• Aozi Feng, Xi Ao, Ning Zhou, Tao Huang, Li Li, Mengnan Zeng, and Jun Lyu.
• Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong 510632, China.
• Int. J. Clin. Pract. 2022 Jan 1; 2022: 54356565435656.

BackgroundThe prognosis is poor when acute pancreatitis (AP) progresses to sepsis; therefore, it is necessary to accurately predict the probability of sepsis and develop a personalized treatment plan to reduce the disease burden of AP patients.MethodsA total of 1295 patients with AP and 43 variables were extracted from the Medical Information Mart for Intensive Care (MIMIC) IV database. The included patients were randomly assigned to the training set and to the validation set at a ratio of 7 : 3. The chi-square test or Fisher's exact test was used to test the distribution of categorical variables, and Student's t-test was used for continuous variables. Multivariate logistic regression was used to establish a prognostic model for predicting the occurrence of sepsis in AP patients. The indicators to verify the overall performance of the model included the area under the receiver operating characteristic curve (AUC), calibration curves, the net reclassification improvement (NRI), the integrated discrimination improvement (IDI), and a decision curve analysis (DCA).ResultsThe multifactor analysis results showed that temperature, phosphate, calcium, lactate, the mean blood pressure (MBP), urinary output, Glasgow Coma Scale (GCS), Charlson Comorbidity Index (CCI), sodium, platelet count, and albumin were independent risk factors. All of the indicators proved that the prediction performance and clinical profitability of the newly established nomogram were better than those of other common indicators (including SIRS, BISAP, SOFA, and qSOFA).ConclusionsThe new risk-prediction system that was established in this research can accurately predict the probability of sepsis in patients with acute pancreatitis, and this helps clinicians formulate personalized treatment plans for patients. The new model can reduce the disease burden of patients and can contribute to the reasonable allocation of medical resources, which is significant for tertiary prevention.Copyright © 2022 Aozi Feng et al.

9 keywords...

hide…