Construction and validation of prediction model on prognosis of moderate to severe traumatic brain injury based on regional cerebral oxygen saturation and transcranial Doppler ultrasound monitoring parameters
10.3760/cma.j.cn501098-20231104-00282
- VernacularTitle:基于局部脑氧饱和度和经颅多普勒超声监测参数的中重型创伤性脑损伤预后预测模型的构建与验证
- Author:
Bingsha HAN
1
;
Jiao LI
;
Yanru LI
;
Ju WANG
;
Zhiqiang REN
;
Jinghe ZHAO
;
Yang LIU
;
Mengyuan XU
;
Guang FENG
Author Information
1. 河南省人民医院、郑州大学人民医院神经外科重症监护室，郑州 450003
- Keywords:
Brain injuries;
Ultrasonography;
Prognosis;
Nomograms;
Near-infrared
- From:
Chinese Journal of Trauma
2024;40(5):411-419
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct a prognostic predictive model for patients with moderate to severe traumatic brain injury (msTBI) based on regional cerebral oxygen saturation (rScO 2) and transcranial Doppler ultrasound (TCD) monitoring parameters and validate its effectiveness. Methods:A retrospective cohort study was conducted to analyze the clinical data of 161 patients with msTBI who were treated at Henan Provincial People′s Hospital from January 2021 to December 2022, including 104 males and 57 females, aged 19-76 years [(53.1±12.8)years]. Glasgow coma scale (GCS) score was 3-12 points [(7.0±1.9)points]. Both rScO 2 and TCD monitoring were performed. Based on the results of prognostic evaluation of patients with the modified Rankin scale (mRS) score at 90 days after discharge, the patients were divided into good prognosis group (mRS score≤3 points, n=88) and poor prognosis group (mRS score of 4-6 points, n=73). The following data of the two groups were collected: the general data, clinical data, rScO 2 monitoring parameters and TCD monitoring parameters. Univariate analysis was employed to compare the differences in the relevant prognostic indicators. Multivariate Logistic stepwise regression analysis was conducted to determine the predictors of poor prognostic outcomes in msTBI patients and regression equations were constructed. A nomogram predictive model based on regression equations was drawn with R language. The discriminability of the model was evaluated by drawing the receiver operating characteristic (ROC) curve, to calculate the area under the curve (AUC), sensitivity, specificity, and Jordan index of the model, and measuring the consistency index (C index). Hosmer-Lemeshow (H-L) goodness of fit test was conducted to evaluate the fit of the model, and the calibration curve was used to evaluate the calibration degree of the model. Decision curve analysis (DCA) was employed to evaluate the clinical benefit and applicability of the model. Results:There were significant differences between the two groups in the clinical data (cerebral hernia formation, GCS on admission, acute physiology and chronic health evaluation II (APACHE II) score on admission, Rotterdam CT score on admission, oxygenation index on admission, mean arterial pressure on admission), rScO 2 monitoring parameters (mean rScO 2, maximum rScO 2, rScO 2 variability), TCD monitoring parameters [peak systolic blood flow velocity (Vs), average blood flow velocity (Vm), pulse index (PI)] ( P<0.05 or 0.01). The results of multivariate Logistic stepwise regression analysis showed that cerebral hernia formation ( OR=9.28, 95% CI 3.40, 25.33, P<0.01), Rotterdam CT score on admission ( OR=1.92, 95% CI 1.32, 2.78, P<0.01), rScO 2 variability ( OR=4.66, 95% CI 1.74, 12.43, P<0.01), Vs ( OR=0.66, 95% CI 0.61, 0.75, P<0.01) and PI ( OR=20.07, 95% CI 4.17, 16.50, P<0.01) were predictive factors for poor prognosis in patients with msTBI. The regression equation was constructed with the forementioned 5 variables: Logit [ P/(1- P)]=2.23×"brain hernia formation"+0.65×"Rotterdam CT score on admission"+1.54×"rScO 2 variability"-0.42×"Vs"+3.00×"PI"-6.75. The AUC of prognostic predictive model of msTBI patients was 0.90 (95% CI 0.85, 0.95), with the sensitivity and specificity of 86.3% and 78.4%, Youden index of 0.65 and C index of 0.90. H-L goodness of fit test showed that the calibration degree of the predictive model was accurate ( χ2 =12.58, P>0.05). The average absolute error of the calibration curve was 0.025, showing that the calibration of the model was good. DCA results showed that this model had higher net return rate than the reference model within the probability range of risk threshold (20%-100%), with good clinical application value in evaluating the risk of poor prognosis of msTBI patients. Conclusion:The model constructed based on the combination of rScO 2 and TCD monitoring parameters (rScO 2 variability, Vs and PI) with multiple clinical indicators (cerebral hernia formation and Rotterdam CT score on admission) has good predictive performance for the prognosis of msTBI.
