Background Predicting prognosis in people with multiple sclerosis (pwMS) at early disease stages still remains an unmet need. Machine learning (ML) strategies demonstrated good reliability when applied for prediction in medicine. This study aimed at developing a predictive algorithm comparing different ML approaches, by using routine demographic, clinical and radiological data from a large multicentric cohort of newly diagnosed pwMS. Methods Demographic, clinical, radiological and biochemical data were retrospectively collected at three Italian MS centers at baseline and four timepoints thereafter (6, 12, 24, and 36 months). Data from the first evaluation and subsequent 2-year follow-up were analyzed, comparing different ML models (Random Forest, Extra Trees, XGBoost, Logistic Regression and Support Vector Classifier) to predict progression independent of relapse activity (PIRA) at year 3. To understand how features impacted the selected model's output, a ML explainability analysis was performed on the whole cohort and on specific subsets of patients, those aged under 45 and those NEDA-3 at the 2-year follow-up. Results Data from 719 pwMS (age 34.6 ± 11.2 years); female sex 501 (70%) were analyzed. Ninety-two pwMS (13%) developed PIRA at year 3. Random Forest achieved the highest score, with a test set area under the ROC curve (AUC) of 0.75 ± 0.06. Features with the highest predictive impact were Expanded Disability Status Scale at 24 months, age at symptom onset and disease duration at baseline. Conclusion Our results showed the feasibility of applying ML techniques to predict short-term PIRA in newly diagnosed pwMS by using routine clinical practice data, paving the way for tailored and personalized approaches.
Machine Learning Analysis Applied to Prediction of Early Progression Independent of Relapse Activity in Multiple Sclerosis Patients
Walter Endrizzi;Stefano Bovo;Flavio Ragni;Monica Moroni;Lorenzo Gios;Marco Chierici;Giuseppe Jurman;Venet Osmani;
2025-01-01
Abstract
Background Predicting prognosis in people with multiple sclerosis (pwMS) at early disease stages still remains an unmet need. Machine learning (ML) strategies demonstrated good reliability when applied for prediction in medicine. This study aimed at developing a predictive algorithm comparing different ML approaches, by using routine demographic, clinical and radiological data from a large multicentric cohort of newly diagnosed pwMS. Methods Demographic, clinical, radiological and biochemical data were retrospectively collected at three Italian MS centers at baseline and four timepoints thereafter (6, 12, 24, and 36 months). Data from the first evaluation and subsequent 2-year follow-up were analyzed, comparing different ML models (Random Forest, Extra Trees, XGBoost, Logistic Regression and Support Vector Classifier) to predict progression independent of relapse activity (PIRA) at year 3. To understand how features impacted the selected model's output, a ML explainability analysis was performed on the whole cohort and on specific subsets of patients, those aged under 45 and those NEDA-3 at the 2-year follow-up. Results Data from 719 pwMS (age 34.6 ± 11.2 years); female sex 501 (70%) were analyzed. Ninety-two pwMS (13%) developed PIRA at year 3. Random Forest achieved the highest score, with a test set area under the ROC curve (AUC) of 0.75 ± 0.06. Features with the highest predictive impact were Expanded Disability Status Scale at 24 months, age at symptom onset and disease duration at baseline. Conclusion Our results showed the feasibility of applying ML techniques to predict short-term PIRA in newly diagnosed pwMS by using routine clinical practice data, paving the way for tailored and personalized approaches.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
