Why the study?
The applicability of surgical risk calculators developed in Europe and the USA has not been adequately examined in low-middle income country settings.
Does the ACS-NSQIP risk calculator predict 30-day mortality more accurately than the CR-POSSUM risk calculator in patients undergoing colorectal surgery in an LMIC setting?
Does the ACS-NSQIP risk calculator predict 30-day mortality more accurately than the CR-POSSUM risk calculator in patients undergoing colorectal surgery in an LMIC setting?
Both ACS-NSQIP and CR-POSSUM risk calculators accurately predict 30-day mortality after colorectal surgery in an LMIC setting, with ACS-NSQIP demonstrating superior overall performance.
Supports ACS-NSQIP over CR-POSSUM for colorectal surgery mortality prediction in LMIC; leaves open prospective validation.
BACKGROUND: Several risk calculating tools have been introduced into clinical practice to provide patients and clinicians with objective, individualised estimates of procedure-related unfavourable outcomes. The currently available risk calculators (RCs) have been developed by well-endowed health systems in Europe and the USA. Applicability of these RCs in low-middle income country (LMIC) settings with wide disparities in patient population, surgical practice and healthcare infrastructure has not been adequately examined. PATIENTS AND METHODS: Through this single tertiary care, LMIC-centre, retrospective cohort study, we investigated the accuracy of the two most widely validated RCs - American College of Surgeons-National Surgical Quality Improvement Program (ACS-NSQIP) RC and ColoRectal Physiological and Operative Severity Score for the Enumeration of Mortality and Morbidity (CR-POSSUM) - for the prediction of mortality in patients undergoing elective and emergency colorectal surgery (CRS) from March 2013 to March 2020. Online RCs were used to predict mortality and other outcomes. Accuracy was assessed by Brier score and C statistic. RESULTS: Of 105 patients, 69 (65.71%) underwent elective and 36 (34.28%) underwent emergency CRS. The 30-day overall mortality was 12 - elective 1 (1.4%) and emergency 11 (30.5%). ACS-NSQIP RC performed better for the prediction of overall ( C statistic 0.939, Brier score 0.065) and emergency ( C statistic 0.840, Brier score 0.152) mortality. However, for elective CRS mortality, Brier scores were similar for both models (0.014), whereas C statistic (0.934 vs. 0.890) value was better for ACS-NSQIP. CONCLUSIONS: Both ACS-NSQIP and CR-POSSUM were accurate for the prediction of CRS mortality. However, compared to CR-POSSUM, ACS-NSQIP performed better. The overall performance of both models is indicative of their wider applicability in LMIC centres also.
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Vashistha et al. (2022) studied this question.
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