ESC-AI HF Readmission Prediction Tool

ESC–AI HF Readmission Prediction Tool: Explanation and clinical context This page implements a transparent prototype logistic model intended to estimate the probability of 30-day all-cause hospital readmission after an index heart failure admission, using routinely available discharge features (age, sex, prior admissions, length of stay, LVEF, systolic blood pressure, heart rate, BUN, sodium, and BNP when available). The implementation intentionally uses an explainable logistic form where each predictor contributes linearly to the log-odds; BNP is transformed using log(BNP+1) to reduce skew. The coefficients embedded in this demonstration are provisional example values to illustrate construction, explainability, and how to embed such a predictor into a WordPress page.

They are not derived from a single validated ESC model and therefore must not be used for clinical decision-making without local validation or replacement with coefficients from a validated model or an externally hosted validated AI service. Reference & further reading: Comprehensive reviews and representative studies on machine learning and statistical models for heart failure readmission prediction are provided to guide replacement of coefficients with a validated model or to inform retraining on local EHR data. Key references include systematic reviews and example ML studies that illustrate common predictors, performance challenges (class imbalance, heterogeneous outcome windows), and the need for external validation.

Please consult: Rahman MS et al., 2023 (Heart Failure emergency readmission prediction; open-access discussion of classical ML methods). Jahangiri S et al., 2024 (nationwide database ML model for 30-day HF readmission). Croon PM et al., 2022 (review of AI-based algorithms for HF readmission and outcomes).

Use these and more recent publications to obtain validated model coefficients or to design a retraining pipeline on local data. How to improve / operationalize: 1) Train and validate a model on your local EHR/hospital dataset with appropriate endpoints (e.g., 30-day all-cause readmission), handling class imbalance and temporal validation. 2) Prefer explainable models (penalized logistic regression, decision trees with SHAP explanations) for clinical adoption, or use black-box models only with explainability layers and robust external validation.

3) Deploy a validated model behind a secure API (HTTPS) and call weights from server-side code (PHP cURL) rather than embedding fixed coefficients if frequent retraining is expected. 4) Include calibration checks (calibration plots, Brier score) and decision-curve analysis before clinical use. References Rahman MS, et al.

Heart Failure Emergency Readmission Prediction Using Classical ML models. Journal / PubMed Central. 2023. (open access discussion and methods).

Jahangiri S, et al. A machine learning model to predict heart failure 30-day readmission using a nationwide hospitalization database. Frontiers in AI.

2024. Croon PM, et al. Current state of artificial intelligence-based algorithms for heart failure outcomes and readmission prediction — systematic review.

2022. For guidance on AI clinical trials and safety considerations consult ESC press materials and trial reports on AI in cardiology.