Contextual Validation of the Prediction of Postoperative Complications of Colorectal Surgery by the “ACS NSQIP^®Risk Calculator” in a Tunisian Center

Abstract

Context: Models for predicting individual risks of surgical complications are advantageous for operative decision making and the nature of postoperative management procedures. Objective: Validate the “ACS NSQIP® Risk Calculator” in the prediction of postoperative complications during colorectal cancer surgery, operated during the years 2015 to 2019. Methods: this is a prognostic validation study of the “ACS NSQIP®” applied retrospectively to patients operated on for colorectal cancer in the surgical department of Farhat Hached hospital, during the 2015 and 2019 5-year term. Three levels of adjustment. Discrimination and calibration were carried out mainly by ROC curves (AUC ⩾ 0.8). Results: In this study, 129 patients were included with a sex ratio of 1.22 and a median age of 62 years. The most common operative procedure was low segmental colectomy with colorectal anastomosis. Thirty-seven patients (28.7%) had at least one postoperative complication. The prediction and cuts-off points values of mortality (AUC = 0.858; CI95% [0.570-0.960]; Cuts-off points = 1.8%), cardiac complications (AUC = 0.824; CI95% [0.658-0.990]; Cuts-off points = 1.8%), thromboembolic complications (AUC = 0.802; CI95% [0.617-0.987]; Cuts-off point = 3.1%), and renal insufficiency (AUC = 0.802; CI95% [ 0.623-0.981]; Cuts-off point = 1.2%) were adjusted according to level 1 of the calculator. Conclusion: This work contextualized the prediction of postoperative complications in colorectal surgery in the university general surgery department of Farhat Hached in Sousse (Tunisia), making it possible to improve the quality and safety of surgical care. The application of the Tunisian mini calculator is recommended as well as the generalization of validation following the development of a generic calculator for all operating procedures.

Development and validation of a predictive model for febrile seizures

“…The AUC was calculated for three sets to assess the model's discriminatory capacity. The discriminatory capacity was categorized as poor (AUC: 0.6–0.69), adequate (AUC: 0.7–0.79), good (AUC: 0.8–0.89), or excellent (AUC: 0.9–1.0) 17 . The validation of the nomogram involved assessing its discrimination (C-statistic) and calibration (calibration plot).…”