Construction and validation of the predictive model for intensive care unit and in-hospital mortality risk in patients with traumatic brain injury
10.3760/cma.j.cn501098-20240114-00065
- VernacularTitle:创伤性脑损伤患者重症监护病房及院内死亡风险预测模型的构建与验证
- Author:
Miao LU
1
;
Jing ZHANG
;
Sai XIN
;
Jiaming ZHANG
;
Lei ZHENG
;
Yun ZHANG
Author Information
1. 南京医科大学附属无锡人民医院急诊医学科，无锡 214023
- Keywords:
Brain injuries;
Prognosis;
Risk factors;
Predictive model
- From:
Chinese Journal of Trauma
2024;40(5):420-431
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct a predictive model for intensive care unit (ICU) and in-hospital mortality risk in patients with traumatic brain injury (TBI) and validate its performance.Methods:A retrospective cohort study was conducted to analyze the clinical data of 3 907 patients with TBI published until May 2018 in the eICU Collaborative Research Database v2.0 (eICU-CRD v2.0), including 2 397 males and 1 510 females, aged 18-92 years [63.0(43.0, 79.0)years]. According to whether the patients died in ICU or at hospital stay, they were divided into ICU survival group ( n=3 575) and ICU mortality group ( n=332), and hospital survival group ( n=3 413) and hospital mortality group ( n=494). The general data, admission diagnosis, laboratory tests, therapeutic interventions, and clinical outcomes were extracted as variables of interest. Univariate analysis and multivariate Logistic regression analysis were conducted on both the survival groups and the mortality groups to identify the independent risk factors that affect ICU and in-hospital mortality in TBI patients, based on which a Logistic regression prediction model was constructed and represented by Nomograms. The extracted dataset was randomly divided into training set ( n=2 735) and validation set ( n=1 172) with a ratio of 7∶3, and was applied for internal validation of the of the predictive model. Meanwhile, the data of TBI patients in the MIMIC-III v1. 4 database were extracted for external validation of the predictive model. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used for discriminability evaluation of the model, and the Hosmer-Lemeshow (H-L) goodness of fit test and calibration curve were used for calibration evaluation of the model. Results:The statistically significant variables identified in the univariate analysis were included in the multivariate logistic regression analysis of ICU mortality and in-hospital mortality risk. The results revealed that acute physiology and chronic health evaluation IV (APACHE IV) score ( OR=1.04, 95% CI 1.03, 1.04, P<0.01), Glasgow coma scale (GCS) ( OR=0.66, 95% CI 0.59, 0.73, P<0.01), cerebral hernia formation ( OR=6.91, 95% CI 3.13, 15.26, P<0.01), international normalized ratio (INR) ( OR=1.33, 95% CI 1.09, 1.62, P<0.01), use of hypertonic saline ( OR=0.45, 95% CI 0.21 0.94, P<0.05), and use of vasoactive agents ( OR=2.19, 95% CI 1.36, 3.52, P<0.01) were independent risk factors for ICU mortality in TBI patients. The age (with 10 years as a grade) ( OR=1.28, 95% CI 1.17, 1.40, P<0.01), APACHE IV score ( OR=1.03, 95% CI 1.02, 1.04, P<0.01), GCS ( OR=0.75, 95% CI 0.71, 0.80, P<0.01), cerebral hernia formation ( OR=6.44, 95% CI 2.99, 13.86, P<0.01), serum creatinine level ( OR=1.07, 95% CI 1.01, 1.15, P<0.05), INR ( OR=1.49, 95% CI 1.20, 1.85, P<0.01), use of hypertonic saline ( OR=0.41, 95% CI 0.21, 0.80, P<0.01), and use of vasoactive agents ( OR=2.27, 95% CI 1.46, 3.53, P<0.01) were independent risk factors of in-hospital mortality of TBI patients. Based on the forementioned independent risk factors for ICU mortality, the model equation was constructed: Logit P (ICU)=7.12+0.03×"APACHE IV score"-0.42×"GCS"+1.93×"cerebral hernia formation"+0.28×"INR"-0.81×"use of hypertonic saline"+0.79×"use of vasoactive agents". Based on the forementioned independent risk factors for in-hospital mortality, the model equation was constructed: Logit P (in-hospital)=2.75+0.25×"age"(with 10 years as a grade)+0.03×"APACHE IV score"-0.28×"GCS"+1.86×"cerebral hernia formation"+0.07×"serum creatinine level"+0.40×"INR"-0.90×"use of hypertonic saline"+0.82×"use of vasoactive agents". In the prediction model for ICU mortality, the AUC of the training set and validation set was 0.95 (95% CI 0.94, 0.97) and 0.91 (95% CI 0.87, 0.95). The result of H-L goodness of fit test of the training set was P=0.495 with the average absolute error in the calibration curve of 0.003, while the result of H-L goodness of fit test of the validation set was P=0.650 with the average absolute error in the calibration curve of 0.012. In the prediction model for in-hospital mortality, the AUC of the training set and validation set was 0.91 (95% CI 0.89, 0.93) and 0.91(95% CI 0.88, 0.94). The result of H-L goodness of fit test of the training set was P=0.670 with the average absolute error in the calibration curve of 0.006, while the result of H-L goodness of fit test of the validation set was P=0.080 with the average absolute error in the calibration curve of 0.021. In the external validation set of ICU mortality risk, the AUC of the prediction model was 0.88 (95% CI 0.86, 0.90), while the result of H-L goodness of fit test was P=0.205 with the average absolute error in the calibration curve of 0.031. In the external validation set of in-hospital mortality risk, the AUC of the prediction model was 0.88 (95% CI 0.85, 0.91), while the result of H-L goodness of fit test was P=0.239 with the average absolute error in the calibration curve of 0.036. The internal and external validation of the model indicated that both the prediction models for ICU and in-hospital mortality had good discriminability and calibration. Conclusion:The ICU mortality prediction model constructed by APACHE IV score, GCS, cerebral hernia formation, use of hypertonic saline, vasoactive agents use of and INR, and the in-hospital mortality prediction model constructed by age grading, APACHE IV score, GCS, cerebral hernia formation, serum creatinine level, hypertonic saline use of, use of vasoactive agents and INR can predict the mortality risk of TBI patients well.
