Evidence map›Paper›PMID 29797470›Full record

ArticleActa paediatrica (Oslo, Norway : 1992)2018

Easy-to-use algorithm would provide faster diagnoses for mucopolysaccharidosis type I and enable patients to receive earlier treatment.

Anna Tylki-Szymańska, Linda De Meirleir, Maja Di Rocco, Waseem M Fathalla, Nathalie Guffon, Christina Lampe, Allan M Lund, Rossella Parini, Frits A Wijburg, Jiri Zeman and 1 more

Open access · hybridAbstract read
In one paragraph

Article in Acta paediatrica (Oslo, Norway : 1992), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.8field-weighted citation impact, top 28% 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

7 citing papers in PubMed, 13 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

11 authors at 11 institutions in 8 countries.

Anna Tylki-SzymańskaDepartment of Pediatrics, Nutrition and Metabolic Diseases, The Children's Memorial Health Institute, Warsaw, Poland.ORCID 0000-0001-7306-6877
Linda De MeirleirDepartment of Paediatric Neurology and Metabolic Diseases, Brussels, Belgium.
Maja Di RoccoUnit of Rare Diseases, Department of Paediatrics, IRCCS Giannina Gaslini, Genova, Italy.
Waseem M FathallaDivision of Child Neurology, Department of Pediatrics, Mafraq Hospital, Bani Yas, Abu Dhabi, United Arab Emirates.
Nathalie GuffonReference Centre of Metabolic Diseases, Hôpital Femme Mère Enfant (HFME), Bron, France.
Christina LampeCenter for Rare Diseases, Clinic for Paediatric and Adolescent Medicine, HELIOS Dr. Horst Schmidt Kliniken Wiesbaden, Wiesbaden, Germany.
Allan M LundCentre for Inherited Metabolic Diseases, Departments of Paediatrics and Clinical Genetics, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.
Rossella PariniRare Metabolic Diseases Unit, Paediatric Clinic, Fondazione MBBM, San Gerardo University Hospital, Monza, Italy.ORCID 0000-0003-4505-1306
Frits A WijburgDepartment of Paediatrics, Academic Medical Center, Amsterdam, The Netherlands.
Jiri ZemanDepartment of Pediatrics and Adolescent Medicine, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic.
Maurizio ScarpaCenter for Rare Diseases, Clinic for Paediatric and Adolescent Medicine, HELIOS Dr. Horst Schmidt Kliniken Wiesbaden, Wiesbaden, Germany.
Amsterdam UMC Location University of Amsterdam · NLAzienda Ospedaliera San Gerardo · ITCharles University · CZChildren's Memorial Health Institute · PLCopenhagen University Hospital · DKHelios Dr. Horst Schmidt Kliniken Wiesbaden · DEHôpital Femme Mère Enfant · FRInstitut des Maladies Métaboliques et Cardiovasculaires · FRIstituto Giannina Gaslini · ITMafraq Hospital · AEUniversity of Padua · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimThe aim of this study was to develop an algorithm to prompt early clinical suspicion of mucopolysaccharidosis type I (MPS I).

methodsAn international working group was established in 2016 that comprised 11 experts in paediatrics, rare diseases and inherited metabolic diseases. They reviewed real-world clinical cases, selected key signs or symptoms based on their prevalence and specificity and reached consensus about the algorithm. The algorithm was retrospectively tested.

resultsAn algorithm was developed. In patients under two years of age, kyphosis or gibbus deformity were the key symptoms that raised clinical suspicion of MPS I and in those over two years they were kyphosis or gibbus deformity, or joint stiffness or contractures without inflammation. The algorithm was tested on 35 cases, comprising 16 Hurler, 10 Hurler-Scheie, and nine Scheie patients. Of these 35 cases, 32 (91%) - 16 Hurler, nine Hurler-Scheie and seven Scheie patients - would have been referred earlier if the algorithm had been used.

conclusionThe expert panel developed and tested an algorithm that helps raise clinical suspicion of MPS I and would lead to a more prompt final diagnosis and allow earlier treatment.

Indexed as

AlgorithmsEarly DiagnosisAge FactorsChildChild, PreschoolConsensusDisease ProgressionFemaleHumansInfant, NewbornInternationalityMaleMucopolysaccharidosis IMultimorbidityNeonatal ScreeningPrognosisAlgorithmDiagnosisKyphosisMucopolysaccharidosisSymptoms

Identifiers

PMID29797470
PMCPMC6055821
OpenAlexW2804556448

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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.