Journal Article

Exploratory analysis of predictive models in the field of myelitis: a systematic review and meta-analysis

Cheng, Yupei, Huang, Jingjie, Wu, Bangqi, Zhao, Yusheng, Zhang, Ziyi

2025

Overview

There 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. To conduct a comprehensive methodological assessment of existing literature concerning myelitis modeling methodologies. We 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. Among 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. Although 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. https://www.crd.york.ac.uk/prospero/, identifier CRD42024623714.

• Publisher: Frontiers Media SA,Frontiers Media S.A
• Subject: Algorithms / Antibodies / Aquaporins / Bacterial infections / Bias / clinical applicability / Deep learning / diagnostic model / Disease prevention / Glycoproteins / Humans / Immunology / Inflammation / Learning algorithms / Machine Learning / Meta-analysis / Multiple sclerosis / Myelitis / Myelitis - diagnosis / Myelitis - etiology / Neurology / Nomograms / Patients / performance evaluation / prediction model / Prediction models / Prognosis / Quality of life / Regression analysis / Spinal cord / Systematic review