A model based on random forests in prediction of 28-day prognosis in patients with severe sepsis/septic shock / 中华危重病急救医学

ABSTRACT

Objective To establish a severe sepsis/septic shock prognosis prediction model based on randomize forest law (RF model), and to evaluate the prognostic value of this model for patients with severe sepsis/septic shock. Methods 497 patients with severe sepsis/septic shock admitted to intensive care unit (ICU) of Zhejiang Hospital from September 2013 to May 2017 were enrolled. The basic data, vital signs and symptoms, biochemical indexes and blood routine indexes on the 1st, 3rd, 5th day and prognosis were collected. According to the 28-day prognosis, the patients were divided into death group and survival group, and the specific indicators about the prognosis of severe sepsis/septic shock were screened. A RF model was constructed by using the specificity indicators. The assessment effectiveness of RF model, sequential organ failure assessment (SOFA), acute physiology and chronic health evaluation Ⅱ(APACHE Ⅱ) were evaluated by receiver operating characteristic (ROC) curve analysis. Results In 497 cases of severe sepsis/septic shock, 201 cases died, 28-day mortality was 40.4%. ① According to the index difference of death group and survival group, 19 specific parameters of the RF model were selected, which included the age; 24-hour urine output, urea nitrogen (BUN), serum creatinine (SCr), platelet count (PLT) on the 1st day; heart rate (HR), mean arterial pressure (MAP), cyanosis and clammy skin on the 3rd day; temperature, HR, MAP, 24-hour urine output, PLT, fever, cyanosis, dyspneic, clammy skin, piebald on the 5th day. ② ROC curve analysis showed that the area under the ROC curve (AUC) of RF model predicting 28-day mortality was higher than that of SOFA and APACHE Ⅱ score on the 1st, 3rd, 5th day (AUC 0.836 vs. 0.643, 0.554, 0.766 and 0.590, 0.670, 0.758). The sensitivity of RF model to predict the 28-day mortality was 86.1%, the specificity was 77.0%, the accuracy was 80.7%. Conclusion The evaluation model based on random forest can effectively predict the death risk of 28-day in patients with severe sepsis/septic shock, and its predictive efficiency is better than that of the SOFA and APACHE Ⅱ score.