Aim To identify distinct phenotypic subgroups in a highly‐dimensional, mixed‐data cohort of individuals with heart failure (HF) with preserved ejection fraction (HFpEF) using unsupervised clustering analysis. Methods and results The study included all Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT) participants from the Americas (n = 1767). In the subset of participants with available echocardiographic data (derivation cohort, n = 654), we characterized three mutually exclusive phenogroups of HFpEF participants using penalized finite mixture model‐based clustering analysis on 61 mixed‐data phenotypic variables. Phenogroup 1 had higher burden of co‐morbidities, natriuretic peptides, and abnormalities in left ventricular structure and function; phenogroup 2 had lower prevalence of cardiovascular and non‐cardiac co‐morbidities but higher burden of diastolic dysfunction; and phenogroup 3 had lower natriuretic peptide levels, intermediate co‐morbidity burden, and the most favourable diastolic function profile. In adjusted Cox models, participants in phenogroup 1 (vs. phenogroup 3) had significantly higher risk for all adverse clinical events including the primary composite endpoint, all‐cause mortality, and HF hospitalization. Phenogroup 2 (vs. phenogroup 3) was significantly associated with higher risk of HF hospitalization but a lower risk of atherosclerotic event (myocardial infarction, stroke, or cardiovascular death), and comparable risk of mortality. Similar patterns of association were also observed in the non‐echocardiographic TOPCAT cohort (internal validation cohort, n = 1113) and an external cohort of patients with HFpEF [Phosphodiesterase‐5 Inhibition to Improve Clinical Status and Exercise Capacity in Heart Failure with Preserved Ejection Fraction (RELAX) trial cohort, n = 198], with the highest risk of adverse outcome noted in phenogroup 1 participants. Conclusions Machine learning‐based cluster analysis can identify phenogroups of patients with HFpEF with distinct clinical characteristics and long‐term outcomes.

To develop and validate a novel, machine learning-derived model to predict the risk of heart failure (HF) among patients with type 2 diabetes mellitus (T2DM). RESEARCH DESIGN AND METHODSUsing data from 8,756 patients free at baseline of HF, with <10% missing data, and enrolled in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial, we used random survival forest (RSF) methods, a nonparametric decision tree machine learning approach, to identify predictors of incident HF. The RSF model was externally validated in a cohort of individuals with T2DM using the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT). RESULTSOver a median follow-up of 4.9 years, 319 patients (3.6%) developed incident HF. The RSF models demonstrated better discrimination than the best performing Coxbased method (C-index 0.77 [95% CI 0.75-0.80] vs. 0.73 [0.70-0.76] respectively) and had acceptable calibration (Hosmer-Lemeshow statistic x 2 5 9.63, P 5 0.29) in the internal validation data set. From the identified predictors, an integer-based risk score for 5-year HF incidence was created: the WATCH-DM (Weight [BMI], Age, hyperTension, Creatinine, HDL-C, Diabetes control [fasting plasma glucose], QRS Duration, MI, and CABG) risk score. Each 1-unit increment in the risk score was associated with a 24% higher relative risk of HF within 5 years. The cumulative 5-year incidence of HF increased in a graded fashion from 1.1% in quintile 1 (WATCH-DM score £7) to 17.4% in quintile 5 (WATCH-DM score ‡14). In the external validation cohort, the RSF-based risk prediction model and the WATCH-DM risk score performed well with good discrimination (C-index 5 0.74 and 0.70, respectively), acceptable calibration (P ‡0.20 for both), and broad risk stratification (5-year HF risk range from 2.5 to 18.7% across quintiles 1-5). CONCLUSIONSWe developed and validated a novel, machine learning-derived risk score that integrates readily available clinical, laboratory, and electrocardiographic variables to predict the risk of HF among outpatients with T2DM.

Background: Type 2 diabetes mellitus (T2DM) is associated with a higher risk for heart failure (HF). The impact of a lifestyle intervention and changes in cardiorespiratory fitness (CRF) and body mass index on risk for HF is not well established. Methods: Participants from the Look AHEAD trial (Action for Health in Diabetes) without prevalent HF were included. Time-to-event analyses were used to compare the risk of incident HF between the intensive lifestyle intervention and diabetes support and education groups. The associations of baseline measures of CRF estimated from a maximal treadmill test, body mass index, and longitudinal changes in these parameters with risk of HF were evaluated with multivariable adjusted Cox models. Results: Among the 5109 trial participants, there was no significant difference in the risk of incident HF (n=257) between the intensive lifestyle intervention and the diabetes support and education groups (hazard ratio, 0.96 [95% CI, 0.75–1.23]) over a median follow-up of 12.4 years. In the most adjusted Cox models, the risk of HF was 39% and 62% lower among moderate fit (tertile 2: hazard ratio, 0.61 [95% CI, 0.44–0.83]) and high fit (tertile 3: hazard ratio, 0.38 [95% CI, 0.24–0.59]) groups, respectively (referent group: low fit, tertile 1). Among HF subtypes, after adjustment for traditional cardiovascular risk factors and interval incidence of myocardial infarction, baseline CRF was not significantly associated with risk of incident HF with reduced ejection fraction. In contrast, the risk of incident HF with preserved ejection fraction was 40% lower in the moderate fit group and 77% lower in the high fit group. Baseline body mass index also was not associated with risk of incident HF, HF with preserved ejection fraction, or HF with reduced ejection fraction after adjustment for CRF and traditional cardiovascular risk factors. Among participants with repeat CRF assessments (n=3902), improvements in CRF and weight loss over a 4-year follow-up were significantly associated with lower risk of HF (hazard ratio per 10% increase in CRF, 0.90 [95% CI, 0.82–0.99]; per 10% decrease in body mass index, 0.80 [95% CI, 0.69–0.94]). Conclusions: Among participants with type 2 diabetes mellitus in the Look AHEAD trial, the intensive lifestyle intervention did not appear to modify the risk of HF. Higher baseline CRF and sustained improvements in CRF and weight loss were associated with lower risk of HF. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT00017953.

Corticosteroids do not increase occurrence of or mortality from bacterial infections in patients with severe alcoholic hepatitis. Further studies are needed to develop strategies of reducing the risk of fungal infection with use of steroids for patients with severe alcoholic hepatitis.

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Key Points Question Is the new, stratified payment adjustment method for the Hospital Readmission Reduction Program associated with an alteration in penalty distribution? Findings This cross-sectional study of 3173 hospitals found that the new payment adjustment method was associated with a reduction in the proportion of hospitals penalized for fiscal year 2019, which corresponds to performance from July 1, 2014, to June 30, 2017, from 79.07% of hospitals (2509 hospitals) to 75.04% (2381 hospitals) compared with the old, nonstratified method. Hospitals with the largest share of patients of low socioeconomic status had the largest reduction. Meaning The new payment adjustment method for the Hospital Readmission Reduction Program was associated with a more equitable distribution of penalties among hospitals, lessening the disproportionate burden carried by hospitals caring for patients of low socioeconomic status.

Background Cardiac infiltration is an important cause of death in sarcoidosis. Tran-sthoracic echocardiography (TTE) has limited sensitivity for the detection of cardiac sarcoidosis (CS). Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) is used to diagnose CS but has limitations of cost and availability. We sought to determine whether TTE- derived global longitudinal strain (GLS) may be used to identify individuals with CS, despite preserved left ventricular ejection fraction (LVEF), and whether abnormal GLS is associated with major cardiovascular events (MCE). Methods We studied 31 patients with biopsy- proven extra- cardiac sarcoidosis, LVEF>50% and LGE on CMR (CS+ group), and 31 patients without LGE (CS−group), matched by age, sex, and severity of lung disease. GLS was measured using vendor- independent speckle tracking software. Parameters of left and right ventricular systolic and diastolic function were also studied. Receiver- operating characteristic curves were used to identify GLS cutoff for CS detection, and Kaplan–Meier plots to determine the ability of GLS to predict MCE. Results LGE was associated with reduced GLS (−19.6±1.9% in CS− vs −14.7±2.4% in CS+, P<.01) and with reduced E/A ratio (1.1±0.3 vs 0.9±0.3, respectively, P =.01). No differences were noted in other TTE parameters. GLS magnitude inversely correlated with LGE burden (r=−.59). GLS cutoff of −17% showed sensitivity and specificity 94% for detecting CS. Patients who experienced MCE had worse GLS than those who did not (−13.4±0.9% vs −17.7±0.4%, P=.0003). Conclusions CS is associated with significantly reduced GLS in the presence of preserved LVEF. GLS measurements may become part of the TTE study performed to screen for CS.