Screening for patients at risk for cardiac amyloidosis via electronic health records: A multicenter machine learning development and validation study

BackgroundTimely detection is crucial to improve outcomes in patients with cardiac amyloidosis (CA) by initiation of life-saving treatments. Although confirmatory bone scintigraphy is highly accurate for CA detection, identifying at-risk patients for referral remains challenging. ObjectivesThis study aimed to develop and validate a machine learning model, Amylo-Detect, using structured multimodal electronic health record (EHR) data to guide referrals for confirmatory scintigraphy and monoclonal protein testing. MethodsConsecutive all-comer patients (n=11,616) referred for bone scintigraphy at the Vienna General Hospital (2010-2023) were retrospectively included. Patients referred before August 2020 formed the development cohort. The remaining patients comprised the internal validation cohort. External validation was performed at the University Hospital Essen (n=1,521). Amylo-Detect was trained using 50 routinely available parameters to predict CA-suggestive uptake (Perugini grade [≥]2) and compared with an existing score and clinical routine. ResultsHigh-grade uptake was present in 388 patients (3.0%). Amylo-Detect demonstrated excellent performance in development (AUC 0.93), independent internal validation (AUC 0.91), and external validation cohort (AUC 0.91), outperforming existing scoring systems and clinical routine. Results were consistent across subgroups, even when crucial predictors were missing. Of the 42/388 (10.8%) patients missed in clinical routine, 12/42 (29%) were additionally detected by Amylo-Detect. The model further conveyed significant prognostic value for mortality and heart failure hospitalization. ConclusionsWe present Amylo-Detect, a validated EHR-based tool for CA risk prediction, available as a web app, allowing application and further evaluation. By improving timely detection and referral, Amylo-Detect promises to address diagnostic delays and improve outcomes. Author summaryCardiac amyloidosis is a progressive and often fatal heart disease that is frequently diagnosed too late, even though effective treatments are now available. A major challenge is recognizing which patients should be referred for specialized testing, because early symptoms are diverse and often non-specific. In this study, we developed and validated a machine learning prediction system, called Amylo-Detect, that uses routinely collected information from electronic health records to identify patients who may be at risk for cardiac amyloidosis. We trained and externally validated the tool using data from 13,137 patients across two hospitals. We found that Amylo-Detect was highly accurate in identifying patients with disease-indicative findings on confirmatory scans and identified a substantial number of patients who were not initially suspected of having cardiac amyloidosis. Amylo-Detect, which we make publicly available via a web app, consistently outperformed existing risk scores and routine clinical decision-making. Our findings suggest that automated analysis of electronic health records can support clinicians in recognizing cardiac amyloidosis earlier, reduce diagnostic delays, and potentially improve patient outcomes by enabling timely treatment.