Victor W Zhong1, Jihad S Obeid2, Jean B Craig2, Emily R Pfaff3, Joan Thomas1, Lindsay M Jaacks4, Daniel P Beavers5, Timothy S Carey6, Jean M Lawrence7, Dana Dabelea8, Richard F Hamman8, Deborah A Bowlby9, Catherine Pihoker10, Sharon H Saydah11, Elizabeth J Mayer-Davis12,13. 1. Department of Nutrition, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA. 2. Biomedical Informatics Center, Medical University of South Carolina, Charleston, SC, USA. 3. North Carolina TraCS Institute, University of North Carolina, Chapel Hill, NC, USA. 4. Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA. 5. Department of Biostatistical Sciences, School of Medicine, Wake Forest University, Winston-Salem, NC, USA. 6. Cecil G. Sheps Center for Health Services Research, University of North Carolina, Chapel Hill, NC, USA. 7. Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, CA, USA. 8. Department of Epidemiology, Colorado School of Public Health, University of Colorado, Aurora, CO, USA. 9. Division of Pediatric Endocrinology, Medical University of South Carolina, Charleston, SC, USA. 10. Department of Washington, University of Washington, Seattle, WA, USA. 11. Centers for Disease Control and Prevention, Division of Diabetes Translation, Atlanta, GA, USA. 12. Department of Nutrition, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA ejmayer_davis@unc.edu. 13. Department of Medicine, School of Medicine, University of North Carolina, Chapel Hill, NC, USA.
Abstract
OBJECTIVE: To develop an efficient surveillance approach for childhood diabetes by type across 2 large US health care systems, using phenotyping algorithms derived from electronic health record (EHR) data. MATERIALS AND METHODS: Presumptive diabetes cases <20 years of age from 2 large independent health care systems were identified as those having ≥1 of the 5 indicators in the past 3.5 years, including elevated HbA1c, elevated blood glucose, diabetes-related billing codes, patient problem list, and outpatient anti-diabetic medications. EHRs of all the presumptive cases were manually reviewed, and true diabetes status and diabetes type were determined. Algorithms for identifying diabetes cases overall and classifying diabetes type were either prespecified or derived from classification and regression tree analysis. Surveillance approach was developed based on the best algorithms identified. RESULTS: We developed a stepwise surveillance approach using billing code-based prespecified algorithms and targeted manual EHR review, which efficiently and accurately ascertained and classified diabetes cases by type, in both health care systems. The sensitivity and positive predictive values in both systems were approximately ≥90% for ascertaining diabetes cases overall and classifying cases with type 1 or type 2 diabetes. About 80% of the cases with "other" type were also correctly classified. This stepwise surveillance approach resulted in a >70% reduction in the number of cases requiring manual validation compared to traditional surveillance methods. CONCLUSION: EHR data may be used to establish an efficient approach for large-scale surveillance for childhood diabetes by type, although some manual effort is still needed. Published by Oxford University Press on behalf of the American Medical Informatics Association 2016. This work is written by US Government employees and is in the public domain in the United States.
OBJECTIVE: To develop an efficient surveillance approach for childhood diabetes by type across 2 large US health care systems, using phenotyping algorithms derived from electronic health record (EHR) data. MATERIALS AND METHODS: Presumptive diabetes cases <20 years of age from 2 large independent health care systems were identified as those having ≥1 of the 5 indicators in the past 3.5 years, including elevated HbA1c, elevated blood glucose, diabetes-related billing codes, patient problem list, and outpatient anti-diabetic medications. EHRs of all the presumptive cases were manually reviewed, and true diabetes status and diabetes type were determined. Algorithms for identifying diabetes cases overall and classifying diabetes type were either prespecified or derived from classification and regression tree analysis. Surveillance approach was developed based on the best algorithms identified. RESULTS: We developed a stepwise surveillance approach using billing code-based prespecified algorithms and targeted manual EHR review, which efficiently and accurately ascertained and classified diabetes cases by type, in both health care systems. The sensitivity and positive predictive values in both systems were approximately ≥90% for ascertaining diabetes cases overall and classifying cases with type 1 or type 2 diabetes. About 80% of the cases with "other" type were also correctly classified. This stepwise surveillance approach resulted in a >70% reduction in the number of cases requiring manual validation compared to traditional surveillance methods. CONCLUSION: EHR data may be used to establish an efficient approach for large-scale surveillance for childhood diabetes by type, although some manual effort is still needed. Published by Oxford University Press on behalf of the American Medical Informatics Association 2016. This work is written by US Government employees and is in the public domain in the United States.
Entities:
Keywords:
ascertainment and classification; automated algorithm; childhood diabetes; electronic health records; surveillance
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