Evidence map›Paper›PMID 41362333›Full record

ArticleTherapeutic advances in neurological disorders2025

From diagnosis to treatment: patterns in disease-modifying therapy initiation in multiple sclerosis.

Stefania Iaquinto, Mina Stanikić, Enriqueta Vallejo-Yagüe, Jens Kuhle, Zina-Mary Manjaly, Patrick Roth, Pasquale Calabrese, Chiara Zecca, Milo A Puhan, Viktor von Wyl

Abstract read
In one paragraph

Article in Therapeutic advances in neurological disorders, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Stefania IaquintoEpidemiology, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.ORCID https://orcid.org/0000-0001-5808-9026
Mina StanikićDepartment of Epidemiology, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.
Enriqueta Vallejo-YagüeEpidemiology, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.
Jens KuhleDepartment of Neurology, University Hospital Basel and University of Basel, Basel, Switzerland.
Zina-Mary ManjalyDepartment of Neurology, Schulthess Clinic, Zurich, Switzerland.
Patrick RothDepartment of Neurology, University Hospital Zurich, Zurich, Switzerland.
Pasquale CalabreseNeuropsychology and Behavioral Neurology Unit, Division of Cognitive and Molecular Neuroscience, University of Basel, Basel, Switzerland.
Chiara ZeccaDepartment of Neurology, Neurocenter of Southern Switzerland, Ospedale Regionale di Lugano, EOC, Lugano, Switzerland.
Milo A PuhanEpidemiology, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.
Viktor von WylDepartment of Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Hirschengraben 84, Zurich 8001, Switzerland Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland.ORCID https://orcid.org/0000-0002-8754-9797

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Over recent decades, several high-efficacy disease-modifying therapies (DMTs) have been approved for multiple sclerosis (MS), prompting a shift from an escalation strategy toward early high-efficacy treatment. Yet, DMT initiation patterns and safety profiles remain unexplored in research relying solely on patient self-reports and have not been examined in Switzerland. Objectives: To investigate (1) time from MS diagnosis to first DMT, (2) temporal trends in initial DMT use, (3) number of suspected adverse drug reactions (sADRs) following the first DMT, and (4) occurrence of any severe sADR. Design: Retrospective cohort study using longitudinal, patient-reported data from the Swiss MS Registry (SMSR) between 1995 and 2024. Methods: We analyzed data from SMSR participants with relapsing-remitting MS or clinically isolated syndrome diagnosed after 1995. Time from diagnosis to first DMT was analyzed using Kaplan-Meier analysis. Temporal trends were assessed through annual distributions of first DMTs. sADR burden was modeled with a two-part zero-inflated negative-binomial model, and severe sADRs (⩾1 severe sADR) were assessed with logistic regression. All models were adjusted for sex and age at DMT initiation. Results: Median time from diagnosis to first DMT decreased from 4.5 months (95% confidence interval (CI): 2-6) in the 1995-2004 diagnosis group to 2 months (95% CI: 1-2) in more recent groups (⩾2010). First DMT use shifted from predominantly low-efficacy to moderate (2013-2020) and, since 2020, to high-efficacy DMTs. Compared to low-efficacy DMTs, moderate (incidence risk ratio (IRR) = 0.63, 95% CI: 0.48-0.84) and high-efficacy DMTs (IRR = 0.55, 95% CI: 0.31-0.98) were associated with fewer sADRs, and high-efficacy DMTs had higher odds of reporting zero sADRs (OR = 8.61, 95% CI: 2.34-31.76). We observed signals of a higher likelihood of severe sADRs in high-efficacy DMTs (OR = 1.41, 95% CI: 0.55-3.24) compared to low-efficacy DMTs, although this was not statistically significant. Conclusion: Patient self-reports can reliably capture trends in DMT use. In Switzerland, the first DMT use has evolved, with patients starting DMTs earlier and more frequently with higher-efficacy DMTs. While moderate and high-efficacy DMTs were associated with fewer sADRs, they may also carry a higher risk of severe sADRs, underscoring the need to balance personal preferences, efficacy, and safety in first-line MS treatment.

Indexed as

disease-modifying therapymultiple sclerosispatient-reported outcomessuspected adverse drug reactions

Identifiers

PMID41362333
PMCPMC12681631

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.