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Updated: Nov 26, 2025
Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
Published on: February 13, 2021
Developing and validating models to predict sudden death and pump failure death in patients with heart failure and
Li Shen1,2, Pardeep S Jhund2, Inder S Anand3
1Department of Medicine, Hangzhou Normal University, Hangzhou, 310003, China.
Insights
New clinical models accurately predict sudden death and pump failure death in heart failure with preserved ejection fraction (HFpEF). These models can identify high-risk patients for clinical trials and potential device interventions.
Area of Science:
- Cardiology
- Clinical Trials
- Predictive Modeling
Background:
- Sudden death (SD) and pump failure death (PFD) are primary causes of mortality in heart failure with preserved ejection fraction (HFpEF).
- Accurate risk stratification for mode-specific death is crucial for patient selection in HFpEF clinical trials, particularly for device interventions.
Purpose of the Study:
- To develop and validate clinical models for predicting the risk of sudden death (SD) and pump failure death (PFD) in patients with HFpEF.
- To assess the performance of these models in identifying high-risk individuals for potential therapeutic strategies.
Main Methods:
- Competing risks regression analysis was employed using data from 4116 patients in the Irbesartan in Heart Failure with Preserved Ejection Fraction (I-Preserve) trial.
- Models were stepwise developed and externally 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.
Main Results:
- Clinical models identified key predictors for SD (older age, male sex, lower LVEF, higher heart rate, diabetes, myocardial infarction history, recent HF hospitalization) and PFD (older age, male sex, lower LVEF/diastolic blood pressure, higher heart rate, diabetes, atrial fibrillation, dyslipidemia).
- Models demonstrated good calibration and discrimination (Harrell's C for SD: 0.71, PFD: 0.78), with robust external validation.
- Inclusion of NT-proBNP significantly improved SD model discrimination and simplified the PFD model.
Conclusions:
- Validated clinical models effectively predict SD and PFD risks in HFpEF patients with good discrimination and calibration.
- These predictive tools can aid in identifying high-risk individuals for targeted device interventions in future HFpEF clinical trials.
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.
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