Unsupervised machine learning identifies symptoms of indigestion as a predictor of acute decompensation and adverse cardiac events in patients with heart failure presenting to the emergency department

Abstract

BackgroundPatients with known heart failure (HF) present to emergency departments (ED) with a plethora of symptoms. Although symptom clusters have been suggested as prognostic features, accurately triaging HF patients is a longstanding challenge. ObjectivesWe sought to use machine learning to identify subtle phenotypes of patient symptoms and evaluate their diagnostic and prognostic value among HF patients seeking emergency care. MethodsThis was a secondary analysis of a prospective cohort study of consecutive patients seen in the ED for chest pain or equivalent symptoms. Independent reviewers extracted clinical data from charts, including nine categories of subjective symptoms reported during initial evaluation. The diagnostic outcome was acute HF exacerbation and prognostic outcome was 30-day major adverse cardiac events (MACE). Outcomes were adjudicated by two independent reviewers. K-means clustering was used to derive latent patient symptom clusters, and their associations with outcomes were assessed using multivariate logistic regression. ResultsSample included 438 patients (age 65±14 years; 45% female, 49% Black, 18% HF exacerbation, 32% MACE). K-means clustering identified three presentation phenotypes: patients with dyspnea only (Cluster A, 40%); patients with indigestion, with or without dyspnea (Cluster B, 23%); patients with neither dyspnea nor indigestion (Cluster C, 37%). Compared to Cluster C, indigestion was a significant predictor of acute HF exacerbation (OR=1.8, 95%CI=1.0–3.4) and 30-day MACE (OR=1.8, 95%CI=1.0–3.1), independent of age, sex, race, and other comorbidities. ConclusionIndigestion symptoms in patients with known HF signify excess risk of adverse events, suggesting that these patients should be triaged as high-risk during initial ED evaluation.