Developing and validating models to predict sudden death and pump failure death in patients with heart failure and preserved ejection fraction

Shen, L. et al. (2021) Developing and validating models to predict sudden death and pump failure death in patients with heart failure and preserved ejection fraction. Clinical Research in Cardiology, 110(8), pp. 1234-1248. (doi: 10.1007/s00392-020-01786-8) (PMID:33301080)

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Background: Sudden death (SD) and pump failure death (PFD) are leading modes of death in heart failure and preserved ejection fraction (HFpEF). Risk stratification for mode-specific death may aid in patient enrichment for new device trials in HFpEF. Methods: Models were derived in 4116 patients in the Irbesartan in Heart Failure with Preserved Ejection Fraction trial (I-Preserve), using competing risks regression analysis. A series of models were built in a stepwise manner, and were validated in the Candesartan in Heart failure: Assessment of Reduction in Mortality and morbidity (CHARM)-Preserved and Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT) trials. Results: The clinical model for SD included older age, men, lower LVEF, higher heart rate, history of diabetes or myocardial infarction, and HF hospitalization within previous 6 months, all of which were associated with a higher SD risk. The clinical model predicting PFD included older age, men, lower LVEF or diastolic blood pressure, higher heart rate, and history of diabetes or atrial fibrillation, all for a higher PFD risk, and dyslipidaemia for a lower risk of PFD. In each model, the observed and predicted incidences were similar in each risk subgroup, suggesting good calibration. Model discrimination was good for SD and excellent for PFD with Harrell’s C of 0.71 (95% CI 0.68–0.75) and 0.78 (95% CI 0.75–0.82), respectively. Both models were robust in external validation. Adding ECG and biochemical parameters, model performance improved little in the derivation cohort but decreased in validation. Including NT-proBNP substantially increased discrimination of the SD model, and simplified the PFD model with marginal increase in discrimination. Conclusions: The clinical models can predict risks for SD and PFD separately with good discrimination and calibration in HFpEF and are robust in external validation. Adding NT-proBNP further improved model performance. These models may help to identify high-risk individuals for device intervention in future trials. Clinical trial registration: I-Preserve: NCT00095238; TOPCAT: NCT00094302; CHARM-Preserved: NCT00634712.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Shen, Dr Li and McMurray, Professor John and Jhund, Dr Pardeep
Authors: Shen, L., Jhund, P. S., Anand, I. S., Carson, P. E., Desai, A. S., Granger, C. B., Køber, L., Komajda, M., McKelvie, R. S., Pfeffer, M. A., Solomon, S. D., Swedberg, K., Zile, M. R., and McMurray, J.
College/School:College of Medical Veterinary and Life Sciences > School of Cardiovascular & Metabolic Health
Journal Name:Clinical Research in Cardiology
ISSN (Online):1861-0692
Published Online:10 December 2020
Copyright Holders:Copyright © 2020 The Authors
First Published:First published in Clinical Research in Cardiology 110(8): 1234-1248
Publisher Policy:Reproduced under a Creative Commons License

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