Data Mining to Identify the Right Interventions for the Right Patient for Heart Failure: A Real-World Study.

Abstract

Objectives: To identify the right interventions for the right heart failure (HF) patients in the real-world setting using machine learning (ML) trained on individual-level clinical data linked with social determinants of health (SDOH) data.

Methods: In this retrospective cohort study, point-of-care claims data from Komodo Health and SDOH data from the National Health and Wellness Survey (NHWS), from January 2014-December 2020, were linked. Data mining was conducted using K-means clustering, an ML tool. Komodo Health data were used to access longitudinal data for the selected patient cohorts and crosssectional data from NHWS for additional patient information. The primary outcome was HF-related hospitalizations; secondary outcomes, all-cause hospitalization and all-cause mortality. Use of digital healthcare (DHC)/non-DHC interventions and related outcomes were also assessed.

Results: The study population included 353 HF patients (mean age, 63.5 years; 57.2% women). The use of non-DHC (75.9%-81.9%) and DHC (4.0%-9.1%) interventions increased from baseline to followup. Overall, 17.0% of patients had HF-related hospitalizations (DHC, 6.9%; non-DHC, 16.5%) and 45.0% had all-cause hospitalization (DHC, 75.0%; non-DHC, 50.9%). Two archetypes with distinct patient profiles were identified. Archetype 1 (vs. 2) characterised by older age, greater disease severity, more comorbidities, more medication use, took steps to prevent heart attack/problems, had better lifestyle, higher HF-related hospitalizations (18.3% vs. 16.3%) and lower all-cause hospitalizations (42.9% vs. 46.3%). The trends remained the same regardless of the intervention type.

Conclusions: Identification of patient archetypes with distinct profiles can be useful to understand underlying disease subtypes, identify specific interventions, predict clinical outcomes, and define the right intervention for the right patient.