Background Surgical risk prediction tools can facilitate shared-decision-making and efficient allocation of perioperative resources. Such tools should be externally validated in target populations prior to implementation. Methods Predicted risk of 30-day mortality was retrospectively derived for surgical patients at Royal Perth Hospital from 2014 to 2021 using the Surgical Outcome Risk Tool (SORT) and the related NZRISK (n=44,031, 53,395 operations). In a sub-population (n=31,153), the Physiology and Operative Severity Score for the enumeration of Mortality (POSSUM) and the Portsmouth variant of this (P-POSSUM) were matched from the Copeland Risk Adjusted Barometer (C2-Ai, Cambridge, UK). The primary outcome was risk score discrimination of 30-day mortality as evaluated by area-under-receiver operator characteristic curve (AUROC) statistics. Calibration plots and outcomes according to risk decile and time were also explored. Results All four risk scores showed high discrimination (AUROC) for 30-day mortality (SORT=0.922, NZRISK=0.909, P-POSSUM=0.893; POSSUM=0.881) but consistently over-predicted risk. SORT exhibited the best discrimination and calibration. Thresholds to denote the highest and second-highest deciles of SORT risk (>3.92% and 1.52-3.92%) captured the majority of deaths (76% and 13% respectively) and hospital-acquired-complications. Year-on-year SORT calibration performance drifted towards over-prediction, reflecting a decrease in 30-day mortality over time despite an increase in the surgical population risk. Conclusions SORT was the best performing risk score in predicting 30-day mortality after surgery. Categorising patients based on SORT into low, medium (80-90th percentile) and high-risk (90-100th percentile) can guide future allocation of perioperative resources. No tools were sufficiently calibrated to support shared-decision-making based on absolute predictions of risk.

The authors have declared no competing interest.

This work was supported by Royal Perth Hospital and the Department of Anaesthesia and Pain Medicine.

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