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.

Background: Postoperative 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. Methods: Data from a prospectively maintained cystectomy database at a single tertiary center ( Results: The 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. Conclusion: Uplift 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.