Existing methods to characterise the evolving condition of traumatic brain injury (TBI) patients in the intensive care unit (ICU) do not capture the context necessary for individualising treatment. We aimed to develop a modelling strategy which integrates all data stored in medical records to produce an interpretable disease course for each TBI patient's ICU stay. From a prospective, European cohort (n=1,550, 65 centres, 19 countries) of TBI patients, we extracted all 1,166 variables collected before or during ICU stay as well as 6-month functional outcome on the Glasgow Outcome Scale-Extended (GOSE). We trained recurrent neural network models to map a token-embedded time series representation of all variables (including missing data) to an ordinal GOSE prognosis every 2 hours. With repeated cross-validation, we evaluated calibration and the explanation of ordinal variance in GOSE with Somers' Dxy. Furthermore, we applied TimeSHAP to calculate the contribution of variables and prior timepoints towards transitions in patient trajectories. Our modelling strategy achieved calibration at 8 hours, and the full range of variables explained up to 52% (95% CI: 50-54%) of the variance in ordinal functional outcome. Up to 91% (90-91%) of this explanation was derived from pre-ICU and admission information. Information collected in the ICU increased explanation (by up to 5% [4-6%]), though not enough to counter poorer performance in longer-stay (>5.75 days) patients. Static variables with the highest contributions were physician prognoses and certain demographic and CT features. Among dynamic variables, markers of intracranial hypertension and neurological function contributed the most. Whilst static information currently accounts for the majority of functional outcome explanation, our data-driven analysis highlights investigative avenues to improve dynamic characterisation of longer-stay patients.

翻译：现有用于描述重症监护室（ICU）中创伤性脑损伤（TBI）患者病情演变的方法，未能捕捉到个体化治疗所需的背景信息。我们旨在开发一种建模策略，整合存储于医疗记录中的所有数据，为每位TBI患者的ICU住院过程生成可解释的疾病病程。从一个前瞻性欧洲队列（n=1,550，65个中心，19个国家）的TBI患者中，我们提取了ICU入院前或住院期间收集的所有1,166个变量，以及基于格拉斯哥预后扩展量表（GOSE）的6个月功能预后。我们训练循环神经网络模型，将包含所有变量（包括缺失数据）的标记嵌入时间序列表示，每2小时映射为一个有序GOSE预后。通过重复交叉验证，我们评估了校准度以及基于Somers' Dxy的有序GOSE方差解释度。此外，我们应用TimeSHAP计算各变量及先前时间点对患者轨迹转变的贡献。我们的建模策略在8小时时达到校准，全变量范围解释了有序功能预后中高达52%（95% CI: 50-54%）的方差。其中高达91%（90-91%）的解释来自ICU前及入院信息。ICU期间收集的信息增加了解释度（最多5% [4-6%]），但不足以抵消长期住院（>5.75天）患者较差的预后表现。贡献最高的静态变量包括医生预后判断、某些人口统计学特征及CT特征。在动态变量中，颅内高压及神经功能标志物贡献最大。尽管静态信息当前占功能预后解释的主要部分，我们的数据驱动分析揭示了改进长期住院患者动态特征描述的探索方向。