AUTHOR=Tang Zhiji , Hu Kun , Yang Ruijin , Zou Mingang , Zhong Ming , Huang Qiangliang , Wei Wenjin , Jiang Qiuhua 

TITLE=Development and validation of a prediction nomogram for a 6-month unfavorable prognosis in traumatic brain-injured patients undergoing primary decompressive craniectomy: An observational study

JOURNAL=Frontiers in Neurology

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2022.944608

DOI=10.3389/fneur.2022.944608

ISSN=1664-2295

ABSTRACT=OBJECTIVE: This study was designed to develop and validate a risk-prediction nomogram to predict 6-month unfavourable prognosis in traumatic brain injuried(TBI) patients undergoing primary decompressive craniectomy(DC).
METHODS: The clinical data of 391 TBI patients with primary DC who were admitted from 2012 to 2020 were reviewed, from which 274 patients were enrolled in the training group, while 117 were enrolled in the internal validation group, randomly. The external data sets containing 80 patients were obtained from another hospital. Independent predictors of 6-month unfavorable prognosis were analyzed using multivariate logistic regression. Furthermore, a nomogram prediction model was constructed using R software. After evaluation the model, internal and external validation was performed to verify the efficiency of the model using the area under the receiver operating characteristic curves and the calibration plots. 
RESULTS: In multivariate analysis, age(p=0.001), Glasgow Score Scale (GCS)(p<0.001), operative blood loss>750ml (p=0.045), completely effaced basal cisterns(p<0.001), intraoperative hypotension(p=0.001), and activated partial thromboplastin time (APTT)>36(p=0.012) were the early independent predictors for 6-month unfavorable prognosis in TBI patients after primary DC. The AUC for training, internal and external validation cohort respectively was 0.93(95%CI, 0.89–0.96, p<0.0001), 0.89(95%CI, 0.82–0.94, p<0.0001) and 0.90(95%CI, 0.84–0.97, p<0.0001) respectively, which indicated that the prediction model had an excellent capability of discrimination. The calibration of model was exhibited by the calibration plots, which showed an optimal concordance between the predicted 6-month unfavourable prognosis probability and actual probability in both training and validation cohorts. 
CONCLUSIONS: This prediction model for 6-month unfavorable prognosis in TBI patients undergoing primary DC can evaluate the prognosis accurately and enhance early identification of high-risk patients.