Development of a Prognostic Model for Predicting Multiple Sclerosis After Optic Neuritis: A Secondary Analysis of Data From the Optic Neuritis Treatment Trial

Predicting the conversion from clinically isolated syndrome to multiple sclerosis: An explainable machine learning approach

INTRODUCTION

Predicting the conversion of clinically isolated syndrome (CIS) to clinically definite multiple sclerosis (CDMS) is critical to personalizing treatment planning and benefits for patients. The aim of this study is to develop an explainable machine learning (ML) model for predicting this conversion based on demographic, clinical, and imaging data.

METHOD

The ML model, Extreme Gradient Boosting (XGBoost), was employed on the public dataset of 273 Mexican mestizo CIS patients with 10-year follow-up. The data was divided into a training set for cross-validation and feature selection, and a holdout test set for final testing. Feature importance was determined using the SHapley Additive Explanations library (SHAP). Then, two experiments were conducted to optimize the model's performance by selectively adding variables and selecting the most contributive variables for the final model.

RESULTS

Nine variables including age, gender, schooling, motor symptoms, infratentorial and periventricular lesion at imaging, oligoclonal band in cerebrospinal fluid, lesion and symptoms types were significant. The model achieved an accuracy of 83.6 %, AUC of 91.8 %, sensitivity of 83.9 %, and specificity of 83.4 % in cross-validation. In the final testing, the model achieved an accuracy of 78.3 %, AUC of 85.8 %, sensitivity of 75 %, and specificity of 81.1 %. Finally, a web-based demo of the model was created for testing purposes.

CONCLUSION

The model, focusing on feature selection and interpretability, effectively stratifies risk for treatment decisions and disability prevention in MS patients. It provides a numerical risk estimate for CDMS conversion, enhancing transparency in clinical decision-making and aiding in patient care.

Development of a predictive model for predicting disability after optic neuritis: a secondary analysis of the Optic Neuritis Treatment Trial

Objective

The present study aimed to develop a prediction model for predicting developing debilities after optic neuritis.

Methods

The data for this research was obtained from the Optic Neuritis Treatment Trial (ONTT). The predictive model was built based on a Cox proportional hazards regression model. Model performance was assessed using Harrell's C-index for discrimination, calibration plots for calibration, and stratification of patients into low-risk and high-risk groups for utility evaluation.

Results

A total of 416 patients participated. Among them, 101 patients (24.3%) experienced disability, which was defined as achieving or surpassing a score of 3 on the expanded disability status scale. The median follow-up duration was 15.5 years (interquartile range, 7.0 to 16.8). Two predictors in the final predictive model included the classification of multiple sclerosis at baseline and the condition of the optic disk in the affected eye at baseline. Upon incorporating these two factors into the model, the model's C-index stood at 0.71 (95% CI, 0.66-0.76, with an optimism of 0.005) with a favorable alignment with the calibration curve. By utilizing this model, the ONTT cohort can be categorized into two risk categories, each having distinct rates of disability development within a 15-year timeframe (high-risk group, 41% [95% CI, 31-49%] and low-risk group, 13% [95% CI, 8.4-17%]; log-rank p-value of <0.001).

Conclusion

This predictive model has the potential to assist physicians in identifying individuals at a heightened risk of experiencing disability following optic neuritis, enabling timely intervention and treatment.

Anti-inflammatory effect of miR-125a-5p on experimental optic neuritis by promoting the differentiation of Treg cells

Methylprednisolone pulse treatment is currently used for optic neuritis. It can speed visual recovery, but does not improve the ultimate visual outcomes. Recent studies have reported that miR-125a-5p has immunomodulatory effects on autoimmune diseases. However, it remains unclear whether miR-125a-5p has effects on optic neuritis. In this study, we used adeno-associated virus to overexpress or silence miR-125a-5p in mice. We found that silencing miR-125a-5p increased the latency of visual evoked potential and aggravated inflammation of the optic nerve. Overexpression of miR-125a-5p suppressed inflammation of the optic nerve, protected retinal ganglion cells, and increased the percentage of Treg cells. Our findings show that miR-125a-5p exhibits anti-inflammatory effects through promoting the differentiation of Treg cells.