Introduction Postoperative complications after major surgery have substantial impacts on morbidity and resource utilisation. We investigated whether adding high-dimensional metabolomic and proteomic data to standard clinical variables would improve the prediction of a range of postoperative complications.

Methods We analysed data from UK Biobank, a large prospective cohort study. Participants who underwent major surgery and had metabolomic and/or proteomic data were included. The primary outcomes were postoperative atrial fibrillation, acute kidney injury, acute myocardial infarction, delirium, stroke and surgical site infection. We trained machine learning models (elastic net penalised regression) with a range of feature sets to predict these outcomes. For outcomes where sample sizes were below recommended levels for predictive modelling, we employed transfer learning from the non-postoperative domain. We compared the predictive performance (AUROC, sensitivity, specificity) of models using only baseline clinical variables with those integrating single- and multiomic datasets.

Results The dataset included 158,156 individuals undergoing qualifying surgery. The numbers of cases with omic data varied across outcome phenotypes and feature sets: metabolomic: 144–1596, proteomic: 27–289 and multiomic: 15–219. Baseline clinical models achieved robust predictive performance (AUROC 0.72–0.88, sensitivity 0.71–0.80). The addition of metabolomic and/or proteomic features, using a variety of integration approaches, provided no clinically meaningful improvement in performance across any of the clinical phenotypes. Transfer learning from the non-postoperative domain improved model performance and stability but did not outperform baseline clinical models.

Conclusions The addition of metabolomic and/or proteomic data from samples collected at a temporal distance from surgery does not improve pre-operative risk prediction compared to standard clinical variables. The lack of incremental predictive value likely reflects the extended gap between biobank sampling and the surgical event. The success of transfer learning from non-postoperative settings suggests shared biological risk between chronic and acute phenotypes.

TRG receives funding from GlaxoSmithKline, Biogen, Novartis and Roche for unrelated research. No other competing interests declared.

This study was funded by a Wellcome Trust GW4-CAT PhD Programme for Health Professionals PhD Fellowship awarded to RAA [316275/Z/24/Z]. RAA, PY, GMK and TRG are supported by the Medical Research Council Integrative Epidemiology Unit at the University of Bristol (RA, TG: MC_UU_00032/3; PY: MC_UU_00032/4; GMK: MC_UU_0032/6). GMK acknowledges additional funding from the Wellcome Trust (grant numbers: 201486/Z/16/Z and 201486/B/16/Z), the Medical Research Council (grant numbers: MR/W014416/1; MR/S037675/1; MR/Z50354X/1; and MR/Z503745/1. BG, TRG, GMK and PY are also supported by the UK National Institute for Health and Care Research (NIHR) Bristol Biomedical Research Centre (grant number: NIHR 203315). The views expressed are those of the authors and not necessarily those of the UK NIHR or the Department of Health and Social Care. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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The analysis was conducted using data from UK Biobank. The UK Biobank study was approved by the North-West Multi-centre Research Ethics Committee and all participants provided written informed consent. This research has been conducted under UK Biobank project number 128619.

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* Part of this work was presented at the Association of Anaesthetists Winter Scientific Meeting 2026, 15-16 January 2026, QEII Centre, Westminster, London. Abstract: Anaesthesia 2026;81:S7-S63. https://doi.org/10.1111/anae.70103.