Anna M Kucharska-Newton1, Matthew Shane Loop2, Manuela Bullo3, Carlton Moore4, Stephanie W Haas5, Lynne Wagenknecht6, Eric A Whitsel7, Gerardo Heiss8. 1. Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America; Department of Epidemiology, College of Public Health, University of Kentucky, Lexington, KY, United States of America. Electronic address: anna_newton@unc.edu. 2. Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America. 3. Hospital Ramos Mejia, Buenos Aires, Argentina. 4. Department of Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America. 5. School of Information and Library Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America. 6. Wake Forest University Population Health Sciences, Winston-Salem, NC, United States of America. 7. Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America; Department of Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America. 8. Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America.
Abstract
OBJECTIVE: Electronic health record (EHR) data are underutilized for abstracting classification criteria for heart disease. We compared extraction of EHR data on troponin I and T levels with human abstraction. METHODS: Using EHR for hospitalizations identified through the Atherosclerosis Risk in Communities (ARIC) Study in four US hospitals, we compared blood levels of troponins I and T extracted from EHR structured data elements with levels obtained through data abstraction by human abstractors to 3 decimal places. Observations were divided randomly 50/50 into training and validation sets. Bayesian multilevel logistic regression models were used to estimate agreement by hospital in first and maximum troponin levels, troponin assessment date, troponin upper limit of normal (ULN), and classification of troponin levels as normal (< ULN), equivocal (1-2× ULN), abnormal (>2× ULN), or missing. RESULTS: Estimated overall agreement in first measured troponin level in the validation data was 88.2% (95% credible interval: 65.0%-97.5%) and 95.5% (91.2-98.2%) for the maximum troponin level observed during hospitalization. The largest variation in probability of agreement was for first troponin measured, which ranged from 66.4% to 95.8% among hospitals. CONCLUSION: Extraction of maximum troponin values during a hospitalization from EHR structured data is feasible and accurate.
OBJECTIVE: Electronic health record (EHR) data are underutilized for abstracting classification criteria for heart disease. We compared extraction of EHR data on troponin I and T levels with human abstraction. METHODS: Using EHR for hospitalizations identified through the Atherosclerosis Risk in Communities (ARIC) Study in four US hospitals, we compared blood levels of troponins I and T extracted from EHR structured data elements with levels obtained through data abstraction by human abstractors to 3 decimal places. Observations were divided randomly 50/50 into training and validation sets. Bayesian multilevel logistic regression models were used to estimate agreement by hospital in first and maximum troponin levels, troponin assessment date, troponin upper limit of normal (ULN), and classification of troponin levels as normal (< ULN), equivocal (1-2× ULN), abnormal (>2× ULN), or missing. RESULTS: Estimated overall agreement in first measured troponin level in the validation data was 88.2% (95% credible interval: 65.0%-97.5%) and 95.5% (91.2-98.2%) for the maximum troponin level observed during hospitalization. The largest variation in probability of agreement was for first troponin measured, which ranged from 66.4% to 95.8% among hospitals. CONCLUSION: Extraction of maximum troponin values during a hospitalization from EHR structured data is feasible and accurate.
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