Machine-learning-based prediction of disability progression in multiple sclerosis: An observational, international, multi-center study

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00064165%3A_____%2F24%3A10496532" target="_blank" >RIV/00064165:_____/24:10496532 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11110/24:10496532

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=sWLOXMVhoo" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=sWLOXMVhoo</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1371/journal.pdig.0000533" target="_blank" >10.1371/journal.pdig.0000533</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Machine-learning-based prediction of disability progression in multiple sclerosis: An observational, international, multi-center study

Popis výsledku v původním jazyce

Background: Disability progression is a key milestone in the disease evolution of people with multiple sclerosis (PwMS). Prediction models of the probability of disability progression have not yet reached the level of trust needed to be adopted in the clinic. A common benchmark to assess model development in multiple sclerosis is also currently lacking. Methods: Data of adult PwMS with a follow-up of at least three years from 146 MS centers, spread over 40 countries and collected by the MSBase consortium was used. With basic inclusion criteria for quality requirements, it represents a total of 15, 240 PwMS. External validation was performed and repeated five times to assess the significance of the results. Transparent Reporting for Individual Prognosis Or Diagnosis (TRIPOD) guidelines were followed. Confirmed disability progression after two years was predicted, with a confirmation window of six months. Only routinely collected variables were used such as the expanded disability status scale, treatment, relapse information, and MS course. To learn the probability of disability progression, state-of-the-art machine learning models were investigated. The discrimination performance of the models is evaluated with the area under the receiver operator curve (ROC-AUC) and under the precision recall curve (AUC-PR), and their calibration via the Brier score and the expected calibration error. All our preprocessing and model code are available at https://gitlab.com/edebrouwer/ms_benchmark, making this task an ideal benchmark for predicting disability progression in MS. Findings: Machine learning models achieved a ROC-AUC of 0DOT OPERATOR  71 +- 0DOT OPERATOR  01, an AUC-PR of 0DOT OPERATOR  26 +- 0DOT OPERATOR  02, a Brier score of 0DOT OPERATOR  1 +- 0DOT OPERATOR  01 and an expected calibration error of 0DOT OPERATOR  07 +- 0DOT OPERATOR  04. The history of disability progression was identified as being more predictive for future disability progression than the treatment or relapses history. Conclusions: Good discrimination and calibration performance on an external validation set is achieved, using only routinely collected variables. This suggests machine-learning models can reliably inform clinicians about the future occurrence of progression and are mature for a clinical impact study.

Název v anglickém jazyce

Machine-learning-based prediction of disability progression in multiple sclerosis: An observational, international, multi-center study

Popis výsledku anglicky

Background: Disability progression is a key milestone in the disease evolution of people with multiple sclerosis (PwMS). Prediction models of the probability of disability progression have not yet reached the level of trust needed to be adopted in the clinic. A common benchmark to assess model development in multiple sclerosis is also currently lacking. Methods: Data of adult PwMS with a follow-up of at least three years from 146 MS centers, spread over 40 countries and collected by the MSBase consortium was used. With basic inclusion criteria for quality requirements, it represents a total of 15, 240 PwMS. External validation was performed and repeated five times to assess the significance of the results. Transparent Reporting for Individual Prognosis Or Diagnosis (TRIPOD) guidelines were followed. Confirmed disability progression after two years was predicted, with a confirmation window of six months. Only routinely collected variables were used such as the expanded disability status scale, treatment, relapse information, and MS course. To learn the probability of disability progression, state-of-the-art machine learning models were investigated. The discrimination performance of the models is evaluated with the area under the receiver operator curve (ROC-AUC) and under the precision recall curve (AUC-PR), and their calibration via the Brier score and the expected calibration error. All our preprocessing and model code are available at https://gitlab.com/edebrouwer/ms_benchmark, making this task an ideal benchmark for predicting disability progression in MS. Findings: Machine learning models achieved a ROC-AUC of 0DOT OPERATOR  71 +- 0DOT OPERATOR  01, an AUC-PR of 0DOT OPERATOR  26 +- 0DOT OPERATOR  02, a Brier score of 0DOT OPERATOR  1 +- 0DOT OPERATOR  01 and an expected calibration error of 0DOT OPERATOR  07 +- 0DOT OPERATOR  04. The history of disability progression was identified as being more predictive for future disability progression than the treatment or relapses history. Conclusions: Good discrimination and calibration performance on an external validation set is achieved, using only routinely collected variables. This suggests machine-learning models can reliably inform clinicians about the future occurrence of progression and are mature for a clinical impact study.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30103 - Neurosciences (including psychophysiology)

Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

PLOS Digital Health

ISSN

2767-3170

e-ISSN

2767-3170

Svazek periodika

3

Číslo periodika v rámci svazku

7

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

25

Strana od-do

e0000533

Kód UT WoS článku

001439561000001

EID výsledku v databázi Scopus

2-s2.0-85201493485