A machine-learning model for accurate diuresis prediction in acute heart failure (DRC-AHF)

We aimed to develop a machine learning-based tool for accurate quantitative prediction of diuretic response in acute heart failure (AHF).

We prospectively enrolled 296 AHF patients (20% female, mean age 68 ± 13 years, mean estimated glomerular filtration rate (eGFR) 62 ± 30 ml/min/1.73m2, median NT-proBNP 7112 [4082-14 400] pg/ml) in a single-centre derivation/validation study. A random forest regression model was developed using derivation (n = 50) and optimization (n = 246) cohorts and externally validated in an independent cohort (n = 50; mean age 69 ± 14 years, mean eGFR 56 ± 27 ml/min/1.73m2). Predictors were eGFR, spot urine sodium (uNa), and urine creatinine (uCr) obtained 2 h after intravenous furosemide.

In the validation cohort (n = 50), the median observed 6 h urine output was 1220 [950-1750] ml vs model-estimated 1446 [957-1749] ml (P = .480). Observed and estimated values showed a strong positive correlation (r = 0.90, 95% CI: 0.83-0.94, P < .0001), with a mean absolute error of 413 ml and a mean bias of 67 ± 383 ml on the Bland-Altman analysis. The model correctly classified 44/50 patients (88%) into predefined diuresis categories (≤900, 900-1800, and ≥1800 ml), compared with 58% for the reference natriuretic response prediction equation. Mean absolute error decreased progressively by approximately 200 ml per 100 additional patients, confirming data-driven performance improvement.

A three-variable machine learning model (eGFR, uNa, and uCr at 2 h post-diuretic) predicted 6 h urine output with high accuracy in AHF and demonstrated adaptive improvement with expanding training data. The calculator is freely available at https://diuresis.umw.edu.pl.