Literature DB >> 33750434

The interoperability between the Spanish version of the International Classification of Diseases and ORPHAcodes: towards better identification of rare diseases.

Juan Rico1, Luis Javier Echevarría-González de Garibay2, María García-López3, Sandra Guardiola-Vilarroig1,4, Luis Alberto Maceda-Roldán5, Óscar Zurriaga1,4,6, Clara Cavero-Carbonell7.   

Abstract

BACKGROUND: Rare diseases present a wide spectrum of clinical manifestations and severity levels and are often poorly known and underrepresented, making them difficult to classify. Diagnoses are usually coded using the International Classification of Diseases (ICD), with its different versions. In Spain, the ICD-10-ES (stem from the ICD-10-CM-Clinical Modification) is used throughout the National Healthcare System since 2016, indistinctively including rare diseases that often lack a specific code. Orphanet aims to provide high-quality resources on rare diseases. The goal was to interrelate the Orphanet classification with the ICD-10-ES in order to engage a tool to track rare diseases diagnosis and characterize the improvement space for the identification of rare diseases patients in the Spanish Healthcare System.
METHODS: 5775 disorder level ORPHAcodes were mapped to ICD-10-ES codes by comparing the descriptors associated in both classifications. ORPHAcodes were then clustered based on their assigned ICD-10-ES chapter and the redundancy of each individual ICD-10-ES code was calculated by counting the ORPHAcodes they mapped to. Three groups were established: Group 1 (1 ORPHAcode per ICD-10-ES), Group 2 (between 2-49 ORPHAcodes per ICD-10-ES) and Group 3 (≥ 50 ORPHAcodes per ICD-10-ES).
RESULTS: Equivalences to 1700 ICD-10-ES codes were established for 5664 ORPHAcodes. The ORPHAcodes distribution within the ICD-10-ES showed an aggregation in the "Q" (> 40%), "G" (> 14%), and "E" (12%) chapters. The availability of ICD-10-ES codes to map ORPHAcodes reached its lowest at the "G" and "Q" chapters with less than 0.2 ICD-10-ES codes available per ORPHAcode. Global ICD-10-ES codes redundancy analysis revealed that only 1055 of the equivalences pertain to group 1. Group 2 contained 3358 equivalences with 634 ICD-10-ES codes while 1322 equivalences were group 3 (11 ICD-10-ES). Within ICD-10-ES chapters, "G" and "Q" contained over 30% and 45% of their own equivalences in the highest redundancy level (group 3) respectively, but under 10% one to one equivalences each (group 1).
CONCLUSIONS: ICD-10-ES codes have not enough specificity to identify rare diseases. Direct mapping between ICD and ORPHAcodes or the integration of ORPHAcodes at the healthcare system for diagnoses codification would enable better detection and epidemiological analysis of rare diseases.

Entities:  

Keywords:  Codification; Diagnoses; Healthcare; ICD-10-ES; ORPHAcode; Public Health; Rare diseases

Mesh:

Year:  2021        PMID: 33750434      PMCID: PMC7941896          DOI: 10.1186/s13023-021-01763-y

Source DB:  PubMed          Journal:  Orphanet J Rare Dis        ISSN: 1750-1172            Impact factor:   4.123


  8 in total

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3.  How many rare diseases are there?

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Review 4.  Rare Disease Terminology and Definitions-A Systematic Global Review: Report of the ISPOR Rare Disease Special Interest Group.

Authors:  Trevor Richter; Sandra Nestler-Parr; Robert Babela; Zeba M Khan; Theresa Tesoro; Elizabeth Molsen; Dyfrig A Hughes
Journal:  Value Health       Date:  2015-08-18       Impact factor: 5.725

5.  Rare diseases in ICD11: making rare diseases visible in health information systems through appropriate coding.

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Journal:  Orphanet J Rare Dis       Date:  2015-03-26       Impact factor: 4.123

6.  SpainUDP: The Spanish Undiagnosed Rare Diseases Program.

Authors:  Estrella López-Martín; Beatriz Martínez-Delgado; Eva Bermejo-Sánchez; Javier Alonso; Manuel Posada
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7.  New primary renal diagnosis codes for the ERA-EDTA.

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Journal:  Nephrol Dial Transplant       Date:  2012-11-22       Impact factor: 5.992

8.  Estimating cumulative point prevalence of rare diseases: analysis of the Orphanet database.

Authors:  Stéphanie Nguengang Wakap; Deborah M Lambert; Annie Olry; Charlotte Rodwell; Charlotte Gueydan; Valérie Lanneau; Daniel Murphy; Yann Le Cam; Ana Rath
Journal:  Eur J Hum Genet       Date:  2019-09-16       Impact factor: 4.246

  8 in total
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1.  Healthcare Burden of Rare Diseases: A Population-Based Study in Tuscany (Italy).

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Journal:  Int J Environ Res Public Health       Date:  2022-06-21       Impact factor: 4.614

  1 in total

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