Department of Neurology, Memory and Aging Center, and Weill Institute for Neurosciences, University of California, San Franscisco, San Francisco, California.
Department of Neurology and Center for Population Brain Health, San Francisco Veterans Affairs Mecical Center, San Francisco, California.
J Neurotrauma. 2019 Sep 1;36(17):2521-2532. doi: 10.1089/neu.2018.6167. Epub 2019 May 24.
Cross-sectional approaches to outcome assessment may not adequately capture heterogeneity in recovery after traumatic brain injury (TBI). Using latent class mixed models (LCMM), a data-driven analytic that identifies groups of patients with similar trajectories, we identified distinct 6 month functional recovery trajectories in a large cohort ( = 1046) of adults 18-70 years of age with complicated mild to severe TBI who participated in the Citicoline Brain Injury Treatment Trial (COBRIT). We used multinomial logistic fixed effect models and backward elimination, forward selection, and forward stepwise selection with several stopping rules to explore baseline predictors of functional recovery trajectory. Based on statistical and clinical considerations, the seven-class model was deemed superior. Visualization of these seven functional recovery trajectories revealed that each trajectory class started at one of three recovery levels at 1 month, which, for ease of reference we labeled groups A-C: Group A, good recovery (two classes; A1 and A2); Group B, moderate disability (two classes; B1 and B2); and Group C, severe disability (three classes; C1, C2, and C3). By 6 months, these three groups experienced dramatically divergent trajectories. Group A experienced stable good recovery (A1, = 115) or dramatic decline (A2, = 4); Group B experienced rapid complete recovery (B1, = 71) or gradual recovery (B2, = 742); Group C experienced dramatic rapid recovery (C1, = 12), no recovery (C2, = 91), or death (C3, = 11). Trajectory class membership was not predicted by citicoline treatment ( = 0.57). The models identified demographic, pre-injury, and injury-related predictors of functional recovery trajectory, including: age, race, education, pre-injury employment, pre-injury diabetes, pre-injury psychiatric disorder, site, Glasgow Coma Scale (GCS) score, post-traumatic amnesia, TBI mechanism, major extracranial injury, hemoglobin, and acute computed tomographic (CT) findings. GCS was the most consistently selected predictor across all models. All models also selected at least one demographic or pre-injury medical predictor. LCMM successfully identified dramatically divergent, clinically meaningful 6 month recovery trajectories with utility to inform clinical trial design.
横断面方法评估结局可能无法充分捕捉创伤性脑损伤 (TBI) 后恢复的异质性。本研究采用潜在类别混合模型 (LCMM) 这一数据驱动的分析方法，可识别具有相似轨迹的患者群体，对参加胞磷胆碱治疗创伤性脑损伤试验 (COBRIT) 的 1046 例年龄在 18-70 岁、有复杂轻度至重度 TBI 的成年人进行了 6 个月的功能恢复轨迹分析。我们使用多项逻辑固定效应模型和向后消除、向前选择、向前逐步选择以及多种停止规则，来探讨功能恢复轨迹的基线预测因素。基于统计学和临床考虑，七类模型被认为是最优的。对这七种功能恢复轨迹的可视化表明，每个轨迹类在 1 个月时均始于三个恢复水平之一，为便于参考，我们将这些水平标记为 A-C 组：A 组，良好恢复（两个类；A1 和 A2）；B 组，中度残疾（两个类；B1 和 B2）；和 C 组，严重残疾（三个类；C1、C2 和 C3）。到 6 个月时，这三组经历了截然不同的轨迹。A 组经历了稳定的良好恢复（A1， = 115）或急剧下降（A2， = 4）；B 组经历了快速完全恢复（B1， = 71）或逐渐恢复（B2， = 742）；C 组经历了急剧快速恢复（C1， = 12）、无恢复（C2， = 91）或死亡（C3， = 11）。LCMM 模型没有预测到胞磷胆碱治疗（ = 0.57）。这些模型确定了功能恢复轨迹的人口统计学、受伤前和受伤相关的预测因素，包括：年龄、种族、教育、受伤前就业、受伤前糖尿病、受伤前精神障碍、损伤部位、格拉斯哥昏迷量表 (GCS) 评分、创伤后遗忘、TBI 机制、主要颅外损伤、血红蛋白和急性计算机断层扫描 (CT) 发现。GCS 是所有模型中选择最一致的预测因素。所有模型还选择了至少一个人口统计学或受伤前医学预测因素。LCMM 成功地识别了具有明显差异的、具有临床意义的 6 个月恢复轨迹，有助于为临床试验设计提供信息。
