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Recommendations for patient screening in ultra-rare inherited metabolic diseases: what have we learned from Niemann-Pick disease type C?

Overview of attention for article published in Orphanet Journal of Rare Diseases, January 2019
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  • Above-average Attention Score compared to outputs of the same age (53rd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (52nd percentile)

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Title
Recommendations for patient screening in ultra-rare inherited metabolic diseases: what have we learned from Niemann-Pick disease type C?
Published in
Orphanet Journal of Rare Diseases, January 2019
DOI 10.1186/s13023-018-0985-1
Pubmed ID
Authors

María-Jesús Sobrido, Peter Bauer, Tom de Koning, Thomas Klopstock, Yann Nadjar, Marc C Patterson, Matthis Synofzik, Chris J Hendriksz

Abstract

Rare and ultra-rare diseases (URDs) are often chronic and life-threatening conditions that have a profound impact on sufferers and their families, but many are notoriously difficult to detect. Niemann-Pick disease type C (NP-C) serves to illustrate the challenges, benefits and pitfalls associated with screening for ultra-rare inborn errors of metabolism (IEMs). A comprehensive, non-systematic review of published information from NP-C screening studies was conducted, focusing on diagnostic methods and study designs that have been employed to date. As a key part of this analysis, data from both successful studies (where cases were positively identified) and unsuccessful studies (where the chosen approach failed to identify any cases) were included alongside information from our own experiences gained from the planning and execution of screening for NP-C. On this basis, best-practice recommendations for ultra-rare IEM screening are provided. Twenty-six published screening studies were identified and categorised according to study design into four groups: 1) prospective patient cohort and family-based secondary screenings (18 studies); 2) analyses of archived 'biobank' materials (one study); 3) medical chart review and bioinformatics data mining (five studies); and 4) newborn screening (two studies). NPC1/NPC2 sequencing was the most common primary screening method (Sanger sequencing in eight studies and next-generation sequencing [gene panel or exome sequencing] in five studies), followed by biomarker analyses (usually oxysterols) and clinical surveillance. Historically, screening for NP-C has been based on single-patient studies, small case series, and targeted cohorts, but the emergence of new diagnostic methods over the last 5-10 years has provided opportunities to screen for NP-C on a larger scale. Combining clinical, biomarker and genetic diagnostic methods represents the most effective way to identify NP-C cases, while reducing the likelihood of misdiagnosis. Our recommendations are intended as a guide for planning screening protocols for ultra-rare IEMs in general.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 77 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 77 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 9 12%
Other 8 10%
Researcher 8 10%
Student > Ph. D. Student 7 9%
Student > Doctoral Student 5 6%
Other 14 18%
Unknown 26 34%
Readers by discipline Count As %
Medicine and Dentistry 16 21%
Biochemistry, Genetics and Molecular Biology 11 14%
Nursing and Health Professions 5 6%
Agricultural and Biological Sciences 3 4%
Immunology and Microbiology 2 3%
Other 16 21%
Unknown 24 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 February 2019.
All research outputs
#13,058,036
of 23,124,001 outputs
Outputs from Orphanet Journal of Rare Diseases
#1,276
of 2,649 outputs
Outputs of similar age
#201,724
of 438,136 outputs
Outputs of similar age from Orphanet Journal of Rare Diseases
#25
of 53 outputs
Altmetric has tracked 23,124,001 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,649 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.6. This one has gotten more attention than average, scoring higher than 51% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 438,136 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 53% of its contemporaries.
We're also able to compare this research output to 53 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.