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Derivation and external validation of a 30-day mortality risk prediction model for older patients having emergency general surgery

Feng S, van Walraven C, Lalu MM, Moloo H, Musselman R, McIsaac DI. Br J Anaesth. 2022; 129(1):33-40. Epub 2022 May 18. DOI: https://doi.org/10.1016/j.bja.2022.04.007


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.

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