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 SAPSⅢ 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 SAPSⅢ score [WBC (×10 9/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%, SAPSⅢ 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 SAPSⅢ 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: χ 2 = 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.
ABSTRACT
Objective:To develop and validate a model for predicting death risk in septic shock patients using LASSO-Logistic methods.Methods:A retrospective cohort study was conducted. Based on the open-source database Medical Information Mart for Intensive Care-Ⅲ v1.4 (MIMIC-Ⅲ v1.4), the septic shock patients meeting the Sepsis-3 criteria were included, and the data on demographic characteristics, major signs, laboratory examinations, hospitalization, and outcomes were extracted. Predictive variables were selected by LASSO regression and predictive models were derived using Logistic regression. The calibration of the model was evaluated using the Hosmer-Lemeshow test and discrimination was evaluated using the receiver operator characteristic curve (ROC curve).Results:A total of 693 patients with septic shock were enrolled, in which 445 patients survived and 248 patients dead within 30 days and the mortality was 35.8%. Logistic regression model was constructed according to nine predictive variables and outcome variables screened by LASSO regression method, which showed that advanced age, Elixhauser index, blood lactic acid (Lac), K + level and mechanical ventilation were associated with increased 30-day mortality [odds ratio ( OR) and 95% confidence interval (95% CI) was 1.023 (1.010-1.037), 1.047 (1.022-1.074), 1.213 (1.133-1.305), 2.241 (1.664-3.057), 2.165 (1.433-3.301), respectively, all P < 0.01], and reduced systolic blood pressure (SBP), diastolic blood pressure (DBP), body temperature, and pulse oxygen saturation (SpO 2) were also associated with increased 30-day mortality [ OR (95% CI) was 0.974 (0.957-0.990), 0.972 (0.950-0.994), 0.693 (0.556-0.857), 0.971 (0.949-0.992), respectively, all P < 0.05]. The calibration curve showed that the predicted risk of septic shock death risk prediction model had good agreement with the real situation. ROC curve analysis showed that the area under the ROC curve (AUC) of the prediction model was 0.839 (95% CI was 0.803-0.876), which could distinguish patients at risk of death from those at risk of survival. Conclusions:The septic shock death risk prediction model has a good ability to identify the 30-day mortality risk of septic shock patients, including nine hospital readily variables (age, Elixhauser index, mechanical ventilation, Lac, K +, SBP, DBP, body temperature and SpO 2). The model could be used by clinicians to calculate the risk of death in septic shock individuals.
