TY - JOUR
T1 - Use of troponins in the classification of myocardial infarction from electronic health records. The Atherosclerosis Risk in Communities (ARIC) Study
AU - Kucharska-Newton, Anna M.
AU - Loop, Matthew Shane
AU - Bullo, Manuela
AU - Moore, Carlton
AU - Haas, Stephanie W.
AU - Wagenknecht, Lynne
AU - Whitsel, Eric A.
AU - Heiss, Gerardo
N1 - Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2022/2/1
Y1 - 2022/2/1
N2 - 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.
AB - 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.
KW - Algorithmic abstraction
KW - Electronic health records
KW - Myocardial infarction
KW - Troponin
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U2 - 10.1016/j.ijcard.2021.12.022
DO - 10.1016/j.ijcard.2021.12.022
M3 - Article
C2 - 34921902
AN - SCOPUS:85121467735
SN - 0167-5273
VL - 348
SP - 152
EP - 156
JO - International Journal of Cardiology
JF - International Journal of Cardiology
ER -