A predictive model for postoperative progressive haemorrhagic injury in traumatic brain injuries

Autor: Tiange Chen, Siming Chen, Yun Wu, Yilei Chen, Lei Wang, Jinfang Liu
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: BMC Neurology, Vol 22, Iss 1, Pp 1-9 (2022)
Druh dokumentu: article
ISSN: 1471-2377
DOI: 10.1186/s12883-021-02541-w
Popis: Abstract Background Progressive haemorrhagic injury after surgery in patients with traumatic brain injury often results in poor patient outcomes. This study aimed to develop and validate a practical predictive tool that can reliably estimate the risk of postoperative progressive haemorrhagic injury (PHI) in patients with traumatic brain injury (TBI). Methods Data from 645 patients who underwent surgery for TBI between March 2018 and December 2020 were collected. The outcome was postoperative intracranial PHI, which was assessed on postoperative computed tomography. The least absolute shrinkage and selection operator (LASSO) regression model, univariate analysis, and Delphi method were applied to select the most relevant prognostic predictors. We combined conventional coagulation test (CCT) data, thromboelastography (TEG) variables, and several predictors to develop a predictive model using binary logistic regression and then presented the results as a nomogram. The predictive performance of the model was assessed with calibration and discrimination. Internal validation was assessed. Results The signature, which consisted of 11 selected features, was significantly associated with intracranial PHI (p
Databáze: Directory of Open Access Journals
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