Implementation of the predictive model of the early recovery program in patients with elective colorectal surgery
Keywords:
model, cancer, surgical patient, colorectal surgery, predictionAbstract
Introduction: Colorectal cancer is the third type of cancer diagnosed. 90% of patients are cured in an early stage, 60% of deaths are prevented with early detection for optimized surgical treatment. The early recovery program in patients with elective colorectal surgery is an evidence-based approach that provides safe care and reduces mortality, morbidity and increases patient satisfaction in the perioperative period.
Objective: To implement the predictive model of the early recovery program in patients with elective colorectal surgery.
Methods: It was carried out a quasi-experimental study in elective surgical patients with colorectal cancer, from January 2018 to January 2022, with a universe of 334 patients and a sample of 268.The information was obtained from the clinical history and autopsy protocols in case of deceased patients. It was performed the binary logistic regression function from the input of the identified risk factors and protective factors for mortality. The variables selected in the equation became predictors of mortality risk.
Results: It was carried out the statistical test of Hosmer and Lemeshow to evaluate the quality of the regression model and it was obtained a good fit. Adherence to the early recovery program in patients with elective colorectal surgery equal to or greater than 80% decreases the probability of mortality by 0,5 times, behavior of the variable as a protective factor. The variables: early readmission, adherence to the early recovery program and duration of the surgical intervention were predictors included in the model.
Conclusions: The optimization of the surgical treatment of the patient with colorectal cancer based on the adherence to the program, uses as a means the quality of the statistical estimations, used in the elaboration, validation and implementation of the developed predictive model.
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