A nomogram model based on LASSO-Cox regression to predict pressure injury risk in mechanically ventilated patients
10.12025/j.issn.1008-6358.2024.20240626
- VernacularTitle:基于LASSO-Cox回归构建列线图模型预测机械通气患者的压力性损伤风险
- Author:
Baihui KANG
1
,
2
;
Meiqiong YAN
2
;
Jian GAO
3
;
Shining CAI
4
;
Jingjing LI
4
Author Information
1. School of Nursing, Fudan University, Shanghai 200032, China
2. Department of Nursing, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
3. Department of Nutrition, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
4. Department of Intensive Care Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
- Publication Type:Originalarticle
- Keywords:
pressure injury;
mechanical ventilation;
nomogram;
LASSO regression;
predictive modeling
- From:
Chinese Journal of Clinical Medicine
2024;31(4):593-602
- CountryChina
- Language:Chinese
-
Abstract:
Objective To construct a nomogram model to predict the risk of pressure injuries (PI) in mechanically ventilated patients in the intensive care unit (ICU). Methods Clinical data of mechanically ventilated patients in the ICU of Zhongshan Hospital, Fudan University from January 1, 2020 to March 15, 2023 were retrospectively collected as the training set, and data from ICU of the same hospital from October 1, 2023 to December 11, 2023 were collected as the external validation set. Risk variables for PI were selected using LASSO regression and Cox proportional hazards model, and a nomogram model was constructed. Receiver operating characteristic (ROC) curve was plotted, and the area under the curve (AUC) was calculated to evaluate the model. Calibration curve and decision curve analysis (DCA) were used to assess the model’s calibration and clinical applicability. The external validation was performed using the validation set data. Results A total of 580 mechanically ventilated patients were included in the training set, with 84 cases (14.5%) of PI. LASSO regression and Cox proportional hazards model selected 10 variables to construct the nomogram model. The ROC curve showed an AUC of 0.830 for predicting PI in mechanically ventilated patients. Calibration curve and DCA indicated good calibration and predictive performance of the model. The external validation set included 100 patients, with 12 cases of PI, and the AUC was 0.870. Calibration curve and DCA showed good model performance. Conclusions The nomogram model based on LASSO-Cox regression has good predictive performance and can be used to screen high-risk population for PI in mechanically ventilated patients.
