AI-guided refinement of coronary revascularization need in patients suspected of acute coronary syndrome

Abstract

Aims: Overdiagnosis in patients suspected of acute coronary syndrome (ACS) leads to unnecessary coronary angiographies, particularly in cases with non-specifically elevated troponin (Trop) levels. We established machine learning (ML) models integrating sequentially available prehospital and in-hospital variables to improve early prediction of the need for coronary re while minimizing overdiagnosis. Methods and results: Retrospective cohort study analysing patients with suspected ACS from 2016 to 2020. Machine learning models were trained using data available at different diagnostic time points, including prehospital assessment, arterial blood gas analysis, full laboratory results, and sequential Trop measurements. A total of 2756 patients were included, identified through emergency physician protocols for ACS-related complaints. Patients with incomplete data or prehospital mortality were excluded.Model performance improved with additional diagnostic data. Model 1 (prehospital data only) achieved an area under the receiver operating characteristic (AUROC) of 0.76 (95% confidence interval [CI] 0.72-0.79), while Model 4 (including sequential Trop testing) reached 0.87 (95% CI 0.83-0.91). Adding early hospital diagnostics (Model 2) significantly improved accuracy compared with Model 1 (0.65 vs. 0.78). Sequential Trop testing in Model 4 did not substantially enhance performance compared with single Trop testing in Model 3 (AUROC 0.87, 95% CI 0.83-0.91 vs. 0.86, 95% CI 0.82-0.91). Misclassification analysis revealed that underdiagnosed patients were typically older females with dyspnoea and known coronary artery disease but no ST-elevations. Overdiagnosed patients had higher body mass index, ST-elevations, regional wall motion abnormalities, and impaired left ventricular ejection fraction but lacked significant sequential Trop elevation. Conclusion: Prehospital assessments combined with early in-hospital diagnostics provide reliable stratification of coronary intervention need, potentially optimizing clinical decision-making and resource utilization.

Ähnliche Arbeiten