Two Actionable Windows: Disentangling Early Mortality From Late Infection Risks Using Time-Resolved AI

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ID: 315763
2026
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Abstract
Abstract Background Postoperative mortality and infection are frequently monitored using uniform clinical and laboratory strategies, despite likely arising from distinct pathophysiological mechanisms. Current one-size-fits-all monitoring may obscure actionable signals and limit the effectiveness of preventive interventions. Aims To disentangle the temporal and physiological drivers of early postoperative mortality and late postoperative infection using time-resolved, explainable machine-learning models, and to identify distinct postoperative risk windows to inform phase-specific clinical interventions. Methods We trained outcome-specific machine-learning models for 30-day mortality and postoperative infection using a retrospective cohort of 32,328 surgical episodes across seven specialties. Models integrated baseline patient characteristics with daily laboratory trajectories from postoperative days (POD) 0–7. Explainable AI techniques were used to quantify time-dependent feature importance, enabling differentiation between early “state”-driven risks and later “trajectory”-driven risks. Results Event rates were 4.6% (1,471/32,328) for mortality and 16.6% (5,374/32,328) for infection. Two distinct postoperative risk phases were identified. Rescue window (POD 0–2): Mortality risk was front-loaded, driven by baseline vulnerability and acute physiological derangements, particularly changes in haemoglobin (bleeding and transfusion) and creatinine (renal dysfunction), with maximal influence within the first 48 hours. Surveillance window (POD 3–7): Infection risk emerged later and was driven by evolving inflammatory trajectories rather than baseline state. Key predictors included CRP kinetics, platelet rebound patterns, and persistent dysglycaemia. Conclusion Postoperative mortality and infection exhibit distinct temporal and physiological signatures. A phase-specific care model is warranted, prioritizing hemodynamic stabilization and renal protection during the early rescue window (POD 0–2), followed by focused surveillance of inflammatory trajectories during the surveillance window (POD ≥3) to enable early infection detection. This framework supports a transition from generic postoperative monitoring to precision, time-adapted care.
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Authors K L Lucas, Y M Wintsch, T Blatter, K Triep, O Endrich, H A Guillen-Ramirez, G Beldi
Journal the british journal of surgery
Year 2026
DOI
10.1093/bjs/znag055.009
URL
https://doi.org/10.1093/bjs/znag055.009
Keywords Keywords not found

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