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Identifying primary care patients at risk for future diabetes and cardiovascular disease using electronic health records
BACKGROUND: Prevention of diabetes and coronary heart disease (CHD) is possible but identification of at-risk patients for targeting interventions is a challenge in primary care. METHODS: We analyzed electronic health record (EHR) data for 122,715 patients from 12 primary care practices. We defined...
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2009
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2753330/ https://www.ncbi.nlm.nih.gov/pubmed/19772639 http://dx.doi.org/10.1186/1472-6963-9-170 |
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author | Hivert, Marie-France Grant, Richard W Shrader, Peter Meigs, James B |
author_facet | Hivert, Marie-France Grant, Richard W Shrader, Peter Meigs, James B |
author_sort | Hivert, Marie-France |
collection | PubMed |
description | BACKGROUND: Prevention of diabetes and coronary heart disease (CHD) is possible but identification of at-risk patients for targeting interventions is a challenge in primary care. METHODS: We analyzed electronic health record (EHR) data for 122,715 patients from 12 primary care practices. We defined patients with risk factor clustering using metabolic syndrome (MetS) characteristics defined by NCEP-ATPIII criteria; if missing, we used surrogate characteristics, and validated this approach by directly measuring risk factors in a subset of 154 patients. For subjects with at least 3 of 5 MetS criteria measured at baseline (2003-2004), we defined 3 categories: No MetS (0 criteria); At-risk-for MetS (1-2 criteria); and MetS (≥ 3 criteria). We examined new diabetes and CHD incidence, and resource utilization over the subsequent 3-year period (2005-2007) using age-sex-adjusted regression models to compare outcomes by MetS category. RESULTS: After excluding patients with diabetes/CHD at baseline, 78,293 patients were eligible for analysis. EHR-defined MetS had 73% sensitivity and 91% specificity for directly measured MetS. Diabetes incidence was 1.4% in No MetS; 4.0% in At-risk-for MetS; and 11.0% in MetS (p < 0.0001 for trend; adjusted OR MetS vs No MetS = 6.86 [6.06-7.76]); CHD incidence was 3.2%, 5.3%, and 6.4% respectively (p < 0.0001 for trend; adjusted OR = 1.42 [1.25-1.62]). Costs and resource utilization increased across categories (p < 0.0001 for trends). Results were similar analyzing individuals with all five criteria not missing, or defining MetS as ≥ 2 criteria present. CONCLUSION: Risk factor clustering in EHR data identifies primary care patients at increased risk for new diabetes, CHD and higher resource utilization. |
format | Text |
id | pubmed-2753330 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2009 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-27533302009-09-29 Identifying primary care patients at risk for future diabetes and cardiovascular disease using electronic health records Hivert, Marie-France Grant, Richard W Shrader, Peter Meigs, James B BMC Health Serv Res Research Article BACKGROUND: Prevention of diabetes and coronary heart disease (CHD) is possible but identification of at-risk patients for targeting interventions is a challenge in primary care. METHODS: We analyzed electronic health record (EHR) data for 122,715 patients from 12 primary care practices. We defined patients with risk factor clustering using metabolic syndrome (MetS) characteristics defined by NCEP-ATPIII criteria; if missing, we used surrogate characteristics, and validated this approach by directly measuring risk factors in a subset of 154 patients. For subjects with at least 3 of 5 MetS criteria measured at baseline (2003-2004), we defined 3 categories: No MetS (0 criteria); At-risk-for MetS (1-2 criteria); and MetS (≥ 3 criteria). We examined new diabetes and CHD incidence, and resource utilization over the subsequent 3-year period (2005-2007) using age-sex-adjusted regression models to compare outcomes by MetS category. RESULTS: After excluding patients with diabetes/CHD at baseline, 78,293 patients were eligible for analysis. EHR-defined MetS had 73% sensitivity and 91% specificity for directly measured MetS. Diabetes incidence was 1.4% in No MetS; 4.0% in At-risk-for MetS; and 11.0% in MetS (p < 0.0001 for trend; adjusted OR MetS vs No MetS = 6.86 [6.06-7.76]); CHD incidence was 3.2%, 5.3%, and 6.4% respectively (p < 0.0001 for trend; adjusted OR = 1.42 [1.25-1.62]). Costs and resource utilization increased across categories (p < 0.0001 for trends). Results were similar analyzing individuals with all five criteria not missing, or defining MetS as ≥ 2 criteria present. CONCLUSION: Risk factor clustering in EHR data identifies primary care patients at increased risk for new diabetes, CHD and higher resource utilization. BioMed Central 2009-09-22 /pmc/articles/PMC2753330/ /pubmed/19772639 http://dx.doi.org/10.1186/1472-6963-9-170 Text en Copyright © 2009 Hivert et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Hivert, Marie-France Grant, Richard W Shrader, Peter Meigs, James B Identifying primary care patients at risk for future diabetes and cardiovascular disease using electronic health records |
title | Identifying primary care patients at risk for future diabetes and cardiovascular disease using electronic health records |
title_full | Identifying primary care patients at risk for future diabetes and cardiovascular disease using electronic health records |
title_fullStr | Identifying primary care patients at risk for future diabetes and cardiovascular disease using electronic health records |
title_full_unstemmed | Identifying primary care patients at risk for future diabetes and cardiovascular disease using electronic health records |
title_short | Identifying primary care patients at risk for future diabetes and cardiovascular disease using electronic health records |
title_sort | identifying primary care patients at risk for future diabetes and cardiovascular disease using electronic health records |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2753330/ https://www.ncbi.nlm.nih.gov/pubmed/19772639 http://dx.doi.org/10.1186/1472-6963-9-170 |
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