28-day sepsis mortality prediction model from combined serial interleukin-6, lactate, and procalcitonin measurements: a retrospective cohort study

Abstract

Abstract Sepsis is a global medical issue owing to its unacceptably high mortality rate. Therefore, an effective approach to predicting patient outcomes is critically needed. We aimed to search for a novel 28-day sepsis mortality prediction model based on serial interleukin-6 (IL-6), lactate (LAC), and procalcitonin (PCT) measurements. We enrolled 367 septic patients based on Sepsis-3 (Third International Consensus Definitions for Sepsis and Septic Shock). Serum IL-6, LAC, and PCT levels were measured serially. Results collected within 24 and 48–72 h of admission were marked as D1 and D3 (e.g., IL-6D1/D3), respectively; the IL-6, LAC, and PCT clearance (IL-6c, LACc, PCTc) at D3 were calculated. Data were split into training and validation cohorts (7:3). Logistic regression analyses were used to select variables to develop models and choose the best one according to the Akaike information criterion (AIC). Receiver operating characteristic curves (ROC), calibration plots, and decision curve analysis (DCA) were used to test model performance. A nomogram was used to validate the model. There were 314 (85.56%) survivors and 53 (14.44%) non-survivors. Logistic regression analyses showed that IL-6D1, IL-6D3, PCTD1, PCTD3, and LACcD3 could be used to develop the best prediction model. The areas under the curves (AUC) of the training (0.849, 95% CI: 0.787–0.911) and validation cohorts (0.828, 95% CI: 0.727–0.929), calibration plot, and the DCA showed that the model performed well. Thus, the predictive value of the risk nomogram was verified. Combining IL-6D1, IL-6D3, PCTD1, PCTD3, and LACcD3 may create an accurate prediction model for 28-day sepsis mortality. Multiple-center research with a larger quantity of data is necessary to determine its clinical utility.