Development and Validation of a Predictive Scoring System for In-hospital Death in Patients With Intra-Abdominal...

Objective: To develop and validate a scoring system to predict the risk of in-hospital death in patients with intra-abdominal infection (IAI). Materials and Methods: Patients with IAI (n = 417) treated at our hospital between June 2010 and May 2020 were retrospectively reviewed. Risk factors for in-hospital death were identified by logistic regression analysis. The regression coefficients of each risk factor were re-assigned using the mathematical transformation principle to establish a convenient predictive scoring system. The scoring system was internally validated by bootstrapping sample method. Results: Fifty-three (53/417, 12.7%) patients died during hospitalization. On logistic regression analysis, high APACHE II score (P = 0.012), pneumonia (P = 0.002), abdominal surgery (P = 0.001), hypoproteinemia (P = 0.025), and chronic renal insufficiency (P = 0.001) were independent risk factors for in-hospital death. On receiver operating characteristic curve analysis, the composite index combining these five risk factors showed a 62.3% sensitivity and 80.2% specificity for predicting in-hospital death (area under the curve: 0.778; 95% confidence interval: 0.711-0.845, P < 0.001). The predictive ability of the composite index was better than that of each independent risk factor. A scoring system (0-14 points) was established by re-assigning each risk factor based on the logistic regression coefficient: APACHE II score (10-15 score, 1 point; >15 score, 4 points); pneumonia (2 points), abdominal surgery (2 points), hypoproteinemia (2 points), and chronic renal insufficiency (4 points). Internal validation by 1,000 bootstrapping sample showed relatively high discriminative ability of the scoring system (C-index = 0.756, 95% confidence interval: 0.753-0.758). Conclusions: The predictive scoring system based on APACHE II score, pneumonia, abdominal surgery, hypoproteinemia, and chronic renal insufficiency can help predict the risk of in-hospital death in patients with IAI.