Heliyon (Sep 2024)
Development and validation of a clinical nomogram for predicting in-hospital mortality in patients with traumatic brain injury prehospital: A retrospective study
Abstract
Objective: Traumatic brain injury (TBI) is among the leading causes of death and disability globally. Identifying and assessing the risk of in-hospital mortality in traumatic brain injury patients at an early stage is challenging. This study aimed to develop a model for predicting in-hospital mortality in TBI patients using prehospital data from China. Methods: We retrospectively included traumatic brain injury patients who sustained injuries due to external forces and were treated by pre-hospital emergency medical services (EMS) at a tertiary hospital. Data from the pre-hospital emergency database were analyzed, including demographics, trauma mechanisms, comorbidities, vital signs, clinical symptoms, and trauma scores. Eligible patients were randomly divided into a training set (241 cases) and a validation set (104 cases) at a 7:3 ratio. Least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression were employed to identify independent risk factors. Analyzed the discrimination, calibration, and net benefit of the nomogram across both groups. Results: 17.40 % (42/241) of TBI patients died in the hospital in the training set, while 18.30 % (19/104) in the validation set. After analysis, chest trauma (odds ratio [OR] = 4.556, 95 % confidence interval [CI] = 1.861–11.152, P = 0.001), vomiting (OR = 2.944, 95%CI = 1.194–7.258, P = 0.019), systolic blood pressure (OR = 0.939, 95%CI = 0.913–0.966, P < 0.001), SpO2 (OR = 0.778, 95%CI = 0.688–0.881, P < 0.001), and heart rate (OR = 1.046, 95%CI = 1.015–1.078, P = 0.003) were identified as independent risk factors for in-hospital mortality in TBI patients. The nomogram based on the five factors demonstrated well-predictive power, with an area under the curve (AUC) of 0.881 in the training set and 0.866 in the validation set. The calibration curve and decision curve analysis showed that the predictive model exhibited good consistency and covered a wide range of threshold probabilities in both sets. Conclusion: The nomogram based on prehospital data demonstrated well-predictive performance for in-hospital mortality in TBI patients, helping prehospital emergency physicians identify and assess severe TBI patients earlier, thereby improving the efficiency of prehospital emergency care.
