Jakob Burcharth

Risk prediction models are frequently used to identify high‐risk patients undergoing emergency laparotomy. The National Emergency Laparotomy Audit (NELA) developed a risk prediction model specifically for emergency laparotomy patients, which was recently updated. In this study, we validated the updated NELA model in an external population. Furthermore, we compared it with three other risk prediction models: the original NELA model, the Portsmouth Physiological and Operative Severity Score for the enUmeration of Mortality and morbidity (P‐POSSUM) model, and the American Society of Anesthesiologists Physical Status (ASA‐PS). We included adult patients undergoing emergency laparotomy at Zealand University Hospital, from March 2017 to January 2019, and Herlev Hospital, from November 2017 to January 2020. Variables included in the risk prediction models were collected retrospectively from the electronic patient records. Discrimination of the risk prediction models was evaluated with area under the curve (AUC) statistics, and calibration was assessed with Cox calibration regression. The primary outcome was 30‐day mortality. Out of 1226 included patients, 146 patients (11.9%) died within 30 days. AUC (95% confidence interval) for 30‐day mortality was 0.85 (0.82–0.88) for the updated NELA model, 0.84 (0.81–0.87) for the original NELA model, 0.81 (0.77–0.84) for the P‐POSSUM model, and 0.76 (0.72–0.79) for the ASA‐PS model. Calibration showed underestimation of mortality risk for both the updated NELA, original NELA and P‐POSSUM models. The updated NELA risk prediction model performs well in this external validation study and may be used in similar settings. However, the model should only be used to discriminate between low‐ and high‐risk patients, and not for prediction of individual risk due to underestimation of mortality.