Dataset Information

Predicting disease severity in multiple sclerosis using multimodal data and machine learning.

ABSTRACT:

Background

Multiple sclerosis patients would benefit from machine learning algorithms that integrates clinical, imaging and multimodal biomarkers to define the risk of disease activity.

Methods

We have analysed a prospective multi-centric cohort of 322 MS patients and 98 healthy controls from four MS centres, collecting disability scales at baseline and 2 years later. Imaging data included brain MRI and optical coherence tomography, and omics included genotyping, cytomics and phosphoproteomic data from peripheral blood mononuclear cells. Predictors of clinical outcomes were searched using Random Forest algorithms. Assessment of the algorithm performance was conducted in an independent prospective cohort of 271 MS patients from a single centre.

Results

We found algorithms for predicting confirmed disability accumulation for the different scales, no evidence of disease activity (NEDA), onset of immunotherapy and the escalation from low- to high-efficacy therapy with intermediate to high-accuracy. This accuracy was achieved for most of the predictors using clinical data alone or in combination with imaging data. Still, in some cases, the addition of omics data slightly increased algorithm performance. Accuracies were comparable in both cohorts.

Conclusion

Combining clinical, imaging and omics data with machine learning helps identify MS patients at risk of disability worsening.

SUBMITTER: Andorra M

PROVIDER: S-EPMC10896787 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

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Publications

Predicting disease severity in multiple sclerosis using multimodal data and machine learning.

Andorra Magi M Freire Ana A Zubizarreta Irati I de Rosbo Nicole Kerlero NK Bos Steffan D SD Rinas Melanie M Høgestøl Einar A EA de Rodez Benavent Sigrid A SA Berge Tone T Brune-Ingebretse Synne S Ivaldi Federico F Cellerino Maria M Pardini Matteo M Vila Gemma G Pulido-Valdeolivas Irene I Martinez-Lapiscina Elena H EH Llufriu Sara S Saiz Albert A Blanco Yolanda Y Martinez-Heras Eloy E Solana Elisabeth E Bäcker-Koduah Priscilla P Behrens Janina J Kuchling Joseph J Asseyer Susanna S Scheel Michael M Chien Claudia C Zimmermann Hanna H Motamedi Seyedamirhosein S Kauer-Bonin Josef J Brandt Alex A Saez-Rodriguez Julio J Alexopoulos Leonidas G LG Paul Friedemann F Harbo Hanne F HF Shams Hengameh H Oksenberg Jorge J Uccelli Antonio A Baeza-Yates Ricardo R Villoslada Pablo P

Journal of neurology 20231222 3

<h4>Background</h4>Multiple sclerosis patients would benefit from machine learning algorithms that integrates clinical, imaging and multimodal biomarkers to define the risk of disease activity.<h4>Methods</h4>We have analysed a prospective multi-centric cohort of 322 MS patients and 98 healthy controls from four MS centres, collecting disability scales at baseline and 2 years later. Imaging data included brain MRI and optical coherence tomography, and omics included genotyping, cytomics and phos ...[more]

PMID: 38133801

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Project description:IntroductionAlzheimer's disease (AD) is a progressive neurodegenerative disorder. Current core cerebrospinal fluid (CSF) AD biomarkers, widely employed for diagnosis, require a lumbar puncture to be performed, making them impractical as screening tools. Considering the role of sleep disturbances in AD, recent research suggests quantitative sleep electroencephalography features as potential non-invasive biomarkers of AD pathology. However, quantitative analysis of comprehensive polysomnography (PSG) signals remains relatively understudied. PSG is a non-invasive test enabling qualitative and quantitative analysis of a wide range of parameters, offering additional insights alongside other biomarkers. Machine Learning (ML) gained interest for its ability to discern intricate patterns within complex datasets, offering promise in AD neuropathology detection. Therefore, this study aims to evaluate the effectiveness of a multimodal ML approach in predicting core AD CSF biomarkers.MethodsMild-moderate AD patients were prospectively recruited for PSG, followed by testing of CSF and blood samples for biomarkers. PSG signals underwent preprocessing to extract non-linear, time domain and frequency domain statistics quantitative features. Multiple ML algorithms were trained using four subsets of input features: clinical variables (CLINVAR), conventional PSG parameters (SLEEPVAR), quantitative PSG signal features (PSGVAR) and a combination of all subsets (ALL). Cross-validation techniques were employed to evaluate model performance and ensure generalizability. Regression models were developed to determine the most effective variable combinations for explaining variance in the biomarkers.ResultsOn 49 subjects, Gradient Boosting Regressors achieved the best results in estimating biomarkers levels, using different loss functions for each biomarker: least absolute deviation (LAD) for the Aβ42, least squares (LS) for p-tau and Huber for t-tau. The ALL subset demonstrated the lowest training errors for all three biomarkers, albeit with varying test performance. Specifically, the SLEEPVAR subset yielded the best test performance in predicting Aβ42, while the ALL subset most accurately predicted p-tau and t-tau due to the lowest test errors.ConclusionsMultimodal ML can help predict the outcome of CSF biomarkers in early AD by utilizing non-invasive and economically feasible variables. The integration of computational models into medical practice offers a promising tool for the screening of patients at risk of AD, potentially guiding clinical decisions.

Dataset Information

Predicting disease severity in multiple sclerosis using multimodal data and machine learning.

Background

Methods

Results

Conclusion

Publications

Predicting disease severity in multiple sclerosis using multimodal data and machine learning.

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