Evidence map›Paper›PMID 32902734›Full record

ArticleJournal of neurology2021

SMILE: a predictive model for Scoring the severity of relapses in MultIple scLErosis.

F Lejeune, A Chatton, D-A Laplaud, E Le Page, S Wiertlewski, G Edan, A Kerbrat, D Veillard, S Hamonic, N Jousset and 6 more

Abstract readMulticenter Study
PubMed Publisher
In one paragraph

Article in Journal of neurology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

16 authors.

F LejeuneNeurology Department and CIC 0004, Nantes University Hospital, Nantes, France.ORCID http://orcid.org/0000-0002-9984-4160
A ChattonMethodS in Patient-Centred Outcomes and HEalth ResEarch (SPHERE) Unit, INSERM, Universities of Nantes and Tours, Nantes, France.
D-A LaplaudNeurology Department and CIC 0004, Nantes University Hospital, Nantes, France.
E Le PageClinical Neuroscience Centre, CIC_P1414 INSERM, Rennes University Hospital, Rennes University, Rennes, France.
S WiertlewskiNeurology Department and CIC 0004, Nantes University Hospital, Nantes, France.
G EdanClinical Neuroscience Centre, CIC_P1414 INSERM, Rennes University Hospital, Rennes University, Rennes, France.
A KerbratClinical Neuroscience Centre, CIC_P1414 INSERM, Rennes University Hospital, Rennes University, Rennes, France.
D VeillardEpidemiology and Public Health Department, Rennes University Hospital, Rennes, France.
S HamonicEpidemiology and Public Health Department, Rennes University Hospital, Rennes, France.
N JoussetNantes Clinical Investigation Centre, Nantes University Hospital, Nantes, France.
F Le FrèreNantes Clinical Investigation Centre, Nantes University Hospital, Nantes, France.
J-C OualletNeurology Department, Magendie Neurocentre, Bordeaux University Hospital, INSERM U1215, Bordeaux, France.
B BrochetNeurology Department, Magendie Neurocentre, Bordeaux University Hospital, INSERM U1215, Bordeaux, France.
A RuetNeurology Department, Magendie Neurocentre, Bordeaux University Hospital, INSERM U1215, Bordeaux, France.
Y FoucherMethodS in Patient-Centred Outcomes and HEalth ResEarch (SPHERE) Unit, INSERM, Universities of Nantes and Tours, Nantes, France.
Laure MichelClinical Neuroscience Centre, CIC_P1414 INSERM, Rennes University Hospital, Rennes University, Rennes, France. laure.michel@chu-rennes.fr.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn relapsing-remitting multiple sclerosis (RRMS), relapse severity and residual disability are difficult to predict. Nevertheless, this information is crucial both for guiding relapse treatment strategies and for informing patients.

objectiveWe, therefore, developed and validated a clinical-based model for predicting the risk of residual disability at 6 months post-relapse in MS.

methodsWe used the data of 186 patients with RRMS collected during the COPOUSEP multicentre trial. The outcome was an increase of ≥ 1 EDSS point 6 months post-relapse treatment. We used logistic regression with LASSO penalization to construct the model, and bootstrap cross-validation to internally validate it. The model was externally validated with an independent retrospective French single-centre cohort of 175 patients.

resultsThe predictive factors contained in the model were age > 40 years, shorter disease duration, EDSS increase ≥ 1.5 points at time of relapse, EDSS = 0 before relapse, proprioceptive ataxia, and absence of subjective sensory disorders. Discriminative accuracy was acceptable in both the internal (AUC 0.82, 95% CI [0.73, 0.91]) and external (AUC 0.71, 95% CI [0.62, 0.80]) validations.

conclusionThe predictive model we developed should prove useful for adapting therapeutic strategy of relapse and follow-up to individual patients.

Indexed as

Multiple SclerosisMultiple Sclerosis, Relapsing-RemittingAdultDisability EvaluationHumansRecurrenceRetrospective StudiesEDSSMultiple sclerosisPredictive modelRelapse phenotypeRelapse recovery

Identifiers

PMID32902734

What OpenQuestion holds

Textmetadata
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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.