Statistical opportunities and challenges of electronic health records Dr Alex Dregan Lecturer in Epidemiology and Public Health email: [email protected]

Statistical opportunities and challenges of electronic health records

Dr Alex DreganLecturer in Epidemiology and Public Health

email: [email protected]

Electronic health records (EHRs)• Capture and integrate data on all aspects of care over time

– various data types, from structured information such as condition diagnosis, lab tests, referrals, drug prescription data, to unstructured data such as clinical narratives

• Growing volume of data – Prescribing, blood pressure, morbidity data is accurate and complete– Data can be related to individual patients' characteristics (sex, age, social class) and practice

aspects (ie practice size, region, number of GPs, auxiliary staff)

• Widespread use in UK primary care– For effective communication, clinical care, service organisation, quality and audit,

professional development and self-directed learning – Potential to supporting undergraduate learning and teaching as it reflects the context in

which the students will be ultimately working– Graduates must be able to use different techniques to record, organise, analyse, and

present information

Guy’s and St Thomas’ NHS Foundation Trust and King’s College London’scomprehensive Biomedical Research Centre

Antibiotic prescribing for acute RTIs

CPRD practices

Control Intervention

Electronic reminder to GP, no or delayed AB prescribing

NICE guidelinesQualitative research

Proportion of consultations for RTI with AB prescribed

Subjects aged 18-59 years consulting for RTI, 60% prescribed AB in 2006

Using EHRs for research – example


Using EHRs for research – example Analysis

• Sample size (Hayes and Bennett (1999)): 47 practice per arm for an 0.8 power to detect 5% difference (ICC=0.23; Ashworth et al., 2005)

• Statistical analysis:

- Intention to treat (ITT) principle - difference in outcome between intervention and control practices

- Cluster-level (practice as a unit) analysis – analysis of covariance framework

- Minimum variance weights (Kerry and Bland, 2001) used to allow for varying number of participants and consultations per practice


5

Prompt Utilisation


RTI consultation and AB prescribing per 1,000 registered participants and proportion (%) of RTI consultations with AB prescribed. Figures are mean (interquartile range) of

practice-specific values for 12 months before- and after- intervention.

Intervention Trial Arm Control Trial Arm Adjusted mean differenced

(95% confidence interval)

P

value Before

After Before After

RTI Consultation rate 219(181; 254)

209 (176;247)

216 (186; 246)

218(184;244)

-9.10 (-21.51;3.30)

0.148

Antibiotic Prescription rate 116 (91; 131)

108 (87;129)

111 (86; 135)

114 (85 ; 128)

-9.69 (-18.63; -0.75)

0.034

AB Prescriptions Per RTI Consultation (%)

53 (46 ;60)

52 (45; 58)

52 (45;60)

52 (45;59)

-1.85 (-3.59;-0.10)

0.038


Intervention utilisation and AB prescribing by quartile of intervention utilisation.

Control practices

Lowest Quartile of Utilisation

(13)

SecondQuartile (13)

ThirdQuartile (13)

Highest Quartile of Utilisation

(13)

Intervention Utilisation (per 1,000 consultations for RTI)

Prompt Views Not applicable 0 (0 ; 0) 16 (0 ; 22) 77 (0 ; 117) 174 (68 ; 248)

Leaflets Printed Not applicable 0 (0 ; 0) 6 (0 ; 0) 18 (0 ; 21) 15 (0 ; 0)

Proportion (%) of RTI consultations with antibiotics prescribed

Before Intervention 52 (45 ; 59) 55 (49 ; 61) 53 (46 ; 59) 55 (51; 63) 50 (41 ; 57)

After Intervention 52 (45 ; 59) 54 (46 ; 63) 54 (51 ; 60) 53 (52;61) 48 (42 ; 54)

Unadjusted mean difference

(95% confidence interval)

0.7 (-0.6 ; 2.0) -1.2 (-5.1 ; 2.8)

-1.0 (-2.9; 0.9) -1.4 (-3.9;1.0) -1.6 (-5.0; 1.7)

Adjusted test for trend across categories (95% confidence interval) -0.64 (-1.23 ; -0.05) P=0.034


Using EHRs for research - example (cont.)

Inflammatory disorders and risk of cardiovascular diseases

• Outcomes– New diagnoses of stroke, CHD, and T2DM. – Multiple morbidity was defined as the occurrence of ≥2 outcomes in a participant.– Mean of CRP values (biomarker)

• Exposure– Chronic inflammatory disorders including psoriasis, Crohn’s disease, Bullous skin

disease, ulcerative colitis, systemic lupus, inflammatory arthritis, and vasculitis

• Statistical analysis– Cox proportional hazards model – Sensitivity analyses using competing risk analysis– Missing indicator variables to deal with missing data– Random-effects meta-analysis


Forest plot displaying random effect meta-analysis of the influence of diverse chronic inflammatory conditions on multiple cardiovascular. HR=Hazard ratios; CI=Confidence

intervals.

Overall (I-squared = 48.7%, p = 0.058)

Inflammatory Arthritis

Crohn's Disease

Ulcerative Colitis

Systemic Vasculitis

Chronic Inflammatory Disease

Bullous Skin Diseases

Psoriasis, Mild

Systemic Autoimmune Disorders

Psoriasis, Severe

1.20 (1.15, 1.26)

1.12 (1.05, 1.18)

1.06 (0.90, 1.24)

1.26 (1.14, 1.40)

1.29 (1.16, 1.44)

HR (95% CI)

1.17 (1.03, 1.33)

1.32 (1.16, 1.50)

100.00

19.94

6.47

11.95

11.24

9.03

23.47

8.96

8.96

1.20 (1.15, 1.26)

1.12 (1.05, 1.18)

1.06 (0.90, 1.24)

1.26 (1.14, 1.40)

1.29 (1.16, 1.44)

HR (95% CI)

1.17 (1.03, 1.33)

1.18 (1.13, 1.23)

1.32 (1.16, 1.50)

1.29 (1.16, 1.50)

100.00

19.94

6.47

11.95

11.24

% Weight

9.03

23.47

8.96

8.96

1 1.25 1.5 1.75

Guy’s and St Thomas’ NHS Foundation Trust and King’s College London’s comprehensive Biomedical Research Centre

Using EHRs for research - example (cont.)Sodium Valproate and risk of stroke - A nested case-control study

A nested case control study was implemented using data from the Clinical Practice Research Datalink (CPRD) (www.cprd.com).

The study population consisted of a cohort of epilepsy (N=15,001) patients treated with at least one AED who were registered with 653 CPRD practices between 1 January 1992 and 31 January 2013.

Exposure: Sodium valproate treatment represented the primary exposure of interest for the present study.

Outcomes: Ischemic stroke

Analysis: Conditional logistic regression analysis


Using EHRs for research - example (cont.)

Case(N=2,002)

Control(N=13,098)

Unadjusted modelOR† (95%CI) p

Fully adjusted modelOR† (95%CI) p

Ever prescribed 681(34) 4,407(34) 1.03(0.93,1.14) 0.555 1.01(0.91,1.12) 0.875

Pre-stroke year 555(28) 3,106(24) 1.27(1.14,1.41) 0.001 1.22(1.09,1.38) 0.001

Number of SV prescriptions

None 1,321(66) 8691(66) Reference Reference

Lowest quarter 227(11) 1,075(8) 1.47(1.26,1.72) 0.001 1.22(1.02,1.45) 0.025

Second quarter 198(10) 1,062(8) 1.28(1.09,1.59) 0.003 1.21(1.02,1.45) 0.033

Third quarter 166(8) 1,100(9) 0.99(0.83,1.18) 0.924 1.00(0.83,1.21) 0.972

Highest quarter 90(5) 1,170(9) 0.49(0.39,0.61) <0.001 0.59(0.46,0.74) <0.001

Time on SV prescriptions

None 1,321(66) 8,915(68) Reference Reference

Lowest quarter 256(13) 962(7) 1.97(1.68,2.29) <0.001 1.62(1.37,1.92) <0.001

Second quarter 194(10) 1,023(8) 1.35(1.14,1.60) 0.001 1.28(1.07,1.54) 0.007

Third quarter 146(7) 1,068(8) 0.92(0.76,1.11) 0.373 0.95(0.78,1.15) 0.584

Highest quarter 85(4) 1,130(9) 0.48(0.38,0.60) 0.001 0.57(0.44,0.72) <0.001

Using EHRs for research - example (cont.)Validity of cancer diagnosis in a primary care database compared with linked

cancer registrations in England. Population-based cohort study

• Population-based cohort study

• The eligible cohort comprised 42,556 participants, registered with English general practices in the CPRD that consented to CR linkage.

• Read and ICD cancer code sets were reviewed and agreed by two authors

• The positive predictive value (PPV), sensitivity, and specificity were estimated using CR as the reference data. Median and interquartile ranges for the difference in date of cancer diagnosis between CPRD and CR databases were estimated for four cancer groups. Because the available CR data included only month and year of cancer diagnosis, a day of diagnosis for each CR case was imputed.


Using EHRs for research - example (cont.) CPRD Cancer registry CPRD total Grand total PPV Sensitivity Specificity

Recorded Not recorded

Colorectal Recorded 1732 43 1775 0.98 0.92 0.99

Not recorded 150 40631

CR total 1882 42556

Lung Recorded 1659 65 1724 0.96 0.94 0.99


CR total 1763 42556

Oesophageal Recorded 872 27 899 0.97 0.92 0.99


CR total 946 42556

Urological Recorded 953 78 1031 0.92 0.85 0.99


CR total 1119 42556


Using EHRs for research - opportunities

• Mining of electronic health records (EHRs) - establishing new patient-stratification principles and for revealing unknown disease correlations

– Identify persons at very high (e.g. >99th percentile, risk scores) risk for a given condition

– Identify novel risk/protective factors for disease onset and progression

• Integrating EHR data with registry data – Link primary care data with genetic data (UK Biobank and CPRD linkage)– Link primary care with registry data (CPRD with National Cancer Registry linkage)

• Developing predictive models for– Therapeutic interventions effectiveness and safety (pharmacovigilance) – use of

propensity score matching to adjust for confounding

• Decision support systems – Synthesize large amounts of information to provide alerts related to adverse events,

patient safety, treatment course


Using EHRs for research Challenges - data

• Concepts– Probability, randomness, variability, statistical errors, central limit theorem

• Data – Reporting – ie CONSORT, TREND, STROBE– Accessing and visualizing - ie manipulation, graphical representation– Interpretation - clinical vs statistical significance, effect size

• Sources of bias– Incomplete data - EHR data are captured at the point of care by GPs, patients who

do not regularly interact with the health system may have incomplete data– Sampling bias, protopathic bias, measurement error, residual bias, confounding by

indication

• Prediction models– Uncovering patterns in patient trajectories through disease and intervention nodes (ie

medication) in a clinical context is statistically and computationally challenging – Inferential methods for clustered, matched, paired, or longitudinal studies– Multiple testing – common in EHRs research


Using EHRs for research Challenges – students & teachers

• Motivation and interests– Most students will be clinicians not researchers - focus on design, choice of analytical

methods, and interpretation of findings?– Statistics is not seen as a ‘core’ subject for medical training – use real-world examples,

uses and abuses of statistics

• Aptitudes – Differences in prior exposure to statistics – group work – Learning disabilities – greater use of technology– Differences in teaching abilites

• Assessment– Formative vs summative assessment– Use of quizzes at the end of each lecture/tutorial? Peer assessment?

• Teaching methods– Online modules/ youtube type learning– Lab-based teaching/use of personal computers during tutorial– Staff shortage – training the teacher?


Using EHRs for research Challenges

• EHRs are becoming common and viewed as a potential tool for healthcare quality assessment, clinical trials and health outcomes research

• Visualizing the data in clinical settings is a challenge, much less applying standard statistical methodology (standard errors and p-values) that may have little or no meaning in very large sample sizes

• Where biostatistics will fit in future education? – Biostatistics is often viewed as a separate entity, and much of it is not directly statistical in

nature, as the issue of how to process such large datasets is a dominating consideration– Public health also requires the analysis of large databases, both specifically and in

relation to issues affecting the ongoing restructuring of the NHS and is also a key area of potential research for biostatistics

– Integrate statistics teaching within the context of epidemiological analysis, medical-decision making, computing, and policy development


Thank you

Questions?


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Statistical opportunities and challenges of electronic health records Dr Alex Dregan Lecturer in Epidemiology and Public Health email: [email protected]