# 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

## TL;DR

This study uses uplift modeling to determine which fluid and norepinephrine regimes help prevent kidney injury after cystectomy surgery.

## Contribution

The study introduces uplift modeling to personalize treatment regimes for preventing postoperative acute kidney injury.

## Key findings

- Uplift models outperformed traditional models in sorting patients by treatment benefit (AUQC: 0.30 vs. 0.06).
- The performance of uplift models was robust to how fluid and norepinephrine regimes were categorized.
- Uplift modeling offers a prescriptive approach to risk assessment for postoperative kidney injury.

## Abstract

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.

Data 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).

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.

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.