Scientific Publications Database

Article Title: Derivation and external validation of a 30-day mortality risk prediction model for older patients having emergency general surgery
Authors: Feng, Simon; van Walraven, Carl; Lalu, Manoj M.; Moloo, Husein; Musselman, Reilly; McIsaac, Daniel I.
Date of Publication:2022
Background: Older people (>= 65 yr) are at increased risk of morbidity and mortality after emergency general surgery. Risk prediction models are needed to guide decision making in this high-risk population. Existing models have substantial limitations and lack external validation, potentially limiting their applicability in clinical use. We aimed to derive and validate, both internally and externally, a multivariable model to predict 30-day mortality risk in older patients undergoing emergency general surgery.Methods After protocol publication, we used the National Surgical Quality Improvement Program (NSQIP) database (2012-6; estimated to contain 90% data from the USA and 10% from Canada) to derive and internally validate a model to predict 30-day mortality for older people having emergency general surgery using logistic regression with elastic net regularisation. Internal validation was done with 10-fold cross-validation. External validation was done using a temporally separate health administrative database exclusively from Ontario, Canada.Results: Overall, 6012 (12.0%) of the 50 221 patients died within 30 days. The model demonstrated strong discrimination (area under the curve [AUC]=0.871) and calibration across the spectrum of observed and predicted risks. Ten-fold internal cross-validation demonstrated minimal optimism (AUC=0.851, optimism 0.019 [standard deviation=0.06]) with excellent calibration. External validation demonstrated lower discrimination (AUC=0.700) and degraded calibration.Conclusion: A multivariable mortality risk prediction model was strongly discriminative and well calibrated internally. However, poor external validation suggests the model may not be generalisable to non-NSQIP data and hospitals. The findings highlight the importance of external validation before clinical application of risk models.