Patients with ATTR amyloidosis often have concomitant polyneuropathy and cardiomyopathy (ATTR-mixed phenotype). However, polyneuropathy is frequently unrecognized. Machine learning powered with explainable artificial intelligence (AI) may improve early ATTR-mixed phenotype detection by identifying at-risk patients and helping clinicians understand key predictors driving the model.

Patients with diagnoses of ATTR (ICD E85.x) or heart failure (ICD I50.x) and peripheral or autonomic neuropathy (ICD G54.x–G64.x, G90.x) were included. Natural language processing (NLP) with Named Entity Recognition was used to process unstructured clinical, echocardiographic, and electrocardiogram data from clinical notes extracted from the Baylor Scott & White Health Epic database. Balanced Random Forest Classifier (BRFC) was trained on the clinical text to predict presence of both ATTR and neuropathy diagnosis codes. Model explanations were yielded by Local Interpretable Model Agnostic Explanations (LIME).

The mean age of the cohort was 71.6 (14.3) years; 47.3% female; 75.8% White, 18.1% Black, and 2.1% Asian; and 89.7% non-Hispanic. The BRFC achieved a sensitivity of 90.0 %, specificity of 86.2%, positive predictive value of 14.1%, negative predictive value of 99.7% and f1 score of 24.5% in identifying ATTR-mixed phenotype (n=412) among patients with heart failure and neuropathy (n=13,500). LIME showed carpal tunnel, joint-swelling, atrial fibrillation, dyspnea, weight loss, tingling, heat and cold intolerance, syncope, paresthesia, and claudication as top predictors for predicting ATTR-mixed phenotype.