Construction and validation of risk prediction model for subdelirium syndrome in ICU
10.3760/cma.j.cn115682-20220819-04067
- VernacularTitle:ICU亚谵妄综合征风险预测模型的构建及验证
- Author:
Jiaxin ZHANG
1
;
Mingtao QUAN
;
Jing ZHANG
;
Weili ZHAN
;
Xueli CHEN
Author Information
1. 遵义医科大学护理学院，遵义 563000
- Keywords:
Intensive Care Units;
Subdelirium syndrome;
Risk factors;
Risk prediction model
- From:
Chinese Journal of Modern Nursing
2023;29(11):1453-1460
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To analyze the risk factors of subdelirium syndrome in Intensive Care Units (ICU) patients, build a risk prediction model and verify the prediction performance of the model.Methods:From July 2021 to February 2022, 443 ICU patients who were admitted to the Affiliated Hospital of Zunyi Medical University were selected by convenient sampling. The patients were divided into subdelirium syndrome group ( n=151) and non-subdelirium syndrome group ( n=292) according to whether they had subdelirium syndrome. The binomial Logistic regression was used to screen out independent influencing factors to construct a risk prediction model of subdelirium syndrome in ICU. The prediction effect of the model was tested by the area under the curve ( AUC) of receiver operating characteristic curve. According to the same standard, 147 ICU patients admitted to the Affiliated Hospital of Zunyi Medical University from March to April 2022 were selected for external validation of the model. Results:The risk prediction model of subdelirium syndrome in ICU was Y=-4.126+1.569×Acute Physiology and Chronic Health Evaluation (APACHE) Ⅱ score+1.261×pain score+1.643×total leukocyte+1.276×albumin +1.530 × operation or not. The internal validation of the model showed that the AUC was 0.878 [95% confidence interval (0.844, 0.912) ], the maximum Youden index was 0.614, the sensitivity was 74.8%, and the specificity was 86.6%. Hosmer-Lemeshow goodness of fit test showed that χ 2=2.743, P>0.05. The external validation of the model showed that the sensitivity was 92.6% and the specificity was 95.7%. Conclusions:This risk prediction model of subdelirium syndrome in ICU has good prediction performance, which can help medical and nursing staff identify high-risk patients with subdelirium syndrome in ICU as soon as possible.
