列线图
医学
逻辑回归
谵妄
重症监护室
曲线下面积
多元统计
败血症
接收机工作特性
重症监护
多元分析
急诊医学
内科学
重症监护医学
机器学习
计算机科学
作者
Qiong Gu,Shizhong Yang,D. W. T. Fei,Yuting Lu,Huijie Yu
标识
DOI:10.1186/s12911-023-02282-5
摘要
To develop a nomogram for predicting the occurrence of sepsis-associated delirium (SAD).Data from a total of 642 patients were retrieved from the Medical Information Mart for Intensive Care (MIMIC III) database to build a prediction model. Multivariate logistic regression was performed to identify independent predictors and establish a nomogram to predict the occurrence of SAD. The performance of the nomogram was assessed in terms of discrimination and calibration by bootstrapping with 1000 resamples.Multivariate logistic regression identified 4 independent predictors for patients with SAD, including Sepsis-related Organ Failure Assessment(SOFA) (p = 0.004; OR: 1.131; 95% CI 1.040 to 1.231), mechanical ventilation (P < 0.001; OR: 3.710; 95% CI 2.452 to 5.676), phosphate (P = 0.047; OR: 1.165; 95% CI 1.003 to 1.358), and lactate (P = 0.023; OR: 1.135; 95% CI 1.021 to 1.270) within 24 h of intensive care unit (ICU) admission. The area under the curve (AUC) of the predictive model was 0.742 in the training set and 0.713 in the validation set. The Hosmer - Lemeshow test showed that the model was a good fit (p = 0.471). The calibration curve of the predictive model was close to the ideal curve in both the training and validation sets. The DCA curve also showed that the predictive nomogram was clinically useful.We constructed a nomogram for the personalized prediction of delirium in sepsis patients, which had satisfactory performance and clinical utility and thus could help clinicians identify patients with SAD in a timely manner, perform early intervention, and improve their neurological outcomes.
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