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Building and validating trend-based multiple sclerosis case definitions: a population-based cohort study for

Naomi C Hamm¹, Ruth Ann Marrie^2,3, Depeng Jiang²

¹Department of Community Health Sciences, University of Manitoba, Max Rady College of Medicine, Winnipeg, Manitoba, Canada lettn@myumanitoba.ca.

|August 16, 2024

Summary

This summary is machine-generated.

New trend-based case definitions for multiple sclerosis (MS) show comparable accuracy to traditional methods. Dynamic classification helps determine the necessary data years for accurate MS case identification.

Keywords:

Chronic Disease EPIDEMIOLOGY Multiple sclerosis STATISTICS & RESEARCH METHODS

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Area of Science:

Epidemiology
Health Informatics

Background:

Accurate identification of multiple sclerosis (MS) cases is crucial for epidemiological studies and healthcare management.
Traditional case definitions may not fully capture disease trends or require extensive data.
Developing dynamic, trend-based case definitions can improve classification accuracy and efficiency.

Purpose of the Study:

To develop and validate model-based, trend-based case definitions for multiple sclerosis (MS).
To apply dynamic classification to determine the average number of data years required for accurate case identification.

Main Methods:

Retrospective cohort study using data from April 2004 to March 2022.
Multivariate generalized linear mixed models were used to construct trend-based case definitions.
Dynamic classification was employed to estimate mean classification time and compare accuracy metrics (sensitivity, specificity, PPV, NPV, PCC, F1-scores) against a deterministic definition.

Main Results:

Trend-based and deterministic case definitions demonstrated high classification accuracy (PCC > 0.94) when using the full study period.
Accuracy metrics, particularly sensitivity and PPV for trend-based definitions, were lower when fewer data years were used.
Dynamic classification identified 5 years as the average trend needed, with reduced accuracy for both definition types when applied to shorter time windows.

Conclusions:

Trend-based case definitions, when validated, offer comparable accuracy to deterministic methods and population-based clinician assessments.
Classification accuracy for both trend-based and deterministic definitions is sensitive to the number of data years utilized.
Dynamic classification is a viable method for determining optimal data duration for trend-based MS case definitions.