论文标题
飞向序的飞跃：通过灵活的建模方法，脑损伤创伤后详细的功能预后
The leap to ordinal: detailed functional prognosis after traumatic brain injury with a flexible modelling approach
论文作者
论文摘要
创伤性脑损伤（TBI）后，患者被接受重症监护病房（ICU）时，早期预后对于基线风险调整和共同的决策至关重要。 TBI结果通常由格拉斯哥的结果量表扩展（GOSE）分为8个，在受伤后6个月的功能恢复水平为8。现有的ICU预后模型预测了一定的GOSE阈值（例如，生存[GOSE> 1]或功能独立性[GOSE> 4]）。我们旨在开发同时预测每个GOSE评分概率的顺序预测模型。在TBI（中心-TBI）患者数据集的ICU层次集ICU阶层的前瞻性队列（n = 1,550，65个中心）中，我们在ICU录取（1,151个预测者）和6个月的GOSE SCORE的24小时内提取了所有临床信息。我们分析了2个设计元素对序数模型性能的影响：（1）基线预测指标集，范围从10个经过验证的预测指标的简明组到所有可能的预测指标的令牌包含的表示，以及（2）建模策略，从序数逻辑回归中，从序数逻辑回归到多基础深度学习。随着重复的K折交叉验证，我们发现扩展基线预测器集显着改善了序数预测性能，而提高分析复杂性并没有提高。通过在简洁集合中添加8个高影响预测因子（2个人口统计学变量，4个蛋白质生物标志物和2个严重性评估）可以实现这些收益中的一半。充其量，序数模型达到了0.76（95％CI：0.74-0.77）序列歧视能力（序列C-指数）和57％（95％CI：54％-60％）的6个月Gose（Somers'D）中序数变化的解释。我们的结果激发了寻找信息性预测因子的较高gose和序数动态预测模型的发展。
When a patient is admitted to the intensive care unit (ICU) after a traumatic brain injury (TBI), an early prognosis is essential for baseline risk adjustment and shared decision making. TBI outcomes are commonly categorised by the Glasgow Outcome Scale-Extended (GOSE) into 8, ordered levels of functional recovery at 6 months after injury. Existing ICU prognostic models predict binary outcomes at a certain threshold of GOSE (e.g., prediction of survival [GOSE>1] or functional independence [GOSE>4]). We aimed to develop ordinal prediction models that concurrently predict probabilities of each GOSE score. From a prospective cohort (n=1,550, 65 centres) in the ICU stratum of the Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) patient dataset, we extracted all clinical information within 24 hours of ICU admission (1,151 predictors) and 6-month GOSE scores. We analysed the effect of 2 design elements on ordinal model performance: (1) the baseline predictor set, ranging from a concise set of 10 validated predictors to a token-embedded representation of all possible predictors, and (2) the modelling strategy, from ordinal logistic regression to multinomial deep learning. With repeated k-fold cross-validation, we found that expanding the baseline predictor set significantly improved ordinal prediction performance while increasing analytical complexity did not. Half of these gains could be achieved with the addition of 8 high-impact predictors (2 demographic variables, 4 protein biomarkers, and 2 severity assessments) to the concise set. At best, ordinal models achieved 0.76 (95% CI: 0.74-0.77) ordinal discrimination ability (ordinal c-index) and 57% (95% CI: 54%-60%) explanation of ordinal variation in 6-month GOSE (Somers' D). Our results motivate the search for informative predictors for higher GOSE and the development of ordinal dynamic prediction models.
