INSIGHT Model: Explainable Artificial Intelligence for Real-World EHR-Based Screening of Transthyretin Amyloidosis Cardiomyopathy.

BACKGROUND: Transthyretin amyloidosis (ATTR) diagnoses are frequently delayed or missed, attenuating treatment benefit. OBJECTIVES: The authors developed an explainable artificial intelligence screening tool to identify high-risk patients who should be considered for diagnostic testing for ATTR-cardiomyopathy (ATTR-CM) from an unselected heart failure (HF) population. METHODS: Electronic health record data (April 2020-March 2023) were used to identify patients with suspected ATTR-CM in an HF cohort. Natural language processing with Named Entity Recognition processed unstructured clinical, echocardiographic, and electrocardiographic data. Clinical ModernBERT predicted suspected ATTR-CM. Local interpretable model-agnostic explanations were provided. The authors examined sensitivity, specificity, positive predictive value, negative predictive value, F1 score, area under the receiver-operating characteristic curve, and key model predictors. RESULTS: This study included 100,290 patients with HF (mean age: 71.6 ± 14.3 years; 47.3% female; 76.2% White; 18.2% Black; 89.7% non-Hispanic). The Clinical ModernBERT model, INSIGHT-CM, identified suspected ATTR-CM (n = 1,108; prevalence 1.2%) with sensitivity 77.5% (95% CI: 75.9%-82.8%), specificity 94.7% (95% CI: 94.4%-95.0%), positive predictive value 19.9% (95% CI: 17.6%-22.36%), negative predictive value 99.6% (95% CI: 99.5%-99.7%), and area under the receiver-operating characteristic curve 92.4% (95% CI: 90.5%-94.3%). CONCLUSIONS: In an unselected HF population with 1% prevalence of suspected ATTR-CM, INSIGHT-CM identified patients among whom 1 in 5 fit the pattern for suspected ATTR-CM, offering meaningful opportunities to improve timely diagnosis and treatment.