Why the study?
Accurate prediction of postoperative mortality after high-risk emergency colorectal cancer resection is essential, prompting investigation into whether adding physiological and surgical measures to standard patient and tumour characteristics improves risk model accuracy.
Does the inclusion of physiological and surgical measures improve the accuracy of risk models for 90-day mortality in patients undergoing emergency colorectal cancer surgery?
Does the inclusion of physiological and surgical measures improve the accuracy of risk models for 90-day mortality in patients undergoing emergency colorectal cancer surgery?
Adding physiological and surgical measures to standard patient and tumor characteristics improves the accuracy of 90-day mortality risk prediction in patients undergoing emergency colorectal cancer surgery.
Adding physiological data improves mortality model discrimination in emergency CRC resection; extends registry models but leaves clinical utility open.
BACKGROUND: Life-saving emergency major resection of colorectal cancer (CRC) is a high-risk procedure. Accurate prediction of postoperative mortality for patients undergoing this procedure is essential for both healthcare performance monitoring and preoperative risk assessment. Risk-adjustment models for CRC patients often include patient and tumour characteristics, widely available in cancer registries and audits. The authors investigated to what extent inclusion of additional physiological and surgical measures, available through linkage or additional data collection, improves accuracy of risk models. METHODS: Linked, routinely-collected data on patients undergoing emergency CRC surgery in England between December 2016 and November 2019 were used to develop a risk model for 90-day mortality. Backwards selection identified a 'selected model' of physiological and surgical measures in addition to patient and tumour characteristics. Model performance was assessed compared to a 'basic model' including only patient and tumour characteristics. Missing data was multiply imputed. RESULTS: Eight hundred forty-six of 10 578 (8.0%) patients died within 90 days of surgery. The selected model included seven preoperative physiological and surgical measures (pulse rate, systolic blood pressure, breathlessness, sodium, urea, albumin, and predicted peritoneal soiling), in addition to the 10 patient and tumour characteristics in the basic model (calendar year of surgery, age, sex, ASA grade, TNM T stage, TNM N stage, TNM M stage, cancer site, number of comorbidities, and emergency admission). The selected model had considerably better discrimination compared to the basic model (C-statistic: 0.824 versus 0.783, respectively). CONCLUSION: Linkage of disease-specific and treatment-specific datasets allowed the inclusion of physiological and surgical measures in a risk model alongside patient and tumour characteristics, which improves the accuracy of the prediction of the mortality risk for CRC patients having emergency surgery. This improvement will allow more accurate performance monitoring of healthcare providers and enhance clinical care planning.
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Blake et al. (2024) studied this question.
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