University of Maryland School of Medicine, R Adams Cowley Shock Trauma Center, Shock Trauma and Anesthesia Research Center, Baltimore, Maryland, USA.
J Trauma Acute Care Surg. 2012 Aug;73(2):419-24; discussion 424-5. doi: 10.1097/TA.0b013e31825ff59a.
We asked whether the advanced machine learning applications used in microarray gene profiling could assess critical thresholds in the massive databases generated by continuous electronic physiologic vital signs (VS) monitoring in the neuro-trauma intensive care unit.
We used Class Prediction Analysis to predict binary outcomes (life/death, good/bad Extended Glasgow Outcome Score, etc.) based on data accrued within 12, 24, 48, and 72 hours after admission to the neuro-trauma intensive care unit. Univariate analyses selected "features," discriminator VS segments or "genes," in each individual's data set. Prediction models using these selected features were then constructed using six different statistical modeling techniques to predict outcome for other individuals in the sample cohort based on the selected features of each individual then cross-validated with a leave-one-out method.
We gleaned complete sets of 588 VS monitoring segment features for each of four periods and outcomes from 52 of 60 patients with severe traumatic brain injury who met study inclusion criteria. Overall, intracranial pressures and blood pressures over time (e.g., intracranial pressure >20 mm Hg for 20 minutes) provided the best discrimination for outcomes. Modeling performed best in the first 12 hours of care and for mortality. The mean number of selected features included 76 predicting 14-day hospital stay in that period, 11 predicting mortality, and 4 predicting 3-month Extended Glasgow Outcome Score. Four of the six techniques constructed models that correctly identified mortality by 12 hours 75% of the time or higher.
Our results suggest that valid prediction models after severe traumatic brain injury can be constructed using gene mapping techniques to analyze large data sets from conventional electronic monitoring data, but that this methodology needs validation in larger data sets, and that additional unstructured learning techniques may also prove useful.
我们想知道，在神经重症监护病房中，连续的电子生理生命体征（VS）监测所产生的海量数据库中，能否利用先进的机器学习应用程序来评估关键阈值。
我们使用分类预测分析来预测二元结局（生与死、良好/不良扩展格拉斯哥结局评分等），依据的是患者入院后 12、24、48 和 72 小时内的数据。单变量分析选择每个个体数据集中的“特征”，即区分 VS 段或“基因”。然后，使用六种不同的统计建模技术，基于每个个体的选定特征，构建使用这些选定特征的预测模型，根据每个个体的选定特征，预测样本队列中其他个体的结局，并采用留一法进行交叉验证。
我们从符合研究纳入标准的 60 例严重创伤性脑损伤患者中，获取了 52 例患者的 4 个时间段和结局的完整的 588 个 VS 监测段特征集。总体而言，颅内压和血压随时间的变化（例如，颅内压>20mmHg 持续 20 分钟）对结局的区分度最好。在护理的前 12 小时内，模型在死亡率方面表现最佳。在该时间段内，用于预测 14 天住院时间、死亡率和 3 个月扩展格拉斯哥结局评分的选定特征数平均分别为 76、11 和 4。6 种技术中有 4 种构建的模型能够在 12 小时内正确识别出 75%或更高的死亡率。
我们的研究结果表明，使用基因映射技术从常规电子监测数据的大型数据集进行分析，可以构建有效的严重创伤性脑损伤后预测模型，但这种方法需要在更大的数据集进行验证，并且可能还需要其他非结构化学习技术来辅助。
