Prediction of who will recover after stroke has been a perennial focus for both researchers and clinicians in the field of neurorehabilitation. The prospects of applying a population-based model to predict outcome in individual patients might ultimately allow more focused approaches to stroke rehabilitation and foster a better distribution of precious health care resources. Aside from anatomical biomarkers, such as the integrity of the corticospinal tract, recent attention has focused on the proportional recovery rule, formally proposed in this journal more than 10 years ago by Prabhakaran et al, who described a surprisingly linear relationship between Fugl-Meyer Assessment upper extremity scores obtained within 3 days after stroke and those obtained at 3 months poststroke, illustrating the general principle of spontaneous recovery with a level of predictability not previously appreciated. This relationship appears to hold for most individuals (so-called “fitters” or “recoverers”), but a subset of individuals (so-called “nonfitters” or “non-recoverers”) fall off the linear regression line. First applied to upper limb motor impairment, the proportional recovery rule has been examined in a variety of motor and nonmotor impairments, and results have generally been in agreement with the initial linear relationship. Recent controversy surrounding the proportional recovery rule has been based on statistical factors such as mathematical coupling and nonlinearity of outcome scales, questioning not only the accuracy but also the underlying validity of this predictive population-based model. Two articles in the current issue of Neurorehabilitation and Neural Repair highlight some of the emerging views and suggestions for future research regarding this model. The first article by Senesh and Reinkensmeyer examines the reasons why “non-fitters” do not recover according to the proportional recovery algorithm. They argue that the local slope of the linear regression reflects the difficulty of test item scores related to arm and hand movement at followup, consistent with the view that non-fitters lack sufficient corticospinal tract. They suggest that at least some nonfitters may have a heightened response to intensive movement training and should be targeted early after stroke for such rehabilitative training. In the second article by Kundert et al, the statistical validity of the proportional recovery rule is examined in the context of recent criticisms regarding its underlying assumptions. Despite 2 recent articles critical of statistical relationships of baseline impairment scores to follow-up scores, especially when used for patient-level predictions, Kundert et al contend that the systematic non-artifactual relationship between initial impairment and motor recovery provides a valid statistical and biologically meaningful model, and that future studies of proportional recovery should use more sophisticated analysis techniques and rigorous methods to assess validity, including comparisons to alternative models.

中文翻译：

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聚光灯下的比例复苏

预测中风后谁会康复一直是神经康复领域的研究人员和临床医生一直关注的焦点。应用基于人群的模型来预测个体患者结果的前景可能最终使中风康复的方法更加集中，并促进宝贵的医疗资源的更好分配。除了解剖学生物标志物，例如皮质脊髓束的完整性，最近的注意力集中在比例恢复规则上，该规则由 Prabhakaran 等人于 10 多年前在本期刊中正式提出，他描述了 Fugl-Meyer 评估之间令人惊讶的线性关系中风后 3 天内和中风后 3 个月内获得的上肢评分，以前所未有的可预测性说明自然恢复的一般原则。这种关系似乎适用于大多数个体（所谓的“适应者”或“恢复者”），但一部分个体（所谓的“非适应者”或“非恢复者”）脱离了线性回归线。首先应用于上肢运动障碍，比例恢复规则已在各种运动和非运动障碍中得到检验，结果普遍与初始线性关系一致。最近围绕比例恢复规则的争议基于统计因素，例如结果量表的数学耦合和非线性，不仅质疑这种基于人群的预测模型的准确性，而且质疑其潜在有效性。本期《神经康复和神经修复》中的两篇文章重点介绍了有关该模型的未来研究的一些新兴观点和建议。Senesh 和 Reinkensmeyer 的第一篇文章探讨了“非拟合者”无法根据比例恢复算法进行恢复的原因。他们认为，线性回归的局部斜率反映了随访时与手臂和手部运动相关的测试项目分数的难度，这与非健康者缺乏足够皮质脊髓束的观点一致。他们建议，至少一些非健身者可能对强化运动训练有更高的反应，应该在中风后尽早进行此类康复训练。在 Kundert 等人的第二篇文章中，在最近对其基本假设提出批评的背景下，审查了比例回收规则的统计有效性。尽管最近有 2 篇文章批评基线损伤评分与随访评分的统计关系，特别是当用于患者水平预测时，Kundert 等人认为，初始损伤和运动恢复之间的系统性非人为关系提供了有效的统计和生物学有意义的模型，并且未来的比例回收研究应该使用更复杂的分析技术和严格的方法来评估有效性，包括与替代模型的比较。