率先在全国汇总的电子健康记录上进行动脉高血压表型分析

Jing Wei Neo, Qihuang Xie, Pei San Ang, Hui Xing Tan, Belinda Foo, Yen Ling Koon, A. Ng, S. Tan, D. Teo, Mun Yee Tham, A. Yap, N. Ng, C. Loke, Li Fung Peck, Huilin Huang, S. Dorajoo
{"title":"率先在全国汇总的电子健康记录上进行动脉高血压表型分析","authors":"Jing Wei Neo, Qihuang Xie, Pei San Ang, Hui Xing Tan, Belinda Foo, Yen Ling Koon, A. Ng, S. Tan, D. Teo, Mun Yee Tham, A. Yap, N. Ng, C. Loke, Li Fung Peck, Huilin Huang, S. Dorajoo","doi":"10.3390/pharma3010010","DOIUrl":null,"url":null,"abstract":"Background: Hypertension is frequently studied in epidemiological studies that have been conducted using retrospective observational data, either as an outcome or a variable. However, there are few validation studies investigating the accuracy of hypertension phenotyping algorithms in aggregated electronic health record (EHR) data. Methods: Utilizing a centralized repository of inpatient EHR data from Singapore for the period of 2019–2020, a new algorithm that incorporates both diagnostic codes and medication details (Diag+Med) was devised. This algorithm was intended to supplement and improve the diagnostic code-only model (Diag-Only) for the classification of hypertension. We computed various metrics (sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV)) to assess the algorithm’s effectiveness in identifying hypertension on 2813 chart-reviewed records. This pool was composed of two patient cohorts: a random sampling of all inpatient admissions (Random Cohort) and a targeted group with atrial fibrillation diagnoses (AF Cohort). Results: The Diag+Med algorithm was more sensitive at detecting hypertension patients in both cohorts compared to the Diag-Only algorithm (83.8 and 87.6% vs. 68.2 and 66.5% in the Random and AF Cohorts, respectively). These improvements in sensitivity came at minimal costs in terms of PPV reductions (88.2 and 90.3% vs. 91.4 and 94.2%, respectively). Conclusion: The combined use of diagnosis codes and specific antihypertension medication exposure patterns facilitates a more accurate capture of patients with hypertension in a database of aggregated EHRs from diverse healthcare institutions in Singapore. The results presented here allow for the bias correction of risk estimates derived from observational studies involving hypertension.","PeriodicalId":74431,"journal":{"name":"Pharmacoepidemiology","volume":"40 S18","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Pioneering Arterial Hypertension Phenotyping on Nationally Aggregated Electronic Health Records\",\"authors\":\"Jing Wei Neo, Qihuang Xie, Pei San Ang, Hui Xing Tan, Belinda Foo, Yen Ling Koon, A. Ng, S. Tan, D. Teo, Mun Yee Tham, A. Yap, N. Ng, C. Loke, Li Fung Peck, Huilin Huang, S. Dorajoo\",\"doi\":\"10.3390/pharma3010010\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Background: Hypertension is frequently studied in epidemiological studies that have been conducted using retrospective observational data, either as an outcome or a variable. However, there are few validation studies investigating the accuracy of hypertension phenotyping algorithms in aggregated electronic health record (EHR) data. Methods: Utilizing a centralized repository of inpatient EHR data from Singapore for the period of 2019–2020, a new algorithm that incorporates both diagnostic codes and medication details (Diag+Med) was devised. This algorithm was intended to supplement and improve the diagnostic code-only model (Diag-Only) for the classification of hypertension. We computed various metrics (sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV)) to assess the algorithm’s effectiveness in identifying hypertension on 2813 chart-reviewed records. This pool was composed of two patient cohorts: a random sampling of all inpatient admissions (Random Cohort) and a targeted group with atrial fibrillation diagnoses (AF Cohort). Results: The Diag+Med algorithm was more sensitive at detecting hypertension patients in both cohorts compared to the Diag-Only algorithm (83.8 and 87.6% vs. 68.2 and 66.5% in the Random and AF Cohorts, respectively). These improvements in sensitivity came at minimal costs in terms of PPV reductions (88.2 and 90.3% vs. 91.4 and 94.2%, respectively). Conclusion: The combined use of diagnosis codes and specific antihypertension medication exposure patterns facilitates a more accurate capture of patients with hypertension in a database of aggregated EHRs from diverse healthcare institutions in Singapore. The results presented here allow for the bias correction of risk estimates derived from observational studies involving hypertension.\",\"PeriodicalId\":74431,\"journal\":{\"name\":\"Pharmacoepidemiology\",\"volume\":\"40 S18\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-03-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Pharmacoepidemiology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3390/pharma3010010\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pharmacoepidemiology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/pharma3010010","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

背景:在使用回顾性观察数据进行的流行病学研究中,高血压经常被作为结果或变量进行研究。然而,很少有验证性研究调查电子健康记录(EHR)汇总数据中高血压表型算法的准确性。研究方法利用新加坡 2019-2020 年住院病人电子病历数据的集中存储库,设计了一种包含诊断代码和用药详情(Diag+Med)的新算法。该算法旨在补充和改进高血压分类的纯诊断代码模型(Diag-Only)。我们计算了各种指标(灵敏度、特异性、阳性预测值 (PPV) 和阴性预测值 (NPV)),以评估该算法在 2813 份病历审查记录中识别高血压的有效性。该数据库由两个患者队列组成:所有住院病人的随机抽样(随机队列)和诊断为心房颤动的目标群体(心房颤动队列)。结果:与单纯诊断算法相比,Diag+Med 算法在两个队列中检测高血压患者的灵敏度更高(随机队列和房颤队列中的灵敏度分别为 83.8% 和 87.6% 对 68.2% 和 66.5%)。灵敏度提高的同时,PPV 的降低幅度却很小(分别为 88.2% 和 90.3% 对 91.4% 和 94.2%)。结论结合使用诊断代码和特定的抗高血压药物接触模式,有助于在新加坡不同医疗机构的电子病历汇总数据库中更准确地捕捉高血压患者。本文介绍的结果可以对高血压观察性研究得出的风险估计值进行偏差校正。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Pioneering Arterial Hypertension Phenotyping on Nationally Aggregated Electronic Health Records
Background: Hypertension is frequently studied in epidemiological studies that have been conducted using retrospective observational data, either as an outcome or a variable. However, there are few validation studies investigating the accuracy of hypertension phenotyping algorithms in aggregated electronic health record (EHR) data. Methods: Utilizing a centralized repository of inpatient EHR data from Singapore for the period of 2019–2020, a new algorithm that incorporates both diagnostic codes and medication details (Diag+Med) was devised. This algorithm was intended to supplement and improve the diagnostic code-only model (Diag-Only) for the classification of hypertension. We computed various metrics (sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV)) to assess the algorithm’s effectiveness in identifying hypertension on 2813 chart-reviewed records. This pool was composed of two patient cohorts: a random sampling of all inpatient admissions (Random Cohort) and a targeted group with atrial fibrillation diagnoses (AF Cohort). Results: The Diag+Med algorithm was more sensitive at detecting hypertension patients in both cohorts compared to the Diag-Only algorithm (83.8 and 87.6% vs. 68.2 and 66.5% in the Random and AF Cohorts, respectively). These improvements in sensitivity came at minimal costs in terms of PPV reductions (88.2 and 90.3% vs. 91.4 and 94.2%, respectively). Conclusion: The combined use of diagnosis codes and specific antihypertension medication exposure patterns facilitates a more accurate capture of patients with hypertension in a database of aggregated EHRs from diverse healthcare institutions in Singapore. The results presented here allow for the bias correction of risk estimates derived from observational studies involving hypertension.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信