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Burden of migraine in Finland: multimorbidity and phenotypic disease networks in occupational healthcare

BACKGROUND: Migraine is a complex neurological disorder with high co-existing morbidity burden. The aim of our study was to examine the overall morbidity and phenotypic diseasome for migraine among people of working age using real world data collected as a part of routine clinical practice. METHODS:...

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Autores principales: Korolainen, Minna A., Tuominen, Samuli, Kurki, Samu, Lassenius, Mariann I., Toppila, Iiro, Purmonen, Timo, Santaholma, Jaana, Nissilä, Markku
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Milan 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6995206/
https://www.ncbi.nlm.nih.gov/pubmed/32005102
http://dx.doi.org/10.1186/s10194-020-1077-x
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author Korolainen, Minna A.
Tuominen, Samuli
Kurki, Samu
Lassenius, Mariann I.
Toppila, Iiro
Purmonen, Timo
Santaholma, Jaana
Nissilä, Markku
author_facet Korolainen, Minna A.
Tuominen, Samuli
Kurki, Samu
Lassenius, Mariann I.
Toppila, Iiro
Purmonen, Timo
Santaholma, Jaana
Nissilä, Markku
author_sort Korolainen, Minna A.
collection PubMed
description BACKGROUND: Migraine is a complex neurological disorder with high co-existing morbidity burden. The aim of our study was to examine the overall morbidity and phenotypic diseasome for migraine among people of working age using real world data collected as a part of routine clinical practice. METHODS: Electronic medical records (EMR) of patients with migraine (n = 17,623) and age- and gender matched controls (n = 17,623) were included in this retrospective analysis. EMRs were assessed for the prevalence of ICD-10 codes, those with at least two significant phi correlations, and a prevalence >2.5% in migraine patients were included to phenotypic disease networks (PDN) for further analysis. An automatic subnetwork detection algorithm was applied in order to cluster the diagnoses within the PDNs. The diagnosis-wise connectivity based on the PDNs was compared between migraine patients and controls to assess differences in morbidity patterns. RESULTS: The mean number of diagnoses per patient was increased 1.7-fold in migraine compared to controls. Altogether 1337 different ICD-10 codes were detected in EMRs of migraine patients. Monodiagnosis was present in 1% and 13%, and the median number of diagnoses was 12 and 6 in migraine patients and controls. The number of significant phi-correlations was 2.3-fold increased, and cluster analysis showed more clusters in those with migraine vs. controls (9 vs. 6). For migraine, the PDN was larger and denser and exhibited one large cluster containing fatigue, respiratory, sympathetic nervous system, gastrointestinal, infection, mental and mood disorder diagnoses. Migraine patients were more likely affected by multiple conditions compared to controls, even if no notable differences in morbidity patterns were identified through connectivity measures. Frequencies of ICD-10 codes on a three character and block level were increased across the whole diagnostic spectrum in migraine. CONCLUSIONS: Migraine was associated with an increased multimorbidity, evidenced by multiple different approaches in the study. A systematic increase in the morbidity across the whole spectrum of ICD-10 coded diagnoses, and when interpreting PDNs, were detected in migraine patients. However, no specific diagnoses explained the morbidity. The results reflect clinical praxis, but also undoubtedly, the pathophysiological phenotypes related to migraine, and emphasize the importance of better understanding migraine-related morbidity.
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spelling pubmed-69952062020-02-04 Burden of migraine in Finland: multimorbidity and phenotypic disease networks in occupational healthcare Korolainen, Minna A. Tuominen, Samuli Kurki, Samu Lassenius, Mariann I. Toppila, Iiro Purmonen, Timo Santaholma, Jaana Nissilä, Markku J Headache Pain Research Article BACKGROUND: Migraine is a complex neurological disorder with high co-existing morbidity burden. The aim of our study was to examine the overall morbidity and phenotypic diseasome for migraine among people of working age using real world data collected as a part of routine clinical practice. METHODS: Electronic medical records (EMR) of patients with migraine (n = 17,623) and age- and gender matched controls (n = 17,623) were included in this retrospective analysis. EMRs were assessed for the prevalence of ICD-10 codes, those with at least two significant phi correlations, and a prevalence >2.5% in migraine patients were included to phenotypic disease networks (PDN) for further analysis. An automatic subnetwork detection algorithm was applied in order to cluster the diagnoses within the PDNs. The diagnosis-wise connectivity based on the PDNs was compared between migraine patients and controls to assess differences in morbidity patterns. RESULTS: The mean number of diagnoses per patient was increased 1.7-fold in migraine compared to controls. Altogether 1337 different ICD-10 codes were detected in EMRs of migraine patients. Monodiagnosis was present in 1% and 13%, and the median number of diagnoses was 12 and 6 in migraine patients and controls. The number of significant phi-correlations was 2.3-fold increased, and cluster analysis showed more clusters in those with migraine vs. controls (9 vs. 6). For migraine, the PDN was larger and denser and exhibited one large cluster containing fatigue, respiratory, sympathetic nervous system, gastrointestinal, infection, mental and mood disorder diagnoses. Migraine patients were more likely affected by multiple conditions compared to controls, even if no notable differences in morbidity patterns were identified through connectivity measures. Frequencies of ICD-10 codes on a three character and block level were increased across the whole diagnostic spectrum in migraine. CONCLUSIONS: Migraine was associated with an increased multimorbidity, evidenced by multiple different approaches in the study. A systematic increase in the morbidity across the whole spectrum of ICD-10 coded diagnoses, and when interpreting PDNs, were detected in migraine patients. However, no specific diagnoses explained the morbidity. The results reflect clinical praxis, but also undoubtedly, the pathophysiological phenotypes related to migraine, and emphasize the importance of better understanding migraine-related morbidity. Springer Milan 2020-01-31 /pmc/articles/PMC6995206/ /pubmed/32005102 http://dx.doi.org/10.1186/s10194-020-1077-x Text en © The Author(s). 2020 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.
spellingShingle Research Article
Korolainen, Minna A.
Tuominen, Samuli
Kurki, Samu
Lassenius, Mariann I.
Toppila, Iiro
Purmonen, Timo
Santaholma, Jaana
Nissilä, Markku
Burden of migraine in Finland: multimorbidity and phenotypic disease networks in occupational healthcare
title Burden of migraine in Finland: multimorbidity and phenotypic disease networks in occupational healthcare
title_full Burden of migraine in Finland: multimorbidity and phenotypic disease networks in occupational healthcare
title_fullStr Burden of migraine in Finland: multimorbidity and phenotypic disease networks in occupational healthcare
title_full_unstemmed Burden of migraine in Finland: multimorbidity and phenotypic disease networks in occupational healthcare
title_short Burden of migraine in Finland: multimorbidity and phenotypic disease networks in occupational healthcare
title_sort burden of migraine in finland: multimorbidity and phenotypic disease networks in occupational healthcare
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6995206/
https://www.ncbi.nlm.nih.gov/pubmed/32005102
http://dx.doi.org/10.1186/s10194-020-1077-x
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