Binary logistic regression to create a predictive model of liver damage in the septic patient
Keywords:
sepsis, liver failure, hepatic insufficiency, forecasting, logistic modelsAbstract
Introduction: sepsis, for all the events it unleashes, affects, directly or indirectly, all the organs. It is common to observe, in the evolution of these patients, the development of dysfunction or hepatic failure that, rarely, it is diagnosed until clinical signs such as jaundice or coagulation disorders appear. Objective: to design a predictive model of liver damage in the septic patient. Method: an observational, retrospective and developmental study of case-control, of case-control, was carried out. A total of 508 deaths were taken with clinical and pathological evidence of sepsis from the Intensive Care Unit that met the intentionality criteria, from January 2006 to December 2015 at “Manuel Fajardo Rivero” Hospital. Of these deaths, 100 cases and 100 controls were taken. Results: the variables that were included in the model, after the binary logistic regression analysis, were: multiple organ dysfunction syndrome, direct bilirubin, alkaline phosphatase, total cholesterol, creatinine, international normalized ratio and platelets. Hosmer-Lemeshow test=1,867 and p=0,985. Sensitivity of 57,69 and specificity of 100. Positive predictive value of 100 and a negative predictive value of 68,57, validity index of 78,00. The area observed under the Receiver Operating Characteristic curve is 0,922, with a significance associated to the calculated statistician of 0,000. Conclusions: the model demonstrated good discriminatory capacity and to be a good predictor of liver damage in the septic patient.Downloads
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