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

Abstract

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: ClinicalTrials.gov NCT00095238; TOPCAT: ClinicalTrials.gov NCT00094302; CHARM-Preserved: ClinicalTrials.gov NCT00634712. Graphic abstract

N-Terminal Pro-B-Type Natriuretic Peptide in Risk Stratification of Heart Failure Patients With Implantable Cardioverter-Defibrillator

“…A variety of studies have demonstrated that NT-proBNP has a stronger relationship with all-cause mortality and pump failure death than SCD ( 23 – 25 , 27 ) by showing a higher HR . Clinical models, including NT-proBNP, to predict pump failure death also showed better discrimination ability than to predict SCD ( 24 , 38 ). These findings indicate that NT-proBNP might not have a direct effect on SCD.…”

“…Otherwise, it might lead to a biased result. However, most studies failed to adjust for LVEDD in their analyses ( 22 , 23 , 26 , 27 , 38 ). Furthermore, although NT-proBNP showed significant associations with SCD ( 22 , 38 ), a cause-and-effect relationship might not exist.…”

“…However, most studies failed to adjust for LVEDD in their analyses ( 22 , 23 , 26 , 27 , 38 ). Furthermore, although NT-proBNP showed significant associations with SCD ( 22 , 38 ), a cause-and-effect relationship might not exist. A variety of studies have demonstrated that NT-proBNP has a stronger relationship with all-cause mortality and pump failure death than SCD ( 23 – 25 , 27 ) by showing a higher HR .…”

Machine learning‐based risk prediction of malignant arrhythmia in hospitalized patients with heart failure

“…also used the I‐PRESERVE trial as the derivation cohort and adopted a competing risk regression model to predict the risk of sudden death. 9 Model performance was found to decline in the external validation of the CHARM‐Preserved trial (C‐statistic 0.68–0.69) and the TOPCAT trial (C‐statistic 0.64–0.73). Because traditional regression methods are difficult to effectively handle high‐dimensional interaction information in large data sets, this mechanistically limits the model's ability to predict complex relationships.…”

“…6 At present, traditional HF risk prediction tools have modest predictive power. [7][8][9] In recent years, machine learning (ML) technology based on data-driven decision-making has developed rapidly and demonstrated great potential in the diagnosis, classification, and prediction of HF. 10,11 Whether the effective prediction of malignant arrhythmia can be realized through ML urgently needs to be explored.…”