Key result
A nomogram-based risk score incorporating routine clinical data and point-of-care BNP demonstrated good discrimination for predicting HFpEF with an overfitting-corrected AUC of 0.77.
Why the study?
Can a nomogram-based risk score using routine clinical data and point-of-care BNP accurately predict HFpEF in a high HIV-prevalence primary care setting?
Cross-Sectional (n=1,508)
No
Can a nomogram-based risk score using routine clinical data and point-of-care BNP accurately predict HFpEF in a high HIV-prevalence primary care setting?
Effect estimate: AUC 0.77 (95% CI 0.71-0.81)
A practical, nomogram-based risk score using routine clinical data and point-of-care BNP can effectively identify individuals at high risk for HFpEF in resource-limited, high HIV-prevalence settings.
BACKGROUND: Antiretroviral therapy (ART)-treated people with HIV (PWH) have increased risk of heart failure with preserved ejection fraction (HFpEF). The diagnosis of HFpEF relies on cardiac imaging and expertise and is challenging in primary care settings. Further, existing risk prediction tools do not account for HIV-related risk. We developed a practical HFpEF risk prediction tool using data from the Prevalence and Phenotype of HIV-HFpEF in South Africa, a cross-sectional observational study that enrolled 1508 adults aged ≥40 years (1008 PWH on ART ≥1 year and 500 people without HIV (PWoH)) at a primary care clinic in South Africa. METHODS: We used four logistic regression models for HFpEF risk, including demographics, clinical factors, B-type natriuretic peptide (BNP) and ECG abnormalities. We compared the models' area under the receiver operating characteristic curve (AUC) with a random forest classifier, translated the final model into a nomogram-based risk score and evaluated its discrimination and calibration through bootstrap internal validation. RESULTS: The median age was 48 years, 77% female, 42% obese, 38% had hypertension and 7.8% met HFpEF criteria (118 participants; 85 (8.4%) among PWH and 33 (6.6%) among PWoH). Two logistic regression models outperformed the random forest classifier. The model that was used for the nomogram included age, sex, HIV status, smoking, hypertension, body mass index and BNP and showed good discrimination (overfitting-corrected AUC 0.77) with good calibration. CONCLUSIONS: The nomogram-based score, developed using routine clinical information and point-of-care BNP, is practical, accessible and may support screening and identification of individuals at high HFpEF risk for targeted referral for diagnosis and care in a sub-Saharan African primary care setting with a high HIV prevalence.
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Honwana et al. (2026) conducted a cross-sectional in Heart failure with preserved ejection fraction (HFpEF) risk (n=1,508). Nomogram-based HFpEF risk prediction score was evaluated on HFpEF diagnosis (model discrimination) (AUC 0.77, 95% CI 0.71-0.81). A nomogram-based risk score incorporating routine clinical data and point-of-care BNP demonstrated good discrimination for predicting HFpEF with an overfitting-corrected AUC of 0.77.
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