Accurate predictions of motor impairment after stroke are of cardinal importance for the patient, clinician, and healthcare system. More than 10 years ago, the proportional recovery rule was introduced by promising that high-fidelity predictions of recovery following stroke were based only on the initially lost motor function, at least for a specific fraction of patients. However, emerging evidence suggests that this recovery rule is subject to various confounds and may apply less universally than previously assumed. Here, we systematically revisited stroke outcome predictions by applying strategies to avoid confounds and fitting hierarchical Bayesian models. We jointly analysed 385 post-stroke trajectories from six separate studies—one of the largest overall datasets of upper limb motor recovery. We addressed confounding ceiling effects by introducing a subset approach and ensured correct model estimation through synthetic data simulations. Subsequently, we used model comparisons to assess the underlying nature of recovery within our empirical recovery data. The first model comparison, relying on the conventional fraction of patients called ‘fitters’, pointed to a combination of proportional to lost function and constant recovery. ‘Proportional to lost’ here describes the original notion of proportionality, indicating greater recovery in case of a more severe initial impairment. This combination explained only 32% of the variance in recovery, which is in stark contrast to previous reports of >80%. When instead analysing the complete spectrum of subjects, ‘fitters’ and ‘non-fitters’, a combination of proportional to spared function and constant recovery was favoured, implying a more significant improvement in case of more preserved function. Explained variance was at 53%. Therefore, our quantitative findings suggest that motor recovery post-stroke may exhibit some characteristics of proportionality. However, the variance explained was substantially reduced compared to what has previously been reported. This finding motivates future research moving beyond solely behaviour scores to explain stroke recovery and establish robust and discriminating single-subject predictions.

中文翻译：

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将比例恢复成比例：脑卒中后运动障碍的贝叶斯模型。

中风后运动障碍的准确预测对于患者，临床医生和医疗保健系统至关重要。十多年前，通过提出比例恢复规则是有希望的，即对中风后恢复的高保真度预测仅基于最初丧失的运动功能，至少针对特定部分患者而言。但是，越来越多的证据表明，这种恢复规则受到各种混淆，并且可能比以前假设的适用范围更广。在这里，我们通过应用避免混淆的策略和拟合分层贝叶斯模型来系统地回顾卒中预后。我们通过六项单独的研究共同分析了385项卒中后轨迹，这是最大的上肢运动恢复总体数据集之一。我们通过引入子集方法解决了令人困惑的天花板效应，并通过合成数据仿真确保了正确的模型估计。随后，我们使用模型比较来评估经验恢复数据中恢复的基本性质。第一次模型比较依靠的是传统的称为“钳工”的患者，指出了与丧失功能成比例和持续恢复的组合。此处的“成比例损失”描述了原始的比例概念，表示在初始损害更为严重的情况下恢复能力更大。这种组合仅解释了恢复差异的32％，这与之前报道的> 80％形成鲜明对比。相反，当分析整个主题（“钳工”和“非钳工”）时，倾向于按比例分配与保留功能成比例且不断恢复的组合，这意味着在保留更多功能的情况下，可以实现更大的改进。解释方差为53％。因此，我们的定量研究结果表明，中风后的运动恢复可能表现出一定比例性。但是，与以前的报告相比，所解释的方差大大减少了。这一发现激发了未来的研究范围，而不仅仅是行为评分，而是用于解释中风恢复并建立可靠且可区分的单项预测。我们的定量研究结果表明，中风后的运动恢复可能表现出一定比例性。但是，与以前的报道相比，所解释的差异大大减少了。这一发现激发了未来的研究范围，而不仅仅是行为评分，而是用于解释中风恢复并建立可靠且可区分的单项预测。我们的定量研究结果表明，中风后的运动恢复可能表现出一定比例性。但是，与以前的报道相比，所解释的差异大大减少了。这一发现激发了未来的研究范围，而不仅仅是行为评分，而是用于解释中风恢复并建立可靠且可区分的单项预测。