Cargando…
Progression to myocardial infarction short-term death based on interval sequential pattern mining
BACKGROUND: Myocardial infarction (MI) is one of the significant cardiovascular diseases (CVDs). According to Taiwanese health record analysis, the hazard rate reaches a peak in the initial year after diagnosis of MI, drops to a relatively low value, and maintains stable for the following years. The...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10416354/ https://www.ncbi.nlm.nih.gov/pubmed/37563547 http://dx.doi.org/10.1186/s12872-023-03393-7 |
_version_ | 1785087753870901248 |
---|---|
author | Wu, Yang-Sheng Taniar, David Adhinugraha, Kiki Wang, Chao-Hung Pai, Tun-Wen |
author_facet | Wu, Yang-Sheng Taniar, David Adhinugraha, Kiki Wang, Chao-Hung Pai, Tun-Wen |
author_sort | Wu, Yang-Sheng |
collection | PubMed |
description | BACKGROUND: Myocardial infarction (MI) is one of the significant cardiovascular diseases (CVDs). According to Taiwanese health record analysis, the hazard rate reaches a peak in the initial year after diagnosis of MI, drops to a relatively low value, and maintains stable for the following years. Therefore, identifying suspicious comorbidity patterns of short-term death before the diagnosis may help achieve prolonged survival for MI patients. METHODS: Interval sequential pattern mining was applied with odds ratio to the hospitalization records from the Taiwan National Health Insurance Research Database to evaluate the disease progression and identify potential subjects at the earliest possible stage. RESULTS: Our analysis resulted in five disease pathways, including “diabetes mellitus,” “other disorders of the urethra and urinary tract,” “essential hypertension,” “hypertensive heart disease,” and “other forms of chronic ischemic heart disease” that led to short-term death after MI diagnosis, and these pathways covered half of the cohort. CONCLUSION: We explored the possibility of establishing trajectory patterns to identify the high-risk population of early mortality after MI. |
format | Online Article Text |
id | pubmed-10416354 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-104163542023-08-12 Progression to myocardial infarction short-term death based on interval sequential pattern mining Wu, Yang-Sheng Taniar, David Adhinugraha, Kiki Wang, Chao-Hung Pai, Tun-Wen BMC Cardiovasc Disord Research BACKGROUND: Myocardial infarction (MI) is one of the significant cardiovascular diseases (CVDs). According to Taiwanese health record analysis, the hazard rate reaches a peak in the initial year after diagnosis of MI, drops to a relatively low value, and maintains stable for the following years. Therefore, identifying suspicious comorbidity patterns of short-term death before the diagnosis may help achieve prolonged survival for MI patients. METHODS: Interval sequential pattern mining was applied with odds ratio to the hospitalization records from the Taiwan National Health Insurance Research Database to evaluate the disease progression and identify potential subjects at the earliest possible stage. RESULTS: Our analysis resulted in five disease pathways, including “diabetes mellitus,” “other disorders of the urethra and urinary tract,” “essential hypertension,” “hypertensive heart disease,” and “other forms of chronic ischemic heart disease” that led to short-term death after MI diagnosis, and these pathways covered half of the cohort. CONCLUSION: We explored the possibility of establishing trajectory patterns to identify the high-risk population of early mortality after MI. BioMed Central 2023-08-10 /pmc/articles/PMC10416354/ /pubmed/37563547 http://dx.doi.org/10.1186/s12872-023-03393-7 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Wu, Yang-Sheng Taniar, David Adhinugraha, Kiki Wang, Chao-Hung Pai, Tun-Wen Progression to myocardial infarction short-term death based on interval sequential pattern mining |
title | Progression to myocardial infarction short-term death based on interval sequential pattern mining |
title_full | Progression to myocardial infarction short-term death based on interval sequential pattern mining |
title_fullStr | Progression to myocardial infarction short-term death based on interval sequential pattern mining |
title_full_unstemmed | Progression to myocardial infarction short-term death based on interval sequential pattern mining |
title_short | Progression to myocardial infarction short-term death based on interval sequential pattern mining |
title_sort | progression to myocardial infarction short-term death based on interval sequential pattern mining |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10416354/ https://www.ncbi.nlm.nih.gov/pubmed/37563547 http://dx.doi.org/10.1186/s12872-023-03393-7 |
work_keys_str_mv | AT wuyangsheng progressiontomyocardialinfarctionshorttermdeathbasedonintervalsequentialpatternmining AT taniardavid progressiontomyocardialinfarctionshorttermdeathbasedonintervalsequentialpatternmining AT adhinugrahakiki progressiontomyocardialinfarctionshorttermdeathbasedonintervalsequentialpatternmining AT wangchaohung progressiontomyocardialinfarctionshorttermdeathbasedonintervalsequentialpatternmining AT paitunwen progressiontomyocardialinfarctionshorttermdeathbasedonintervalsequentialpatternmining |