{"title":"量化心房颤动的危险因素:一个大型电子患者数据库的回顾性回顾。","authors":"Jaclyn Rivington, Patrick Twohig","doi":"10.4022/jafib.2365","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Despite the numerous comorbidities associated with atrial fibrillation (AF), the relative risk has been varying and not well-documented.</p><p><strong>Aim: </strong>To quantify the risk of diseases associated with AF.</p><p><strong>Methods: </strong>Population-based retrospective analysis in IBM Explorys (1999-2019), an electronic database with over 63 million patients in the United States. Odds ratios were calculated between AF and other diseases. AF patients were also stratified by age, gender, and race to assess trends of AF in different demographic groups.</p><p><strong>Results: </strong>1,812,620 patients had AF in the database. Congestive heart failure had the highest association with AF (OR 42.95). Cardiomyopathy, coronary artery disease, hypertension, and myocardial infarction all had odds greater than 15. Anemia of chronic disease and chronic kidney disease had odds greater than 18, the highest for chronic inflammatory conditions. Other conditions commonly associated with AF were found to have odds less than 8, including hyperthyroidism, alcohol use, and sleep apnea. Helicobacter pylori infection had the lowest odds at 1.98.</p><p><strong>Conclusions: </strong>Epidemiologic information could be integrated with current clinical algorithms to more rapidly identify patients at risk of AF.</p>","PeriodicalId":15072,"journal":{"name":"Journal of atrial fibrillation","volume":"13 3","pages":"2365"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8691333/pdf/jafib-13-02365.pdf","citationCount":"0","resultStr":"{\"title\":\"Quantifying Risk Factors for Atrial Fibrillation: Retrospective Review of a Large Electronic Patient Database.\",\"authors\":\"Jaclyn Rivington, Patrick Twohig\",\"doi\":\"10.4022/jafib.2365\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Despite the numerous comorbidities associated with atrial fibrillation (AF), the relative risk has been varying and not well-documented.</p><p><strong>Aim: </strong>To quantify the risk of diseases associated with AF.</p><p><strong>Methods: </strong>Population-based retrospective analysis in IBM Explorys (1999-2019), an electronic database with over 63 million patients in the United States. Odds ratios were calculated between AF and other diseases. AF patients were also stratified by age, gender, and race to assess trends of AF in different demographic groups.</p><p><strong>Results: </strong>1,812,620 patients had AF in the database. Congestive heart failure had the highest association with AF (OR 42.95). Cardiomyopathy, coronary artery disease, hypertension, and myocardial infarction all had odds greater than 15. Anemia of chronic disease and chronic kidney disease had odds greater than 18, the highest for chronic inflammatory conditions. Other conditions commonly associated with AF were found to have odds less than 8, including hyperthyroidism, alcohol use, and sleep apnea. Helicobacter pylori infection had the lowest odds at 1.98.</p><p><strong>Conclusions: </strong>Epidemiologic information could be integrated with current clinical algorithms to more rapidly identify patients at risk of AF.</p>\",\"PeriodicalId\":15072,\"journal\":{\"name\":\"Journal of atrial fibrillation\",\"volume\":\"13 3\",\"pages\":\"2365\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8691333/pdf/jafib-13-02365.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of atrial fibrillation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4022/jafib.2365\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2020/10/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q3\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of atrial fibrillation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4022/jafib.2365","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2020/10/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"Medicine","Score":null,"Total":0}
Quantifying Risk Factors for Atrial Fibrillation: Retrospective Review of a Large Electronic Patient Database.
Background: Despite the numerous comorbidities associated with atrial fibrillation (AF), the relative risk has been varying and not well-documented.
Aim: To quantify the risk of diseases associated with AF.
Methods: Population-based retrospective analysis in IBM Explorys (1999-2019), an electronic database with over 63 million patients in the United States. Odds ratios were calculated between AF and other diseases. AF patients were also stratified by age, gender, and race to assess trends of AF in different demographic groups.
Results: 1,812,620 patients had AF in the database. Congestive heart failure had the highest association with AF (OR 42.95). Cardiomyopathy, coronary artery disease, hypertension, and myocardial infarction all had odds greater than 15. Anemia of chronic disease and chronic kidney disease had odds greater than 18, the highest for chronic inflammatory conditions. Other conditions commonly associated with AF were found to have odds less than 8, including hyperthyroidism, alcohol use, and sleep apnea. Helicobacter pylori infection had the lowest odds at 1.98.
Conclusions: Epidemiologic information could be integrated with current clinical algorithms to more rapidly identify patients at risk of AF.
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