Accepted for/Published in: JMIR Formative Research
Date Submitted: Jul 8, 2025
Open Peer Review Period: Jul 8, 2025 - Sep 2, 2025
Date Accepted: Dec 17, 2025
(closed for review but you can still tweet)
Stratified Causal Inference for ICU Risk Prediction: Informatics-Based Modelling of Anaesthetic Drug Combinations
ABSTRACT
Background:
Postoperative intensive care unit (ICU) admission affects 15-20% of surgical patients and represents a major source of morbidity and healthcare costs. Current anaesthetic dosing relies on empirical guidelines rather than individualised risk assessment. We developed a counterfactual dose-response model to identify optimal fentanyl-propofol combinations.
Objective:
To develop and evaluate a stratified, causal machine learning framework using electronic health record data to identify optimal fentanyl–propofol dose combinations and predict postoperative ICU admission risk, enabling precision anesthesia and individualised clinical decision support.
Methods:
We analysed perioperative electronic health records of 67,134 surgical procedures from UC Irvine Medical Centre (2017-2022). A hierarchical learning framework was used to estimate causal effects while controlling for confounding variables. Six dose-sensitive subgroups were identified through stratified analysis. The primary endpoint was postoperative ICU admission.
Results:
High-dose combinations (fentanyl >3 mcg/kg, propofol >3.5mg/kg) increased ICU admissions’ absolute risk difference by 36% (absolute risk increase; 95% CI = [0.57, 0.83], p<0.001). Six patient subgroups demonstrated distinct dose-response patterns, with vulnerable populations (low haemoglobin, high glucose, elevated creatinine) showing elevated risk even at standard doses (ICU risk >44% vs. 40% in general population). The optimal dose range for decision-making was determined to be 1.75–2.25 mcg/kg for propofol and 1.5–2.25 mg/kg for fentanyl.
Conclusions:
Fentanyl-propofol combinations exhibit complex, nonlinear dose-response relationships with ICU admission risk. High-dose combinations markedly increase risk through synergistic effects, while specific patient subgroups require enhanced monitoring even at standard doses. These findings support the development of individualised dosing algorithms and risk assessment tools that could prevent thousands of ICU admissions annually. Clinical Trial: N/A
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