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Chart validation of an algorithm for identifying hereditary progressive muscular dystrophy in healthcare claims

BACKGROUND: Muscular dystrophies (MDs) are a group of inherited conditions characterized by progressive muscle degeneration and weakness. The rarity and heterogeneity of the population with MD have hindered therapeutic developments as well as epidemiological and health outcomes research. The objecti...

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Autores principales: Chen, Xiaoxue, Agiro, Abiy, Martin, Ann S., Lucas, Ann M., Haynes, Kevin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6688201/
https://www.ncbi.nlm.nih.gov/pubmed/31399066
http://dx.doi.org/10.1186/s12874-019-0816-7
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author Chen, Xiaoxue
Agiro, Abiy
Martin, Ann S.
Lucas, Ann M.
Haynes, Kevin
author_facet Chen, Xiaoxue
Agiro, Abiy
Martin, Ann S.
Lucas, Ann M.
Haynes, Kevin
author_sort Chen, Xiaoxue
collection PubMed
description BACKGROUND: Muscular dystrophies (MDs) are a group of inherited conditions characterized by progressive muscle degeneration and weakness. The rarity and heterogeneity of the population with MD have hindered therapeutic developments as well as epidemiological and health outcomes research. The objective of the study was to develop and validate a case-finding algorithm utilizing administrative claims data to identify and characterize patients with MD. METHODS: This retrospective cohort study used medical chart validation to evaluate an ICD-9/10 coding algorithm in a large commercial claims database. Patients were identified who had ≥2 office visits with a diagnosis of hereditary progressive MDs from January 1, 2013 through December 31, 2016, were male, and younger than 18 years at the time of first MD diagnosis. Cases who met the algorithm were then validated against medical charts. Diagnoses of MD and specific type (Duchenne, Becker, or other MD) were confirmed by medical chart review by trained reviewers. Positive predictive value (PPV) and 95% confidence intervals (CI) were calculated using a 2 × 2 contingence table. Patient demographic, clinical, and health utilization characteristics were summarized using basic descriptive statistics. RESULTS: Charts were obtained and reviewed for 109 patients who met the algorithm. The PPV of the case-identifying algorithm for MD was 95% (95% CI 88–98%). Of the 103 confirmed MD cases, 87 patients (85%, 95% CI 76–91%) had Duchenne or Becker MD; 76 patients (74%, 95% CI 64–82%) had Duchenne MD, and 11 patients (11%, 95% CI 5–18%) had Becker MD. A total of 74 (67.9%) patients had ≥1 pediatric complex chronic condition (other than neurologic/neuromuscular disease); 54 (49.5%) had cardiovascular conditions; 14 (12.8%) had respiratory conditions; 50 (45.9%) had bone-related issues; 11 (10.1%) had impaired growth; and 6 (5.5%) had puberty delay. CONCLUSIONS: The results of this study demonstrate that the case-finding algorithm accurately identified patients with MD, primarily Duchenne MD, within a large administrative database. The algorithm, which was constructed using a few items easily accessible from claims, can be used to facilitate epidemiological and health outcomes research in the Duchenne patient population. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12874-019-0816-7) contains supplementary material, which is available to authorized users.
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spelling pubmed-66882012019-08-14 Chart validation of an algorithm for identifying hereditary progressive muscular dystrophy in healthcare claims Chen, Xiaoxue Agiro, Abiy Martin, Ann S. Lucas, Ann M. Haynes, Kevin BMC Med Res Methodol Research Article BACKGROUND: Muscular dystrophies (MDs) are a group of inherited conditions characterized by progressive muscle degeneration and weakness. The rarity and heterogeneity of the population with MD have hindered therapeutic developments as well as epidemiological and health outcomes research. The objective of the study was to develop and validate a case-finding algorithm utilizing administrative claims data to identify and characterize patients with MD. METHODS: This retrospective cohort study used medical chart validation to evaluate an ICD-9/10 coding algorithm in a large commercial claims database. Patients were identified who had ≥2 office visits with a diagnosis of hereditary progressive MDs from January 1, 2013 through December 31, 2016, were male, and younger than 18 years at the time of first MD diagnosis. Cases who met the algorithm were then validated against medical charts. Diagnoses of MD and specific type (Duchenne, Becker, or other MD) were confirmed by medical chart review by trained reviewers. Positive predictive value (PPV) and 95% confidence intervals (CI) were calculated using a 2 × 2 contingence table. Patient demographic, clinical, and health utilization characteristics were summarized using basic descriptive statistics. RESULTS: Charts were obtained and reviewed for 109 patients who met the algorithm. The PPV of the case-identifying algorithm for MD was 95% (95% CI 88–98%). Of the 103 confirmed MD cases, 87 patients (85%, 95% CI 76–91%) had Duchenne or Becker MD; 76 patients (74%, 95% CI 64–82%) had Duchenne MD, and 11 patients (11%, 95% CI 5–18%) had Becker MD. A total of 74 (67.9%) patients had ≥1 pediatric complex chronic condition (other than neurologic/neuromuscular disease); 54 (49.5%) had cardiovascular conditions; 14 (12.8%) had respiratory conditions; 50 (45.9%) had bone-related issues; 11 (10.1%) had impaired growth; and 6 (5.5%) had puberty delay. CONCLUSIONS: The results of this study demonstrate that the case-finding algorithm accurately identified patients with MD, primarily Duchenne MD, within a large administrative database. The algorithm, which was constructed using a few items easily accessible from claims, can be used to facilitate epidemiological and health outcomes research in the Duchenne patient population. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12874-019-0816-7) contains supplementary material, which is available to authorized users. BioMed Central 2019-08-09 /pmc/articles/PMC6688201/ /pubmed/31399066 http://dx.doi.org/10.1186/s12874-019-0816-7 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Chen, Xiaoxue
Agiro, Abiy
Martin, Ann S.
Lucas, Ann M.
Haynes, Kevin
Chart validation of an algorithm for identifying hereditary progressive muscular dystrophy in healthcare claims
title Chart validation of an algorithm for identifying hereditary progressive muscular dystrophy in healthcare claims
title_full Chart validation of an algorithm for identifying hereditary progressive muscular dystrophy in healthcare claims
title_fullStr Chart validation of an algorithm for identifying hereditary progressive muscular dystrophy in healthcare claims
title_full_unstemmed Chart validation of an algorithm for identifying hereditary progressive muscular dystrophy in healthcare claims
title_short Chart validation of an algorithm for identifying hereditary progressive muscular dystrophy in healthcare claims
title_sort chart validation of an algorithm for identifying hereditary progressive muscular dystrophy in healthcare claims
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6688201/
https://www.ncbi.nlm.nih.gov/pubmed/31399066
http://dx.doi.org/10.1186/s12874-019-0816-7
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