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Background:Despite recent advances, patients with heart failure (HF) often experience repeat hospitalizations and worsening clinical trajectories from inadequate decongestion. Evidence-based approaches for optimizing interventions in the acute hospital setting for patients with decompensated HF are needed. We evaluated whether machine learning (ML) models can accurately predict next-day levels for decongestion surrogates in hospitalized HF patients. Hypothesis:ML can accurately predict body weight, hematocrit, creatinine, and potassium values in the next 24 hours in hospitalized HF patients. Methods:We utilized national Veterans Affairs (VA) databases to study all patients admitted with HF from January 2014 to July 2022. Records including at least one value for at least one biomarker of interest (body weight, hematocrit, creatinine, and potassium) were included. Patients were randomly split into training (80%), validation (10%), and test (10%) datasets. We trained a recurrent neural network to predict each biomarker’s value on admission day n+1 using data until day n, simulating a scenario where a clinician monitors response to treatment (e.g., diuresis) over a 24-hour cycle. The model that performed best on the validation set was evaluated on the test set. The R², mean absolute error (MAE), and feature importance were determined. Results:We identified 589,114 admissions involving 124,163 unique patients. The mean (SD) age on admission was 72 (10) years; 98% were male, 69% were white, and 25% were Black. The performance (R², MAE) for each biomarker model was as follows: body weight (0.94, 6.15 lb.), creatinine (0.92, 0.21 mg/dL), hematocrit (0.86, 1.7%), and potassium (0.53, 0.27 mmol/L). The top predictive features across all models were intravenous or oral diuretic use, patient age, and diastolic blood pressure. The predicted 24-hour change in each biomarker based on total daily diuretic dose for five representative patients is demonstrated in the Figure. Conclusions:ML can accurately predict the 24-hour body weight, hematocrit, creatinine, and potassium values in hospitalized HF patients, suggesting the potential for AI to guide acute in-hospital management. 

SARS-CoV-2 has caused symptomatic COVID-19 and widespread death across the globe. We sought to determine genetic variants contributing to COVID-19 susceptibility and hospitalization in a large biobank linked to a national United States health system. We identified 19,168 (3.7%) lab-confirmed COVID-19 cases among Million Veteran Program participants between March 1, 2020, and February 2, 2021, including 11,778 Whites, 4,893 Blacks, and 2,497 Hispanics. A multi-population genome-wide association study (GWAS) for COVID-19 outcomes identified four independent genetic variants (rs8176719, rs73062389, rs60870724, and rs73910904) contributing to COVID-19 positivity, including one novel locus found exclusively among Hispanics. We replicated eight of nine previously reported genetic associations at an alpha of 0.05 in at least one population-specific or the multi-population meta-analysis for one of the four MVP COVID-19 outcomes. We used rs8176719 and three additional variants to accurately infer ABO blood types. We found that A, AB, and B blood types were associated with testing positive for COVID-19 compared with O blood type with the highest risk for the A blood group. We did not observe any genome-wide significant associations for COVID-19 severity outcomes among those testing positive. Our study replicates prior GWAS findings associated with testing positive for COVID-19 among mostly White samples and extends findings at three loci to Black and Hispanic individuals. We also report a new locus among Hispanics requiring further investigation. These findings may aid in the identification of novel therapeutic agents to decrease the morbidity and mortality of COVID-19 across all major ancestral populations.