BMJ Open€¦ · Khunti, Kamlesh; University of Leicester, Department of Health Sciences Seshasai, Sreenivasa; St Georges, University of London, Cardiovascular & Cell Sciences Ray,

For peer review only

Estimating the Economic Burden of Cardiovascular Events in Patients Receiving Lipid Modifying Therapy in the United

Kingdom Using Real World Data

Journal: BMJ Open

Manuscript ID bmjopen-2016-011805

Article Type: Research

Date Submitted by the Author: 09-Mar-2016

Complete List of Authors: Danese, Mark; Outcomes Insights, Inc, Outcomes Research Gleeson, Michelle; Outcomes Insights, Inc, Outcomes Research Kutikova, Lucie; Amgen Europe GmbH, Health Economics

Griffiths, Robert; Outcomes Insights, Inc, Outcomes Research Azough, Ali; Amgen Ltd Uxbridge, Health Economics Khunti, Kamlesh; University of Leicester, Department of Health Sciences Seshasai, Sreenivasa; St Georges, University of London, Cardiovascular & Cell Sciences Ray, Kausik; Imperial College London School of Public Health, Medicine

<b>Primary Subject Heading</b>:

Health services research

Secondary Subject Heading: Health economics, Cardiovascular medicine

Keywords: HEALTH ECONOMICS, CARDIOLOGY, HEALTH SERVICES ADMINISTRATION & MANAGEMENT, Coronary heart disease < CARDIOLOGY

For peer review only - http://bmjopen.bmj.com/site/about/guidelines.xhtml

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Title:

Estimating the Economic Burden of Cardiovascular Events in Patients Receiving Lipid Modifying

Therapy in the United Kingdom Using Real World Data

Authors:

Mark D. Danese, MHS, PhD (1); Michelle Gleeson, PhD (1); Lucie Kutikova, PhD (2); Robert I.

Griffiths, MS, ScD (1, 3); Ali Azough, MSc (4); Kamlesh Khunti, PhD, MD, FRCGP, FRCP (5); Sreenivasa

Rao Kondapally Seshasai, MBBS, MD, MRCP, MPhil, PhD (6); Kausik K. Ray, MD, FAHA (7)

Affiliations:

(1) Outcomes Insights, Inc., Outcomes Research, Westlake Village, CA; (2) Amgen (Europe) GmbH,

Health Economics, Zug, Switzerland; (3) University of Oxford, Oxford, UK; (4) Amgen Ltd, Health

Economics, Uxbridge, UK; (5) Diabetes Research Centre, University of Leicester, Leicester, UK; (6) St

George’s, Cardiovascular & Cell Sciences, University of London, London, UK; (7) School of Public

Health, Department of Medicine, Imperial College London, London, UK

Key words:

Economics, utilization, cardiovascular disease, costs, treatment patterns, LDL

Corresponding author contact information:

Mark Danese, MHS, PhD

2801 Townsgate Road, Suite 330

Westlake Village, CA 91361

805-498-0034 (office)

805-715-8106 (fax)

[email protected] (email)

Word Count:

3,774 (excluding abstract, tables, acknowledgements, and references)

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1. Abstract (299 out of 300 words max)

1.1 Objectives

To characterize the direct medical costs of cardiovascular (CV) events among individuals receiving

lipid modifying therapy.

1.2 Design

Retrospective cohort study using Clinical Practice Research Datalink records from 2006-2012 to

identify individuals with their first and second CV-related hospitalizations (First Event and Second

Event cohorts). Within-person differences were used to estimate CV-related outcomes.

1.3 Setting

Patients in the United Kingdom (UK) who had their first CV event between January 2006 and March

2012.

1.4 Participants

Patients ≥ 18 years who had a CV event and received at least 2 lipid modifying therapy prescriptions

within 180 days beforehand.

1.5 Primary and secondary outcome measures

Direct medical costs were estimated in 3 periods: baseline (pre-event), acute (6 months afterward)

and long-term (subsequent 30 months). Primary outcomes included incremental direct medical

costs, resource utilization, and total costs per period.

1.6 Results

There were 24,093 patients in the First Event cohort of whom 5,274 were included in the Second

Event cohort. The mean incremental acute CV event costs for the First Event and Second Event

cohorts were: CABG/PTCA £5635 and £5823, myocardial infarction £4275 and £4301, ischemic

stroke £3512 and £4572, heart failure £2444 and £3461, unstable angina £2179 and £2489 and

transient ischemic attack £1537 and £1814. The mean incremental long-term costs were: heart

failure £848 and £2829, myocardial infarction £922 and £1385, ischemic stroke £973 and £682,

transient ischemic attack £705 and £1692, unstable angina £328 and £677, and CABG/PTCA £-368

and £599. Hospitalization accounted for the majority of all costs. Higher comorbidity burden was

associated with higher long-term costs.

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1.7 Conclusions

Revascularization and myocardial infarction were associated with the highest incremental costs

following a CV event. Based on real-world data, the economic burden of CV events in the UK is

substantial, particularly among those with greater comorbidity burden.

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2. Article Summary

Strengths and limitations of this study:

• All estimates were calculated using real-world data for the United Kingdom.

• Incremental costs were estimated using within-person differences to minimize confounding.

• All patients received lipid modifying therapy, maximizing the relevance for this population.

• Censoring in the data may affect the cost estimates, particularly for long-term costs.

• The estimates were based on external cost data applied to general practitioner and inpatient

hospitalization utilization, with limited information on outpatient specialist care.

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3. Introduction

Cardiovascular (CV) disease is a major cause of premature death worldwide and an important

source of disability. Elevated levels of blood lipids represent a major risk factor for the development

of coronary heart disease including myocardial infarction (MI) and angina, as well as other CV

events including stroke, transient ischemic attack (TIA) and peripheral arterial disease (PAD). A

history of these events places individuals at higher risk of experiencing subsequent CV events. 1

A meta-analysis of 21 trials of >170,000 randomized patients demonstrated that the incidence of

major CV events was reduced by approximately 25% for each mmol/L reduction in low-density

lipoprotein (LDL) cholesterol.2 Therefore, reducing serum cholesterol levels is the cornerstone of

cardiovascular disease prevention efforts.3 Drug treatment with lipid modifying therapy (LMT) to

lower lipids, especially statins, is commonly used with the goal of reducing population CV event

rates. In addition to the potential public health benefits, lower CV event rates from reducing LDL

should also have economic benefits in terms of cost-offsets from prevented CV events. However,

many patients do not attain target lipid levels for a variety of reasons including the use of lower-

than-optimal doses, non-compliance, and very high levels of LDL that cannot be sufficiently lowered

with existing therapies. Thus, these individuals carry a higher risk of CV events such as MI and

stroke that could be reduced with greater LDL reductions and/or improved adherence.1,3

In the United Kingdom (UK), studies of the cost of CV events have not been conducted to date,

which is likely due to two challenges. First, cost information is generally not included with clinical

data in the United Kingdom (UK). Therefore, analysing costs requires a complicated process of

merging information from disparate sources. Second, the linkage to Hospital Episode Statistics

(HES) data has relatively recently become available to provide the detailed information necessary

for estimating costs based on Healthcare Resource Groups (HRGs).

To date, CV event cost estimates have been based primarily on expert opinion regarding utilization,

combined with UK-specific costs for the resources.4 Although studies of CV event costs have been

previously published, they are generally limited to analysing short-term costs using US data

sources.5,6,7,8,9,10

While these studies include patients receiving LMT, they tend to focus on patients

hospitalized for CV events, or patients with atherosclerosis, hypertension, or acute coronary

syndrome; they do not provide estimates specific to the LMT population who may experience a

different distribution of CV events with different patterns of resource use. Having analyses in the

LMT population provides useful evidence to physicians, payers, or other decision-makers interested

in the burden of CVD and related health economic analyses.

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The present study was designed to provide evidence-based estimates of healthcare resource use

(HRU) and costs specifically for the LMT population. The primary objective was to estimate the

short- and long-term HRU and costs associated with specific new CV events (including both initial

and subsequent events) in UK patients receiving LMT. A secondary objective was to explore the

effects of comorbidity burden on these costs.

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4. Methods

4.1 Study Overview

This was a retrospective cohort study to estimate the resource utilization and direct medical costs

associated with CV events in patients already receiving LMT prior to the event. Primary care data

were obtained from electronic medical records in the Clinical Practice Research Datalink (CPRD), and

inpatient data were obtained from the linked Hospital Episode Statistics (HES) data.

4.2 Data Sources

This was an observational cohort study.11

The CPRD contains anonymized information from general

practitioners (GP) on demographics, symptoms, diagnoses, test results, and referrals to secondary

care, and is a widely used database globally.12

These patients are broadly representative of the UK

general population in terms of age, sex, and ethnicity. At the time this study was conducted, the

CPRD-HES linkage included hospitalized care but did not include data for outpatients or for accident

and emergency visits.

4.3 Patient Populations

The study population included adult patients (≥ 18 years) who were alive and observable in both the

CPRD and HES data as of January 1, 2005, and were hospitalized for their first CV event between

January 2006 and March 31, 2012 (end of HES observation period). Only patients who had a CV

event in the HES data were included in the study. CV events were defined as hospitalizations with a

primary ICD-10 diagnosis code for myocardial infarction (MI), ischemic stroke (IS), heart failure (HF),

transient ischemic attack (TIA), or unstable angina (UA), or other hospitalizations for

revascularization that included coronary artery bypass graft (CABG) or percutaneous transluminal

coronary angioplasty (PTCA). Percutaneous coronary intervention (PCI [angioplasty with stenting])

was included with PTCA. Patients with a history of an acute event (MI or IS) in the CPRD data before

2006 were also excluded. Prescription records for LMT were used to identify patients receiving

treatment to reduce serum lipid levels. To ensure that patients were receiving LMT prior to their

first CV event, patients had to have received at least two prescriptions for LMT in the 180 days prior

to the index date. LMT included statins, ezetimibe, fibrates, nicotinic acid, and bile acid

sequestrants.

The date of the event that qualified the patient to be in a cohort was defined as the “index” date.

To rule out previous events more accurately, patients were required to have at least 12 months of

data prior to the CV event date; we also required at least 30 days of follow-up afterward to properly

categorize the index event. Patients who met these inclusion criteria were included in the First

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Event cohort. In addition, a Second Event cohort was created using patients who had a subsequent

CV event, the date of which was their index date. (See Supplementary Materials Fig. 1)

Medical history of any of the following conditions in the CPRD was also captured: diabetes,

peripheral artery (vascular) disease, abdominal aortic aneurysm, transient ischemic attack, ischemic

heart disease (excluding MI or UA), carotid artery disease, or angina pectoris. These conditions

were identified using READ codes based on code lists from the Quality Outcomes Framework (QOF)

where possible.13

4.4 Study Design

This was a pre-post design, using patients as their own controls, and was used to reduce the

influence of potential confounders that could have influenced the CV event costs. Patients were

selected at the time they had their qualifying CV event. To estimate costs related to CV events, we

estimated costs both before and after the CV event and calculated the incremental difference.

The assessment of demographics and comorbidity information was based on information present at

the time of the index CV event. Baseline utilization and costs for all cohorts were estimated in the

12-month period prior to the first CV event. The follow-up period for the assessment of HRU and

costs started with the date of the index CV event and continued for 36 months, or until the end of

data availability within the HES database (31 March 2012). Patients were also censored at date of

death, date of a subsequent CV hospitalization event, date of last known up-to-standard CPRD

record for the patient in the practice, or 31 March 2012, whichever came first.

For the subset of First Event cohort patients who had a second CV event, the baseline period was

the 12-month period before the second event and the follow-up period was the 36-month period

after it. Because follow-up was censored at subsequent CV events, costs were not double-counted

between the First Event and Second Event cohorts. This facilitated reporting costs for first and

second events separately as well as reporting pooled results.

The primary time periods for the analyses were the first 6 months after the index event (“acute

period”), and the subsequent 30 months after the acute period (“long-term period”). These

intervals were selected to align with time frames provided in a National Institute for Health and

Care Excellence (NICE) clinical guideline for lipid modification.14

Long-term outcomes were

annualized for easier interpretation. For analyses of incremental utilization and cost in this cohort,

the 12-month period before the first CV event was used as the baseline for calculating all cost

differences, including those in the Second Event cohort. This was done to have a consistent

reference cost for the calculation of incremental costs across cohorts.

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4.5 Study Endpoints

4.5.1 Primary Endpoints

The primary endpoints in this study were resource utilization and direct medical costs for the acute

period after a CV hospitalization, and the subsequent long-term period. Both total and incremental

CV-related costs were estimated, including hospitalizations (HES), outpatient referrals (CPRD),

primary care office visits (CPRD), and medications (CPRD). Medications included lipid-lowering

therapy, antihypertensive therapy, antithrombotic therapy, and anti-diabetic therapy (see

Supplemental Materials Table 1).

All events within 30 days after the initial event were used to categorize the event type. Multiple

events occurring within this 30-day window were assigned to a group using the highest event from

the following hierarchy: MI, UA, IS, CABG, PTCA, HF, TIA. (For example, someone with a TIA

followed by MI then CABG within a 30-day window would be assigned to the MI group.) This

ensured that temporally close observations were not double-counted, and reduced censoring due

to subsequent events. All hospitalizations were identified using ICD-10 codes for diagnoses and

Office of Population, Censuses and Surveys (OPCS) codes for procedures (see Supplementary

Materials Table 2).

4.6 Costing

4.6.1 Data sources

Unit costs were attributed to the identified resource use category for each individual patient and

aggregated across all patients. Unit costs for secondary care (hospitalization) and drugs were

derived from 2014 UK National Health Service (NHS) sources to ensure transparency and relevancy

in the source of cost data. 15,16

Primary care unit costs were estimated from the Personal Social

Services Research Unit 2014 costs. 17

Office visits in surgery or clinic were priced at £46 (11.7 minute visit) and telephone consultations at

£28 (7.1 minute consult) based on 2014 data.17

Outpatient referrals listed in the CPRD data were

assumed to have occurred and costs were assigned using NHS reference costs for 2013-2014

according to specialty type.15

Costs for hospitalizations were based on Healthcare Resource Group

(HRG) Reference Costs and were assigned using the 2013/2014 HRG4+ Reference Costs Grouper

software.16

All available diagnosis and procedure codes were used to assign the hospital cost. Drug

costs were based on the NHS Electronic Drug Tariff for England & Wales using data from September

2014 where possible, augmented with other months in 2014 for drugs that did not have a

September 2014 cost. Generic prices were used where possible.18

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4.7 Analyses

Estimation of patient costs was performed by multiplying the quantity of each resource used by the

corresponding unit cost of the resource. Costs were grouped into hospital, drug, and office visit

(including outpatient referral). Acute and long-term incremental and total costs were also stratified

by the type of index event. Exploratory analyses of acute and long-term incremental costs were

stratified by the Charlson Comorbidity Index19

and by age group. Comorbid conditions for the

Charlson Comorbidity Index were identified using a study that provided UK-specific code lists for

each condition.20

Because patients were censored during the follow up period, we used inverse probability weighting

to account for censoring and death.21,22

This facilitated the summation of costs, and the calculation

of incremental costs over the time intervals in the study, even when different numbers of patients

were present in each interval. To combine costs and align with the acute and long-term periods, the

first year costs were divided into two 6-month time intervals, while the remaining costs were

divided into annual time intervals. Death was treated as a censoring event to align with economic

model inputs that require the annual cost of care for survivors (i.e., attenuation of costs due to

death is incorporated into the economic models23

). Variances were estimated using bootstrapping

of the inverse probability weighted estimates for each time interval. Utilization estimates were

analyzed similarly to cost estimates to account for censoring and death.

Patient demographic and clinical characteristics were described using means and standard

deviations for continuous variables, and percentages for categorical variables. Analyses of mortality

rates were based on deaths from all causes divided by person-years of follow-up until death or a

censoring event, and are expressed per 100 person-years. All analyses were conducted in R (version

3.1.3).24

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5. Results

5.1 Cohort Overview

Demographic, baseline characteristics, and mortality rates are summarized by cohort in Table 1.

There were 24,093 patients in the First Event cohort. Of these, 5,274 (22%) had a subsequent CV

event in our study time horizon and were also included in the Second Event cohort. The mean age

at the time of their CV event was 73 years in both cohorts, the proportion male was 59% and 60%

respectively, the mean body mass index was 29 kg/m2 in both cohorts, and baseline LDL cholesterol

levels were 2.3 and 2.2 mmol/L respectively. Within 6 months of the index event, 11.5% of the First

Event and 13.9% of the Second Event cohort died. All-cause mortality rates were higher in the 6-

month acute period (28.5 and 36.5 per 100 person-years, respectively), and lower in the 30-month

long-term period (5.4 to 6.2 per 100 person-years, respectively). Figure 2 shows the distribution of

CV event type for each of the cohorts. Approximately 97% of patients were included in the study

based on receiving a statin as their LMT prescription within 180 days prior to their first CV event

(Table 1).

5.2 CV Event Costs

The incremental CV event costs in the acute period were £3504 in the First Event cohort and £3968

in the Second Event cohort (Table 2). The incremental cost for both cohorts combined was £3577.

These costs were mostly due to hospitalization costs, which were £3338 and £3737 respectively,

and £3400 averaged across both cohorts. The incremental costs in the subsequent 30 months were

£361 and £1018 per year, respectively, and £439 averaged across both cohorts.

In terms of index event subgroups, the highest acute period incremental CV event costs were for

the CABG/PTCA, myocardial infarction, and ischemic stroke patients in both cohorts (Table 2). The

mean costs for the First Event and Second Event cohorts were as follows: CABG/PTCA £5635 and

£5823, myocardial infarction £4275 and £4301, ischemic stroke £3512 and £4572, heart failure

£2444 and £3461, unstable angina £2179 and £2489 and transient ischemic attack £1537 and

£1814. The subsequent long-term incremental costs (per year) were more heterogeneous and were

as follows for the First Event and Second Event cohorts, respectively: heart failure £8848 and

£2829, myocardial infarction £922 and £1385, ischemic stroke £973 and £682, transient ischemic

attack £705 and £1692, unstable angina £328 and £677, and CABG/PTCA £-368 and £599.

Figure 2 shows stratified incremental costs for each cohort by comorbidity status, and Table 3

shows the corresponding rate of office visits, subsequent CV event rate, death rate, and the initial

CV hospitalization length of stay. In the First Event cohort, higher comorbidity scores from the

Charlson Comorbidity Index were associated with comparable acute period costs and higher long-

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term period costs. In the Second Event cohort, both acute and long-term annual costs increased

with the Charlson score. These trends were generally mirrored by corresponding utilization and

event rate patterns in Table 3. Costs stratified by age group did not vary as systematically, and are

provided in Supplementary Materials.

Overall and hospital-specific total (i.e., non-incremental) costs in the 12-month baseline period, the

6-month acute period, and the 30-month long-term period are provided in Table 2. In terms of total

costs within the baseline period, the highest was for patients with heart failure, whose annual cost

was £3149 in the First Event cohort and £2738 in the Second Event cohort. In the acute period, the

highest total cost was for CABG/PTCA, which was £6843 in the First Event cohort and £6630 in the

Second Event cohort. In the long-term period, the highest annualized cost was in patients with

heart failure, which was £3608 in the First Event cohort and £5554 in the Second Event cohort.

In the acute period after the index event, there were 5.0 and 6.1 more office visits as well as 1.5 and

1.6 more non-CV hospitalizations per person and 1 CV hospitalization per person, for the First Event

and Second Event cohorts respectively. During the long-term period afterwards, there were 2.9 and

4.8 more office visits, as well as 0.2 and 0.5 more non-CV hospitalizations compared to the baseline

period. Mean length of stay for the index hospitalization was shortest for unstable angina (4.5 and

4.9 days) and longest for ischemic stroke (22.5 and 26.7 days).

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6. Discussion

Estimating resource use and the associated costs of managing CVD is important for estimating the

value of interventions that reduce the risk of CV events. To date, these estimates have been based

on expert opinion regarding utilization, combined with UK-specific costs for the resources.14

To our

knowledge, this is the first study both to measure utilization in LMT patients with CV events using

comprehensive real-world data and to combine this information with costs from the UK perspective.

These data show that the cost of care for patients experiencing a CV event was generally very high

in the acute period following the event. In particular, patients with a second CV event had higher

incremental CV-related costs. These incremental CV-related costs were generally much smaller in

the long-term period, although they were almost always still positive (i.e., greater than the baseline

pre-CV event period). The exception to this trend was for CABG/PTCA in the First Event cohort,

which suggests that this procedure might have long-term cost offsets in patients who had these

procedures in the absence of one of the other qualifying CV events. The incremental long-term cost

in the Second Event cohort was the lowest in CABG/PTCA patients.

It is also interesting that the First Event and Second Event cohorts often had similar incremental

acute CV-related costs. This is likely related to hospitalization costs, which were the dominant cost

driver during our study’s time horizon. In addition, it appears that comorbidity burden is an

important contributor to incremental long-term costs as shown in Figure 2. The modest association

of age and long-term costs in Figure 3 is most likely caused by increases in comorbidity burden with

increasing age in the 3 younger age groups.

It is important to highlight the clinical implications of these results. Cardiovascular events are

multifactorial, and CV event costs might vary according to the cardiovascular risk factors of the

patient population. Because our focus was on estimating costs in patients at risk of atherosclerotic

vascular events, we required patients to have received at least 2 LMT prescriptions within 180 days.

While one would expect CV event costs to be similar in other populations, these results are most

relevant for patients receiving LMT. The fact that many of these patients, most of whom were

receiving antithrombotic and antihypertensive treatment, went on to experience subsequent CV

events highlights the limitations of available interventions in a population with substantial need.

Furthermore, the progressively more costly results by higher comorbidity risk score indicate that

comorbid conditions are another important factor for these patients.

The primary value of these analyses is that they are derived from current, real-world data in the UK.

The CPRD data reflect the medical records of approximately 6% of all patients in clinical practice in

the UK, and are considered to be a very reliable source of longitudinal patient data, supporting over

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1500 publications.25,26

Patients were included based on hospitalizations in the HES data, so they

align with intense resource utilization that is relevant to supporting economic modeling efforts. This

is in contrast to events recorded in the CPRD general practitioner data that may not reflect

hospitalization, depending on the reason for recording a diagnosis code at a particular office visit.27

Finally, the perspective of the NHS was used for costing where possible, to ensure alignment with

the payer for the majority of services provided in the UK.

It must be emphasized, though, that our findings merit careful interpretation in light of potential

limitations of our data and the analyses. For instance, we used external data sources for 2014 for

costing and these may not reflect the actual resources used for the patients in this study. This is

most likely to affect the hospitalization costs, since the costs of office visits and drugs are much

smaller and less variable. We included the cost of referral visits, based on referrals recorded in the

CPRD referral file. We cannot ascertain whether these visits actually occurred, so the utilization of

these services (and hence, the costs) may be over-estimated. On the contrary, not every visit to a

specialist is recorded by the general practitioner, causing cost underestimation and

counterbalancing the potential bias to some degree.

Importantly, we included all hospitalizations for each patient in our utilization and cost analyses,

and not just CV hospitalizations. This may lead to more noise in the data if there were to be no

relationship between the CV events and hospitalizations for other, non-CV diseases. However, we

have minimized this with a pre-post design over a relatively short period of 3 years, and by

calculating incremental costs. In terms of medications, we focused on anti-thrombotic, anti-

hypertensive, and anti-diabetic drugs, which are likely to be important. We did not analyze every

prescription in the CPRD data due to the variety of drugs, doses, and prescriptions; therefore, the

drug costs may be under-estimated. Drug costs are generally low, so this is not likely to be a large

bias.

Finally, we used inverse probability of censoring weights to combine data across time periods to

reduce the loss of information about costs and the incremental cost of CV events. However,

information from patients who did not survive, or who were censored, is still lost and is estimated

by using the data from the remaining patients in the study. For patients censored due to the end of

their data, the missing data is likely to be missing at random and should not introduce a substantial

bias. However, for patients who died or were censored due to a subsequent CV hospitalization, the

costs estimated in this study may under-estimate the actual cost of care over time if the patients

who were censored were higher cost patients. This may at least partly explain the lower cost

estimates in the oldest patients in Figure 3.

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Overall, this analysis provides a real-world evidence-based analysis of the resources used, and the

associated costs, in managing CV events in the UK. The burden is higher in higher risk patients. The

results demonstrate the substantial economic burden and unmet medical need for patients with CV

events in the UK despite the use of lipid modifying therapies.

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7. Figures

Figure 1: Distribution of Types of Index Events By Cohort

Note: MI = myocardial infarction, IS = ischemic stroke, HF = heart failure, TIA = transient ischemic

attack, UA = unstable angina, CABG = coronary artery bypass graft, and PTCA = percutaneous

transluminal coronary angioplasty.

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Figure 2: Incremental Costs (£) by Charlson Comorbidity Score and Cohort

Note: Costs in 2014 £ based on assignment of unit costs to utilization as described in Methods.

Patients stratified by Charlson Comorbidity Index Score, which is a function of the number of

comorbid conditions and their association with mortality. Higher scores indicate a higher mortality

risk.

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8. Tables

Table 1. Baseline Characteristics

Variable Level Cohort

First Event

(n =24,093)

Second Event

(n = 5274)

Continuous Variables N Mean SD N Mean SD

Age at Index 24,093 72.7 11.1 5274 72.8 10.8

BMI 15,005 28.8 5.7 3457 28.7 5.6

Systolic BP 23,107 136.1 19.2 5119 132.6 20.1

LDL Cholesterol 13,247 2.4 2.3 2803 2.2 0.9

Total Cholesterol 19,693 4.4 1.4 4267 4.2 1.4

Triglycerides 15,064 1.6 1.2 3143 1.6 1.0

Categorical Variables N % N %

Lipid Modifying

Therapy

(before or on

index date)*

Low dose statin 1918 8.0 428 8.1

Moderate dose

statin 18,556 77.0

3980 75.5

High dose statin 2886 12.0 695 13.2

Ezetimibe 465 1.9 104 2.0

All others 268 1.1 67 1.3

Year of Index

Event

2006 3948 16.4 425 8.1

2007 3975 16.5 782 14.8

2008 3858 16.0 877 16.6

2009 4023 16.7 986 18.7

2010 3801 15.8 980 18.6

2011 3630 15.1 987 18.7

2012 858 3.6 237 4.5

Age Group < 60 3170 13.2 652 12.4

60 - 69 4892 20.3 1007 19.1

70 - 79 8864 36.8 2092 39.7

≥ 80 7167 29.8 1523 28.9

Gender Male 14,221 59.0 3177 60.3

Female 9872 41.0 2097 39.8

Smoking Status Current 2867 11.9 488 9.3

Former 9020 37.4 2248 42.6

Never 6483 26.9 1375 26.1

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First Event

(n =24,093)

Second Event

(n = 5274)

N % N %

Charlson

Comorbidity

Index (Score)

None 7199 29.9 831 15.8

One 5378 22.3 1029 19.5

Two or higher 11,516 47.8 3414 64.7

Risk Factors Hypertension 10,291 42.7 2364 44.8

Diabetes 7343 30.5 1905 36.1

COPD 1958 8.1 524 9.9

CKD 6038 25.1 1815 34.4

AF 3085 12.8 1014 19.2

CV Conditions AAA 143 0.6 25 0.5

Angina 7239 30.1 1861 35.3

PVD 1972 8.2 524 9.9

TIA 2005 8.3 442 8.4

Cardiac Ischemia 10,261 42.6 2508 47.6

Carotid Stenosis 223 0.9 54 1.02

Other

medications

Anti-hypertensive 20,973 87.1 4979 94.4

Anti-thrombotic 18,818 78.1 4901 92.9

Anti-diabetic 6073 25.2 1599 30.3

Mortality Rate (per 100 person-years) Person

years

Died Rate Person

years

Died Rate

Acute 0-6 months 9686 2764 28.5 2006 733 36.5

Long-term 7-36 months 22,798 1240 5.4 3967 246 6.2

Note: COPD = Chronic Obstructive Pulmonary Disease; CKD = Chronic Kidney Disease; AF = Arterial

Fibrillation; AAA = Abdominal Aortic Aneurysm; PVD = Peripheral Vascular Disease; TIA = Transient

Ischemic Attack; BMI = Body Mass Index; SBP = Systolic Blood Pressure; LDL = Low-Density

Lipoprotein. * LMT refers to the closest prescription to the index date within 180 days prior to it.

See Supplementary Materials for definitions of medication categories.

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CPRD Utilization and Cost


Table 2. CV Event-Related Costs by Cohort and Index Event

Total Costs (in £) Incremental Total Costs (in £)

Cohort Group n

Baseline (1 year

before first CV

event) [Mean (SE)]

Months 1-6 [Mean

(SE)]

Months 7-36

Annualized [Mean

(SE)]

Months 1-6 [Mean

(SE)]

Months 7-36

Annualized [Mean

(SE)]

First

Event

Myocardial Infarction 4468 2266.34 (163.45) 5297.59 (115.51) 2350.66 (165.54) 4275.41 (102.41) 922.43 (155.58)

Unstable Angina 5801 1965.61 (86.76) 3133.12 (61.44) 2081.84 (65.15) 2179.24 (48.51) 328.45 (61)

Ischemic Stroke 3489 1962.42 (119.73) 4389.27 (102.29) 2526.63 (276.42) 3512.25 (102.82) 972.62 (257.48)

PTCA/CABG* 5082 2412.2 (95.69) 6843.38 (94.67) 1855.11 (110.02) 5635.19 (87.29) -368.26 (103.58)

Heart Failure 3596 3148.72 (136.77) 3988.86 (111.87) 3607.86 (313.16) 2443.58 (95.22) 847.55 (247.64)

Transient Ischemic Attack 1657 2109.76 (215.46) 2488.83 (89.42) 2362.10 (152.91) 1536.88 (69.22) 704.76 (147.47)

All Patients (hospital) 24,093 1565.41 (56.72) 4059.91 (38.39) 1432.92 (59.15) 3337.51 (37.75) 203.18 (48.2)

All Patients (total) 24,093 2301.34 (57.26) 4594.16 (39.01) 2262.92 (60.37) 3504.01 (38.04) 361.11 (48.79)

Second

Event


Unstable Angina 1,347 2795.44 (398.16) 3925.66 (204.31) 2844.49 (247.33) 2489.48 (132.63) 676.82 (278.78)

Ischemic Stroke 532 2202.96 (143.98) 5607.47 (273.97) 2870.35 (281.51) 4572.28 (280.27) 682.29 (438.78)

PTCA/CABG* 1256 1635.76 (63.68) 6630.29 (208.77) 2121.06 (302.87) 5823.12 (202.55) 598.64 (296.25)

Heart Failure 1104 2737.60 (97.24) 4818.24 (318.44) 5554.17 (1732.62) 3460.91 (312.14) 2829.02 (1643.39)


All Patients (hospital) 5274 1588.26 (100.82) 4530.08 (100.6) 2061.65 (237.53) 3736.68 (85.06) 762.21 (237.68)

All Patients (total) 5274 2380.42 (100.97) 5147.79 (100.62) 2998.11 (242.14) 3967.74 (84.73) 1017.68 (239.47)

First and

Second

Combined


Unstable Angina 7148 2121.98 (111.68) 3261.27 (65.83) 2179.64 (75.16) 2229.42 (50.81) 373.15 (74.47)

Ischemic Stroke 4021 1994.31 (91.59) 4533.08 (103.14) 2545.1 (274.62) 3637.86 (103.05) 953.48 (261.93)

PTCA/CABG* 6338 2258.28 (69.8) 6802.98 (86.73) 1894.5 (89.56) 5669.47 (85.13) -220.73 (83.14)

Heart Failure 4700 3052.04 (112.99) 4144.89 (116.03) 3881.8 (344.1) 2635.35 (105.35) 1128.86 (300.08)



All Patients (total) 29,367 2315.55 (43.78) 4681.04 (41.45) 2351.28 (59.09) 3576.82 (36.18) 438.89 (54)

* Note: PTCA/CABG = Percutaneous Transluminal Coronary Angioplasty / Coronary Artery Bypass Graft. The proportions of revascularization events that

were CABG were 42% (2137/5082), 35% (442/1256) and 41% (2579/6338) for the First Event, Second Event, and All (combined) cohorts respectively.

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Table 3: Event Rates by Charlson Comorbidity Score and Cohort

Comorbidity score 0 Comorbidity score 1 Comorbidity score 2+

Acute Long term Acute Long term Acute Long term

First Event

CV hospitalization LOS (mean days) 7.6 NA 9.6 NA 11.4 NA

Office visits (per person year) 23.2 18.1 26.6 22.9 31.8 29.7

CV event rate (per 100 person years) 23.4 6.0 23.4 9.5 28.8 12.2

Death rate (per 100 person years) 13.5 2.4 22.7 4.8 42.0 8.4

Second Event





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9. Additional Study Information

9.1 Contributorship:

Design: All authors

Analyses: MDD, MG, RIG

Initial draft: MDD, MG

Revisions: All authors

Final approval: All authors

Agreement to be accountable: All authors

9.2 Funding:

This study was funded by Amgen, Inc.

9.3 Competing interests:

Mark Danese, Michelle Gleeson, and Robert Griffiths work with Outcomes Insights, Inc., which

was funded to conduct this study. Lucie Kutikova and Ali Azough are employees of Amgen, Inc.

Kamlesh Khunti has acted as a consultant and speaker for Novartis, Novo Nordisk, Sanofi-

Aventis, Lilly, Merck Sharp & Dohme, Janssen, Astra Zeneca and Boehringer Ingelheim. He has

received grants in support of investigator and investigator-initiated trials from Novartis, Novo

Nordisk, Sanofi-Aventis, Lilly, Pfizer, Boehringer Ingelheim, Merck Sharp & Dohme, Janssen and

Roche, and has served on advisory boards for Lilly, Sanofi-Aventis, Merck Sharp & Dohme, Novo

Nordisk, Boehringer Ingelheim, Janssen and Astra Zeneca. SR Kondapally Seshasai has provided

consulting to Amgen and received grants from Kowa and Sanofi. Kausik K. Ray has provided

consulting to Amgen, Sanofi, Pfizer, Regeneron, Astra Zeneca, Kowa, Aegerion, Merck Sharp &

Dohme, Lilly and ISIS, and received grants from Sanofi, Regeneron, Amgen, Pfizer and Merck

Sharp & Dohme through his institution.

9.4 Acknowledgments:

KK would like to acknowledge support for his work from the National Institute for Health

Research (NIHR) and Collaboration for Leadership in Applied Health Research and Care

(CLAHRC) East Midlands. Dr. Khunti’s participation was supported by the National Institute for

Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care in

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East Midlands (CLAHRC EM). The views expressed are those of the authors and not necessarily

those of the NHS, the NIHR or the Department of Health.

9.5 Data sharing:

To ensure patient privacy and confidentiality, the CPRD data cannot be shared.

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10. References

1 The Fifth Joint Task Force of the European Society of Cardiology and Other Societies on

Cardiovascular Disease Prevention in Clinical Practice. European guidelines on cardiovascular

disease prevention in clinical practice (version 2012). European Heart Journal. 2012; 33: 1635-

1701.

2 Cholesterol Treatment Trialists’ (CTT) Collaboration, Baigent C, Blackwell L, Emberson Efficacy

and safety of more intensive lowering of LDL cholesterol: a meta-analysis of data from 170,000

participants in 26 randomised trials. Lancet. 2010;13;376:1670-1681.

3 JBS3 Board. Joint British Societies’ consensus recommendations for the prevention of

cardiovascular disease (JBS3). Heart. 2014; 100: ii1-ii67.

4 National Institute for Health and Care Excellence. NICE clinical guideline CG181. Lipid

modification: Cardiovascular risk assessment and the modification of blood lipids for the

primary and secondary prevention of cardiovascular disease. Appendices. National Clinical

Guideline Center, UK. July 2014, page 589.

5 Chapman RH, Liu LZ, Girase PG, Straka RJ. Determining initial and follow-up costs of

cardiovascular events in a US managed care population. BMC Cardiovasc Disord. 2011;11:11.

6 Ohsfeldt RL, Gandhi SK, Fox KM, Bullano MF, Davidson M. Medical and cost burden of

atherosclerosis among patients treated in routine clinical practice. J Med Econ. 2010;13:500-

507.

7 Duh MS, Fulcher NM, White LA, Jayawant SS, Ramamurthy P, Moyneur E, Ong SH. Costs

associated with cardiovascular events in patients with hypertension in US managed care

settings. J Am Soc Hypertension. 2009;3:403-415.

8 Eisenstein EL, Shaw LK, Anstrom KJ, Nelson CL, Hakim Z, Hasselblad V, Mark DB. Assessing the

clinical and economic burden of coronary artery disease: 1986-1998. Med Care. 2001;39:824-

835.

9 Etemad LR, McCollam PL. Total first-year costs of acute coronary syndrome in a managed care

setting. J Manag Care Pharm. 2005;11:300-306.

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10 Straka RJ, Liu LZ, Girase PS, DeLorenzo A, Chapman RH. Incremental cardiovascular costs and

resource use associated with diabetes: an assessment of 29,863 patients in the US managed-

care setting. Cardiovasc Diabetol. 2009;8:53.

11 Herrett E, Gallagher AM, Bhaskaran K, et al. Data Resource Profile: Clinical Practice Research

Datalink (CPRD). Int J Epidemiol. 2015;44(3):827-836. doi:10.1093/ije/dyv098.

12 Williams T, van Staa T, Puri S, Eaton S. Recent advances in the utility and use of the General

Practice Research Database as an example of a UK Primary Care Data resource. 2012; 3:89-99.

13 http://www.hscic.gov.uk/qofbrv28, accessed January 20, 2015




Guideline Center, UK. July 2014, page 589, accessed January 20, 2015

15https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/397469/03

a_2013-14_National_Schedule_-_CF-NET_updated.xls, accessed January 20, 2015

16 http://www.hscic.gov.uk/casemix/costing, accessed January 20, 2015

17 http://www.pssru.ac.uk/project-pages/unit-costs/2014/, accessed January 20, 2015

18 http://www.ppa.org.uk/edt/September_2014/mindex.htm, accessed January 20, 2015

19 Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic

comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373-

383. doi:10.1016/0021-9681(87)90171-8.

20 Kahn N, Perera R, Harper S, Rose PW. Adaptation and validation of the Charlson Index for

Read/OXMIS coded databases, BMC Family Practice 2010, 11:1

21 Lin DY. Linear regression analysis of censored medical costs. Biostatistics. 2000. 1:35-47.

22 Griffiths RI, Gleeson ML, Danese MD, O’Hagan A. Inverse Probability Weighted Least Squares

Regression in the Analysis of Time-Censored Cost Data: An Evaluation of the Approach Using

SEER-Medicare. Value Health. 2012 Jul;15(5):656-63.

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23 Siebert U, Alagoz O, Bayoumi AM, et al. State-Transition Modeling: A report of the ISPOR-

SMDM modeling good research practices task force – 3. Medical Decision Making. 2012. 32:

690-700.

24 Comprehensive R Archive Network website: http://cran.r-project.org. Accessed 15

September 2015

25 Herrett E. Validation and validity of diagnoses in the General Practice Research Database: a

systematic review. British Journal of Clinical Pharmacology. 2009. 69: 4-14.

26 Clinical Practice Research Datalink website: http://www.cprd.com/intro.asp. Accessed 15

September 2015.

27 Herrett E, Shah AD, Boggon R, Denaxas S, Smeeth L, van Staa T, Timmis A, and Hemingway H.

Completeness and diagnostic validity of recording acute myocardial infarction events in primary

care, hospital care, disease registry, and national mortality records: cohort study. BMJ. 2013;

346:f2350. doi: 10.1136/bmj.f2350

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Supplementary Materials

Version date: 3/5/2016 4:34 PM Page 1 of 4


Figures:

Figure 1: Overview of Study Design

Note: The study sample was selected in the following way. We identified unique patients with CV

hospitalizations in HES from 2006 to 2012 (105,526), selected those with linked, up-to-standard CPRD

data (69,248), selected those who received LMT within 180 days (28,051), and then selected those

without a prior history of myocardial infarction or ischemic stroke in the GP data (24,093).

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Figure 2: Incremental Costs (£) by Age Group and Cohort


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Tables:

Table 1: Codes to Identify Medications*

Category BNF Chapter

Number

BNF Chapter Title

Diabetes 060101 Insulins

060102 Antidiabetic Drugs

Anti-coagulant 020802 Oral Anticoagulants

020900 Antiplatelet Drugs

Anti-hypertensive 020400 Beta-adrenoceptor Blocking Drugs

020501 Vasodilator Antihypertensive Drugs

020502 Centrally Acting Antihypertensive Drugs

020503 Adrenergic Neurone Blocking Drugs

020504 Alpha-adrenoceptor Blocking Drugs

020505 Drugs Affecting The Renin-angiotensin System

020602 Calcium-channel Blockers

Lipid modifying

therapy

021204 Statins*

021202 Ezetimibe

021203 Fibrates

021205 Nicotinic acid

021201 Bile acid sequestrants

*Statin intensity as defined below was based on Stone NJ, et al. American College of

Cardiology/American Heart Association Task Force on Practice Guidelines. 2013 ACC/AHA guideline on

the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults: a report of the

American College of Cardiology/American Heart Association Task Force on Practice Guidelines.

Circulation. 2014 Jun 24;129 (25 Suppl 2):S1-45. doi: 10.1161/01.cir.0000437738.63853.7a. PMID:

24222016.

High-intensity statins: Atorvastatin 40, 80 mg; Rosuvastatin 20, 40 mg; Simvastatin 80 mg

Medium-intensity statins: Fluvastatin 80 mg; Simvastatin 20, 40 mg; Atorvastatin 10, 20 mg;

Rosuvastatin 5, 10 mg; Lovastatin 40 mg; Pravastatin 40, 80 mg; Pitavastatin 2, 4 mg

Low-intensity statins: Fluvastatin 20-40 mg; Simvastatin 10 mg; Lovastatin 20 mg;

Pravastatin 10-20 mg

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Table 2: Codes to Identify Hospitalizations

Hospitalization Diagnosis Code(s) Condition Type of Code

I21, I22 Myocardial infarction ICD-10 diagnosis

I20.0, I20.9, I24.0, I24.8, I24.9 Unstable angina ICD-10 diagnosis

I63 Ischemic stroke ICD-10 diagnosis

I11.0, I13.0, I13.2, I50 Heart failure ICD-10 diagnosis

G45.8, G45.9 Transient ischemic attack ICD-10 diagnosis

K40

Saphenous vein graft

replacement of coronary artery

OPCS procedure for CABG

K41 Other autograft replacement of

coronary artery


K42 Allograft replacement of

coronary artery


K43 Prosthetic replacement of

coronary artery


K44 Other replacement of coronary

artery


K45 Connection of thoracic artery to

coronary artery


K46 Other bypass of coronary artery OPCS procedure for CABG

K49 Transluminal balloon angioplasty

of coronary artery

OPCS procedure for PTCA

K50 Other therapeutic transluminal

operations on coronary artery


K75 Percutaneous transluminal

balloon angioplasty and insertion

of stent into coronary artery


Note: The number of characters sufficient to capture the entire set of codes is provided above.

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STROBE 2007 (v4) Statement—Checklist of items that should be included in reports of cohort studies

Section/Topic Item

# Recommendation Reported on page #

Title and abstract 1 (a) Indicate the study’s design with a commonly used term in the title or the abstract p.3 (abstract)

(b) Provide in the abstract an informative and balanced summary of what was done and what was found p.3 (abstract) and

p.5 (article

summary)

Introduction

Background/rationale 2 Explain the scientific background and rationale for the investigation being reported p.6 (introduction)

Objectives 3 State specific objectives, including any prespecified hypotheses p.7 (introduction)

Methods

Study design 4 Present key elements of study design early in the paper p.8 (study overview

and data sources)

Setting 5 Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data

collection

p.8-10 (methods)

Participants 6 (a) Give the eligibility criteria, and the sources and methods of selection of participants. Describe methods of follow-up p.8 (patient

populations)

(b) For matched studies, give matching criteria and number of exposed and unexposed Not applicable

Variables 7 Clearly define all outcomes, exposures, predictors, potential confounders, and effect modifiers. Give diagnostic criteria, if

applicable

p.10 (study

endpoints)

Data sources/

measurement

8* For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe

comparability of assessment methods if there is more than one group

p.8 (data sources)

Bias 9 Describe any efforts to address potential sources of bias p.9 (study design)

Study size 10 Explain how the study size was arrived at Supplementary

materials, figure 1

Quantitative variables 11 Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen and

why

p.10 (costing)

Statistical methods 12 (a) Describe all statistical methods, including those used to control for confounding p.11 (analyses)

(b) Describe any methods used to examine subgroups and interactions p.11 (analyses)

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(c) Explain how missing data were addressed p.11 (analyses)

(d) If applicable, explain how loss to follow-up was addressed p.11 (analyses)

(e) Describe any sensitivity analyses Not applicable

Results

Participants 13* (a) Report numbers of individuals at each stage of study—eg numbers potentially eligible, examined for eligibility, confirmed

eligible, included in the study, completing follow-up, and analysed

Supplementary

materials, figure 1

(b) Give reasons for non-participation at each stage Supplementary

materials, figure 1

(c) Consider use of a flow diagram Supplementary

materials, figure 1

Descriptive data 14* (a) Give characteristics of study participants (eg demographic, clinical, social) and information on exposures and potential

confounders

p.12 (Results) and

p.20 (table 1) and

p.18 (figure 1)

(b) Indicate number of participants with missing data for each variable of interest p.20 (table 1)

(c) Summarise follow-up time (eg, average and total amount) p.20 (table 1)

Outcome data 15* Report numbers of outcome events or summary measures over time p.20 (table 1)

Main results 16 (a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their precision (eg, 95% confidence

interval). Make clear which confounders were adjusted for and why they were included

p.12 (Results) and

p.21 (table 2)

(b) Report category boundaries when continuous variables were categorized p.20 (table 1)

(c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time period Not applicable

Other analyses 17 Report other analyses done—eg analyses of subgroups and interactions, and sensitivity analyses p.20 (table 2), p.18

(figure 2), and p.22

(table 3)

Discussion

Key results 18 Summarise key results with reference to study objectives p.14 (discussion)

Limitations

Interpretation 20 Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results from

similar studies, and other relevant evidence

p.15 (discussion)

Generalisability 21 Discuss the generalisability (external validity) of the study results p.15 (discussion)

Other information

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Funding 22 Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on

which the present article is based

p.1 (disclosures)

*Give information separately for cases and controls in case-control studies and, if applicable, for exposed and unexposed groups in cohort and cross-sectional studies.

Note: An Explanation and Elaboration article discusses each checklist item and gives methodological background and published examples of transparent reporting. The STROBE

checklist is best used in conjunction with this article (freely available on the Web sites of PLoS Medicine at http://www.plosmedicine.org/, Annals of Internal Medicine at

http://www.annals.org/, and Epidemiology at http://www.epidem.com/). Information on the STROBE Initiative is available at www.strobe-statement.org.

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Estimating the Economic Burden of Cardiovascular Events in Patients Receiving Lipid Modifying Therapy in the United

Kingdom

Journal: BMJ Open

Manuscript ID bmjopen-2016-011805.R1

Article Type: Research

Date Submitted by the Author: 08-Jun-2016

Complete List of Authors: Danese, Mark; Outcomes Insights, Inc, Outcomes Research Gleeson, Michelle; Outcomes Insights, Inc, Outcomes Research Kutikova, Lucie; Amgen Europe GmbH, Health Economics

Griffiths, Robert; Outcomes Insights, Inc, Outcomes Research Azough, Ali; Amgen Ltd Uxbridge, Health Economics Khunti, Kamlesh; University of Leicester, Department of Health Sciences Seshasai, Sreenivasa; St Georges, University of London Ray, Kausik; Imperial College London School of Public Health, Medicine

<b>Primary Subject Heading</b>:

Health services research

Secondary Subject Heading: Health economics, Cardiovascular medicine

Keywords: HEALTH ECONOMICS, CARDIOLOGY, HEALTH SERVICES ADMINISTRATION & MANAGEMENT, Coronary heart disease < CARDIOLOGY


BMJ Open on June 27, 2020 by guest. P

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Title:

Estimating the Economic Burden of Cardiovascular Events in Patients Receiving Lipid-Modifying

Therapy in the United Kingdom

Authors:

Mark D. Danese, MHS, PhD (1); Michelle Gleeson, PhD (1); Lucie Kutikova, PhD (2); Robert I.

Griffiths, MS, ScD (1, 3); Ali Azough, MSc (4); Kamlesh Khunti, PhD, MD, FRCGP, FRCP (5); Sreenivasa

Rao Kondapally Seshasai, MBBS, MD, MRCP, MPhil, PhD (6); Kausik K. Ray, MD, FAHA (7)

Affiliations:

(1) Outcomes Insights, Inc., Outcomes Research, Westlake Village, CA; (2) Amgen (Europe) GmbH,

Health Economics, Zug, Switzerland; (3) University of Oxford, Oxford, UK; (4) Amgen Ltd, Health

Economics, Uxbridge, UK; (5) Diabetes Research Centre, University of Leicester, Leicester, UK; (6) St

George’s, University of London, London, UK; (7) School of Public Health, Department of Medicine,

Imperial College London, London, UK

Key words:

Economics, utilization, cardiovascular disease, costs, treatment patterns, LDL

Corresponding author contact information:

Mark Danese, MHS, PhD

2801 Townsgate Road, Suite 330

Westlake Village, CA 91361

805-498-0034 (office)

805-715-8106 (fax)

[email protected] (email)

Word Count:

3,774 (excluding abstract, tables, acknowledgements, and references)

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1. Abstract (300 out of 300 words max)

1.1 Objectives

To characterize the costs to the United Kingdom (UK) National Health Service of cardiovascular (CV)

events among individuals receiving lipid modifying therapy.

1.2 Design

Retrospective cohort study using Clinical Practice Research Datalink records from 2006-2012 to

identify individuals with their first and second CV-related hospitalizations (First Event and Second

Event cohorts). Within-person differences were used to estimate CV-related outcomes.

1.3 Setting

Patients in the UK who had their first CV event between January 2006 and March 2012.

1.4 Participants

Patients ≥ 18 years who had a CV event and received at least 2 lipid modifying therapy prescriptions

within 180 days beforehand.

1.5 Primary and secondary outcome measures

Direct medical costs (2014 £) were estimated in 3 periods: baseline (pre-event), acute (6 months

afterward) and long-term (subsequent 30 months). Primary outcomes included incremental costs,

resource utilization, and total costs per period.

1.6 Results

There were 24,093 patients in the First Event cohort of whom 5,274 were included in the Second

Event cohort. The mean incremental acute CV event costs for the First Event and Second Event

cohorts were: CABG/PTCA £5635 and £5823, myocardial infarction £4275 and £4301, ischemic

stroke £3512 and £4572, heart failure £2444 and £3461, unstable angina £2179 and £2489 and

transient ischemic attack £1537 and £1814. The mean incremental long-term costs were: heart

failure £848 and £2829, myocardial infarction £922 and £1385, ischemic stroke £973 and £682,

transient ischemic attack £705 and £1692, unstable angina £328 and £677, and CABG/PTCA £-368

and £599. Hospitalization accounted for 95% of acute and 61% of long-term incremental costs.

Higher comorbidity was associated with higher long-term costs.

1.7 Conclusions

Revascularization and myocardial infarction were associated with the highest incremental costs

following a CV event. Based on real-world data, the economic burden of CV events in the UK is

substantial, particularly among those with greater comorbidity burden.

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2. Article Summary

Strengths and limitations of this study:

• All estimates were calculated using real-world data for the United Kingdom.

• Incremental costs were estimated using within-person differences to minimize confounding.

• All patients received lipid modifying therapy, maximizing the relevance for this population.

• Censoring in the data may affect the cost estimates, particularly for long-term costs.

• The estimates were based on external cost data applied to general practitioner and inpatient

hospitalization utilization, with limited information on outpatient specialist care.

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3. Introduction

Cardiovascular (CV) disease is a major cause of premature death worldwide and an important

source of disability. Elevated levels of blood lipids represent a major risk factor for the development

of coronary heart disease including myocardial infarction (MI) and angina, as well as other CV

events including stroke, transient ischemic attack (TIA) and peripheral arterial disease (PAD). A

history of these events places individuals at higher risk of experiencing subsequent CV events. 1

A meta-analysis of 21 trials of >170,000 randomized patients demonstrated that the incidence of

major CV events was reduced by approximately 25% for each mmol/L reduction in low-density

lipoprotein (LDL) cholesterol.2 Therefore, reducing serum cholesterol levels is the cornerstone of

cardiovascular disease prevention efforts.3 Drug treatment with lipid modifying therapy to lower

lipids, especially statins, is commonly used with the goal of reducing population CV event rates. In

addition to the potential public health benefits, lower CV event rates from reducing LDL should also

have economic benefits in terms of cost-offsets from prevented CV events. However, many patients

do not attain target lipid levels for a variety of reasons including the use of lower-than-optimal

doses, non-compliance, and very high levels of LDL that cannot be sufficiently lowered with existing

therapies. Thus, these individuals carry a higher risk of CV events such as MI and stroke that could

be reduced with greater LDL reductions and/or improved adherence.1,3

In the United Kingdom (UK), studies of the cost of CV events have not been conducted to date,

which is likely due to two challenges. First, cost information is generally not included with clinical

data in the United Kingdom (UK). Therefore, analysing costs requires a complicated process of

merging information from disparate sources. Second, the linkage to Hospital Episode Statistics

(HES) data has relatively recently become available to provide the detailed information necessary

for estimating costs based on Healthcare Resource Groups (HRGs).

To date, CV event cost estimates have been based primarily on expert opinion regarding utilization,

combined with UK-specific costs for the resources.4 Although studies of CV event costs have been

previously published, they are generally limited to analysing short-term costs using US data

sources.5,6,7,8,9,10

While these studies include patients receiving lipid-modifying therapy, they tend

to focus on patients hospitalized for CV events, or patients with atherosclerosis, hypertension, or

acute coronary syndrome; they do not provide estimates specific to the lipid-modifying therapy

population who may experience a different distribution of CV events with different patterns of

resource use. Having analyses in the lipid-modifying therapy population provides useful evidence to

physicians, payers, or other decision-makers interested in the burden of CVD and related health

economic analyses.

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The present study was designed to provide evidence-based estimates of healthcare resource use

(HRU) and costs specifically for the lipid-modifying therapy population. The primary objective was to

estimate the short- and long-term HRU and costs associated with specific new CV events (including

both initial and subsequent events) in UK patients receiving lipid-modifying therapy. A secondary

objective was to explore the effects of comorbidity burden on these costs.

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4. Methods

4.1 Study Overview

This was a retrospective cohort study to estimate resource utilization and direct medical costs after

surviving a first or second CV event in patients receiving lipid-modifying therapy therapy prior to the

event. Primary care data were obtained from electronic medical records in the Clinical Practice

Research Datalink (CPRD), and inpatient data were obtained from the linked Hospital Episode

Statistics (HES) data.

4.2 Data Sources

This was an observational cohort study.11

The CPRD contains anonymized information from general

practitioners (GP) on demographics, symptoms, diagnoses, test results, and referrals to secondary

care, and is a widely used database globally.12

These patients are broadly representative of the UK

general population in terms of age, sex, and ethnicity. At the time this study was conducted, the

CPRD-HES linkage included hospitalized care but did not include data for outpatients or for accident

and emergency visits.

4.3 Patient Populations

The study population included adult patients (≥ 18 years) who were alive and observable in both the

CPRD and HES data as of January 1, 2005, and were hospitalized for their first CV event between

January 2006 and March 31, 2012 (end of HES observation period). Only patients who had a CV

event in the HES data were included in the study. CV events were defined as hospitalizations with a

primary ICD-10 diagnosis code for myocardial infarction (MI), ischemic stroke (IS), heart failure (HF),

transient ischemic attack (TIA), or unstable angina (UA), or other hospitalizations for

revascularization that included coronary artery bypass graft (CABG) or percutaneous transluminal

coronary angioplasty (PTCA). Percutaneous coronary intervention (PCI [angioplasty with stenting])

was included with PTCA. Patients with a history of an acute event (MI or IS) in the CPRD data before

2006 were also excluded. Prescription records for lipid-modifying therapy were used to identify

patients receiving treatment to reduce serum lipid levels. To ensure that patients were receiving

lipid-modifying therapy prior to their first CV event, patients had to have received at least two

prescriptions for lipid-modifying therapy in the 180 days prior to the index date. Lipid-modifying

therapy included statins, ezetimibe, fibrates, nicotinic acid, and bile acid sequestrants.

The date of the event that qualified the patient to be in a cohort was defined as the “index” date.

To rule out previous events more accurately, patients were required to have at least 12 months of

data prior to the CV event date; we also required at least 30 days of follow-up afterward to properly

categorize the index event. Patients who met these inclusion criteria were included in the First

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Event cohort. In addition, a Second Event cohort was created using patients who had a subsequent

CV event, the date of which was their index date (see Figure 1 in Supplementary Materials).

Medical history of any of the following conditions in the CPRD was also captured: diabetes,

peripheral artery (vascular) disease, abdominal aortic aneurysm, transient ischemic attack, ischemic

heart disease (excluding MI or UA), carotid artery disease, or angina pectoris. These conditions

were identified using READ codes based on code lists from the Quality Outcomes Framework (QOF)

where possible.13

4.4 Study Design

This was a pre-post design, using patients as their own controls, and was used to reduce the

influence of potential confounders that could have influenced the CV event costs. Patients were

selected at the time they had their qualifying CV event. To estimate costs related to first and

second CV events compared to no CV event, we estimated costs after each CV event and calculated

the incremental difference from the period before the first CV event.

The assessment of demographics and comorbidity information was based on information present at

the time of the index CV event. Baseline utilization and costs for all cohorts were estimated during

the 12-month period prior to the first CV event for all patients. The follow-up period for the

assessment of HRU and costs started with the date of the index CV event and continued for 36

months, or until the end of data availability within the HES database (31 March 2012). Patients

were also censored at date of death, date of a subsequent CV hospitalization event, date of last

known up-to-standard CPRD record for the patient in the practice, or 31 March 2012, whichever

came first.

For the subset of First Event cohort patients who had a second CV event, the baseline period was

the 12-month period before the second event and the follow-up period was the 36-month period

after it. Because follow-up was censored at subsequent CV events, costs were not double-counted

between the First Event and Second Event cohorts. This facilitated reporting costs for first and

second events separately as well as reporting pooled results.

The primary time periods for the analyses were the first 6 months after the index event (“acute

period”), and the subsequent 30 months after the acute period (“long-term period”). These

intervals were selected to align with time frames provided in a National Institute for Health and

Care Excellence (NICE) clinical guideline for lipid modification.14

Long-term outcomes were

annualized for easier interpretation. For analyses of incremental utilization and cost in this cohort,

the 12-month period before the first CV event was used as the baseline for calculating all cost

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differences, including those in the Second Event cohort. This was done to have a consistent

reference cost for the calculation of incremental costs across cohorts.

4.5 Study Endpoints

4.5.1 Primary Endpoints

The primary endpoints in this study were resource utilization and direct medical costs for the acute

period after a CV hospitalization, and the subsequent long-term period. Both total and incremental

CV-related costs were estimated, including hospitalizations (HES), outpatient referrals (CPRD),

primary care office visits (CPRD), and medications (CPRD). Medications included lipid-lowering

therapy, antihypertensive therapy, antithrombotic therapy, and anti-diabetic therapy (see

Supplemental Materials Table 1).

All events within 30 days after the initial event were used to categorize the event type. Multiple

events occurring within this 30-day window were assigned to a group using the highest event from

the following hierarchy: MI, UA, IS, CABG, PTCA, HF, TIA. (For example, someone with a TIA

followed by MI then CABG within a 30-day window would be assigned to the MI group.) This

ensured that temporally close observations were not double-counted, and reduced censoring due

to subsequent events. All hospitalizations were identified using ICD-10 codes for diagnoses and

Office of Population, Censuses and Surveys (OPCS) codes for procedures (see Supplementary

Materials Table 2).

4.6 Costing

4.6.1 Data sources

Unit costs were attributed to the identified resource use category for each individual patient and

aggregated across all patients. Unit costs for secondary care (hospitalization) and drugs were

derived from 2014 UK National Health Service (NHS) sources to ensure transparency and relevancy

in the source of cost data. 15,16

Primary care unit costs were estimated from the Personal Social

Services Research Unit 2014 costs. 17

Office visits in surgery or clinic were priced at £46 (11.7 minute visit) and telephone consultations at

£28 (7.1 minute consult) based on 2014 data.17

Outpatient referrals listed in the CPRD data were

assumed to have occurred and costs were assigned using NHS reference costs for 2013-2014

according to specialty type.15

Costs for hospitalizations were based on Healthcare Resource Group

(HRG) Reference Costs and were assigned using the 2013/2014 HRG4+ Reference Costs Grouper

software.16

All available diagnosis and procedure codes were used to assign the hospital cost. Drug

costs were based on the NHS Electronic Drug Tariff for England & Wales using data from September

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2014 where possible, augmented with other months in 2014 for drugs that did not have a

September 2014 cost. Generic prices were used where possible.18

4.7 Analyses

Estimation of patient costs was performed by multiplying the quantity of each resource used by the

corresponding unit cost of the resource. Costs were grouped into hospital, drug, and office visit

(including outpatient referral). Acute and long-term incremental and total costs were also stratified

by the type of index event. Exploratory analyses of acute and long-term incremental costs were

stratified by the Charlson Comorbidity Index19

and by age group. Comorbid conditions for the

Charlson Comorbidity Index were identified using a study that provided UK-specific code lists for

each condition.20

Because patients were censored during the follow up period, we used inverse probability weighting

to account for censoring and death.21,22

This facilitated the summation of costs, and the calculation

of incremental costs over the time intervals in the study, even when different numbers of patients

were present in each interval. To combine costs and align with the acute and long-term periods, the

first year costs were divided into two 6-month time intervals, while the remaining costs were

divided into annual time intervals. Death was treated as a censoring event to align with economic

model inputs that require the annual cost of care for survivors (i.e., attenuation of costs due to

death is incorporated into the economic models23

). Variances were estimated using bootstrapping

of the inverse probability weighted estimates for each time interval. Utilization estimates were

analyzed similarly to cost estimates to account for censoring and death.

Patient demographic and clinical characteristics were described using means and standard

deviations for continuous variables, and percentages for categorical variables. Analyses of mortality

rates were based on deaths from all causes divided by person-years of follow-up until death or a

censoring event, and are expressed per 100 person-years. All analyses were conducted in R (version

3.1.3).24

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5. Results

5.1 Cohort Overview

Demographic, baseline characteristics, and mortality rates are summarized by cohort in Table 1.

There were 24,093 patients in the First Event cohort. Of these, 5,274 (22%) had a subsequent CV

event in our study time horizon and were also included in the Second Event cohort. The mean age

at the time of their CV event was 73 years in both cohorts, the proportion male was 59% and 60%

respectively, the mean body mass index was 29 kg/m2 in both cohorts, and baseline LDL cholesterol

levels were 2.3 and 2.2 mmol/L respectively. Within 6 months of the index event, 11.5% of the First

Event and 13.9% of the Second Event cohort died. All-cause mortality rates were higher in the 6-

month acute period (28.5 and 36.5 per 100 person-years, respectively), and lower in the 30-month

long-term period (5.4 to 6.2 per 100 person-years, respectively). Figure 1 shows the distribution of

CV event type for each of the cohorts. Approximately 97% of patients were included in the study

based on receiving a statin as their lipid-modifying therapy prescription within 180 days prior to

their first CV event (Table 1).

5.2 CV Event Costs

The incremental CV event costs in the acute period were £3504 in the First Event cohort and £3968

in the Second Event cohort (Table 2). The incremental cost for both cohorts combined was £3577.

Hospitalization costs were £3338 and £3737 respectively, and £3400 averaged across both cohorts.

The incremental costs in the subsequent 30 months were £361 and £1018 per year, respectively,

and £439 averaged across both cohorts. Hospitalization accounted for 95% of acute and 61% of

long-term incremental costs.

In terms of index event subgroups, the highest acute period incremental CV event costs were for

the CABG/PTCA, myocardial infarction, and ischemic stroke patients in both cohorts (Table 2). The

mean costs for the First Event and Second Event cohorts were as follows: CABG/PTCA £5635 and

£5823, myocardial infarction £4275 and £4301, ischemic stroke £3512 and £4572, heart failure

£2444 and £3461, unstable angina £2179 and £2489 and transient ischemic attack £1537 and

£1814. The subsequent long-term incremental costs (per year) were more heterogeneous and were

as follows for the First Event and Second Event cohorts, respectively: heart failure £8848 and

£2829, myocardial infarction £922 and £1385, ischemic stroke £973 and £682, transient ischemic

attack £705 and £1692, unstable angina £328 and £677, and CABG/PTCA £-368 and £599.

Figure 2 shows stratified incremental costs for each cohort by comorbidity status, and Table 3

shows the corresponding rate of office visits, subsequent CV event rate, death rate, and the initial

CV hospitalization length of stay. In the First Event cohort, higher comorbidity scores from the

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Charlson Comorbidity Index were associated with comparable acute period costs and higher long-

term period costs. In the Second Event cohort, both acute and long-term annual costs increased

with the Charlson score. These trends were generally mirrored by corresponding utilization and

event rate patterns in Table 3. Costs stratified by age group did not vary as systematically (see

Figure 2 in Supplementary Materials).

Overall and hospital-specific total (i.e., non-incremental) costs in the 12-month baseline period, the

6-month acute period, and the 30-month long-term period are provided in Table 2. In terms of total

costs within the baseline period, the highest was for patients with heart failure, whose annual cost

was £3149 in the First Event cohort and £2738 in the Second Event cohort. In the acute period, the

highest total cost was for CABG/PTCA, which was £6843 in the First Event cohort and £6630 in the

Second Event cohort. In the long-term period, the highest annualized cost was in patients with

heart failure, which was £3608 in the First Event cohort and £5554 in the Second Event cohort.

In the acute period after the index event, there were 5.0 and 6.1 more office visits as well as 1.5 and

1.6 more non-CV hospitalizations per person and 1 CV hospitalization per person, for the First Event

and Second Event cohorts respectively. During the long-term period afterwards, there were 2.9 and

4.8 more office visits, as well as 0.2 and 0.5 more non-CV hospitalizations compared to the baseline

period. Mean length of stay for the index hospitalization was shortest for unstable angina (4.5 and

4.9 days) and longest for ischemic stroke (22.5 and 26.7 days).

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6. Discussion

Estimating resource use and the associated costs of managing CVD is important for estimating the

value of interventions that reduce the risk of CV events. To date, these estimates have been based

on expert opinion regarding utilization, combined with UK-specific costs for the resources.14

To our

knowledge, this is the first study both to measure utilization in lipid-modifying therapy patients with

CV events using comprehensive real-world data and to combine this information with costs from

the UK perspective.

These data show that the cost of care for patients experiencing a CV event was generally very high

in the acute period following the event. In particular, patients with a second CV event had higher

incremental CV-related costs. These incremental CV-related costs were generally much smaller in

the long-term period, although they were almost always still positive (i.e., greater than the baseline

pre-CV event period). The exception to this trend was for CABG/PTCA in the First Event cohort,

which suggests that this procedure might have long-term cost offsets in patients who had these

procedures in the absence of one of the other qualifying CV events. The incremental long-term cost

in the Second Event cohort was the lowest in CABG/PTCA patients.

It is also interesting that the First Event and Second Event cohorts often had similar incremental

acute CV-related costs. This is likely related to hospitalization costs, which were the dominant cost

driver during our study’s time horizon. In addition, it appears that comorbidity burden is an

important contributor to incremental long-term costs as shown in Figure 2. The modest association

of age and long-term costs in Supplementary Materials Figure 2 is most likely caused by increases in

comorbidity burden with increasing age in the 3 younger age groups. We were not able to evaluate

the effects of compliance and persistence with lipid-modifying therapy on clinical and economic

outcomes. However, a recent study showed that 43% of patients discontinued their statins within

12 months of initiation, highlighting the importance of doing such research in the future. 25

It is important to highlight the clinical implications of these results. Cardiovascular events are

multifactorial, and CV event costs might vary according to the cardiovascular risk factors of the

patient population. Because our focus was on estimating costs in patients at risk of atherosclerotic

vascular events, we required patients to have received at least 2 lipid-modifying therapy

prescriptions within 180 days. While one would expect CV event costs to be similar in other

populations, these results are most relevant for patients receiving lipid-modifying therapy. The fact

that many of these patients, most of whom were receiving antithrombotic and antihypertensive

treatment, went on to experience subsequent CV events highlights the limitations of available

interventions in a population with substantial need. Furthermore, the progressively more costly

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results by higher comorbidity risk score indicate that comorbid conditions are another important

factor for these patients.

The primary value of these analyses is that they are derived from current, real-world data in the UK.

The CPRD data reflect the medical records of approximately 6% of all patients in clinical practice in

the UK, and are considered to be a very reliable source of longitudinal patient data, supporting over

1500 publications.26,27

Patients were included based on hospitalizations in the HES data, so they

align with intense resource utilization that is relevant to supporting economic modeling efforts. This

is in contrast to events recorded in the CPRD general practitioner data that may not reflect

hospitalization, depending on the reason for recording a diagnosis code at a particular office visit.28

Finally, the perspective of the NHS was used for costing where possible, to ensure alignment with

the payer for the majority of services provided in the UK.

It must be emphasized, though, that our findings merit careful interpretation in light of potential

limitations of our data and the analyses. For instance, we used external data sources for 2014 for

costing and these may not reflect the actual resources used for the patients in this study. This is

most likely to affect the hospitalization costs, since the costs of office visits and drugs are much

smaller and less variable. We included the cost of referral visits, based on referrals recorded in the

CPRD referral file. We cannot ascertain whether these visits actually occurred, so the utilization of

these services (and hence, the costs) may be over-estimated. On the contrary, not every visit to a

specialist is recorded by the general practitioner, causing cost underestimation and

counterbalancing the potential bias to some degree.

Importantly, we included all hospitalizations for each patient in our utilization and cost analyses,

and not just CV hospitalizations. This may lead to more noise in the data if there were to be no

relationship between the CV events and hospitalizations for other, non-CV diseases. However, we

have minimized this with a pre-post design over a relatively short period of 3 years, and by

calculating incremental costs. In terms of medications, we focused on anti-thrombotic, anti-

hypertensive, and anti-diabetic drugs, which are likely to be important. We did not analyze every

prescription in the CPRD data due to the variety of drugs, doses, and prescriptions; therefore, the

drug costs may be under-estimated. Drug costs are generally low, so this is not likely to be a large

bias. Also, if the initial CV event lead to additional resource utilization for other reasons (e.g.,

incidentally discovered conditions), these would be included in the incremental costs. Along these

lines, older patients may receive different care than younger patients, so the incremental costs

might vary by age.

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Finally, we used inverse probability of censoring weights to combine data across time periods to

reduce the loss of information about costs and the incremental cost of CV events. However,

information from patients who did not survive, or who were censored, is still lost and is estimated

by using the data from the remaining patients in the study. For patients censored due to the end of

their data, the missing data is likely to be missing at random and should not introduce a substantial

bias. However, for patients who died or were censored due to a subsequent CV hospitalization, the

costs estimated in this study may under-estimate the actual cost of care over time if the patients

who were censored were higher cost patients. This may at least partly explain the lower cost

estimates in the oldest patients (see Figure 2 in Supplementary Materials).

Overall, this analysis provides a real-world evidence-based analysis of the resources used, and the

associated costs, in managing CV events in the UK. The burden is higher in higher risk patients. The

results demonstrate the substantial economic burden and unmet medical need for patients with CV

events in the UK despite the use of lipid modifying therapies.

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7. Figures

Figure 1: Distribution of Types of Index Events By Cohort

Note: MI = myocardial infarction, IS = ischemic stroke, HF = heart failure, TIA = transient ischemic

attack, UA = unstable angina, CABG = coronary artery bypass graft, and PTCA = percutaneous

transluminal coronary angioplasty.

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Figure 2: Incremental Costs (£) by Charlson Comorbidity Score and Cohort


Patients stratified by Charlson Comorbidity Index Score, which is a function of the number of

comorbid conditions and their association with mortality. Higher scores indicate a higher mortality

risk.

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8. Tables

Table 1. Baseline Characteristics


First Event

(n =24,093)

Second Event

(n = 5274)

Continuous Variables N Mean SD N Mean SD

Age at Index 24,093 72.7 11.1 5274 72.8 10.8

BMI 15,005 28.8 5.7 3457 28.7 5.6

Systolic BP 23,107 136.1 19.2 5119 132.6 20.1

LDL Cholesterol 13,247 2.4 2.3 2803 2.2 0.9

Total Cholesterol 19,693 4.4 1.4 4267 4.2 1.4

Triglycerides 15,064 1.6 1.2 3143 1.6 1.0

Categorical Variables N % N %

Lipid Modifying

Therapy

(before or on

index date)*

Low dose statin 1918 8.0 428 8.1

Moderate dose

statin 18,556 77.0

3980 75.5

High dose statin 2886 12.0 695 13.2

Ezetimibe 465 1.9 104 2.0

All others 268 1.1 67 1.3

Year of Index

Event

2006 3948 16.4 425 8.1

2007 3975 16.5 782 14.8

2008 3858 16.0 877 16.6

2009 4023 16.7 986 18.7

2010 3801 15.8 980 18.6

2011 3630 15.1 987 18.7

2012 858 3.6 237 4.5

Age Group < 60 3170 13.2 652 12.4

60 - 69 4892 20.3 1007 19.1

70 - 79 8864 36.8 2092 39.7

≥ 80 7167 29.8 1523 28.9

Gender Male 14,221 59.0 3177 60.3

Female 9872 41.0 2097 39.8

Smoking Status Current 2867 11.9 488 9.3

Former 9020 37.4 2248 42.6

Never 6483 26.9 1375 26.1

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First Event

(n =24,093)

Second Event

(n = 5274)

N % N %

Charlson

Comorbidity

Index (Score)

None 7199 29.9 831 15.8

One 5378 22.3 1029 19.5

Two or higher 11,516 47.8 3414 64.7

Risk Factors Hypertension 10,291 42.7 2364 44.8

Diabetes 7343 30.5 1905 36.1

COPD 1958 8.1 524 9.9

CKD 6038 25.1 1815 34.4

AF 3085 12.8 1014 19.2

CV Conditions AAA 143 0.6 25 0.5

Angina 7239 30.1 1861 35.3

PVD 1972 8.2 524 9.9

TIA 2005 8.3 442 8.4

Cardiac Ischemia 10,261 42.6 2508 47.6

Carotid Stenosis 223 0.9 54 1.02

Other

medications

Anti-hypertensive 20,973 87.1 4979 94.4

Anti-thrombotic 18,818 78.1 4901 92.9

Anti-diabetic 6073 25.2 1599 30.3

Mortality Rate (per 100 person-years) Person

years

Died Rate Person

years

Died Rate

Acute 0-6 months 9686 2764 28.5 2006 733 36.5

Long-term 7-36 months 22,798 1240 5.4 3967 246 6.2

Note: COPD = Chronic Obstructive Pulmonary Disease; CKD = Chronic Kidney Disease; AF = Arterial

Fibrillation; AAA = Abdominal Aortic Aneurysm; PVD = Peripheral Vascular Disease; TIA = Transient

Ischemic Attack; BMI = Body Mass Index; SBP = Systolic Blood Pressure; LDL = Low-Density

Lipoprotein. * Lipid-modifying therapy refers to the closest prescription to the index date within

180 days prior to it. See Supplementary Materials for definitions of medication categories.

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Table 2. CV Event-Related Costs by Cohort and Index Event

Total Costs (in £) Incremental Total Costs (in £)

Cohort Group n

Baseline (1 year

before first CV

event) [Mean (SE)]

Months 1-6 [Mean

(SE)]

Months 7-36

Annualized [Mean

(SE)]

Months 1-6 [Mean

(SE)]

Months 7-36

Annualized [Mean

(SE)]

First

Event


Unstable Angina 5801 1965.61 (86.76) 3133.12 (61.44) 2081.84 (65.15) 2179.24 (48.51) 328.45 (61)

Ischemic Stroke 3489 1962.42 (119.73) 4389.27 (102.29) 2526.63 (276.42) 3512.25 (102.82) 972.62 (257.48)

PTCA/CABG* 5082 2412.2 (95.69) 6843.38 (94.67) 1855.11 (110.02) 5635.19 (87.29) -368.26 (103.58)

Heart Failure 3596 3148.72 (136.77) 3988.86 (111.87) 3607.86 (313.16) 2443.58 (95.22) 847.55 (247.64)



All Patients (total) 24,093 2301.34 (57.26) 4594.16 (39.01) 2262.92 (60.37) 3504.01 (38.04) 361.11 (48.79)

Second

Event


Unstable Angina 1,347 2795.44 (398.16) 3925.66 (204.31) 2844.49 (247.33) 2489.48 (132.63) 676.82 (278.78)

Ischemic Stroke 532 2202.96 (143.98) 5607.47 (273.97) 2870.35 (281.51) 4572.28 (280.27) 682.29 (438.78)

PTCA/CABG* 1256 1635.76 (63.68) 6630.29 (208.77) 2121.06 (302.87) 5823.12 (202.55) 598.64 (296.25)

Heart Failure 1104 2737.60 (97.24) 4818.24 (318.44) 5554.17 (1732.62) 3460.91 (312.14) 2829.02 (1643.39)


All Patients (hospital) 5274 1588.26 (100.82) 4530.08 (100.6) 2061.65 (237.53) 3736.68 (85.06) 762.21 (237.68)

All Patients (total) 5274 2380.42 (100.97) 5147.79 (100.62) 2998.11 (242.14) 3967.74 (84.73) 1017.68 (239.47)

First and

Second

Combined


Unstable Angina 7148 2121.98 (111.68) 3261.27 (65.83) 2179.64 (75.16) 2229.42 (50.81) 373.15 (74.47)

Ischemic Stroke 4021 1994.31 (91.59) 4533.08 (103.14) 2545.1 (274.62) 3637.86 (103.05) 953.48 (261.93)

PTCA/CABG* 6338 2258.28 (69.8) 6802.98 (86.73) 1894.5 (89.56) 5669.47 (85.13) -220.73 (83.14)

Heart Failure 4700 3052.04 (112.99) 4144.89 (116.03) 3881.8 (344.1) 2635.35 (105.35) 1128.86 (300.08)



All Patients (total) 29,367 2315.55 (43.78) 4681.04 (41.45) 2351.28 (59.09) 3576.82 (36.18) 438.89 (54)

* Note: PTCA/CABG = Percutaneous Transluminal Coronary Angioplasty / Coronary Artery Bypass Graft. The proportions of revascularization events that

were CABG were 42% (2137/5082), 35% (442/1256) and 41% (2579/6338) for the First Event, Second Event, and All (combined) cohorts respectively.

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Table 3: Event Rates by Charlson Comorbidity Score and Cohort

Comorbidity score 0 Comorbidity score 1 Comorbidity score 2+

Acute Long term Acute Long term Acute Long term

First Event





Second Event





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9. Additional Study Information

9.1 Contributorship:

Design: All authors

Analyses: MDD, MG, RIG

Initial draft: MDD, MG

Revisions: All authors

Final approval: All authors

Agreement to be accountable: All authors

9.2 Funding:

This study was funded by Amgen, Inc.

9.3 Competing interests:

Mark Danese, Michelle Gleeson, and Robert Griffiths work with Outcomes Insights, Inc., which

was funded to conduct this study. Lucie Kutikova and Ali Azough are employees of Amgen, Inc.

Kamlesh Khunti has acted as a consultant and speaker for Novartis, Novo Nordisk, Sanofi-

Aventis, Lilly, Merck Sharp & Dohme, Janssen, Astra Zeneca and Boehringer Ingelheim. He has

received grants in support of investigator and investigator-initiated trials from Novartis, Novo

Nordisk, Sanofi-Aventis, Lilly, Pfizer, Boehringer Ingelheim, Merck Sharp & Dohme, Janssen and

Roche, and has served on advisory boards for Lilly, Sanofi-Aventis, Merck Sharp & Dohme, Novo

Nordisk, Boehringer Ingelheim, Janssen and Astra Zeneca. SR Kondapally Seshasai has provided

consulting to Amgen and received grants from Kowa and Sanofi. Kausik K. Ray has provided

consulting to Amgen, Sanofi, Pfizer, Regeneron, Astra Zeneca, Kowa, Aegerion, Merck Sharp &

Dohme, Lilly and ISIS, and received grants from Sanofi, Regeneron, Amgen, Pfizer and Merck

Sharp & Dohme through his institution.

9.4 Acknowledgments:

KK would like to acknowledge support for his work from the National Institute for Health

Research (NIHR) and Collaboration for Leadership in Applied Health Research and Care

(CLAHRC) East Midlands. Dr. Khunti’s participation was supported by the National Institute for

Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care in

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East Midlands (CLAHRC EM). The views expressed are those of the authors and not necessarily

those of the NHS, the NIHR or the Department of Health.

9.5 Data sharing:

To ensure patient privacy and confidentiality, the CPRD data cannot be shared.

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10. References

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Cardiovascular Disease Prevention in Clinical Practice. European guidelines on cardiovascular

disease prevention in clinical practice (version 2012). European Heart Journal. 2012; 33: 1635-

1701.

2 Cholesterol Treatment Trialists’ (CTT) Collaboration, Baigent C, Blackwell L, Emberson Efficacy

and safety of more intensive lowering of LDL cholesterol: a meta-analysis of data from 170,000

participants in 26 randomised trials. Lancet. 2010;13;376:1670-1681.

3 JBS3 Board. Joint British Societies’ consensus recommendations for the prevention of

cardiovascular disease (JBS3). Heart. 2014; 100: ii1-ii67.




Guideline Center, UK. July 2014, page 589.

5 Chapman RH, Liu LZ, Girase PG, Straka RJ. Determining initial and follow-up costs of

cardiovascular events in a US managed care population. BMC Cardiovasc Disord. 2011;11:11.

6 Ohsfeldt RL, Gandhi SK, Fox KM, Bullano MF, Davidson M. Medical and cost burden of

atherosclerosis among patients treated in routine clinical practice. J Med Econ. 2010;13:500-

507.

7 Duh MS, Fulcher NM, White LA, Jayawant SS, Ramamurthy P, Moyneur E, Ong SH. Costs

associated with cardiovascular events in patients with hypertension in US managed care

settings. J Am Soc Hypertension. 2009;3:403-415.

8 Eisenstein EL, Shaw LK, Anstrom KJ, Nelson CL, Hakim Z, Hasselblad V, Mark DB. Assessing the

clinical and economic burden of coronary artery disease: 1986-1998. Med Care. 2001;39:824-

835.

9 Etemad LR, McCollam PL. Total first-year costs of acute coronary syndrome in a managed care

setting. J Manag Care Pharm. 2005;11:300-306.

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10 Straka RJ, Liu LZ, Girase PS, DeLorenzo A, Chapman RH. Incremental cardiovascular costs and

resource use associated with diabetes: an assessment of 29,863 patients in the US managed-

care setting. Cardiovasc Diabetol. 2009;8:53.

11 Herrett E, Gallagher AM, Bhaskaran K, et al. Data Resource Profile: Clinical Practice Research

Datalink (CPRD). Int J Epidemiol. 2015;44(3):827-836. doi:10.1093/ije/dyv098.

12 Williams T, van Staa T, Puri S, Eaton S. Recent advances in the utility and use of the General

Practice Research Database as an example of a UK Primary Care Data resource. 2012; 3:89-99.

13 http://www.hscic.gov.uk/qofbrv28, accessed January 20, 2015




Guideline Center, UK. July 2014, page 589, accessed January 20, 2015

15https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/397469/03

a_2013-14_National_Schedule_-_CF-NET_updated.xls, accessed January 20, 2015

16 http://www.hscic.gov.uk/casemix/costing, accessed January 20, 2015

17 http://www.pssru.ac.uk/project-pages/unit-costs/2014/, accessed January 20, 2015

18 http://www.ppa.org.uk/edt/September_2014/mindex.htm, accessed January 20, 2015

19 Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic

comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373-

383. doi:10.1016/0021-9681(87)90171-8.

20 Kahn N, Perera R, Harper S, Rose PW. Adaptation and validation of the Charlson Index for

Read/OXMIS coded databases, BMC Family Practice 2010, 11:1

21 Lin DY. Linear regression analysis of censored medical costs. Biostatistics. 2000. 1:35-47.

22 Griffiths RI, Gleeson ML, Danese MD, O’Hagan A. Inverse Probability Weighted Least Squares

Regression in the Analysis of Time-Censored Cost Data: An Evaluation of the Approach Using

SEER-Medicare. Value Health. 2012 Jul;15(5):656-63.

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23 Siebert U, Alagoz O, Bayoumi AM, et al. State-Transition Modeling: A report of the ISPOR-

SMDM modeling good research practices task force – 3. Medical Decision Making. 2012. 32:

690-700.

24 Comprehensive R Archive Network website: http://cran.r-project.org. Accessed 15

September 2015

25 Nordstrom Beth L, Collins Jenna M, Donaldson Robert, Engelman WA, Tockhorn A, Zhu Y, and

Zhao Zhenxiang. Treatment patterns and lipid levels among patients with high-risk

atherosclerotic CVD in the UK. Br J Cardiol. 2015; 22:147-54. doi:10.5837/bjc.2015.034

26 Herrett E. Validation and validity of diagnoses in the General Practice Research Database: a

systematic review. British Journal of Clinical Pharmacology. 2009. 69: 4-14.

27 Clinical Practice Research Datalink website: http://www.cprd.com/intro.asp. Accessed 15

September 2015.

28 Herrett E, Shah AD, Boggon R, Denaxas S, Smeeth L, van Staa T, Timmis A, and Hemingway H.

Completeness and diagnostic validity of recording acute myocardial infarction events in primary

care, hospital care, disease registry, and national mortality records: cohort study. BMJ. 2013;

346:f2350. doi: 10.1136/bmj.f2350

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Distribution of Types of Index Events By Cohort Figure 1

127x97mm (300 x 300 DPI)

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Incremental Costs (£) by Charlson Comorbidity Score and Cohort

Figure 2

127x97mm (300 x 300 DPI)

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Figures:

Figure 1: Overview of Study Design

Note: The study sample was selected in the following way. We identified unique patients with CV

hospitalizations in HES from 2006 to 2012 (105,526), selected those with linked, up-to-standard CPRD

data (69,248), selected those who received LMT within 180 days (28,051), and then selected those

without a prior history of myocardial infarction or ischemic stroke in the GP data (24,093).

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Figure 2: Incremental Costs (£) by Age Group and Cohort


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Tables:

Table 1: Codes to Identify Medications*

Category BNF Chapter

Number

BNF Chapter Title

Diabetes 060101 Insulins

060102 Antidiabetic Drugs

Anti-coagulant 020802 Oral Anticoagulants

020900 Antiplatelet Drugs

Anti-hypertensive 020400 Beta-adrenoceptor Blocking Drugs

020501 Vasodilator Antihypertensive Drugs

020502 Centrally Acting Antihypertensive Drugs

020503 Adrenergic Neurone Blocking Drugs

020504 Alpha-adrenoceptor Blocking Drugs

020505 Drugs Affecting The Renin-angiotensin System

020602 Calcium-channel Blockers

Lipid modifying

therapy

021204 Statins*

021202 Ezetimibe

021203 Fibrates

021205 Nicotinic acid

021201 Bile acid sequestrants

*Statin intensity as defined below was based on Stone NJ, et al. American College of

Cardiology/American Heart Association Task Force on Practice Guidelines. 2013 ACC/AHA guideline on

the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults: a report of the

American College of Cardiology/American Heart Association Task Force on Practice Guidelines.

Circulation. 2014 Jun 24;129 (25 Suppl 2):S1-45. doi: 10.1161/01.cir.0000437738.63853.7a. PMID:

24222016.

High-intensity statins: Atorvastatin 40, 80 mg; Rosuvastatin 20, 40 mg; Simvastatin 80 mg

Medium-intensity statins: Fluvastatin 80 mg; Simvastatin 20, 40 mg; Atorvastatin 10, 20 mg;

Rosuvastatin 5, 10 mg; Lovastatin 40 mg; Pravastatin 40, 80 mg; Pitavastatin 2, 4 mg

Low-intensity statins: Fluvastatin 20-40 mg; Simvastatin 10 mg; Lovastatin 20 mg;

Pravastatin 10-20 mg

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Table 2: Codes to Identify Hospitalizations

Hospitalization Diagnosis Code(s) Condition Type of Code

I21, I22 Myocardial infarction ICD-10 diagnosis

I20.0, I20.9, I24.0, I24.8, I24.9 Unstable angina ICD-10 diagnosis

I63 Ischemic stroke ICD-10 diagnosis

I11.0, I13.0, I13.2, I50 Heart failure ICD-10 diagnosis

G45.8, G45.9 Transient ischemic attack ICD-10 diagnosis

K40

Saphenous vein graft

replacement of coronary artery


K41 Other autograft replacement of

coronary artery


K42 Allograft replacement of

coronary artery


K43 Prosthetic replacement of

coronary artery


K44 Other replacement of coronary

artery


K45 Connection of thoracic artery to

coronary artery


K46 Other bypass of coronary artery OPCS procedure for CABG

K49 Transluminal balloon angioplasty

of coronary artery


K50 Other therapeutic transluminal

operations on coronary artery


K75 Percutaneous transluminal

balloon angioplasty and insertion

of stent into coronary artery


Note: The number of characters sufficient to capture the entire set of codes is provided above.

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STROBE 2007 (v4) Statement—Checklist of items that should be included in reports of cohort studies

Section/Topic Item

# Recommendation Reported on page #

Title and abstract 1 (a) Indicate the study’s design with a commonly used term in the title or the abstract p.3 (abstract)

(b) Provide in the abstract an informative and balanced summary of what was done and what was found p.3 (abstract) and

p.5 (article

summary)

Introduction

Background/rationale 2 Explain the scientific background and rationale for the investigation being reported p.6 (introduction)

Objectives 3 State specific objectives, including any prespecified hypotheses p.7 (introduction)

Methods

Study design 4 Present key elements of study design early in the paper p.8 (study overview

and data sources)

Setting 5 Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data

collection

p.8-10 (methods)

Participants 6 (a) Give the eligibility criteria, and the sources and methods of selection of participants. Describe methods of follow-up p.8 (patient

populations)

(b) For matched studies, give matching criteria and number of exposed and unexposed Not applicable

Variables 7 Clearly define all outcomes, exposures, predictors, potential confounders, and effect modifiers. Give diagnostic criteria, if

applicable

p.10 (study

endpoints)

Data sources/

measurement

8* For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe

comparability of assessment methods if there is more than one group

p.8 (data sources)

Bias 9 Describe any efforts to address potential sources of bias p.9 (study design)

Study size 10 Explain how the study size was arrived at Supplementary

materials, figure 1

Quantitative variables 11 Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen and

why

p.10 (costing)

Statistical methods 12 (a) Describe all statistical methods, including those used to control for confounding p.11 (analyses)

(b) Describe any methods used to examine subgroups and interactions p.11 (analyses)

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(c) Explain how missing data were addressed p.11 (analyses)

(d) If applicable, explain how loss to follow-up was addressed p.11 (analyses)

(e) Describe any sensitivity analyses Not applicable

Results

Participants 13* (a) Report numbers of individuals at each stage of study—eg numbers potentially eligible, examined for eligibility, confirmed

eligible, included in the study, completing follow-up, and analysed

Supplementary

materials, figure 1

(b) Give reasons for non-participation at each stage Supplementary

materials, figure 1

(c) Consider use of a flow diagram Supplementary

materials, figure 1

Descriptive data 14* (a) Give characteristics of study participants (eg demographic, clinical, social) and information on exposures and potential

confounders

p.12 (Results) and

p.20 (table 1) and

p.18 (figure 1)

(b) Indicate number of participants with missing data for each variable of interest p.20 (table 1)

(c) Summarise follow-up time (eg, average and total amount) p.20 (table 1)

Outcome data 15* Report numbers of outcome events or summary measures over time p.20 (table 1)

Main results 16 (a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their precision (eg, 95% confidence

interval). Make clear which confounders were adjusted for and why they were included

p.12 (Results) and

p.21 (table 2)

(b) Report category boundaries when continuous variables were categorized p.20 (table 1)

(c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time period Not applicable

Other analyses 17 Report other analyses done—eg analyses of subgroups and interactions, and sensitivity analyses p.20 (table 2), p.18

(figure 2), and p.22

(table 3)

Discussion

Key results 18 Summarise key results with reference to study objectives p.14 (discussion)

Limitations

Interpretation 20 Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results from

similar studies, and other relevant evidence

p.15 (discussion)

Generalisability 21 Discuss the generalisability (external validity) of the study results p.15 (discussion)

Other information

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Funding 22 Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on

which the present article is based

p.1 (disclosures)

*Give information separately for cases and controls in case-control studies and, if applicable, for exposed and unexposed groups in cohort and cross-sectional studies.

Note: An Explanation and Elaboration article discusses each checklist item and gives methodological background and published examples of transparent reporting. The STROBE

checklist is best used in conjunction with this article (freely available on the Web sites of PLoS Medicine at http://www.plosmedicine.org/, Annals of Internal Medicine at

http://www.annals.org/, and Epidemiology at http://www.epidem.com/). Information on the STROBE Initiative is available at www.strobe-statement.org.

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Consolidated Health Economic Evaluation Reporting Standards – CHEERS Checklist 1

CHEERS Checklist Items to include when reporting economic evaluations of health interventions

The ISPOR CHEERS Task Force Report, Consolidated Health Economic Evaluation Reporting Standards (CHEERS)—Explanation and Elaboration: A Report of the ISPOR Health Economic Evaluations Publication Guidelines Good Reporting Practices Task Force, provides examples and further discussion of the 24-item CHEERS Checklist and the CHEERS Statement. It may be accessed via the Value in Health or via the ISPOR Health Economic Evaluation Publication Guidelines – CHEERS: Good Reporting Practices webpage: http://www.ispor.org/TaskForces/EconomicPubGuidelines.asp

Section/item Item No

Recommendation Reported on page No/ line No

Title and abstract Title 1 Identify the study as an economic evaluation or use more

specific terms such as “cost-effectiveness analysis”, and describe the interventions compared.

Abstract 2 Provide a structured summary of objectives, perspective, setting, methods (including study design and inputs), results (including base case and uncertainty analyses), and conclusions.

Introduction Background and objectives

3 Provide an explicit statement of the broader context for the study.

Present the study question and its relevance for health policy or practice decisions.

Methods Target population and subgroups

4 Describe characteristics of the base case population and subgroups analysed, including why they were chosen.

Setting and location 5 State relevant aspects of the system(s) in which the decision(s) need(s) to be made.

Study perspective 6 Describe the perspective of the study and relate this to the costs being evaluated.

Comparators 7 Describe the interventions or strategies being compared and state why they were chosen.

Time horizon 8 State the time horizon(s) over which costs and consequences are being evaluated and say why appropriate.

Discount rate 9 Report the choice of discount rate(s) used for costs and outcomes and say why appropriate.

Choice of health outcomes

10 Describe what outcomes were used as the measure(s) of benefit in the evaluation and their relevance for the type of analysis performed.

Measurement of effectiveness

11a Single study-based estimates: Describe fully the design features of the single effectiveness study and why the single study was a sufficient source of clinical effectiveness data.

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Not a decision analysis

Not comparative

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Not applicable(all costs from 2014)

No benefits measured

Not applicable




11b Synthesis-based estimates: Describe fully the methods used for identification of included studies and synthesis of clinical effectiveness data.

Measurement and valuation of preference based outcomes

12 If applicable, describe the population and methods used to elicit preferences for outcomes.

Estimating resources and costs

13a Single study-based economic evaluation: Describe approaches used to estimate resource use associated with the alternative interventions. Describe primary or secondary research methods for valuing each resource item in terms of its unit cost. Describe any adjustments made to approximate to opportunity costs.

13b Model-based economic evaluation: Describe approaches and data sources used to estimate resource use associated with model health states. Describe primary or secondary research methods for valuing each resource item in terms of its unit cost. Describe any adjustments made to approximate to opportunity costs.

Currency, price date, and conversion

14 Report the dates of the estimated resource quantities and unit costs. Describe methods for adjusting estimated unit costs to the year of reported costs if necessary. Describe methods for converting costs into a common currency base and the exchange rate.

Choice of model 15 Describe and give reasons for the specific type of decision-analytical model used. Providing a figure to show model structure is strongly recommended.

Assumptions 16 Describe all structural or other assumptions underpinning the decision-analytical model.

Analytical methods 17 Describe all analytical methods supporting the evaluation. This could include methods for dealing with skewed, missing, or censored data; extrapolation methods; methods for pooling data; approaches to validate or make adjustments (such as half cycle corrections) to a model; and methods for handling population heterogeneity and uncertainty.

Results Study parameters 18 Report the values, ranges, references, and, if used, probability

distributions for all parameters. Report reasons or sources for distributions used to represent uncertainty where appropriate. Providing a table to show the input values is strongly recommended.

Incremental costs and outcomes

19 For each intervention, report mean values for the main categories of estimated costs and outcomes of interest, as well as mean differences between the comparator groups. If applicable, report incremental cost-effectiveness ratios.

Characterising uncertainty

20a Single study-based economic evaluation: Describe the effects of sampling uncertainty for the estimated incremental cost and incremental effectiveness parameters, together with the impact

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Not applicable

Not applicable

Not applicable

Not applicable

Not a decision model


Page 10



Not applicable




of methodological assumptions (such as discount rate, study perspective).

20b Model-based economic evaluation: Describe the effects on the results of uncertainty for all input parameters, and uncertainty related to the structure of the model and assumptions.

Characterising heterogeneity

21 If applicable, report differences in costs, outcomes, or cost-effectiveness that can be explained by variations between subgroups of patients with different baseline characteristics or other observed variability in effects that are not reducible by more information.

Discussion Study findings, limitations, generalisability, and current knowledge

22 Summarise key study findings and describe how they support the conclusions reached. Discuss limitations and the generalisability of the findings and how the findings fit with current knowledge.

Other Source of funding 23 Describe how the study was funded and the role of the funder

in the identification, design, conduct, and reporting of the analysis. Describe other non-monetary sources of support.

Conflicts of interest 24 Describe any potential for conflict of interest of study contributors in accordance with journal policy. In the absence of a journal policy, we recommend authors comply with International Committee of Medical Journal Editors recommendations.

For consistency, the CHEERS Statement checklist format is based on the format of the CONSORT statement checklist The ISPOR CHEERS Task Force Report provides examples and further discussion of the 24-item CHEERS Checklist and the CHEERS Statement. It may be accessed via the Value in Health link or via the ISPOR Health Economic Evaluation Publication Guidelines – CHEERS: Good Reporting Practices webpage: http://www.ispor.org/TaskForces/EconomicPubGuidelines.asp The citation for the CHEERS Task Force Report is: Husereau D, Drummond M, Petrou S, et al. Consolidated health economic evaluation reporting standards (CHEERS)—Explanation and elaboration: A report of the ISPOR health economic evaluations publication guidelines good reporting practices task force. Value Health 2013;16:231-50.

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Not applicable

Not applicable

Table 2, Figure 2

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