Author + information
- Cesare Russo,
- Xiao Shao,
- Zhenchao Guo,
- Chelsea Jin,
- Leah Burns,
- Alice Goshorn and
- Sanjay Doddamani
Background: Heart failure (HF) clinical heterogeneity constitutes a dilemma for risk stratification and treatment. Unsupervised phenotyping techniques might help in characterizing HF subpopulations and improving risk stratification.
Methods: Electronic medical records (EMRs) for chronic stable HF patients were extracted from the Geisinger Health System. Patients were represented by a set of 39 demographic, clinical and echocardiographic variables within a +/- 90 day window from an index echocardiogram. Latent class analysis (LCA) was applied to detect distinct HF phenotypes. One and two year outcomes were then compared between the phenotype classes.
Results: The analysis included 907 HF patients (mean age 68.6, 40.5% female). The LCA identified 4 classes with discreet clinical and echocardiographic characteristics (Table). The 4 groups showed significant differences in 1- and 2-year mortality, and 1-year all-cause rehospitalizations (Table). Compared to Class 1, the hazard ratios for 2-year mortality (95% confidence interval) adjusted for age, gender, and past hospitalization were 2.86 (1.38-5.96) for Class 2, 3.17 (1.51-6.65) for Class 3, and 8.98 (4.30-18.75) for Class 4.
Conclusions: In chronic stable HF, LCA identified clinically distinct subpopulations with different survival and readmission rates. Such approach may improve HF risk stratification with potential implications for clinical trial design.
Poster Hall, Hall C
Friday, March 17, 2017, 3:45 p.m.-4:30 p.m.
Session Title: Acute Heart Failure: Evaluating Strategies to Prevent Readmissions
Abstract Category: 13. Heart Failure and Cardiomyopathies: Clinical
Presentation Number: 1163-270
- 2017 American College of Cardiology Foundation