Intensive care for patients with traumatic brain injury (TBI) aims to optimize intracranial pressure (ICP) and cerebral perfusion pressure (CPP). The transformation of ICP and CPP time-series data into a dynamic prediction model could aid clinicians to make more data-driven treatment decisions. We retrained and externally validated a machine learning model to dynamically predict the risk of mortality in patients with TBI. Retraining was done in 686 patients with 62,000 h of data and validation was done in two international cohorts including 638 patients with 60,000 h of data. The area under the receiver operating characteristic curve increased with time to 0.79 and 0.73 and the precision recall curve increased with time to 0.57 and 0.64 in the Swedish and American validation cohorts, respectively. The rate of false positives decreased to ≤2.5%. The algorithm provides dynamic mortality predictions during intensive care that improved with increasing data and may have a role as a clinical decision support tool.

创伤性脑损伤 (TBI) 患者的重症监护旨在优化颅内压 (ICP) 和脑灌注压 (CPP)。将 ICP 和 CPP 时间序列数据转换为动态预测模型可以帮助临床医生做出更多数据驱动的治疗决策。我们重新训练并在外部验证了机器学习模型，以动态预测 TBI 患者的死亡风险。对 686 名患者进行了再培训，数据为 62,000 小时，并在两个国际队列中进行了验证，其中包括 638 名患者，数据为 60,000 小时。在瑞典和美国的验证队列中，受试者工作特征曲线下面积随时间增加至 0.79 和 0.73，精确召回曲线分别随时间增加至 0.57 和 0.64。误报率降至≤2.5%。