Electronic Phenotype for Advanced Chronic Kidney Disease in a Veteran Health Care System Clinical Database: Systems-Based Strategy for Model Development and Evaluation
BackgroundIdentifying 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....
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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JMIR Publications
2023-07-01
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| Series: | Interactive Journal of Medical Research |
| Online Access: | https://www.i-jmr.org/2023/1/e43384 |
| _version_ | 1797733997607911424 |
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| author | Gajapathiraju Chamarthi Tatiana Orozco Popy Shell Devin Fu Jennifer Hale-Gallardo Huanguang Jia Ashutosh M Shukla |
| author_facet | Gajapathiraju Chamarthi Tatiana Orozco Popy Shell Devin Fu Jennifer Hale-Gallardo Huanguang Jia Ashutosh M Shukla |
| author_sort | Gajapathiraju Chamarthi |
| collection | DOAJ |
| description |
BackgroundIdentifying 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.
ObjectiveThis 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.
MethodsUsing 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.
ResultsOf 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.
ConclusionsWhile 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. |
| first_indexed | 2024-03-12T12:37:50Z |
| format | Article |
| id | doaj.art-e6195aa8abb04236b8ce3922a0d94830 |
| institution | Directory Open Access Journal |
| issn | 1929-073X |
| language | English |
| last_indexed | 2024-03-12T12:37:50Z |
| publishDate | 2023-07-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | Interactive Journal of Medical Research |
| spelling | doaj.art-e6195aa8abb04236b8ce3922a0d948302023-08-29T00:06:27ZengJMIR PublicationsInteractive Journal of Medical Research1929-073X2023-07-0112e4338410.2196/43384Electronic Phenotype for Advanced Chronic Kidney Disease in a Veteran Health Care System Clinical Database: Systems-Based Strategy for Model Development and EvaluationGajapathiraju Chamarthihttps://orcid.org/0000-0001-5304-4718Tatiana Orozcohttps://orcid.org/0000-0001-9618-6927Popy Shellhttps://orcid.org/0000-0002-1519-4632Devin Fuhttps://orcid.org/0000-0003-0419-7675Jennifer Hale-Gallardohttps://orcid.org/0000-0002-5536-3066Huanguang Jiahttps://orcid.org/0009-0006-8534-951XAshutosh M Shuklahttps://orcid.org/0000-0002-1471-0324 BackgroundIdentifying 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. ObjectiveThis 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. MethodsUsing 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. ResultsOf 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. ConclusionsWhile 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.https://www.i-jmr.org/2023/1/e43384 |
| spellingShingle | Gajapathiraju Chamarthi Tatiana Orozco Popy Shell Devin Fu Jennifer Hale-Gallardo Huanguang Jia Ashutosh M Shukla Electronic Phenotype for Advanced Chronic Kidney Disease in a Veteran Health Care System Clinical Database: Systems-Based Strategy for Model Development and Evaluation Interactive Journal of Medical Research |
| title | Electronic Phenotype for Advanced Chronic Kidney Disease in a Veteran Health Care System Clinical Database: Systems-Based Strategy for Model Development and Evaluation |
| title_full | Electronic Phenotype for Advanced Chronic Kidney Disease in a Veteran Health Care System Clinical Database: Systems-Based Strategy for Model Development and Evaluation |
| title_fullStr | Electronic Phenotype for Advanced Chronic Kidney Disease in a Veteran Health Care System Clinical Database: Systems-Based Strategy for Model Development and Evaluation |
| title_full_unstemmed | Electronic Phenotype for Advanced Chronic Kidney Disease in a Veteran Health Care System Clinical Database: Systems-Based Strategy for Model Development and Evaluation |
| title_short | Electronic Phenotype for Advanced Chronic Kidney Disease in a Veteran Health Care System Clinical Database: Systems-Based Strategy for Model Development and Evaluation |
| title_sort | electronic phenotype for advanced chronic kidney disease in a veteran health care system clinical database systems based strategy for model development and evaluation |
| url | https://www.i-jmr.org/2023/1/e43384 |
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