Age, Pulse, Urea and Albumin (APUA) Model: A Tool for Predicting in-Hospital Mortality of Community-Acquired Pneumonia Adapted for Patients with Type 2 Diabetes

OBJECTIVE: The aim of this study was to develop a tool for predicting in-hospital mortality of community-acquired pneumonia (CAP) in patients with type 2 diabetes (T2DM). METHODS: A retrospective study was conducted on 531 CAP patients with T2DM at The First Hospital of Qinhuangdao. The primary outcome was in-hospital mortality. Variables to develop the nomogram were selected using multiple logistic regression analysis. Discrimination was evaluated using receiver operating characteristic (ROC) curve. Calibration was evaluated using the Hosmer–Lemeshow test and calibration plot. RESULTS: Multiple logistic regression analysis showed that age, pulse, urea and albumin (APUA) were independent risk predictors. Based on these results, we developed a nomogram (APUA model) for predicting in-hospital mortality of CAP in T2DM patients. In the training set, the area under the curve (AUC) of the APUA model was 0.814 (95% CI: 0.770–0.853), which was higher than the AUCs of albumin alone, CURB-65 and Pneumonia Severity Index (PSI) class (p<0.05). The Hosmer–Lemeshow test (χ(2)=5.298, p=0.808) and calibration plot (p=0.802) showed excellent agreement between the predicted possibility and the actual observation in the APUA model. The results of the validation set were similar to those of the training set. CONCLUSION: The APUA model is a simple and accurate tool for predicting in-hospital mortality of CAP, adapted for patients with T2DM. The predictive performance of the APUA model was better than CURB-65 and PSI class.