A pragmatic nomogram using routinely collected clinical variables to screen prevalent HFpEF: development and temporal validation

TL;DR

This study created a tool to estimate the likelihood of heart failure with preserved ejection fraction using common clinical data, helping prioritize patients for further testing.

Contribution

A new nomogram using non-imaging variables for HFpEF screening, validated over time with real-world data.

Findings

The nomogram uses nine clinical variables and shows good discrimination (C-index 0.762–0.783).

Calibration plots confirmed strong agreement between predicted and observed probabilities.

The model is robust and reliable for triaging patients in resource-limited settings.

Abstract

To develop and temporally validate a pragmatic nomogram based on routinely available clinical and laboratory variables to estimate the individualized probability of prevalent heart failure with preserved ejection fraction (HFpEF) at the index assessment, and to support screening or triage, and prioritization for confirmatory echocardiography in settings where comprehensive imaging resources are limited.

A total of 2187 cases were collected for the prediction model. Another 2026 cases from a new data set were utilized for performing independent temporal validation. The LASSO regression analysis was used to control possible variables. A final screening or triage nomogram for HFpEF was established based on logistic regression, and the discrimination and calibration of the established nomogram were evaluated by bootstrapping with 1000 resamples.

The final nomogram for screening or triage prevalent HFpEF was constructed using nine predictors retained in the final model: Age, systolic blood pressure (SBP), monocyte ratio (MONO%), red cell distribution width–coefficient of variation (RDW-CV), fasting glucose (GLU), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), Urea, and immunoglobulin G (IgG). The model demonstrated good discrimination, with a C-index of 0.762 in the training cohort and 0.783 in the validation cohort. Calibration plots showed good agreement between predicted and observed probabilities in both cohorts. Internal and temporal validation indicated that the model was robust and reliable.

This cross-sectional screening prediction nomogram estimates individualized risk of prevalent HFpEF using readily non-imaging variables and may support screening or triage and efficient allocation of echocardiography resources across hospital and community workflows. The tool is intended to prioritize referral for echocardiographic confirmation rather than replace guideline-based diagnosis. Prospective multicenter studies are warranted to evaluate transportability, clinical utility, and implementation impact.

The online version contains supplementary material available at 10.1186/s12872-026-05619-w.

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