Меры по согласованию и износостойкости в рамках системы многоразового склероза-системного обзора доказательств и применимости используемых систем скоринга в клинических и реальных данных.

Introduction

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system (CNS), characterized by heterogenous clinical phenotypes and disease trajectories. A growing body of evidence suggests that this heterogeneity cannot be understood purely by neuroimmunological mechanisms; rather, it is shaped in tandem by the broader health context in which the disease unfolds. In particular, multimorbidity and frailty have emerged as potentially relevant factors to explain the variance of disease heterogeneity [ 1 , 2 , 3 , 4 ]. Frailty describes a condition with a decline in physical and psychosocial abilities that leads to reduced homeostasis and resilience [ 5 ].

Illustratively, approximately 50% of people with MS (pwMS) harbor at least one comorbidity at diagnosis. Overall comorbidity burden rises as patients grow older and the disease endures, especially under consideration of the increasing incidence and prevalence of MS with higher age [ 6 , 7 , 8 , 9 ]. The most frequent conditions include affective disorders (depressive and anxiety disorders), hypertension and dyslipidemia [ 7 , 10 ]. Comorbidities are linked to delayed diagnosis of MS, faster disability accrual, increased mortality and poorer health‐related quality of life [ 1 , 11 , 12 , 13 ]. They also complicate therapeutic decision‐making, because polypharmacy and drug–drug interactions undermine adherence and might attenuate treatment efficacy [ 3 , 14 , 15 ].

In parallel, frailty provides a complementary yet conceptually distinct construct that captures diminished physiological reserve across physical, cognitive and psychosocial domains [ 16 , 17 , 18 , 19 ]. In pwMS, frailty seems to emerge earlier compared to healthy controls. Frailty has been linked to higher disability, increased fall risk, higher relapse rates, and secondary progressive MS [ 4 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ]. Importantly, frailty only partially overlaps with neurological disability: moderate‐to‐severe frailty curtails physical activity independently of Expanded Disability Status Scale scores [ 22 , 23 ].

Capturing both comorbidity burden and frailty status is therefore critical for research design and patient management. Simple tallies of co‐existing conditions, however, might disregard differences in severity and clinical impact [ 27 ]. Although numerous generic indices exist for comorbidity and frailty, no MS‐specific tools have been validated, and their external validity within MS cohorts remains uncertain.

To extend the scope of earlier narrative overviews and smaller investigations, we performed a systematic literature review (SLR) that synthesizes the current body of evidence on instruments used to quantify comorbidity burden and frailty in pwMS. Notably, although frailty indices have received more emphasis recently, no systematic appraisal of comorbidity scales or indices in MS has been published to date, a gap this review directly addresses. Using prespecified criteria, we evaluated the methodological integrity, internal and external validity, and pragmatic feasibility of the identified indices. This framework allowed us to determine their clinical utility and to ascertain whether the accumulated evidence supports routine adoption of these measures within contemporary MS research agendas and care pathways.

Methods

Search Strategy and Study Selection

The SLR was conducted according to the PRISMA 2020 methodology [ 28 ]. A comprehensive literature search was performed across three major electronic databases—Medline, Embase, and the Cochrane Library. The core search terms were “Multiple Sclerosis,” combined with “comorbidity” or “frailty,” using both keyword and subject heading searches (e.g., MeSH terms in Medline). Where possible, filters were applied to limit results to English‐language publications. Citation tracking of key articles was conducted to identify any additional relevant studies that may not have appeared in the initial electronic search.

Studies were deemed eligible if they: Were published between January 2014 and August 2024. The rationale for restricting the publication date to the pre‐defined time frame was to ensure methodological homogeneity in comorbidity coding and to reduce heterogeneity due to evolution in MS diagnostic criteria, aiming for a pragmatic yet comprehensive timeframe. Additionally, a pilot search found no studies that applied frailty measures prior to 2014, indicating that the cut‐off does not omit substantive evidence. Report any measure of comorbidity and/or frailty. Were published in English. Primarily investigated adults with a confirmed diagnosis of MS. Reported original data or were systematic reviews/meta‐analyses specifically assessing, validating, or applying comorbidity scales/indices or frailty indices in pwMS. Described the operational definitions or scoring algorithms used for these indices/scales.

Exclusion criteria included studies that: Report measures unrelated to comorbidity or frailty. Were conducted exclusively in healthy populations or in conditions other than MS. Involved only in vitro or animal models. Were published as narrative reviews, letters, editorials, expert opinion pieces, or case reports without systematic assessment or empirical data.

Subsequently, two trained reviewers (M.L.A. and F.F.) independently conducted a two‐step screening process: first, titles and abstracts were examined to exclude obviously irrelevant studies; second, the full‐text versions of the remaining articles were assessed to determine final inclusion. To ensure extensive coverage of both comorbidity and frailty dimensions, one reviewer focused on identifying studies relevant to frailty indices (M.L.A.), while the other (F.F.) focused on comorbidity‐related measures. Any disagreements regarding inclusion at either screening stage were resolved through discussion or, when necessary, consultation with a third reviewer (GB). In accordance with PRISMA 2020 methodology [ 28 ], each step of the search process and the study identification/inclusion/exclusion is transparently documented in a flow‐chart (Figure 1 ) and a screening list (File S1 , “Screening List”) including all relevant information (measures reported in the respective study, full study title, first study author, journal, date of publication and full‐text link).

Data Extraction and Grading of Evidence

Data extraction was performed independently by the two main reviewers (MLA and FF) using a standardized form designed to capture relevant study characteristics. The extracted items included: (i) full study title, first author, publication date, and journal; (ii) study design (e.g., cross‐sectional, longitudinal, randomized controlled trial); (iii) characteristics of participants (e.g., total sample size, MS subtype); (iv) the specific comorbidity or frailty measure(s) utilized; (v) operational definitions and scoring criteria for each measure; (vi) key findings related to comorbidity burden or frailty status in pwMS; and (vii) reported effect sizes or summary statistics where applicable.

Each study underwent assessment against predefined criteria separately assessed for each measure such as adequacy of sample size, clarity of inclusion/exclusion criteria, validation or reliability testing of the indices/scales, and overall study design (see File S2 , “Full study list”). Any disagreements in risk of bias assessment were resolved by discussion and, if necessary, adjudication by a third reviewer (G.B.).

Studies were then stratified into categories based on the comorbidity and/or frailty measures employed. A qualitative synthesis was then performed to collate and compare the scales' operational properties, psychometric characteristics (e.g., validity, reliability, responsiveness), and clinical utility. Strengths and limitations of each instrument were critically appraised, with particular emphasis on their applicability in clinical routine. Strengths and limitations as well as both internal and external validity were qualitatively assessed.

To determine the overall quality of the body of evidence for each measure, four trained raters (M.L.A., F.F., R.H., G.B.) independently applied the GRADE (Grading of Recommendations Assessment, Development and Evaluation) methodology [ 29 ]. This process involved assessing the risk of bias, consistency of findings, precision of estimates, and likelihood of publication bias across included studies. Quality of each study was evaluated by two independent reviewers (M.L.A., F.F.) oriented on the Newcastle–Ottawa Scale (NOS). Disagreement was resolved on the item level through consensus during discussions with arbitration by a senior reviewer (R.H. or G.B.) when necessary, ensuring an objective and reproducible appraisal for every included study.

Discrepancies in GRADE ratings were resolved by consensus discussion, ensuring a robust evaluation of each instrument's evidence base. Finally, drawing upon both the evidence quality (GRADE) and the feasibility analysis, each scale was given a recommendation status by the four raters: Reserved for specialized settings/studies: limited or emerging evidence; recommended primarily for research or niche clinical scenarios. Advanced/aspirational measurable: promising but requiring further validation or resources; feasible for centers with extensive capacity for advanced assessments. Core measurable: well‐supported by evidence and feasible for broad clinical implementation; recommended for routine use in MS populations.

Results

Here, the results of the SLR are narratively summarized within the framework of the two domains of interest: comorbidities and frailty. A detailed flowchart of the selection process is presented in Figure 1 and main results are presented in Tables 1 and 2 (with a concise report in Table S1 ). Specific risk of bias assessments for each study, as well as detailed information on individual study results are provided in File S2 .

Measures of Comorbidities in pwMS

The search process resulted in 3997 records for comorbidity measures, of which a total of 31 studies (Files S1 and S2 ) on four comorbidity measures were identified: Charlson Comorbidity Index (CCI), Elixhauser Comorbidity Index (ECI), Self‐Reported Comorbidity Questionnaire (SCQ) and a self‐reported comorbidity questionnaire specifically validated in a cohort of pwMS (SRQ‐MS). Together these studies encompassed a total sample size of 231,873 pwMS ( S2 , “Full study list”), the majority of whom could be attributed to retrospective data studies ( n = 220,292 pwMS). Table 1 provides a detailed overview of every identified comorbidity measure.

Charlson Comorbidity Index

The CCI is a weighted scoring system designed to quantify the overall comorbidity burden. The original CCI encompasses 17 categories of chronic conditions (e.g., heart disease, diabetes, cancer), each assigned a weight (1–6) based on severity and mortality risk, yielding a total score predicting 10‐year mortality [ 41 ]. The CCI has been extensively validated in different disease‐specific cohorts and was consistently, although to differing degrees, associated with adverse outcomes in a myriad of health conditions, increased healthcare utilization and particularly mortality [ 42 , 43 , 44 , 45 ]. Notably, there are several modifications to the original CCI, enabling calculation from ICD (International Classification of Disease) administrative codes, which have been widely applied in registries or claims‐based studies [ 46 , 47 , 48 ].

Multiple studies have applied the CCI in pwMS. For example, a UK study ( n = 2526 pwMS, 9980 controls) showed significantly higher baseline CCI scores in pwMS (OR for CCI ≥ 5 = 3.26), indicating elevated comorbidity burden. Following diagnosis, pwMS had increased risk of new comorbidities, especially cardiovascular and mental disorders. Higher baseline CCI scores predicted increased 5‐year mortality [ 49 ]. A Danish study in pwMS > 50 years confirmed increased comorbidity and demonstrated survival disadvantages associated with MS diminished at high CCI (≥ 4) [ 50 ]. A Canadian study found that higher CCI predicted hospitalization risk, though unrelated to MS‐specific hospitalizations [ 51 ]. While evidence on relapse risk is inconsistent, higher CCI consistently predicts disability progression and reaching disability milestones earlier [ 52 , 53 , 54 , 55 ].

Although formal time data are lacking, practical experience indicates that completing a full CCI assessment,requiring review of the patient's comprehensive medical record,generally takes on the order of 10 to 15 min. Depending on electronic medical record (EMR) structure, the CCI can be readily integrated into clinical practice via administrative codes, reflecting moderate to high real‐world applicability, as evidenced by its extensive use in registry research. The original CCI demonstrates high inter‐rater and test–retest reliability, provided data documentation is thorough and systematic [ 45 , 56 , 57 , 58 ].

However, several limitations must be considered. The fixed weighting of comorbidities may not reflect changing mortality risks over time, necessitating periodic recalibration. Numerous CCI adaptations and modifications might create variability across studies and reduce comparability [ 41 , 46 , 47 , 59 , 60 ]. The CCI poses specific limitations for MS cohorts as it captures few mental‐health disorders, even though psychiatric comorbidities are known to significantly influence MS outcomes, yet assigns weight to conditions that are seldom encountered in this population, such as Human Immunodeficiency Virus (HIV) infections/Acquired Immunodeficiency Syndrome (AIDS) [ 61 , 62 , 63 , 64 ]. Additionally, most validation studies in MS rely on retrospective administrative datasets with potential coding inaccuracies and incomplete data capture, contributing to a moderate‐to‐low evidence quality rating (Table 1 ).

Although originally not developed for MS, the CCI constitutes a valuable tool for summarizing comorbidities and predicting key outcomes such as mortality. Nonetheless, clinicians and researchers must account for its inherent limitations when interpreting findings, including currently unadjusted comorbidity weighting, limited psychiatric coverage, as well as limited validation in MS with restricted data source reliability. Therefore, the current status of the CCI was unanimously categorized as an advanced/aspirational instrument.

Elixhauser Comorbidity Index

The Elixhauser Comorbidity Index (ECI), originally derived from ICD‐9 diagnostic codes, comprises 30 binary and unweighted comorbidity measures; over the past two decades, it has undergone several refinements and modifications, including adaptations to ICD‐10 and efforts to construct and validate ECI composite scores [ 65 , 66 , 67 ]. Its validity in mortality prediction has been supported by key studies examining the original and adapted versions, demonstrating good‐to‐excellent prognostic performance [ 46 , 48 , 66 , 68 ]. Yet, ECI has only been infrequently applied in retrospective MS studies, mostly utilizing administrative or registry data. For example, one large US hospital‐based study found that nearly half of MS patients had ≥ 2 ECI‐defined comorbidities; ≥ 3 comorbidities almost doubled the odds of 30‐ and 90‐day readmission, thus supporting the ECI's clinical utility [ 69 ]. Additionally, one claims‐based study on specialty pharmacy using ECI primarily for model adjustment found that increased ECI scores were associated with higher risks of subsequent relapse during follow‐up [ 70 ]. Similarly, another large MS versus non‐MS comparative study and a few other retrospective studies used the ECI as a continuous covariate as adjustment for health status when modeling different outcomes (e.g., mortality, differences in prescription, etc.) confirm that comorbidity burden (as quantified by ECI) meaningfully affects MS outcomes [ 71 , 72 , 73 ].

While the ECI is particularly well‐suited to real‐world and retrospective data sources, its complexity in the assessment of comorbidities and lack of proper composite scores limit routine clinical use. Retrospective analyses introduce potential biases (e.g., undercoding, residual confounding), restricting evidence quality [ 69 , 70 , 74 ]. Robust associations have consistently been demonstrated between the ECI and outcomes such as mortality and healthcare utilization; however, evidence for its direct validation in MS cohorts remains scarce [ 67 ]. Likewise, a broad systematic review not confined to MS populations affirms the ECI's construct validity for predicting mortality across diverse patient groups, yet it highlights the need for disease‐specific validation,such as in individuals with MS [ 48 ].

Currently, the ECI is best suited for specialized research contexts (e.g., registries, administrative databases) rather than routine MS practice, given its complexity, data requirements, limited MS validation, and variable clinician familiarity (Table 1 ).

Self‐Administered Comorbidity Questionnaire and Self‐Reported Comorbidity Questionnaire in Multiple Sclerosis

The conventional Self‐Administered Comorbidity Questionnaire (SCQ) is a patient‐reported checklist of common comorbid conditions originally developed to capture comorbidities without specific EMR or chart review [ 75 ]. It presents respondents with a predefined catalog of medical conditions,for example, hypertension, cardiac disease, pulmonary disease, diabetes mellitus, and arthritis,while also permitting the reporting of additional illnesses not captured by the list. In its original configuration, the instrument provides respondents with 13 specific disorders and reserves space for two supplementary comorbidities. For each listed condition, respondents indicate its presence, after which supplementary items ascertain whether they are undergoing treatment and whether the illness imposes functional limitations. The SCQ has been validated as a predictor of mortality in disease‐specific populations, is comparable to other comorbidity indices with regard to accurately capturing comorbidity and has been associated with impaired quality of life across various populations [ 75 , 76 , 77 , 78 , 79 ].

In MS, evidence for the SCQ is limited and primarily derived from the HOLISM (Health Outcomes and Lifestyle in a Sample of People With Multiple Sclerosis) cohort study, where a modified SCQ assessed comorbidity associations with patient‐reported outcomes [ 80 , 81 , 82 , 83 , 84 ]. About half of MS participants reported at least one comorbidity, consistent with data from other registries [ 83 , 85 ]. Studies found associations between SCQ‐defined comorbidity burden and reduced quality of life as well as self‐reported depression, though their methodology and lack of objective endpoints preclude robust internal validation and limits generalizability [ 80 , 81 , 82 ]. A derivative questionnaire and as such a similar tool, the self‐report questionnaire (SRQ‐MS), demonstrated high agreement (up to κ > 0.82) with EMR for clearly defined comorbidities like hypertension, diabetes, coronary artery disease, autoimmune disorders, and mood disorders, validated amongst pwMS. Prospective studies using SRQ‐MS consistently linked higher comorbidity counts with worse physical function, increased hospitalization, higher relapse rates, and reduced quality of life [ 13 , 86 , 87 , 88 , 89 ]. However despite good internal validity, external validation in broader MS populations is lacking.

Both SCQ and SRQ‐MS are practical, patient‐driven instruments easily integrated into clinical or research settings, evidenced by high participation rates in registries even when deployed digitalized via an online format (e.g., NARCOMS, HOLISM) [ 83 , 85 ]. However, both questionnaires have limited applicability in real‐world data and retrospective analyses, as scores cannot be reconstructed accurately from existing clinical or administrative data and lack robust external validation [ 86 ]. Additionally, SCQ in particular lacks sufficient quantitative studies and data on MS‐relevant endpoints. SCQ and SRQ‐MS were therefore rated as reserved for specialized settings/studies (Table 1 ).

Measures of Frailty in MS Patients

For frailty, 193 records were found, of which a total of 9 studies on three frailty measures (Table 2 ; Files S1 and S2 ) were identified: Fried Frailty (FF), Frailty Index (FI) and Tilburg Frailty Indicator (TFI). Together these studies comprised a total sample size of 1941 pwMS. Table 2 provides a detailed overview of every identified frailty measure.

Fried Frailty Criteria

The Fried frailty criteria (FFC) define frailty as a biological syndrome characterized by the presence of at least three of the following five symptoms: unintentional weight loss (within 1 year), muscle weakness (measured by handgrip strength), fatigue (assessed using a standardized scale), slowed motor skills (evaluated by walking speed), and reduced physical activity (determined via questionnaire) [ 17 ]. These criteria are well‐established, with a clear operational definition and strong predictive value for adverse health outcomes in older adults. The assessment comprises a physical examination and a questionnaire. One of its key strengths is its widespread use and high validity, with strong predictive value [ 30 , 31 ].

However, evidence supporting the application of FFC in pwMS remains limited. To date, only one study involving 80 pwMS has investigated FFC in a descriptive, cross‐sectional study conducted at a single centre [ 25 ]. Here, the prevalence of frailty was higher amongst pwMS compared to healthy controls.

However, the use of FFC for frailty assessment in pwMS is subject to significant bias. The FFC was originally validated in older adults and may not be directly applicable to pwMS, who often include a substantial proportion of younger and middle‐aged individuals. Additionally, inconsistency and imprecision may arise due to potential discrepancies in the subjective components of the assessment, such as fatigue, low physical activity, grip strength, and walking speed. The generalizability and reliability of FFC in pwMS remain undetermined. Consequently, based on the GRADE framework, the quality of evidence supporting its use in pwMS is considered very low.

Despite these limitations, the implementation of the FFC in clinical practice is feasible, as the assessment is both inexpensive and time‐efficient (requiring less than 10 min, according to Dent et al., 2016) and can be conducted by trained personnel [ 30 ]. Still, retrospective assessment is unlikely to be viable, as key data, such as grip strength measurements, are not routinely available in clinical records.

While the FFC demonstrates moderate clinical applicability/feasibility, its applicability to real‐world data in pwMS is low. Therefore, we propose that the Fried Frailty Criteria is reserved for specific research settings or specialized studies (Table 2 ).

Frailty Index

The Cumulative Deficit Model calculates the FI based on health‐related deficits categorized as symptoms, signs, disabilities, and diseases [ 90 ]. FI scores range continuously from zero to one, requiring at least 30–40 continuous, ordinal, or binary items, with higher accuracy from more items meeting specific criteria [ 91 ]. The FI robustly predicts mortality and adverse health outcomes [ 34 ]. It increases by ~0.03 points annually in elderly individuals, a finding validated across studies [ 34 , 92 , 93 ]. Overall, the FI shows strong validity, reliability, predictive capacity and generalizability [ 32 , 94 ].

Eight studies have applied FI in MS (seven cross‐sectional, one longitudinal; total n = 1182 pwMS). Three studies used an identical FI version, reporting FI scores of 0.1–0.15 in relapsing MS and 0.15–0.27 in progressive MS, demonstrating external validity [ 20 , 21 , 26 ]. Multiple studies showed FI correlates positively with Expanded Disability Status Scale (EDSS)‐measured disability as well as age, comorbidity burden, BMI, education, and disease duration [ 20 , 21 , 23 , 24 , 25 , 26 ]. PwMS defined as frail per FI also show worse walking performance, increased fall risk, and impaired sleep [ 22 , 23 , 33 ].

Although adapted for pwMS, FI has limitations including assessment complexity, inconsistent standardization, variable data sources, subjective items, and limited validation, resulting in very low evidence quality for pwMS. Clinical feasibility is low to moderate, hindered by long assessment times (10–60 min), required expertise, and limited routine clinical data availability. Standardization, digital automation, and further MS‐specific studies could improve feasibility. Currently, the FI is suitable primarily for specialized research settings, with potential for wider clinical applicability if these challenges are addressed (Table 2 ).

Tilburg Frailty Indicator ( TFI )

The Tilburg frailty indicator (TFI) is a self‐administered questionnaire originally validated for community‐dwelling individuals over 70 years old [ 37 , 95 ]. It comprises 25 items across physical, psychological, and social domains, with strong validity, reliability, and good predictive value for quality of life, disability, and mortality [ 36 , 37 , 38 , 39 , 40 , 95 ].

To date, only one study has used TFI in pwMS, confirming associations between frailty, EDSS‐measured disability, autonomy impairment, and reduced quality of life, though without significant correlation to psychological or social domains [ 35 ].

The TFI is brief, cost‐effective, and patient‐completed within 15 min, making it feasible for clinical use [ 30 ]. However, use in retrospective studies is limited by unavailable clinical record data, low generalizability in younger populations like pwMS, and potential recall and reporting biases. Its validation primarily in older adults further limits evidence quality in MS populations, resulting in very low evidence quality for pwMS. Thus, clinical applicability and feasibility in MS is moderate, applicability to real‐world data is low, and current use should remain confined to specialized research settings (Table 2 ).

Further Frailty Scores

The Clinical Frailty Scale (CFS) is a widely used, cost‐efficient tool for assessing frailty, consisting of seven or nine stages from “very fit” to “severely frail” [ 96 ]. It demonstrates strong validity and predicts adverse outcomes, including mortality in hospitalized older adults [ 30 , 97 , 98 , 99 ]. Administration by trained personnel takes under 5 min, enabling practical clinical integration [ 30 ].

However, limitations exist, including reliance on subjective clinical judgment and the frequent unavailability of required data in EMR. Critically, the CFS has not been studied in pwMS and may inadequately capture frailty complexity in this younger population. The scale was previously deemed unsuitable for younger COVID‐19 patients, further questioning its appropriateness for pwMS [ 100 ].

Overall, clinical applicability in pwMS is moderate, given feasibility but absent validation. Applicability to real‐world MS data remains low; thus, we recommend the CFS exclusively for specialized research settings (Table 2 ).

Discussion

Enhancing Comprehensive Health Assessment in MS Care

Integrating comorbidity and frailty measures into MS care likely provides a more comprehensive assessment of overall patient health, potentially improving clinical outcomes. Comorbidity and frailty measures add critical context to traditional clinical assessments, capturing complex interactions between MS progression, concurrent health conditions, and patients' functional resilience. While direct evidence in MS specifically supporting improved outcomes through integrated assessment remains limited, broader evidence from chronic illness management suggests that holistic evaluation correlates with enhanced health outcomes and more targeted interventions.

Complementary Value of Comorbidity and Frailty Measures

Comorbidity and frailty, though related, are not identical concepts and each offers unique insights. Comorbidity measures quantify the presence and impact of multiple chronic conditions on overall health outcomes, typically focusing on disease‐specific morbidity and mortality. Frailty measures, conversely, assess physiological vulnerability and functional resilience, capturing diminished capacity to cope with stressors, which may not be adequately reflected by comorbidity alone. Evidence from geriatric populations indicates that assessing both frailty and comorbidities simultaneously provides complementary information, better predicting adverse outcomes than either measure alone [ 101 , 102 , 103 ]. Whether this combined benefit extrapolated from other chronic‐disease settings translates to MS remains debatable. As such, further MS‐specific research is required to confirm, or refute, this assumption and to explore instruments that can jointly capture comorbidity and frailty within this population.

Optimal Measures Considering Data Quality and MS ‐Specific Issues

None of the currently available measures of comorbidity and frailty provide sufficiently robust evidence to warrant routine use in MS populations. For frailty, several investigated scores have been summarized in a review without considering the different forms of measurements by the different scores [ 4 ]. For research, given the variability in real‐world data quality, the CCI and ECI appear promising for integrating into existing administrative datasets due to their reliance on standardized coding. SRQ‐MS and SCQ, while providing patient‐centred data, are better suited for prospectively designed studies with predefined variables.

The Need for MS ‐Specific Instruments

There is a growing appreciation of the need for developing or refining systematic, validated instruments to measure comorbidity and frailty specifically tailored to the MS population [ 104 ]. Such specialized tools could integrate additional conditions, modifiable risk factors (e.g., smoking, physical inactivity), and multidimensional assessments of psychosocial well‐being, thereby improving prognostic accuracy and enabling earlier, more targeted interventions [ 2 , 105 ]. These MS‐specific instruments would have the potential to address current gaps in clinical and research practice by providing tailored, sensitive, and actionable insights into patient health. Future studies should therefore consider specific validation and subsequently calibration of comorbidity and frailty measures against relevant outcomes such as relapse rate, disability progression, and treatment safety. Additionally, comparative studies across registries, observational cohorts, and interventional trials should be performed to establish the most prognostic/predictive selection of comorbidity and frailty metrics. Lastly, feasibility studies in routine clinics should be conducted to develop streamlined versions of comorbidity and frailty measures that can be completed within standard consultation times (e.g., EMR‐derived comorbidity counts combined with a brief frailty screen).

Limitations

This systematic review should be interpreted considering some constraints. The available evidence is predominantly cross‐sectional, limiting our ability to interpret temporal relationships. Additionally, the highly heterogeneous study quality and designs, ranging from small single‐centre cohorts to larger administrative database analyses, along with inconsistent operational definitions of measures complicate direct comparisons and consequently potentially biased conclusions. However, we systematically appraised each measure using the GRADE approach after a thorough risk of bias assessment, supporting the overall robustness of the patterns we report.

Conclusion

Several measures have been used to investigate comorbidities and frailty in pwMS. Nevertheless, current instruments are underpinned by a limited evidence base and insufficient validation, and their real‐world applicability is further restricted by a lack of specificity for MS populations. At present, amongst the identified measures the CCI for comorbidities and the FI for frailty, offer provisional options for use, provided their limitations are acknowledged. Importantly, no single measure has displayed sufficient evidence to warrant general, routine use in MS populations. While potentially useful for research purposes, further validation and standardization are required to ensure their applicability in real‐world use. Future studies should focus on developing and validating MS‐specific comorbidity and frailty measures with weightings calibrated to MS‐specific outcomes and consider head‐to‐head comparison trials to identify the most prognostically informative selection of individual metrics.

Conflicts of Interest

Marie Louise Aicher: nothing to disclose. Fabian Föttinger: has participated in meetings sponsored by, received speaker honoraria and/or travel funding from Biogen, Novartis and Merck. Robert Hoepner: received speaker/advisor honorary from Merck, Novartis, Roche, Biogen, Alexion, Sanofi, Janssen, Bristol‐Myers Squibb, Teva/Mepha and Almirall. He received research support within the last 3 years from Chiesi, and Bristol‐Myers Squibb. He also received research grants from the Swiss MS Society, the SITEM Insel Support Fund and is a member of the Advisory Board of the Swiss and International MS Society. He also serves as deputy editor in chief for the Journal of Central Nervous System Disease and is part of the ECTRIMS Young Investigator Committee. Andrew Chan: Received speakers'/board honoraria from Actelion (Janssen/J&J), Alexion, Almirall, argenx, Biogen, Celgene (BMS), Genzyme/Sanofi, Horizon Therapeutics, Merck KGaA, Novartis, Roche, and Teva, all for hospital research funds. He received research support from Biogen, CSL Behring, Genzyme, Roche and UCB, the European Union, and the Swiss National Foundation. He serves as associate editor of the European Journal of Neurology, and on the editorial board for Clinical and Translational Neuroscience. Thomas Berger: has participated in meetings sponsored by and received honoraria (lectures, advisory boards, consultations) from pharmaceutical companies marketing treatments for MS: Allergan, Bayer, Biogen, Bionorica, BMS, Eisai, Genesis, GSK, Jazz Pharma, Horizon, Janssen‐Cilag, MedDay, Merck, Neuraxpharm, Newbridge, Novartis, Octapharma, Roche, Sandoz, Sanofi, Teva, TG Pharmaceuticals and UCB. He serves as a member of DSMB (Cellerys AG) and IRAP (TG Therapeutics) clinical trial boards. His institution has received financial support in the past 12 months by unrestricted research grants (Biogen, Bayer, BMS, Merck, Novartis, Roche, Sanofi, Teva) and for participation in clinical trials in multiple sclerosis sponsored by Alexion, Bayer, Biogen, Merck, Novartis, Octapharma, Roche, Sanofi, Teva. Gabriel Bsteh: has participated in meetings sponsored by, received speaker honoraria or travel funding from Biogen, BMS, Janssen, Lilly, Medwhizz, Merck, Neuraxpharm, Novartis, Roche, Sanofi‐Genzyme and Teva, and received honoraria for consulting Adivo Associates, Biogen, BMS, Janssen, Merck, Novartis, Roche, Sanofi‐Genzyme and Teva. He has received unrestricted research grants from BMS and Novartis. He serves on the Executive Committee of the European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS) and the Board of Directors of the International Multiple Sclerosis VisualSystem Consortium (IMSVISUAL).

Supporting information

File S1: Screening list.

File S2: Full study list.

Table S1: ene70425‐sup‐0003‐TableS1.docx.

Acknowledgement

Open Access funding provided by Medizinische Universitat Wien/KEMÖ.

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study. Studies used for the article are published in the Supporting Information .

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