Evidence map›Paper›PMID 31411330›Full record

ArticleRheumatology (Oxford, England)2020

Improvement of MEFV gene variants classification to aid treatment decision making in familial Mediterranean fever.

Matteo Accetturo, Angela Maria D'Uggento, Piero Portincasa, Alessandro Stella

Open access · hybridAbstract read
In one paragraph

Article in Rheumatology (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
1.5field-weighted citation impact, top 18% 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

20 citing papers in PubMed, 38 citations in OpenAlex.

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  8. Genewise detection of variants inFrontiers in genetics · 2024
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  18. In-silico Analysis ofInternational journal of molecular sciences · 2020
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 1 country.

Matteo AccetturoR&D Department, BioTechnology Services srl, Foggia, Italy.
Angela Maria D'UggentoDepartment of Economics and Finance, Italy.
Piero PortincasaDivision of Internal Medicine, Clinica Medica A. Murri, Department of Biomedical Sciences and Human Oncology, Italy.
Alessandro StellaLaboratory of Medical Genetics, Department of Biomedical Sciences and Human Oncology, Università degli Studi di Bari Aldo Moro, AOU Policlinico di Bari, Bari, Italy.
Ministero dell'Economia e delle Finanze · ITUniversity of Bari Aldo Moro · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveFMF is an inherited autoinflammatory syndrome caused by mutations in the MEFV gene. MEFV variants are still largely classified as acvariant of uncertain significance, or with unresolved classification, posing significant challenges in FMF diagnosis. Rare Exome Variant Ensemble Learner (REVEL) is a recently developed variant metapredictor tool. To reduce the number of MEFV variants with ambiguous classification, we extracted REVEL scores for all missense variants present in the INFEVERS database, and analysed its correlation with expert-based classification and localization in the MEFV-encoded pyrin functional domains.

methodsThe data set of 216 MEFV missense variants was divided into four categories (likely benign, variant of uncertain significance, likely pathogenic and unresolved). Variants were plotted onto the pyrin protein, the distribution of REVEL scores in each category was computed and means, confidence intervals, and area under the receiver operating curve were calculated.

resultsWe observed a non-random distribution of pathogenic variants along the pyrin functional domains. The REVEL scores demonstrated a good correlation with the consensus classification of the International Study Group for Systemic Autoinflammatory Diseases. Sensitivity, specificity and accuracy were calculated for different cut-off values of REVEL scores and a gene-specific-threshold of 0.298 was computed with confidence boundary limits. This cut-off value allowed us to propose a reclassification of 96 MEFV gene variants, thus reducing the variant of uncertain significance proportion from 61.6% to 17.6%.

conclusionThe combination of available expert information with sensitive predictor tools could result in a more accurate interpretation of clinical consequences of MEFV gene variants, and to a better genetic counselling and patient management.

Indexed as

Clinical Decision RulesClassificationClinical Decision-MakingDatabases, GeneticDiscriminant AnalysisDisease ManagementFamilial Mediterranean FeverGenetic CounselingGenetic VariationHumansMutation, MissensePyrinStatistics as TopicSupport Vector MachineMEFV protein, humanPyrinfamilial Mediterranean fevergeneticsMEFVvariant interpretation

Identifiers

PMID31411330
PMCPMC7188344
OpenAlexW2968363351

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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.