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  • Title: [Establishment and evaluation of early in-hospital death prediction model for patients with acute pancreatitis in intensive care unit].
    Author: Yu L, Zhou X, Li Y, Liu M.
    Journal: Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2023 Aug; 35(8):865-869. PubMed ID: 37593868.
    Abstract:
    OBJECTIVE: To investigate the death risk prediction factors of acute pancreatitis (AP) patients in intensive care unit (ICU), and to establish a death prediction model and evaluate its efficacy. METHODS: A retrospective cohort study was conducted using the data in the Medical Information Mart for Intensive Care-III (MIMIC-III). The clinical data of 285 AP patients admitted to the ICU in the database were collected, including age, gender, blood routine and blood biochemical indicators, comorbidities, simplified acute physiology score III (SAPS III) and hospital prognosis. By using univariate analysis, the differences in the clinical data of the patients were compared between the two groups. Binary multivariate Logistic regression analysis was used to screen out independent predictors of in-hospital death in AP patients. A death prediction model was established, and the nomogram was drawn. The receiver operator characteristic curve (ROC curve) was plotted, and the area under the ROC curve (AUC) was used to test the discrimination of the prediction model. In addition, the prediction model was compared with the SAPS III score in predicting in-hospital death. The calibration ability of the prediction model was evaluated by the Hosmer-Lemeshow goodness of fit test, and a calibration map was drawn to show the calibration degree of the prediction model. RESULTS: Among 285 patients with AP, 29 patients died in the hospital and 256 patients survived. Univariate analysis showed that the patients in the death group were older than those in the survival group (years old: 70±17 vs. 58±16), and had higher white blood cell count (WBC), total bilirubin (TBil), serum creatinine (SCr), blood urea nitrogen (BUN), red blood cell volume distribution width (RDW), proportion of congestive heart failure and SAPS III score [WBC (×109/L): 18.5 (13.9, 24.3) vs. 13.2 (9.3, 17.9), TBil (μmol/L): 29.1 (15.4, 66.7) vs. 16.2 (10.3, 29.1), SCr (μmol/L): 114.9 (88.4, 300.6) vs. 79.6 (53.0, 114.9), BUN (mmol/L): 13.9 (9.3, 17.8) vs. 6.1 (3.7, 9.6), RDW: 0.152 (0.141, 0.165) vs. 0.141 (0.134, 0.150), congestive heart failure: 34.5% vs. 14.8%, SAPS III score: 66 (52, 90) vs. 39 (30, 48), all P < 0.05]. Multivariate Logistic regression analysis showed that age [odds ratio (OR) = 1.038, 95% confidence interval (95%CI) was 1.005-1.073], WBC (OR = 1.103, 95%CI was 1.038-1.172), TBil (OR = 1.247, 95%CI was 1.066-1.459), BUN (OR = 1.034, 95%CI was 1.014-1.055) and RDW (OR = 1.344, 95%CI was 1.024-1.764) were independent risk predictors of in-hospital death in patients with AP. Logistic regression model was established: Logit(P) = 0.037×age+0.098×WBC+0.221×TBil+0.033×BUN+0.296×RDW-12.133. ROC curve analysis showed that the AUC of the Logistic regression model for predicting the in-hospital death of patients with AP was 0.870 (95%CI was 0.794-0.946), the sensitivity was 86.2%, and the specificity was 78.5%, indicating that the model had good predictive performance, and it was superior to the SAPS III score [AUC was 0.831 (95%CI was 0.754-0.907), the sensitivity was 82.8%, and the specificity was 75.4%]. A nomogram model was established based on the result of multivariate Logistic regression analysis. The calibration map showed that the calibration curve of the nomogram model was very close to the standard curve, with the goodness of fit test: χ2 = 6.986, P = 0.538, indicating that the consistency between the predicted death risk of the nomogram model and the actual occurrence risk was relatively high. CONCLUSIONS: The older the AP patient is, the higher the WBC, TBil, BUN, and RDW, and the greater the risk of hospital death. The death prediction Logistic regression model and nomogram model constructed based on the above indicators have good discrimination ability and high accuracy for high-risk patients with hospital death, which can accurately predict the probability of death in AP patients and provide a basis for prognosis judgment and clinical treatment of AP patients.
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