Machine learning to define phenotypes and outcomes of patients hospitalized for heart failure with preserved ejection fraction: Findings from ASCEND-HF.

BACKGROUND: Heart Failure with Preserved Ejection Fraction (HFpEF) is a heterogenous disease with few therapies proven to provide clinical benefit. Machine learning can characterize distinct phenotypes and compare outcomes among patients with HFpEF who are hospitalized for acute HF. METHODS: We applied hierarchical clustering using demographics, comorbidities, and clinical data on admission to identify distinct clusters in hospitalized HFpEF (ejection fraction >40%) in the ASCEND-HF trial. We separately applied a previously developed latent class analysis (LCA) clustering method and compared in-hospital and long-term outcomes across cluster groups. RESULTS: Of 7141 patients enrolled in the ASCEND-HF trial, 812 (11.4%) were hospitalized for HFpEF and met the criteria for complete case analysis. Hierarchical Cluster 1 included older women with atrial fibrillation (AF). Cluster 2 had elevated resting blood pressure. Cluster 3 had young men with obesity and diabetes. Cluster 4 had low resting blood pressure. Mortality at 180 days was lowest among Cluster 3 (KM event-rate 6.2 [95% CI: 3.5, 10.9]) and highest among Cluster 4 (18.8 [14.6, 24.0], P < .001). Twenty four-hour urine output was higher in Cluster 3 (2700 mL [1800, 3975]) than Cluster 4 (2100 mL [1400, 3055], P < .001). LCA also identified four clusters: A) older White or Asian women, B) younger men with few comorbidities, C) older individuals with AF and renal impairment, and D) patients with obesity and diabetes. Mortality at 180 days was lowest among LCA Cluster B (KM event-rate 5.5 [2.0, 10.3]) and highest among LCA Cluster C (26.3 [19.2, 35.4], P < .001). CONCLUSIONS: In patients hospitalized for HFpEF, cluster analysis demonstrated distinct phenotypes with differing clinical profiles and outcomes.

Ambient air pollution and emergency department visits for asthma: a multi-city assessment of effect modification by age.

Previous studies have found strong associations between asthma morbidity and major ambient air pollutants. Relatively little research has been conducted to assess whether age is a factor conferring susceptibility to air pollution-related asthma morbidity. We investigated the short-term relationships between asthma emergency department (ED) visits and ambient ozone (O3), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and fine particulate matter (PM2.5) in Atlanta (1993-2009), Dallas (2006-2009), and St. Louis (2001-2007). City-specific daily time-series analyses were conducted to estimate associations by age group (0-4, 5-18, 19-39, 40-64, and 65+ years). Sub-analyses were performed stratified by race and sex. City-specific rate ratios (RRs) were combined by inverse-variance weighting to provide an overall association for each strata. The overall RRs differed across age groups, with associations for all pollutants consistently strongest for children aged 5-18 years. The patterns of association across age groups remained generally consistent when models were stratified by sex and race, although the strong observed associations among 5-18 year olds appeared to be partially driven by non-white and male patients. Our findings suggest that age is a susceptibility factor for asthma exacerbations in response to air pollution, with school-age children having the highest susceptibility.