Center for Musculoskeletal Surgery, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Berlin, Germany.
Berlin Institute of Health at Charité - Universitätsmedizin Berlin, BIH Biomedical Innovation Academy, Charitéplatz 1, 10117, Berlin, Germany.
Sci Rep. 2024 May 23;14(1):11838. doi: 10.1038/s41598-024-62720-w.
5q-spinal muscular atrophy (SMA) is a neuromuscular disorder (NMD) that has become one of the first 5% treatable rare diseases. The efficacy of new SMA therapies is creating a dynamic SMA patient landscape, where disease progression and scoliosis development play a central role, however, remain difficult to anticipate. New approaches to anticipate disease progression and associated sequelae will be needed to continuously provide these patients the best standard of care. Here we developed an interpretable machine learning (ML) model that can function as an assistive tool in the anticipation of SMA-associated scoliosis based on disease progression markers. We collected longitudinal data from 86 genetically confirmed SMA patients. We selected six features routinely assessed over time to train a random forest classifier. The model achieved a mean accuracy of 0.77 (SD 0.2) and an average ROC AUC of 0.85 (SD 0.17). For class 1 'scoliosis' the average precision was 0.84 (SD 0.11), recall 0.89 (SD 0.22), F1-score of 0.85 (SD 0.17), respectively. Our trained model could predict scoliosis using selected disease progression markers and was consistent with the radiological measurements. During post validation, the model could predict scoliosis in patients who were unseen during training. We also demonstrate that rare disease data sets can be wrangled to build predictive ML models. Interpretable ML models can function as assistive tools in a changing disease landscape and have the potential to democratize expertise that is otherwise clustered at specialized centers.
5q 脊髓性肌萎缩症（SMA）是一种神经肌肉疾病（NMD），现已成为首批可治疗的 5%罕见病之一。新 SMA 疗法的疗效正在创造一个动态的 SMA 患者群体，其中疾病进展和脊柱侧弯发展起着核心作用，但仍难以预测。需要新的方法来预测疾病进展和相关后遗症，以便为这些患者提供最佳的护理标准。在这里，我们开发了一种可解释的机器学习（ML）模型，该模型可以作为基于疾病进展标志物预测 SMA 相关脊柱侧弯的辅助工具。我们收集了 86 名经基因确认的 SMA 患者的纵向数据。我们选择了六个特征，这些特征是在治疗过程中通常会随时间评估的，以训练随机森林分类器。该模型的平均准确率为 0.77（SD 0.2），平均 ROC AUC 为 0.85（SD 0.17）。对于类别 1“脊柱侧弯”，平均精度为 0.84（SD 0.11），召回率为 0.89（SD 0.22），F1 得分为 0.85（SD 0.17）。我们的训练模型可以使用选定的疾病进展标志物预测脊柱侧弯，与放射学测量结果一致。在验证后，该模型可以预测在训练期间未见过的患者的脊柱侧弯。我们还证明，罕见病数据集可以被处理以构建预测性 ML 模型。可解释的 ML 模型可以作为不断变化的疾病景观中的辅助工具，并且有可能将专业知识民主化，而这些专业知识目前集中在专门的中心。
