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Supplementary data from: Integration of intraoperative data in interpretable machine learning models to predict postoperative AKI in noncardiac surgery patients

Do, Justin1; Shah, Karan1; Xu, Melissa1; Kim, Andrew2; Suresh, Vivaswat3; Guggilla, Nidhir2; Li, Michael4; Kothari, Rishi4

Published Jun 09, 2026 on Dryad. https://doi.org/10.5061/dryad.z08kprrw7

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Abstract

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