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Developing and Validating Risk Scores for Predicting Major Cardiovascular Events Using Population Surveys Linked with Electronic Health Insurance Records
A risk prediction model for major cardiovascular events was developed using population survey data linked to National Health Insurance (NHI) claim data and the death registry. Another set of population survey data were used to validate the model. The model was built using the Nutrition and Health Su...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8835337/ https://www.ncbi.nlm.nih.gov/pubmed/35162342 http://dx.doi.org/10.3390/ijerph19031319 |
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author | Chang, Hsing-Yi Fang, Hsin-Ling Huang, Ching-Yu Chiang, Chi-Yung Chuang, Shao-Yuan Hsu, Chih-Cheng Cheng, Hao-Min Chen, Tzen-Wen Yao, Wei-Cheng Pan, Wen-Harn |
author_facet | Chang, Hsing-Yi Fang, Hsin-Ling Huang, Ching-Yu Chiang, Chi-Yung Chuang, Shao-Yuan Hsu, Chih-Cheng Cheng, Hao-Min Chen, Tzen-Wen Yao, Wei-Cheng Pan, Wen-Harn |
author_sort | Chang, Hsing-Yi |
collection | PubMed |
description | A risk prediction model for major cardiovascular events was developed using population survey data linked to National Health Insurance (NHI) claim data and the death registry. Another set of population survey data were used to validate the model. The model was built using the Nutrition and Health Survey in Taiwan (NAHSIT) collected from 1993–1996 and linked with 10 years of events from NHI data. Major adverse cardiovascular events (MACEs) were identified based on hospital admission or death from coronary heart disease or stroke. The Taiwanese Survey on Hypertension, Hyperglycemia, and Hyperlipidemia (TwSHHH), conducted in 2002 was used for external validation. The NAHSIT data consisted of 1658 men and 1652 women aged 35–70 years. The incidence rates for MACE per 1000 person-years were 13.77 for men and 7.76 for women. These incidence rates for the TwSHHH were 7.27 for men and 3.58 for women. The model had reasonable discrimination (C-indexes: 0.76 for men; 0.75 for women), thus can be used to predict MACE risks in the general population. NHI data can be used to identify disease statuses if the definition and algorithm are clearly defined. Precise preventive health services in Taiwan can be based on this model. |
format | Online Article Text |
id | pubmed-8835337 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-88353372022-02-12 Developing and Validating Risk Scores for Predicting Major Cardiovascular Events Using Population Surveys Linked with Electronic Health Insurance Records Chang, Hsing-Yi Fang, Hsin-Ling Huang, Ching-Yu Chiang, Chi-Yung Chuang, Shao-Yuan Hsu, Chih-Cheng Cheng, Hao-Min Chen, Tzen-Wen Yao, Wei-Cheng Pan, Wen-Harn Int J Environ Res Public Health Article A risk prediction model for major cardiovascular events was developed using population survey data linked to National Health Insurance (NHI) claim data and the death registry. Another set of population survey data were used to validate the model. The model was built using the Nutrition and Health Survey in Taiwan (NAHSIT) collected from 1993–1996 and linked with 10 years of events from NHI data. Major adverse cardiovascular events (MACEs) were identified based on hospital admission or death from coronary heart disease or stroke. The Taiwanese Survey on Hypertension, Hyperglycemia, and Hyperlipidemia (TwSHHH), conducted in 2002 was used for external validation. The NAHSIT data consisted of 1658 men and 1652 women aged 35–70 years. The incidence rates for MACE per 1000 person-years were 13.77 for men and 7.76 for women. These incidence rates for the TwSHHH were 7.27 for men and 3.58 for women. The model had reasonable discrimination (C-indexes: 0.76 for men; 0.75 for women), thus can be used to predict MACE risks in the general population. NHI data can be used to identify disease statuses if the definition and algorithm are clearly defined. Precise preventive health services in Taiwan can be based on this model. MDPI 2022-01-25 /pmc/articles/PMC8835337/ /pubmed/35162342 http://dx.doi.org/10.3390/ijerph19031319 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Chang, Hsing-Yi Fang, Hsin-Ling Huang, Ching-Yu Chiang, Chi-Yung Chuang, Shao-Yuan Hsu, Chih-Cheng Cheng, Hao-Min Chen, Tzen-Wen Yao, Wei-Cheng Pan, Wen-Harn Developing and Validating Risk Scores for Predicting Major Cardiovascular Events Using Population Surveys Linked with Electronic Health Insurance Records |
title | Developing and Validating Risk Scores for Predicting Major Cardiovascular Events Using Population Surveys Linked with Electronic Health Insurance Records |
title_full | Developing and Validating Risk Scores for Predicting Major Cardiovascular Events Using Population Surveys Linked with Electronic Health Insurance Records |
title_fullStr | Developing and Validating Risk Scores for Predicting Major Cardiovascular Events Using Population Surveys Linked with Electronic Health Insurance Records |
title_full_unstemmed | Developing and Validating Risk Scores for Predicting Major Cardiovascular Events Using Population Surveys Linked with Electronic Health Insurance Records |
title_short | Developing and Validating Risk Scores for Predicting Major Cardiovascular Events Using Population Surveys Linked with Electronic Health Insurance Records |
title_sort | developing and validating risk scores for predicting major cardiovascular events using population surveys linked with electronic health insurance records |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8835337/ https://www.ncbi.nlm.nih.gov/pubmed/35162342 http://dx.doi.org/10.3390/ijerph19031319 |
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