AUTHOR=Huber Markus , Furrer Marc A. , Jardot François , Wuethrich Patrick Y. 

TITLE=Uplift modeling to determine which fluid-norepinephrine regime results in a postoperative acute kidney injury-free recovery in patients scheduled for cystectomy and urinary diversions

JOURNAL=Frontiers in Medicine

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1542797

DOI=10.3389/fmed.2025.1542797

ISSN=2296-858X

ABSTRACT=BackgroundPostoperative acute kidney injury (PO-AKI) remains common after surgery. Although risk prediction models for PO-AKI exist, it is still unknown which intraoperative regime in terms of fluid and norepinephrine administration is beneficial for a specific patient. We thus aim to investigate the potential of uplift modeling—a framework combining causal inference and machine learning—in identifying patients for which certain fluid and norepinephrine regimes result in a PO-AKI-free recovery.MethodsData from a prospectively maintained cystectomy database at a single tertiary center (N = 1,482, period 2000–2020) were used. Total intraoperative fluid balance (TIFB) and norepinephrine (NE) administration were dichotomized into a high TIFB/low NE and a low TIFB/high NE regime. Primary outcome was PO-AKI. Confounding was addressed with inverse probability of treatment weighting. Uplift was defined as the difference in likelihood of no PO-AKI with a high TIFB/low NE versus low TIFB/high NE treatment regime. We modeled uplift using logistic regression and random forests as outcome models. Model performance was evaluated with the area under the Qini curve (AUQC).ResultsThe uplift models demonstrated a higher ability (AUQC: 0.30, 95%-CI: 0.26–0.30) compared to a random sorting strategy (0.06, 95%-CI: 0.02–0.06) or a traditional prediction model (AUQC: 0.06, 95%-CI: 0.03–0.06) for PO-AKI in sorting patients according to the expected treatment benefit from either a high TIFB / low NE or a low TIFB / high NE regime. The performance of the uplift models is robust with respect to the fluid-NE dichotomization.ConclusionUplift modeling provides a clinically relevant step toward personalized medicine by considering the incremental benefit of an alternative treatment versus a control treatment on a patient’s outcome, thus moving from a predictive toward a prescriptive risk assessment. We demonstrated the overall higher clinical utility of an uplift modeling approach compared to a prediction model of baseline PO-AKI risk in sorting patients according to the expected treatment benefit from either a high total intraoperative fluid balance / low norepinephrine regime or a low total intraoperative fluid balance / high norepinephrine regime with respect to postoperative acute kidney injury.