Pre-AF 13: An Interpretable Atrial Fibrillation Risk Score Mined from Discharge Reports

2026-06-09

AI Summary

Existing atrial fibrillation (AF) risk scores, which rely heavily on common factors such as advanced age and hypertension, struggle to effectively stratify intermediate-term risk among patients with cardiovascular disease. This study addresses this limitation by developing an NLP pipeline that combines rule-based parsing with Transformer-based named entity recognition to extract structured features from discharge summaries. Using these features, an interpretable machine learning model was trained via LightAutoML. The work proposes Pre-AF 13, a novel 13-variable score for intermediate-term AF risk prediction, and a streamlined 9-variable linear version, Pre-AF 9, designed for rapid bedside assessment. Validated in a cohort of 45,000 patients, Pre-AF 13 achieved an AUC of 0.725 for predicting AF within 24 months—significantly outperforming traditional scores like CHARGE-AF (AUC 0.53–0.64)—and successfully stratified AF incidence from 7% to 36%.

Abstract

Background. Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia and a major determinant of prognosis. Established AF risk scores rely on factors (older age, hypertension) nearly ubiquitous among patients with cardiovascular disease (CVD), offering limited stratification in this high-risk group. Most target long-term (5-10 year) rather than medium-term prediction. We developed interpretable ML models predicting AF risk over a 24-month and entire follow-up horizon in CVD patients using routinely collected hospital data. Methods. Single-center retrospective study of electronic health records from the National Research Cardiology Center (Russia) for patients aged >=18 with CVD but without pre-existing AF, hospitalized more than once between January 2012 and May 2019. A custom NLP pipeline transformed unstructured discharge reports into 73 structured features, combining a rule-based parser with transformer-based NER. Using LightAutoML we built a full model (73 features), a simple model (reduced subset), and a linear model for a bedside risk score. Performance was assessed by ROC AUC, compared with CHARGE-AF, C2HEST, MHS, and HAVOC, and interpreted via SHAP. Results. Of 80,576 records from 45,000 patients, 17,562 met inclusion criteria; 1,438 (8.19%) developed AF. The full model reached ROC AUC 0.735 (24-month) and 0.696 (entire follow-up); the simple model was nearly identical (0.725, 0.696). All non-linear models outperformed the four clinical risk scores (ROC AUC 0.53-0.64). The simple model uses 13 features and is named Pre-AF 13. SHAP identified age and left atrial volume as dominant predictors. A linear risk score (Pre-AF 9) stratified observed 24-month AF incidence from ~7% to 36%. Conclusion. Interpretable ML models built from routinely collected EHR data identify high-AF-risk CVD patients, outperforming established clinical risk scores.

Problem

• atrial fibrillation
• risk prediction
• cardiovascular disease
• medium-term risk
• clinical risk scores

Innovation

• interpretable machine learning
• natural language processing
• atrial fibrillation risk prediction
• electronic health records
• SHAP interpretability