Home Tele-Monitoring Reduces Hospitalization for Heart Failure in an NHS Service: A Propensity-Matched Analysis

Y. Zhang, C. Kambhampati, J.G.F. Cleland, J. Caffarel, H. Reiter,

K.M. Goode, R. Dierckx, D.N. Davis

Contents

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• Introduction

• Patients and Methods

• Results

• Conclusion

Introduction

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• Heart Failure (HF) is one of the most common, costly, disabling, and potentially deadly medical conditions

• According to several large trails, Home Tele-Monitoring (HTM) may reduce mortality and possibly hospitalizations for heart failure (HF)

• Whether HTM is more or less effective in clinical practice than in clinical trials is unknown, because there is by definition no control group.

- Hull has the unique advantage of having a powerful longitudinal chronic HF data set (Hull-Lifelab) that enables Propensity Score (PS) matching analysis to be performed.

Patients and Methods

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• Study Patients

– As part of the local heart failure service provision for the City of Kingston-upon-Hull

• 225 patients were enrolled in the HTM service (04/2008 – 05/2012 )

• 1822 patients had been registered in an out-patient service during the same time , and have consented for their data to be entered into the Hull-Lifelab study

– The HTM care was performed with the use of

• The Philips Motiva system

– The HTM care was supported by centralised clinical and technical triage provided in partnership between Hull and East Yorkshire NHS Trust and the University of Hull.

Patients and Methods

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– Hull HTM Service Model

• A 2-tiered service model, but with the technical triage and clinical triage roles performed by the designated telehealth nurse.

• The telehealth nurses are not the clinical responders and this responsibility is passed to a prescribing nurse or doctor.

• Implemented within secondary care with direct access to medical and specialist nurse support.

Patient

Technical TriageClinical Triage

Clinical Responder

2-tiered with merged technical/clinical triage roles

Patients and Methods

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• Clinical event data

– Collected up to September 2011

• Assembly of study cohort - data extraction

– 119 variables were selected as initial candidate covariates, which stored in 10 different data Hull-Lifelab:

Main enrolment, Physical examination, ECG test, Echo examination, Blood test, Drug history, PFT test, Clinical examination, QoL survey, and Device implant

– Two criteria were defined to extract baseline characteristics of the initial candidate covariates :

• Baseline characteristics should be measured after medicine titration period

• Baseline for different covariates should be measured in an as close time slot as possible : [-3, +3] days

Patients and Methods

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– A four-step pre-processing scheme to limit the missing value percentage and impute the missing values

– 645 patients remain in the control group and 106 patients in the HTM group

Only patients having records in Physical examination, ECG test, Echo Examination, Blood test, and Drug history within a specified time window around the Selected Baseline Dates (SBD) will be selected

Initial candidate covariates (ICC) saved in other tables are extracted using specified time windows around the SBD

ICC with missing value percentage more than 30% are excluded from further analysis

Imputing missing values with mean/mode of the covariate

Patients and Methods

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– 54 features were selected from the initial candidate variables to be used for the calculation of PS, including

Age, Sex, Weight, NT-proBNP, Blood pressure, Creatinine, Urea, Heart rate, Diabetic, Hypertension, Beta blocker, etc.

• Assembly of study cohort - propensity score matching

– Calculated using a non-parsimonious multivariable logistic regression model

– 80 pairs of patients with a similar PS (to two decimal places) were matched

Patients and Methods

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• Assessment of baseline covariate balance

Before PS match After PS match

Control group (n=645) HTM group (n=106) Control group (n=80) HTM group (n=80)

Mean StdDev Mean StdDev Mean StdDev Mean StdDev

Age 70.72 11.44 68.58 12.22 68.26 12.19 69.33 11.51

Men(%)) 406 (62.9) 66 (62.2) 52 (65.0) 51 (63.7)

BMI 25.13 6.21 29.34 6.55 29.46 7.02 29.35 6.55

Alkaline phophatase 80.99 42.27 86.62 45.23 82.81 40.45 84.04 28.99

Creatinine 118.30 62.04 125.90 59.77 117.34 68.52 120.34 38.48

NT-proBNP 1826.20 3203.17 4189.88 6241.13 2907.41 4607.53 2856.75 3194.94

Urea 8.36 4.59 10.07 5.75 8.96 5.45 9.36 4.16

Left atrium 4.16 0.75 4.32 0.77 4.17 0.79 4.22 0.59

LVEDD 5.84 0.88 5.95 0.81 5.92 0.83 5.97 0.81

Rate 68.03 14.47 78.05 15.67 73.75 14.78 75.45 14.23

Systolic BP 126.84 20.60 122.69 23.59 126.76 20.31 124.68 24.24

Diastolic BP 74.29 12.14 72.21 13.66 74.09 11.85 72.10 12.95

Sodium 137.77 2.97 137.50 3.28 137.80 3.10 137.84 3.06

…

Patients and Methods

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• Statistical analysis

– Outcomes of interest were (time to first event) all-cause hospitalization or death, cardiovascular (CV) hospitalization or death, all-cause mortality and CV mortality.

– For risk distribution and statistical comparison, the Kaplan-Meier estimation method (with 95% confidence level for survivor analysis) was used.

Results

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Control group

HTM group

Results

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At two year:

Risk in HTM group Risk in Control group P value

all-cause hospitalization or death 37.30% 59.50% 0.008

CV hospitalization or death 34.20% 51.80% 0.046

all-cause mortality 23.40% 21.20% 0.531

CV mortality 16.20% 13.90% 0.629

Conclusion

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In this analysis, HTM was associated with a lower incidence of (all-cause or CV) hospitalisation or death but not all-cause or CV mortality

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