Validation of Medicaid Claims-based Diagnosis of Myocardial Infarction Using an HIV Clinical Cohort

Abstract

Background In non-experimental comparative effectiveness research using healthcare databases, outcome measurements must be validated to evaluate and potentially adjust for misclassification bias. We aimed to validate claims-based myocardial infarction algorithms in a Medicaid population using an HIV clinical cohort as the gold standard. Methods Medicaid administrative data were obtained for the years 2002–2008 and linked to the UNC CFAR HIV Clinical Cohort based on social security number, first name and last name and myocardial infarction were adjudicated. Sensitivity, specificity, positive predictive value, and negative predictive value were calculated. Results There were 1,063 individuals included. Over a median observed time of 2.5 years, 17 had a myocardial infarction. Specificity ranged from 0.979–0.993 with the highest specificity obtained using criteria with the ICD-9 code in the primary and secondary position and a length of stay ≥ 3 days. Sensitivity of myocardial infarction ascertainment varied from 0.588–0.824 depending on algorithm. Conclusion: Specificities of varying claims-based myocardial infarction ascertainment criteria are high but small changes impact positive predictive value in a cohort with low incidence. Sensitivities vary based on ascertainment criteria. Type of algorithm used should be prioritized based on study question and maximization of specific validation parameters that will minimize bias while also considering precision.

Types of Myocardial Infarction Among Human Immunodeficiency Virus–Infected Individuals in the United States

“…We therefore ascertained potential MIs using cardiac biomarkers in addition to diagnoses. While diagnoses alone such as from billing data are commonly used for ascertainment, possibly with concomitant verification using other data elements 4,16,37–40 , the sensitivity of this approach is not optimal 41 . We have previously demonstrated that using clinical diagnoses alone results in missing substantial numbers of T2MI in particular 20 .…”

Position matters: Validation of medicare hospital claims for myocardial infarction against medical record review in the atherosclerosis risk in communities study

“…Recent studies examining the ability of International Classification of Diseases, Clinical Modification 9th revision (ICD9) discharge codes to identify MI cases were more likely to report the positive predictive value (PPV) of definitions rather than their sensitivity and specificity. Published studies reporting sensitivity and specificity were either conducted over 10 years ago or used selected populations that may not generalize to the current US population. It is also important to investigate the effect of relaxing the standard restriction of discharge code position in the MI case definition because a validation study of MI in an HIV population showed evidence of higher sensitivity when removing these restrictions …”

“…Published studies reporting sensitivity and specificity were either conducted over 10 years ago or used selected populations that may not generalize to the current US population. It is also important to investigate the effect of relaxing the standard restriction of discharge code position in the MI case definition because a validation study of MI in an HIV population showed evidence of higher sensitivity when removing these restrictions …”

Opioid tolerance and clinically recognized opioid poisoning among patients prescribed extended‐release long‐acting opioids

“…We used validated ICD‐9‐CM codes to identify opioid poisoning events, with prior studies finding that these codes have a specificity of 99.9% and sensitivity of 25%, and are helpful tools to enable population‐level research monitoring overdose rates . In studies estimating relative effects, high outcome specificity is valued over outcome sensitivity, as perfect specificity results in unbiased relative effect estimates even when sensitivity is low, assuming nondifferential sensitivity between exposure groups …”