退伍军人医疗保健系统临床数据库中的晚期慢性肾病电子表型:基于系统的模型开发和评估策略。

IF 1.9 Q3 MEDICINE, RESEARCH & EXPERIMENTAL
Gajapathiraju Chamarthi, Tatiana Orozco, Popy Shell, Devin Fu, Jennifer Hale-Gallardo, Huanguang Jia, Ashutosh M Shukla
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引用次数: 0

摘要

背景:在临床数据库中识别晚期(第 4 期和第 5 期)慢性肾脏病(CKD)组群非常复杂,而且往往不可靠。准确识别这些患者可以满足这些人群的特殊临床和研究需求:本研究是一项基于系统的策略,旨在识别所有患有晚期慢性肾脏病的退伍军人,以便随后将其纳入临床试验。我们的目的是在电子健康记录(EHR)数据库中检查常规使用的诊断代码和基于估计肾小球滤过率(eGFR)的晚期 CKD 表型的普遍性和准确性。我们试图开发一种实用的电子病历表型,以改善地区退伍军人医疗保健系统对晚期 CKD 队列的实时识别:利用退伍军人事务信息学和计算基础设施服务,我们根据最近 12 个月内 eGFR 值≤30 ml-min-1-1.73 m-2 或现有国际疾病分类 (ICD)-10 中晚期 CKD 诊断代码(N18.4 和 N18.5)的组合,提取了晚期 CKD 退伍军人的源队列。我们使用之前公布的各种 EHR 表型(即晚期 CKD 诊断代码、使用最新的单个 eGFR -1-1.73 m-2、使用两个 eGFR 值)以及我们对高风险、中风险和低风险晚期 CKD 队列的操作性 EHR 表型估算了晚期 CKD 的患病率。我们通过检查 6 个月随访期间 eGFR -1-1.73 m-2 持续下降的可能性来评估这些表型的准确性:在北佛罗里达州/南乔治亚州退伍军人医疗系统(NF/SG VHS)的 133756 名在职退伍军人中,我们发现了 1759 名患有晚期非透析性慢性肾功能衰竭的退伍军人。其中,1102 名退伍军人(62.9%)拥有晚期 CKD 诊断代码;1391 名退伍军人(79.1%)的 eGFR 指数为 -1-1.73 m-2;928 名退伍军人(52.7%)、480 名退伍军人(27.2%)和 315 名退伍军人(17.9%)分别拥有高风险、中风险和低风险晚期 CKD。根据 EHR 表型的不同,NF/SG VHS 退伍军人的晚期 CKD 患病率介于 1% 和 1.5% 之间。在 6 个月的随访中,根据 ICD-10 编码定义的退伍军人处于晚期 CKD 阶段的概率为 65.3%,而根据 eGFR 值定义的退伍军人处于晚期 CKD 阶段的概率为 90%。根据我们的表型,94.2% 的高危人群、71% 的中危人群和 16.1% 的低危人群仍处于晚期 CKD 类别:结论:虽然晚期 CKD 的患病率在不同 EHR 表型之间的差异有限,但通过分层使用两个 eGFR 值可以提高准确性。我们报告了一个基于电子病历的实用模型的开发情况,该模型可在地区退伍军人医疗保健系统中实时识别晚期 CKD,其分层方法可满足有进展至终末期肾病风险的人群的需求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Electronic Phenotype for Advanced Chronic Kidney Disease in a Veteran Health Care System Clinical Database: Systems-Based Strategy for Model Development and Evaluation.

Electronic Phenotype for Advanced Chronic Kidney Disease in a Veteran Health Care System Clinical Database: Systems-Based Strategy for Model Development and Evaluation.

Electronic Phenotype for Advanced Chronic Kidney Disease in a Veteran Health Care System Clinical Database: Systems-Based Strategy for Model Development and Evaluation.

Background: Identifying advanced (stages 4 and 5) chronic kidney disease (CKD) cohorts in clinical databases is complicated and often unreliable. Accurately identifying these patients can allow targeting this population for their specialized clinical and research needs.

Objective: This study was conducted as a system-based strategy to identify all prevalent Veterans with advanced CKD for subsequent enrollment in a clinical trial. We aimed to examine the prevalence and accuracy of conventionally used diagnosis codes and estimated glomerular filtration rate (eGFR)-based phenotypes for advanced CKD in an electronic health record (EHR) database. We sought to develop a pragmatic EHR phenotype capable of improving the real-time identification of advanced CKD cohorts in a regional Veterans health care system.

Methods: Using the Veterans Affairs Informatics and Computing Infrastructure services, we extracted the source cohort of Veterans with advanced CKD based on a combination of the latest eGFR value ≤30 ml·min-1·1.73 m-2 or existing International Classification of Diseases (ICD)-10 diagnosis codes for advanced CKD (N18.4 and N18.5) in the last 12 months. We estimated the prevalence of advanced CKD using various prior published EHR phenotypes (ie, advanced CKD diagnosis codes, using the latest single eGFR <30 ml·min-1·1.73 m-2, utilizing two eGFR values) and our operational EHR phenotypes of a high-, intermediate-, and low-risk advanced CKD cohort. We evaluated the accuracy of these phenotypes by examining the likelihood of a sustained reduction of eGFR <30 ml·min-1·1.73 m-2 over a 6-month follow-up period.

Results: Of the 133,756 active Veteran enrollees at North Florida/South Georgia Veterans Health System (NF/SG VHS), we identified a source cohort of 1759 Veterans with advanced nondialysis CKD. Among these, 1102 (62.9%) Veterans had diagnosis codes for advanced CKD; 1391(79.1%) had the index eGFR <30 ml·min-1·1.73 m-2; and 928 (52.7%), 480 (27.2%), and 315 (17.9%) Veterans had high-, intermediate-, and low-risk advanced CKD, respectively. The prevalence of advanced CKD among Veterans at NF/SG VHS varied between 1% and 1.5% depending on the EHR phenotype. At the 6-month follow-up, the probability of Veterans remaining in the advanced CKD stage was 65.3% in the group defined by the ICD-10 codes and 90% in the groups defined by eGFR values. Based on our phenotype, 94.2% of high-risk, 71% of intermediate-risk, and 16.1% of low-risk groups remained in the advanced CKD category.

Conclusions: While the prevalence of advanced CKD has limited variation between different EHR phenotypes, the accuracy can be improved by utilizing two eGFR values in a stratified manner. We report the development of a pragmatic EHR-based model to identify advanced CKD within a regional Veterans health care system in real time with a tiered approach that allows targeting the needs of the groups at risk of progression to end-stage kidney disease.

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来源期刊
Interactive Journal of Medical Research
Interactive Journal of Medical Research MEDICINE, RESEARCH & EXPERIMENTAL-
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