Raj Rahul, Wennervirta Jenni M, Tjerkaski Jonathan, Luoto Teemu M, Posti Jussi P, Nelson David W, Takala Riikka, Bendel Stepani, Thelin Eric P, Luostarinen Teemu, Korja Miikka
Department of Neurosurgery, Helsinki University Hospital and University of Helsinki, Helsinki, Finland.
Analytics and AI Development Services, HUS IT Management, Helsinki University Hospital, Helsinki, Finland.
NPJ Digit Med. 2022 Jul 18;5(1):96. doi: 10.1038/s41746-022-00652-3.
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有助于临床医生做出更多基于数据的治疗决策。我们重新训练并对外验证了一个机器学习模型，以动态预测TBI患者的死亡风险。在686例患者中利用62000小时的数据进行了重新训练，并在两个国际队列中进行了验证，这两个队列包括638例患者及60000小时的数据。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，精确召回率曲线随时间增加至0.57和0.64。假阳性率降至≤2.5%。该算法在重症监护期间提供动态死亡预测，预测结果随数据增加而改善，可能作为一种临床决策支持工具发挥作用。
