利用行政健康和心脏设备登记数据区分一级和二级预防植入式心律转复除颤器

IF 2.5 Q2 CARDIAC & CARDIOVASCULAR SYSTEMS CJC Open Pub Date : 2024-07-01 DOI:10.1016/j.cjco.2024.02.003
Isaac Robinson , Daniel Daly-Grafstein MSc , Mayesha Khan MA , Andrew D. Krahn MD , Nathaniel M. Hawkins MD , Jeffrey R. Brubacher MD , John A. Staples MD, MPH
{"title":"利用行政健康和心脏设备登记数据区分一级和二级预防植入式心律转复除颤器","authors":"Isaac Robinson ,&nbsp;Daniel Daly-Grafstein MSc ,&nbsp;Mayesha Khan MA ,&nbsp;Andrew D. Krahn MD ,&nbsp;Nathaniel M. Hawkins MD ,&nbsp;Jeffrey R. Brubacher MD ,&nbsp;John A. Staples MD, MPH","doi":"10.1016/j.cjco.2024.02.003","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><p>Administrative health data and cardiac device registries can be used to empirically evaluate outcomes and costs after implantable cardioverter defibrillator (ICD) implantation. These datasets often have incomplete information on the indication for implantation (primary vs secondary prevention of sudden cardiac death).</p></div><div><h3>Methods</h3><p>We used 16 years of population-based cardiac device registry and administrative health data from British Columbia, Canada, to derive and internally validate statistical models that predict the likely indication for ICD implantation. We used chart review data as the reference standard for ICD indication in the Cardiac Device Registry database (CDR; 2004-2012 [Cardiac Services BC]) and nonmissing indication as the reference standard in the Heart Information System registry database (HEARTis; 2013-2019 [Cardiac Services BC]). We created 3 logistic regression prediction models in each database: one using only registry data, one using only administrative data, and one using both registry and administrative data. We assessed the predictive performance of each model using standard metrics after optimism correction with 200 bootstrap resamples.</p></div><div><h3>Results</h3><p>Models that used registry data alone demonstrated excellent predictive performance (sensitivity ≥ 89%; specificity ≥ 87%). Models that used only administrative data performed well (sensitivity ≥ 84%; specificity ≥ 70%). Models that used both registry and administrative data showed modest gains over those that used registry data alone (sensitivity ≥ 90%; specificity ≥ 89%).</p></div><div><h3>Conclusions</h3><p>Administrative health data and cardiac device registry data can distinguish secondary prevention ICDs from primary prevention ICDs with acceptable sensitivity and specificity. Imputation of missing ICD indication might make these data resources more useful for research and health system monitoring.</p></div>","PeriodicalId":36924,"journal":{"name":"CJC Open","volume":null,"pages":null},"PeriodicalIF":2.5000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589790X24001318/pdfft?md5=b6f80249de14a72de0fb35d00c943801&pid=1-s2.0-S2589790X24001318-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Distinguishing Primary Prevention From Secondary Prevention Implantable Cardioverter Defibrillators Using Administrative Health and Cardiac Device Registry Data\",\"authors\":\"Isaac Robinson ,&nbsp;Daniel Daly-Grafstein MSc ,&nbsp;Mayesha Khan MA ,&nbsp;Andrew D. Krahn MD ,&nbsp;Nathaniel M. Hawkins MD ,&nbsp;Jeffrey R. Brubacher MD ,&nbsp;John A. Staples MD, MPH\",\"doi\":\"10.1016/j.cjco.2024.02.003\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><p>Administrative health data and cardiac device registries can be used to empirically evaluate outcomes and costs after implantable cardioverter defibrillator (ICD) implantation. These datasets often have incomplete information on the indication for implantation (primary vs secondary prevention of sudden cardiac death).</p></div><div><h3>Methods</h3><p>We used 16 years of population-based cardiac device registry and administrative health data from British Columbia, Canada, to derive and internally validate statistical models that predict the likely indication for ICD implantation. We used chart review data as the reference standard for ICD indication in the Cardiac Device Registry database (CDR; 2004-2012 [Cardiac Services BC]) and nonmissing indication as the reference standard in the Heart Information System registry database (HEARTis; 2013-2019 [Cardiac Services BC]). We created 3 logistic regression prediction models in each database: one using only registry data, one using only administrative data, and one using both registry and administrative data. We assessed the predictive performance of each model using standard metrics after optimism correction with 200 bootstrap resamples.</p></div><div><h3>Results</h3><p>Models that used registry data alone demonstrated excellent predictive performance (sensitivity ≥ 89%; specificity ≥ 87%). Models that used only administrative data performed well (sensitivity ≥ 84%; specificity ≥ 70%). Models that used both registry and administrative data showed modest gains over those that used registry data alone (sensitivity ≥ 90%; specificity ≥ 89%).</p></div><div><h3>Conclusions</h3><p>Administrative health data and cardiac device registry data can distinguish secondary prevention ICDs from primary prevention ICDs with acceptable sensitivity and specificity. Imputation of missing ICD indication might make these data resources more useful for research and health system monitoring.</p></div>\",\"PeriodicalId\":36924,\"journal\":{\"name\":\"CJC Open\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2024-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2589790X24001318/pdfft?md5=b6f80249de14a72de0fb35d00c943801&pid=1-s2.0-S2589790X24001318-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"CJC Open\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2589790X24001318\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CARDIAC & CARDIOVASCULAR SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"CJC Open","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2589790X24001318","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CARDIAC & CARDIOVASCULAR SYSTEMS","Score":null,"Total":0}
引用次数: 0

摘要

背景行政健康数据和心脏设备登记可用于对植入式心律转复除颤器(ICD)植入后的疗效和成本进行经验性评估。我们利用加拿大不列颠哥伦比亚省 16 年的基于人口的心脏设备登记和行政健康数据,推导并在内部验证了预测 ICD 植入可能适应症的统计模型。我们使用病历审查数据作为心脏设备登记数据库(CDR;2004-2012 年 [Cardiac Services BC])中 ICD 适应症的参考标准,并使用心脏信息系统登记数据库(HEARTis;2013-2019 年 [Cardiac Services BC])中的非遗漏适应症作为参考标准。我们在每个数据库中创建了 3 个逻辑回归预测模型:一个仅使用登记数据,一个仅使用管理数据,一个同时使用登记数据和管理数据。结果仅使用登记处数据的模型表现出卓越的预测性能(灵敏度≥ 89%;特异性≥ 87%)。仅使用行政数据的模型表现良好(灵敏度≥ 84%;特异度≥ 70%)。结论行政健康数据和心脏设备登记数据能以可接受的灵敏度和特异性区分二级预防 ICD 和一级预防 ICD。对缺失的 ICD 适应症进行估算可能会使这些数据资源在研究和卫生系统监测方面更加有用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Distinguishing Primary Prevention From Secondary Prevention Implantable Cardioverter Defibrillators Using Administrative Health and Cardiac Device Registry Data

Background

Administrative health data and cardiac device registries can be used to empirically evaluate outcomes and costs after implantable cardioverter defibrillator (ICD) implantation. These datasets often have incomplete information on the indication for implantation (primary vs secondary prevention of sudden cardiac death).

Methods

We used 16 years of population-based cardiac device registry and administrative health data from British Columbia, Canada, to derive and internally validate statistical models that predict the likely indication for ICD implantation. We used chart review data as the reference standard for ICD indication in the Cardiac Device Registry database (CDR; 2004-2012 [Cardiac Services BC]) and nonmissing indication as the reference standard in the Heart Information System registry database (HEARTis; 2013-2019 [Cardiac Services BC]). We created 3 logistic regression prediction models in each database: one using only registry data, one using only administrative data, and one using both registry and administrative data. We assessed the predictive performance of each model using standard metrics after optimism correction with 200 bootstrap resamples.

Results

Models that used registry data alone demonstrated excellent predictive performance (sensitivity ≥ 89%; specificity ≥ 87%). Models that used only administrative data performed well (sensitivity ≥ 84%; specificity ≥ 70%). Models that used both registry and administrative data showed modest gains over those that used registry data alone (sensitivity ≥ 90%; specificity ≥ 89%).

Conclusions

Administrative health data and cardiac device registry data can distinguish secondary prevention ICDs from primary prevention ICDs with acceptable sensitivity and specificity. Imputation of missing ICD indication might make these data resources more useful for research and health system monitoring.

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
CJC Open
CJC Open Medicine-Cardiology and Cardiovascular Medicine
CiteScore
3.30
自引率
0.00%
发文量
143
审稿时长
60 days
期刊最新文献
Cardiac Papillary Fibroelastoma and Cerebrovascular Events: A Systematic Review Late-Onset Mitral Valve Prosthesis Dehiscence With Severe Paravalvular Leak—Infectious Versus Noninfectious Etiology Dilemma: A Case Report Assessing the Safety of Early Repatriation for Stable ST-Segment Elevation Myocardial Infarction Patients After Primary Percutaneous Coronary Intervention Coronary Sinus Reducer Improves Myocardial Perfusion in a Patient With Angina, Hypertrophic Cardiomyopathy, and Coronary Microvascular Disease Fractured and Entrapped Coronary Angioplasty Balloon Successfully Managed with Rotational Atherectomy
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1