Phenotyping of Elderly Patients With Heart Failure Focused on Noncardiac Conditions: A Latent Class Analysis From a Multicenter Registry of Patients Hospitalized With Heart Failure.
Ryo NakamaruYasuyuki ShiraishiNozomi NiimiTakashi KohnoYuji NagatomoMakoto TakeiTakenori IkomaKei NishikawaMunehisa SakamotoShintaro NakanoShun KohsakaTsutomu YoshikawaPublished in: Journal of the American Heart Association (2023)
Background The burden of noncardiovascular conditions is becoming increasingly prevalent in patients with heart failure (HF). We aimed to identify novel phenogroups incorporating noncardiovascular conditions to facilitate understanding and risk stratification in elderly patients with HF. Methods and Results Data from a total of 1881 (61.2%) patients aged ≥65 years were extracted from a prospective multicenter registry of patients hospitalized for acute HF (N=3072). We constructed subgroups of patients with HF with preserved ejection fraction (HFpEF; N=826, 43.9%) and those with non-HFpEF (N=1055, 56.1%). Latent class analysis was performed in each subgroup using 17 variables focused on noncardiovascular conditions (including comorbidities, Clinical Frailty Scale, and Geriatric Nutritional Risk Index). The latent class analysis revealed 3 distinct clinical phenogroups in both HFpEF and non-HFpEF subgroups: (1) robust physical and nutritional status (Group 1: HFpEF, 41.2%; non-HFpEF, 46.0%); (2) multimorbid patients with renal impairment (Group 2: HFpEF, 40.8%; non-HFpEF, 41.9%); and (3) malnourished patients (Group 3: HFpEF, 18.0%; non-HFpEF, 12.1%). After multivariable adjustment, compared with Group 1, patients in Groups 2 and 3 had a higher risk for all-cause death over the 1-year postdischarge period (hazard ratio [HR], 2.79 [95% CI, 1.64-4.81] and HR, 2.73 [95% CI, 1.39-5.35] in HFpEF; HR, 1.96 [95% CI, 1.22-3.14] and HR, 2.97 [95% CI, 1.64-5.38] in non-HFpEF; respectively). Conclusions In elderly patients with HF, the phenomapping focused on incorporating noncardiovascular conditions identified 3 phenogroups, each representing distinct clinical outcomes, and the discrimination pattern was similar for both patients with HFpEF and non-HFpEF. This classification provides novel risk stratification and may aid in clinical decision making.
Keyphrases
- ejection fraction
- end stage renal disease
- heart failure
- newly diagnosed
- chronic kidney disease
- peritoneal dialysis
- aortic stenosis
- prognostic factors
- decision making
- machine learning
- patient reported outcomes
- randomized controlled trial
- physical activity
- respiratory failure
- big data
- acute heart failure
- left ventricular
- deep learning
- electronic health record
- open label
- acute respiratory distress syndrome
- cardiac resynchronization therapy