Evidence map›Paper›PMID 34009478›Full record

ReviewCurrent neurology and neuroscience reports2021

Infection Mitigation Strategies for Multiple Sclerosis Patients on Oral and Monoclonal Disease-Modifying Therapies.

Tyler Ellis Smith, Ilya Kister

Open access · bronzeAbstract readReview
In one paragraph

Review in Current neurology and neuroscience reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
1.8field-weighted citation impact, top 15% of its field
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

14 citing papers in PubMed, 14 citations in OpenAlex.

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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

2 authors at 1 institution in 1 country.

Tyler Ellis SmithDepartment of Neurology, NYU-Multiple Sclerosis Care Center, NYU School of Medicine, New York, NY, USA. Tyler.Smith@nyulangone.org.ORCID http://orcid.org/0000-0001-6836-8192
Ilya KisterDepartment of Neurology, NYU-Multiple Sclerosis Care Center, NYU School of Medicine, New York, NY, USA.ORCID http://orcid.org/0000-0003-3549-949X
Multiple Sclerosis Center Of Northeastern New York · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purpose of reviewThe newer, higher-efficacy disease-modifying therapies (DMTs) for multiple sclerosis (MS)-orals and monoclonals-have more profound immunomodulatory and immunosuppressive properties than the older, injectable therapies and require risk mitigation strategies to reduce the risk of serious infections. This review will provide a systematic framework for infectious risk mitigation strategies relevant to these therapies. RECENT

findingsWe classify risk mitigation strategies according to the following framework: (1) screening and patient selection, (2) vaccinations, (3) antibiotic prophylaxis, (4) laboratory and MRI monitoring, (5) adjusting dose and frequency of DMT, and (6) behavioral modifications to limit the risk of infection. We systematically apply this framework to the infections for which risk mitigations are available: hepatitis B, herpetic infections, progressive multifocal leukoencephalopathy, and tuberculosis. We also discuss up-to-date recommendations regarding COVID-19 vaccinations for patients on DMTs. We offer a practical, comprehensive, DMT-specific framework of derisking strategies designed to minimize the risk of infections associated with the newer MS therapies.

Indexed as

COVID-19InfectionsMultiple SclerosisHumansImmunosuppressive AgentsSARS-CoV-2Immunosuppressive AgentsAdverse eventsDisease-modifying therapyInfectionsMultiple sclerosisRisk mitigationTreatment

Identifiers

PMID34009478
PMCPMC8132488
OpenAlexW3161034062

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.