(Press-News.org) Machine learning models can reliably inform clinicians about the disability progression of multiple sclerosis, according to a new study published this week in the open-access journal PLOS Digital Health by Edward De Brouwer of KU Leuven, Belgium, and colleagues.
Multiple sclerosis (MS) is a chronic progressive autoimmune disease that leads to severe disability over time through a complex pattern of progression, recovery, and relapse. Its global prevalence has increased by more than 30% over the last decade. Yet there are few tools that can predict the progression of MS to help clinicians and patients make life planning and treatment decision-making.
In the new study, De Brouwer and colleagues used data on 15,240 adults with at least three years of MS history who were being treated at 146 MS centers in 40 countries. Data on two years of each patient’s disease progression was used to train state-of-the-art machine learning models to predict the probability of disease progression over the subsequent months and years. The models were trained and validated using strict clinical guidelines, promoting applicability of the models in clinical practice. While individual models varied in performance across different patient subgroups, the models had an average area under the ROC curve (ROC-AUC) of 0.71 ± 0.01. The study found that the history of disability progression was more predictive for future disability progression than treatment or relapse history.
The authors conclude that the models developed in the study have the potential to greatly enhance planning for individuals with MS and could be evaluated in a clinical impact study.
De Brouwer adds: “Using the clinical history of more than 15,000 people with multiple sclerosis, we trained a machine learning model capable of reliably predicting the probability of disability progression in the next two years. The model only uses routinely collected clinical variables, which makes it widely applicable. Our rigorous benchmarking and external validation support the vast potential of machine learning models for helping patients planning their lives and clinicians optimizing treatment strategies.”
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In your coverage, please use this URL to provide access to the freely available article in PLOS Digital Health: https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0000533
Citation: De Brouwer E, Becker T, Werthen-Brabants L, Dewulf P, Iliadis D, Dekeyser C, et al. (2024) Machine-learning-based prediction of disability progression in multiple sclerosis: An observational, international, multi-center study. PLOS Digit Health 3(7): e0000533. https://doi.org/10.1371/journal.pdig.0000533
Author Countries: Argentina, Australia, Belgium, Canada, Czech Republic, Greece, Hungary, Italy, Ireland, Kuwait, Lebanon, Malta, The Netherlands, Oman, Spain, Saudi Arabia, Switzerland, Turkey, Romania, United Kingdom, United States
Funding: This study was funded by the Research Foundation Flanders (FWO) and the Flemish government through the Onderzoeksprogramma Artificiele Intelligentie (AI) Vlaanderen program (https://www.flandersairesearch.be/en). This funding was awarded to YM, LB, TD, DD, WW, and BDB and funded EBD, TB, LWB, PD, DI, MS, YM, LB, TD, DD, WW, and BDB. EDB was also concomitantly funded by a FWO-SB fellowship (1S98821N - https://fwo.be). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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Novel human lung organoids can form lifelike models for tuberculosis infection, and might be used to test efficacy of anti-TB drugs.
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Article URL: http://journals.plos.org/plospathogens/article?id=10.1371/journal. ppat.1012295
Article Title: Advances in an In Vitro Tuberculosis Infection Model Using Human Lung Organoids for Host-Directed Therapies
Author Countries: Republic of Korea
Funding: This research was supported by the Korea National Institutes of Health (NIH) (No. 2021-ER2001-00) awarded to E.M.K., the Korea Institute of Toxicology, Republic of Korea (No. 1711195891) awarded to E.M.K., the Korea Environment Industry & Technology ...
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PHILADELPHIA – There are many important reasons for keeping cuts and sores clean, but new research from the Perelman School of Medicine at the University of Pennsylvania shows that a certain bug, Alcaligenes faecalis (A. faecalis), can facilitate healing of hard-to-treat wounds among people with diabetes. While there are many studies done on potentially harmful bacteria in wounds, the researchers discovered that A. faecalis, a bacterium found in many types of chronic wounds, actually boosts healing of diabetic wounds. The researchers found that the beneficial bacterium can promote ...
