Table 1_Exploratory analysis of predictive models in the field of myelitis: a systematic review and meta-analysis.xlsx

BackgroundThere has been a significant increase in the number of diagnostic and predictive models for myelitis. These models aim to provide clinicians with more accurate diagnostic tools and predictive methods through advanced data analysis and machine learning techniques. However, despite the growing number of such models, their effectiveness in clinical practice and their quality and applicability in future research remain unclear.
ObjectiveTo conduct a comprehensive methodological assessment of existing literature concerning myelitis modeling methodologies.
MethodsWe queried PubMed, Web of Science, and Embase for publications through October 23, 2024. Extracted parameters covered: study design, data origin, outcome criteria, cohort size, predictors, modeling techniques, and validation metrics. Methodological quality was evaluated using the PROBAST instrument, assessing potential biases and clinical applicability.
ResultsAmong the 11 included studies, six focused on predictive diagnostic models, while five were centered on prognostic models. Modeling approaches comprised: logistic regression (n=6), Cox regression (n=2), deep learning (n=1), joint modeling (n=1), and hybrid machine learning/scoring algorithms (n=1). Multivariable logistic regression was the most frequently employed modeling algorithm in the current field. The most commonly used predictors for training diagnostic or prognostic models in myelitis were sex (n=6) and age (n=4). PROBAST evaluation indicated: (1) High bias risk (n=6): primarily from suboptimal data sourcing and analytical reporting gaps; (2) Unclear risk (n=4): mainly due to non-transparent analytical workflows; (3) Low risk (n=1). Pooled AUC for eight validated models reached 0.83 (95%CI: 0.75–0.91), demonstrating robust discriminative capacity.
ConclusionAlthough existing models demonstrate good discrimination in predicting myelitis, according to the PROBAST criteria, only one study exhibited a low risk of bias; analysis of data accessibility indicated that the model from only one study was directly available for public use. Consequently, future research should prioritize the development of models with larger cohort sizes, rigorous methodological design, high reporting transparency, and validation through multicenter external studies, enabling direct clinical translation to enhance their application value in clinical practice and improve healthcare delivery.
Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42024623714.

背景：当前针对脊髓炎的诊断与预测模型数量呈显著增长趋势。此类模型旨在通过先进数据分析与机器学习技术，为临床医师提供更为精准的诊断工具与预测方法。然而，尽管此类模型的数量不断增加，其在临床实践中的有效性，以及在未来研究中的质量与适用性仍尚不明确。
目的：对现有有关脊髓炎建模方法学的文献开展全面的方法学评估。
方法：本研究检索了截至2024年10月23日发表于PubMed、Web of Science及Embase的相关文献。提取的参数涵盖：研究设计、数据来源、结局指标、队列规模、预测因子、建模技术及验证指标。采用预测模型偏倚风险评估工具（PROBAST）对方法学质量进行评估，以分析潜在偏倚与临床适用性。
结果：本研究共纳入11项研究，其中6项聚焦于预测性诊断模型，5项以预后模型为核心。建模方法包括：逻辑回归（n=6）、Cox回归（n=2）、深度学习（n=1）、联合建模（n=1）以及混合型机器学习/评分算法（n=1）。多变量逻辑回归是当前该领域最常用的建模算法。在脊髓炎诊断或预后模型的训练中，最常用的预测因子为性别（n=6）与年龄（n=4）。PROBAST评估结果显示：(1) 高偏倚风险（n=6）：主要源于数据获取欠佳与分析报告存在缺失；(2) 偏倚风险不明（n=4）：主要因分析流程不透明；(3) 低偏倚风险（n=1）。8项经过验证的模型的综合AUC达0.83（95%CI：0.75~0.91），表明其判别能力良好。
结论：尽管现有模型在脊髓炎预测中展现出良好的判别能力，但根据PROBAST标准，仅1项研究存在低偏倚风险；对数据可及性的分析显示，仅1项研究的模型可直接公开使用。因此，未来研究应优先开发具备大规模队列、严谨方法学设计、高报告透明度，并通过多中心外部验证的模型，以实现直接的临床转化，提升其在临床实践中的应用价值，优化医疗服务质量。
系统综述注册信息：https://www.crd.york.ac.uk/prospero/，标识符CRD42024623714。