Here we show that patients with Chronic Heart Heart who receive Home Telehealth Monitoring equipment have better survival rates and spend more days alive and out of hospital.

1. Outcome assessment of
heart failure patients in
Hull and East Yorkshire
under telehealth care
5th July 2016
John Stamford
Chandra Kambhampati
Steffen Pauws
Andrew L Clark

2. Content
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• iCase EPSRC Project Overview [EP/L505468/1]
• Introduce Home Telehealth Monitoring (HTM)
– What is HTM?
– Literature
• Longitudinal Dataset
– Hull Lifelab
– Matching and extracting patients
• Evaluation
– Results

3. Project Overview
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• Project:
– Predictive Algorithms for Telehealth Service
Improvement and Evaluation (PATSIE)
• Overall project scope…
1. Better models of mortality and hospitalization risk
facilitating patient selection for telehealth
2. Accurate impact analysis of telehealth service delivery
with respect to outcomes, quality of care and cost-
effectiveness

4. The Impact of Heart Failure
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Impact
• 2006/2007 England and Wales
• 250,000 hospital deaths and discharges
• 65,000 of them being a first time diagnosis
(Cleland 2011)
• £563 million per year in the UK (Cleland 2011)
• 1 million inpatient bed days per year (NICE 2012)
• 5 million people in the USA (Soran 2008)
• 10 million in Europe (Giamouzis 2012)
• Readmission
• 30% within 3 months (Zhang 2013)
• 50% within 6 months (Woodend 2008, Giamouzis 2012)

5. What is Home Telehealth Monitoring?
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Patient
Measurements Criteria
Resting heart rate < 50 beats/min
Or
> 80 beats/min
Systolic blood pressure < 90 mm Hg
Or
> 140 mm Hg
Weight Change Change > 2kg
Cleland (2005), Dendale (2014)

6. How does HTM perform (literature)?
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(Inglis 2011)

7. Our Work
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• Aims
– Analyse the effectiveness of HTM for patients with CHF
• Hull-Lifelab (Clark et al 2014)
– 6,300 patients
• 380 HTM Patients
– 129 useable
• The problem
– Matching the HTM patients with similar patients
Tables
Baseline
No. Records
Total
No. Records
No.
Variables
Blood/Laboratory 5,802 18,412 75
Medication 6,287 14,342 131
Echocardiogram 6,021 13,314 78
Examination 6,003 14,155 52
QoL 4,488 10,130 181
History 6,254 - 70
Hospitalisation - 27,667 48
Mortality - 6,287 112

8. Baseline (Before Matching)
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Variable All Normal Care HTM P-value
Number of Patients 3214 3085 129 NA
Age 72.2 (11.3) 72.4 (11.2) 67.6 (11.4) < 0.001
Female 40% 40.70% 20.90% < 0.001
ACE 55.40% 54.60% 78.30% < 0.001
Betablocker 58% 57.30% 79.20% < 0.001
Digoxin 15.50% 15% 30.20% < 0.001
Diuretic 68% 67% 97.20% < 0.001
Calcium Channel Blocker 2.90% 3% 0% < 0.001
Furosemide EquivDailyDose (mg) 36.3 (43.4) 34.9 (42.3) 78.1 (52.5) < 0.001
Warfarin 23.60% 23.10% 36.80% 0.002
BP Systolic (mmHg) 139.1 (25) 139.5 (25) 126.2 (23.5) < 0.001
BP Diastolic (mmHg) 79 (14) 79.1 (13.9) 74.7 (14) 0.002
NYHA Exam >= 3 32% 31.70% 40.40% 0.077
NTproBNP (ngL) 2018.1 (3941.2) 1977.3 (3844.2) 3115 (5913.7) 0.077
Creatinine (umolL) 106.3 (55.2) 105.7 (54.8) 125.5 (63.5) 0.003
Urea (mmolL) 7.9 (5.1) 7.8 (4.9) 9.8 (7.8) 0.015
Sodium (mmolL) 137.9 (3.2) 138 (3.2) 136.7 (3.6) < 0.001
Myocardial Infarction 12.40% 11.80% 29.90% < 0.001
Coronary Artery Bypass Graft 6.30% 6% 16.90% < 0.001

9. Problem
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?
HTM Patients
Comparison Patients
?
2. Match
Hull-Lifelab Dataset

10. Propensity Matching
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• Estimating the likelihood of receiving treatment based on
covariate scores (Osborn 2005, Austin 2009)
• Logistic Regression Model
𝑝(𝑥) = 𝛽0 + 𝛽1 𝑥1 + ⋯ + 𝛽 𝑛 𝑥 𝑛 + 𝜀
– Dependent Variable
• if the patient received HTM
– Independent Variables
• age, gender and weight together with laboratory variables
(sodium (mmol/L), urea (mmol/L) and amino-terminal pro-B-
type natriuretic peptide (NTproBNP) (ng/L)) and medication
(furosemide (mg) and betablocker use)
– p(x) difference is < 0.02

11. Baseline (After Matching)
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Variable All Normal Care HTM P-value
Number of Patients 202 101 101 NA
Age 68.3 (12.5) 68.9 (12.9) 67.8 (12.2) 0.56
Female 25.70% 28.70% 22.80% 0.42
ACE 74.80% 71.30% 78.20% 0.33
Betablocker 80.70% 82.20% 79.20% 0.72
Digoxin 26.70% 24.80% 28.70% 0.63
Diuretic 96.50% 96% 97% 1.00
Calcium Channel Blocker 1% 2% 0% <0.001
Furosemide EquivDailyDose (mg) 76.8 (48.4) 78.8 (48.8) 74.9 (48.2) 0.56
Warfarin 37.10% 38.60% 35.60% 0.77
BP Systolic (mmHg) 126.9 (24.4) 127.4 (25.3) 126.4 (23.6) 0.78
BP Diastolic (mmHg) 75.7 (14.1) 76.6 (14.5) 74.9 (13.7) 0.39
NYHA Exam >= 3 39.60% 39.60% 39.60% 1.00
NTproBNP (ngL) 3575.8 (6070.7) 4015.9 (5931.5) 3163.7 (6207.5) 0.39
Creatinine (umolL) 126.1 (66.7) 126.9 (68.5) 125.2 (65) 0.87
Urea (mmolL) 10 (7.4) 10.3 (6.9) 9.8 (8) 0.64
Sodium (mmolL) 136.9 (3.8) 137 (3.9) 136.8 (3.7) 0.76
Myocardial Infarction 15.60% 5.20% 27.10% < 0.001
Coronary Artery Bypass Graft 11.60% 9.10% 14.30% 0.47

12. Results
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13. Survival Analysis
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Alive Dead Total
HTM 93 8 101
Normal Care 81 20 101
Total 174 28 202
One Year Mortality (p = 0.025)
• The normal care group had a greater likelihood of dying
within the first year (HR: 3.20, 95% CI: 1.40 – 7.28, p =
0.006).

14. Survival Estimates (Kaplan Meier)
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(Log rank test p = 0.00345)
Cox Proportional Hazard Ratio
Hazard Ratio 3.2
95% CI 1.404– 7.28
P-value 0.006

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First to Event Analysis
(Composite Death or Hospitalisation)
(Log rank test p = 0.11)
Cox Proportional Hazard Ratio
Hazard Ratio 0.74
95% CI 0.51 – 1.07
P-value 0.11

16. Number of Hospitalisation
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17. Days Alive and Out of Hospital (DAOH)
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• Repeat Event Analysis
• Each patient has a max DAOH of 365 days
• HTM Group had 3273 more days (alive and out of hospital)
• Patients receiving HTM had an average of 32.4 more DAOH
than the normal care group (95% CI: 10.1 – 54.7 days, p =
0.005).
DAOH %DAOH
HTM 35,385.3 96%
Normal Care 32,112.4 87%

18. Conclusion
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• Results show, in Hull and East Riding of Yorkshire…
– HTM patients have better survival rates
– Less likely to die within one year
– Have more days alive and out of hospital
• Propensity matching
– Reduces differences in baseline characteristics
– Allows valid outcome assessment
• Repeat event analysis (DAOH) overcomes limitation of first to event
analysis
• Possible future work
– Understand the results
– Develop models to…
• Identify which patients would be more likely to benefit from HTM
• Develop models to predict hospitalisation events

19. References
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• Austin, P. C. Discussion of ‘A critical appraisal of propensity-score matching in the medical literature between 1996 and 2003, Stat. Med., vol. 28, no. 15, pp. 1999–
2011, 2009.
• Clark, A. L. Cleland, J. G. F. Goode, K Kazmi, S. The Hull-Lifelab Dataset - A longitudinal cohort study of patients diagnosed with Heart Failure, 2014.
• Cleland, J.G., et al., Noninvasive home telemonitoring for patients with heart failure at high risk of recurrent admission and death: the Trans-European Network-
Home-Care Management System (TEN-HMS) study. J Am Coll Cardiol, 2005. 45(10): p. 1654-64
• Cleland, J.G., et al., The national heart failure audit for England and Wales 2008-2009. Heart, 2011. 97(11): p. 876-86
• Dendale, P., et al., Effect of a telemonitoring-facilitated collaboration between general practitioner and heart failure clinic on mortality and rehospitalization rates
in severe heart failure: the TEMA-HF 1 (TElemonitoring in the MAnagement of Heart Failure) study. European Journal of Heart Failure, 2012. 14(3): p. 333-340
• Giamouzis, G. Mastrogiannis, D. Koutrakis, K. Karayannis, G. Parisis, C. Rountas, C. Adreanides, E. Dafoulas, G. E. Stafylas, P. C. Skoularigis, J. Giacomelli, S.
Olivari, Z. and Triposkiadis, F. “Telemonitoring in chronic heart failure: A systematic review,” Cardiol. Res. Pract., vol. 1, 2012.
• Inglis, S. C., Clark, R. A., McAlister, F. A., Stewart, S., & Cleland, J. G. F. (2011). Which components of heart failure programmes are effective? A systematic review
and meta-analysis of the outcomes of structured telephone support or telemonitoring as the primary component of chronic heart failure management in 8323
patients: Abridged Coc. European Journal of Heart Failure, 13(9), 1028–1040.
• NICE, “NICE guidance recommends new treatment for some people with chronic heart failure,” 2012 Available: http://www.nice.org.uk/news/press-and-
media/nice-guidance-recommends-new-treatment-for-some-people-with-chronic-heart-failure.
• Osborn ,C. E. Statistical Applications For Health Information Management. Jones & Bartlett Learning, 2005.
• Soran, O.Z., et al., A randomized clinical trial of the clinical effects of enhanced heart failure monitoring using a computer-based telephonic monitoring system in
older minorities and women. J Card Fail, 2008. 14(9): p. 711-7
• NHS (2014) - http://www.nhs.uk/conditions/Heart-failure/Pages/Introduction.aspx
• Woodend, A.K., et al., Telehome monitoring in patients with cardiac disease who are at high risk of readmission. Heart Lung, 2008. 37(1): p. 36-45.
• Zhang, J. Goode, K. M. Rigby, A. Balk, A. H. M. M. and Cleland, J. G. “Identifying patients at risk of death or hospitalisation due to worsening heart failure using
decision tree analysis: Evidence from the Trans-European Network-Home-Care Management System (TEN-HMS) Study,” Int J Cardiol, vol. 163, no. 2, pp. 149–
156, Feb. 2013