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Purpose. Acute cholangitis (AC) is a widespread acute inflammatory disease and the main cause of septic shock, which has a high death rate in hospitals. At present, the prediction models for short-term mortality of AC patients are still not ideal. We aimed at developing a new model that could forecast the short-term mortality rate of AC patients. Methods. Data were extracted from the Medical Information Mart for Intensive Care IV version 2.0 (MIMIC-IV v2.0). There were a total of 506 cases of AC patients that were included. Patients were given a 7 : 3 split between the training set and the validation set after being randomly assigned to one of the groups. Multivariate logistic regression was used to create an AC patient predictive nomogram for 30-day mortality. The overall efficacy of the model is evaluated using the area under the receiver operating characteristic curve (AUC), the calibration curve, the net reclassification improvement (NRI), the integrated discrimination improvement (IDI), and a decision curve analysis (DCA). Results. Out of 506 patients, 14.0% (71 patients) died. The training cohort had 354 patients, and the validation cohort had 152 patients. GCS, SPO2, albumin, AST/ALT, glucose, potassium, PTT, and peripheral vascular disease were the independent risk factors according to the multivariate analysis results. The newly established nomogram had better prediction performance than other common scoring systems (such as SOFA, OASIS, and SAPS II). For two cohorts, the calibration curve demonstrated coherence between the nomogram and the ideal observation P > 0.05. The clinical utility of the nomogram in both sets was revealed by decision curve analysis. Conclusion. The novel prognostic model was effective in forecasting the 30-day mortality rate for acute cholangitis patients.

Purpose. The available nomograms used to predict acute pancreatitis (AP) are not comprehensive. We sought to investigate the effect of red blood cell distribution width (RDW)-albumin ratio (RA) on prognosis of patients with AP and develop a new nomogram to identify AP patients at high risk for mortality. Methods. We used data from the Medical Information Mart for Intensive Care IV version 2.0 (MIMIC-IV v2.0). A total of 487 patients with acute pancreatitis were included. Patients enrolled in the study were randomly assigned to the training set and validation set at a 7 : 3 ratio. According to the 30-day mortality rate, the data were divided into a survival group and a death group. Multivariate logistic regression was used to establish a prognostic nomogram for predicting the 30-day mortality in AP patients. The area under the receiver operating characteristic curve (AUC), calibration curve, the net reclassification improvement (NRI), the integrated discrimination improvement (IDI), and a decision curve analysis (DCA) are used to verify the overall performance of the model. Results. Among 487 patients, 54 patients died (11.1%). 338 patients were assigned to the training cohort and 149 were assigned to the validation cohort. The multivariate analysis results showed that RA, age, heart rate, temperature, AST/ALT, BUN, hemoglobin, potassium, and bilirubin were independent risk factors. The prediction performance of the newly established nomogram was better than those of other common scoring systems (including SOFA, OASIS, and APSIII). The nomogram suggests that RA (OR = 1.706, 95% CI: 1.367–2.185) is the most significant laboratory test indicator influencing prognosis. Conclusion. The new nomogram incorporating RA performed well in predicting AP short-term mortality. A prospective study with a larger sample is needed to validate our findings.