Predicting risk of secondary progression in multiple sclerosis: A nomogram

Area of Science:

• Neurology
• Clinical Prediction Models
• Epidemiology

Background:

• Multiple sclerosis (MS) is a chronic neurological disease.
• Predicting conversion to secondary progressive multiple sclerosis (SPMS) is crucial for timely intervention.
• Early identification of high-risk patients can optimize treatment strategies.

Purpose of the Study:

• To develop and validate a predictive tool (nomogram) for estimating an individual's risk of converting to SPMS at the time of MS diagnosis.
• To identify key factors influencing the conversion to SPMS.

Main Methods:

• A nomogram was derived from a large cohort of 8825 relapsing-onset MS patients in Sweden.
• Survival analysis models were employed to identify significant predictors.
• The nomogram's accuracy was validated in three independent cohorts totaling 6358 patients.

Main Results:

• Key predictors for SPMS conversion included sex, birth year, initial Expanded Disability Status Scale (EDSS) score, age at first EDSS, and age at disease onset.
• The nomogram demonstrated strong predictive accuracy, with internal validation at 84% and external validation ranging from 77% to 87% across different cohorts.
• These factors were significant for predicting SPMS conversion at 10, 15, and 20 years.

Conclusions:

• The developed SPMS nomogram serves as a valuable tool for early-phase MS patient counseling and clinical decision-making.
• This nomogram can facilitate prompt and effective treatment initiation for individuals identified as having a higher risk of progressing to SPMS.
• Utilizing this tool may lead to improved patient outcomes by enabling proactive management strategies.