Assessment of Administrative Data to Identify Acute Myocardial Infarction in Electronic Health Records

Mentz, Robert J.; Newby, L. Kristin; Neely, Ben; Lucas, Joseph E.; Pokorney, Sean D.; Rao, Meena P.; Jackson, Larry R.; Grau‐Sepulveda, Maria V.; Smerek, Michelle; Barth, Pamela; Nelson, Charlotte L.; Pencina, Michael J.; Shah, Bimal R.

doi:10.1016/j.jacc.2016.03.511

Cited by 10 publications

(6 citation statements)

References 6 publications

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“…Second, several studies and reports in literature have demonstrated the weakness in using administrative data for identifying the AMI phenotypes [38,39]. This is mainly because of inconsistent definitions and heterogeneity of data.…”

Section: Discussionmentioning

confidence: 99%

Cannabis Use Is an Independent Predictor for Acute Myocardial Infarction Related Hospitalization in Younger Population

Patel

Manocha

Patel

et al. 2020

Journal of Adolescent Health

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Section: Discussionmentioning

confidence: 99%

Cannabis Use Is an Independent Predictor for Acute Myocardial Infarction Related Hospitalization in Younger Population

Patel

Manocha

Patel

et al. 2020

Journal of Adolescent Health

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“…The selection of which codes from within the administrative dataset were used to denote the occurrence of an MI is also a potential limitation. Several different approaches have been described, 13 and the approach used in this study, when compared against 2 alternative strategies, yielded the best performance. Our study may also be criticized for its reliance on the ICD-9 coding scheme, which was phased out in 2015.…”

Section: Discussionmentioning

confidence: 99%

Postoperative Myocardial Infarction in Administrative Data vs Clinical Registry: A Multi-Institutional Study

Etzioni

Lessow

Bordeianou

et al. 2018

Journal of the American College of Surgeons

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“…These algorithms were adapted from multiple sources. 17–20 Duplicate potential events within a given month were removed.…”

Section: Methodsmentioning

confidence: 99%

Validity of Cardiovascular Data From Electronic Sources

Ahmad¹,

Chan²,

Rosenman³

et al. 2017

Circulation

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Background Understanding the validity of data from electronic data research networks is critical to national research initiatives and learning healthcare systems for cardiovascular care. Our goal was to evaluate the degree of agreement of electronic data research networks compared with data collected by standardized research approaches in a cohort study. Methods We linked individual-level data from The Multi-Ethnic Study of Atherosclerosis (MESA), a community-based cohort, with HealthLNK, a 2006–2012 database of electronic health records (EHRs) from six, Chicago health systems. To evaluate the correlation and agreement of blood pressure (BP) in HealthLNK as compared with in-person MESA examinations, and BMI in HealthLNK compared with MESA, we used Pearson Correlation Coefficients and Bland-Altman plots. Using diagnoses in MESA as the criterion standard, we calculated the performance of HealthLNK for hypertension (HTN), obesity, and diabetes diagnosis using ICD-9 codes and clinical data. We also identified potential myocardial infarctions (MIs), strokes, and heart failure events in HealthLNK and compared them with adjudicated events in MESA. Results Of the 1,164 MESA participants enrolled at the Chicago Field Center, 802 (68.9%) participants had data in HealthLNK. The correlation was low for systolic BP (0.39; P<0.0001). Compared with MESA, HealthLNK overestimated systolic BP by 6.5 mmHg (95%CI: 4.2, 7.8). There was a high correlation between BMI in MESA and HealthLNK (0.94; P<0.0001). HealthLNK underestimated BMI by 0.3 kg/m2 (95%CI: −0.4, −0.1). Using ICD-9 codes and clinical data, the sensitivity and specificity of HealthLNK queries for HTN were 82.4% and 59.4%, for obesity were 73.0% and 89.8%, and for diabetes were 79.8% and 93.3%. Compared with adjudicated CVD events in MESA, the concordance rates for MI, stroke, and heart failure were, respectively, 41.7% (5/12), 61.5% (8/13), and 62.5% (10/16). Conclusions These findings illustrate the limitations and strengths of electronic data repositories compared with information collected by traditional standardized epidemiologic approaches for the ascertainment of CVD risk factors and events.

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Assessment of Administrative Data to Identify Acute Myocardial Infarction in Electronic Health Records

Cited by 10 publications

References 6 publications

Cannabis Use Is an Independent Predictor for Acute Myocardial Infarction Related Hospitalization in Younger Population

Cannabis Use Is an Independent Predictor for Acute Myocardial Infarction Related Hospitalization in Younger Population

Postoperative Myocardial Infarction in Administrative Data vs Clinical Registry: A Multi-Institutional Study

Validity of Cardiovascular Data From Electronic Sources

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