Integration of Intraoperative Data in Interpretable Machine Learning Models to Predict Postoperative AKI in Noncardiac Surgery Patients

Authors

• Justin Do, Thomas Jefferson UniversityFollow
• Karan H. Shah, Thomas Jefferson UniversityFollow
• Melissa Xu, Thomas Jefferson UniversityFollow
• Andrew Hyunwoo Kim
• Vivaswat Suresh
• Nidhir Guggilla
• Michael Li, Thomas Jefferson UniversityFollow
• Rishi Kothari, Thomas Jefferson UniversityFollow

Document Type

Article

Publication Date

6-20-2026

Comments

This article is the author’s final published version in JAMIA Open, Volume 9, Issue 3, 2026, Article number ooag092.

The published version is available at https://doi.org/10.1093/jamiaopen/ooag092. Copyright © The Author(s) 2026.

Abstract

OBJECTIVES: We aimed to (1) quantify changes in discrimination when adding intraoperative data to preoperative data and (2) compare tabular machine learning with feature engineering against a time-aware LSTM-based model.

MATERIALS AND METHODS: Retrospective cohort of 46 204 adults undergoing 57 055 eligible noncardiac surgery in the INSPIRE database. We extracted 38 preoperative and 49 intraoperative variables; acute kidney injury (AKI) was defined by KDIGO serum creatinine criteria and modeled as stage 2/3 postoperative AKI. Models were trained on preoperative-only and combined pre- and intraoperative data. Intraoperative series were summarized using eight statistical features for tabular models or integrated directly using an MLP+LSTM architecture.

RESULTS: GBT with combined features achieved the highest AUROC (0.896, 95% CI, 0.878-0.914), followed by combined AutoGluon (0.893, 95% CI, 0.877-0.909) and preoperative-only GBT (0.891, 95% CI, 0.871-0.910). ASA-PS ≥3 (AUROC 0.723, 95% CI, 0.700-0.746) and adapted GS-AKI (AUROC 0.719, 95% CI, 0.700-0.739) underperformed machine-learning models. The hybrid MLP+LSTM model did not outperform simpler tabular models (AUROC 0.870, 95% CI, 0.848-0.892).

DISCUSSION: The small gain from adding low-frequency intraoperative summaries suggests that most discriminative information for stage 2/3 postoperative AKI was available before surgery.

CONCLUSION: Preoperative tabular ML models provided excellent prediction of postoperative AKI, and added limited incremental discrimination at the available sampling frequency. Future work should evaluate whether higher-frequency intraoperative signals better leverage time-aware architectures.

Recommended Citation

Do, Justin; Shah, Karan H.; Xu, Melissa; Kim, Andrew Hyunwoo; Suresh, Vivaswat; Guggilla, Nidhir; Li, Michael; and Kothari, Rishi, "Integration of Intraoperative Data in Interpretable Machine Learning Models to Predict Postoperative AKI in Noncardiac Surgery Patients" (2026). Department of Anesthesiology Faculty Papers. Paper 110.
https://jdc.jefferson.edu/anfp/110

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.

PubMed ID

42327625

Language

English