Objective Evaluation of serum neurofilament light chain (sNfL), measured using high‐throughput assays on widely accessible platforms in large, real‐world MS populations, is a critical step for sNfL to be utilized in clinical practice. Methods Multiple Sclerosis Partners Advancing Technology and Health Solutions (MS PATHS) is a network of healthcare institutions in the United States and Europe collecting standardized clinical/imaging data and biospecimens during routine clinic visits. sNfL was measured in 6974 MS and 201 healthy control (HC) participants, using a high‐throughput, scalable immunoassay. Results Elevated sNfL levels for age (sNfL‐E) were found in 1238 MS participants (17.8%). Factors associated with sNfL‐E included male sex, younger age, progressive disease subtype, diabetes mellitus, impaired renal function, and active smoking. Higher body mass index (BMI) was associated with lower odds of elevated sNfL. Active treatment with disease‐modifying therapy was associated with lower odds of sNfL‐E. MS participants with sNfL‐E exhibited worse neurological function (patient‐reported disability, walking speed, manual dexterity, and cognitive processing speed), lower brain parenchymal fraction, and higher T2 lesion volume. Longitudinal analyses revealed accelerated short‐term rates of whole brain atrophy in sNfL‐E participants and higher odds of new T2 lesion development, although both MS participants with or without sNfL‐E exhibited faster rates of whole brain atrophy compared to HC. Findings were consistent in analyses examining age‐normative sNfL Z‐scores as a continuous variable. Interpretation Elevated sNfL is associated with clinical disability, inflammatory disease activity, and whole brain atrophy in MS, but interpretation needs to account for comorbidities including impaired renal function, diabetes, and smoking.

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Multiple sclerosis (MS) phenotypes provide useful disease descriptions but lack complete information regarding the continuing disease process. Disease activity and progression are meaningful modifiers of the MS phenotypes which can further guide prognosis, therapeutic decisions, and clinical trial designs and outcomes, which were not explicitly documented in patients’ electronic medical records (EMRs). We aimed to detect disease activity and progression in patients with MS from clinical notes in the EMR using Natural Language Processing and Machine Learning models. Using randomly selected progress notes from MS patients at the University of Rochester MS clinic, we integrated NLP and machine learning technologies to predict selected phenotype modifiers that represent disease activity and progression. The method was evaluated by the performance of both the NLP models and machine learning models, as well as the interpretability of the integrated method. We identified 460 progress notes from 287 MS patients. The NLP model had an average of 0.92 in precision, 0.87 in recall, and 0.89 in F-score for entity extraction. It had an average of 0.85 in precision, 0.84 in recall, and 0.85 in F-score for entity relation extraction. The sensitivities and specificities of the classification algorithms in predicting phenotype modifiers were: 67% and 93% for predicting modifier “Active”, 61% and 82% for predicting modifier “Worsening”, 92% and 98% for predicting modifier “Progression”, 80% and 94% for predicting modifier “New MRI Lesion”, respectively. We showed that the integrated method of NLP with machine learning classification is capable of detecting evidence of disease activity and clinical progression from clinical notes. The classification algorithms yielded interpretable and largely clinically relevant features (symptoms and clinical conditions) that were persistently associated with disease activity and progression. This method holds promise for facilitating the screening of MS clinical trial participants and potentially identifying early evidence of disease progression.

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