Evidence map›Paper›PMID 39267157›Full record

ArticleOrphanet journal of rare diseases2024

Pareto-principle in rare disease education: assessing the representation of common rare diseases in medical education and coding systems.

Alexandra Berger, Kai Lars Grimm, Richard Noll, Thomas Of Wagner

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Article in Orphanet journal of rare diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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0cells of the map it votes in
4citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Alexandra BergerFrankfurt Reference Centre for Rare Diseases, Goethe University Frankfurt, University Hospital, Theodor-Stern-Kai 7, 60590, Frankfurt am Main, Germany. a.berger@med.uni-frankfurt.de.ORCID 0000-0002-0982-6613
Kai Lars GrimmFrankfurt Reference Centre for Rare Diseases, Goethe University Frankfurt, University Hospital, Theodor-Stern-Kai 7, 60590, Frankfurt am Main, Germany.
Richard NollInstitute of Medical Informatics (IMI), Goethe University Frankfurt, University Hospital, Theodor Stern Kai 7, 60590, Frankfurt am Main, Germany.
Thomas Of WagnerFrankfurt Reference Centre for Rare Diseases, Goethe University Frankfurt, University Hospital, Theodor-Stern-Kai 7, 60590, Frankfurt am Main, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe Pareto Principle asserts that a large portion of results can be achieved with a small amount of effort. Wakap et al. found that around 80% of individuals with rare diseases (RD) suffer from one of 149 specific rare diseases. A significant challenge in the RD domain is the lack of information, compounded by the fact that most RD are not specifically codifiable in the ICD-10, leading to a deficit in reliable epidemiological data. Additionally, time constraints in medical education hinder the comprehensive teaching of all RD, contributing to the diagnostic odyssey problem through failure of recognizing diseases. We identified the most and second most prevalent RD (prevalences of 1-5/10,000 and 1-9/100,000, respectively) from the Orphanet Epidemiology File, totaling 454 diseases. We investigated the feasibility of specific coding using ICD-10-GM and whether these diseases were explicitly listed in the subject catalog (GK) of the second state examination in human medicine in Germany. A two-sided chi-square test was employed to identify statistically significant differences between prevalence groups.

resultsOut of 454 diseases, a total of 34% could be specifically coded in ICD-10-GM, with 49% of diseases in the 1-5/10,000 prevalence range (153 RD) and 26% in the 1-9/100,000 range (301 RD) having specific codes. Approximately 15% of all investigated diseases were part of the GK, with 25% of the most prevalent and 10% of the second most prevalent RD group, respectively. Statistically significant differences were observed between prevalence groups concerning the presence of a specific ICD-10-GM code and inclusion in the GK.

conclusionOnly 49% of the most prevalent RD can be specifically coded, highlighting the challenge of limited epidemiological data on RD. In Germany, the Alpha-ID was introduced in addition to ICD-10 in the inpatient setting to obtain more valid epidemiological data on RD. Recognizing the Pareto Principle's applicability, the study emphasizes the importance of including the most common rare diseases in medical education. While recognizing the limitations, especially in covering ultra-rare diseases, the study underscores the potential benefits of enhancing medical curricula to improve rare disease awareness and diagnostic accuracy.

Indexed as

Education, MedicalInternational Classification of DiseasesRare DiseasesGermanyHumansICD-10 codingMedical educationMost prevalent rare diseases

Identifiers

PMID39267157
PMCPMC11396778

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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.