P4343Reconciling acute coronary syndrome diagnoses between linked administrative data and hospital medical records in medical research

Abstract Background/Introduction Administrative data incorporating the International Classification of Diseases 10th Revision (ICD-10) is commonly used in cardiac research. Using patient records, diagnoses are systematically coded by trained coders who have limited/no clinical experience. Therefore, it is important to understand how systematically coded cardiac diagnoses compare with clinically assessed diagnoses to better analyse and interpret studies that have used linked administrative data to adjudicate patient's diagnosis. Purpose To assess the agreement between the acute coronary syndrome (ACS) diagnoses according to linked data compared to those extracted from hospital medical records by clinicians participating in a national registry and determine the factors associated with diagnoses disagreement. Methods The rate of ST-elevation myocardial infarction (STEMI), non-STEMI (NSTEMI) and unstable angina (UA) obtained from the medical records, from admission to discharge, for the nationwide SNAPSHOT ACS audit in 2012 were compared to the corresponding ICD-10 Australian Modification (ICD-10-AM) codes using linked data from 6 jurisdictions covering all Australian states (6) and territories (2). The proportions of the overall agreement (OA), the positive agreement (PA) and the Cohen's weighted kappa and the 95% confidence interval (CI) were derived using both data sources for STEMI, NSTEMI and UA individually, where kappa≥0.8 confers strong agreement and 0.6≤kappa<0.8 moderate agreement. The factors associated with the diagnostic disagreement were explored using multilevel multivariable logistic regression model (backward selection method), accounting for the hospital clustering effect. Results Overall, 3130 patients had both medical records and linked data available for comparison. The degree of agreement was greatest for STEMI and lowest for UA (STEMI: OA=97%, PA=85%, kappa (95% CI)=0.84 (0.81, 0.87); NSTEMI: OA=91%, PA=81%, kappa (95% CI)=0.76 (0.73,0.79); UA: OA = 81%, PA=53%, kappa (95% CI)=0.41 (0.38, 0.45)). Further, the independent factors associated with the disagreement between the medical records and the linked data were the diagnosis of UA (UA vs. STEMI (odds ratio (95% CI)): 6.85 (4.12, 11.40)), not receiving revascularisation (2.27 (1.69, 3.03)), and the state where the ICD-10-AM was coded (p=0.007) (see Figure). Figure 1 Conclusion This study suggests that the agreement between the systematically coded diagnoses from linked administrative data and the diagnosis from the clinical assessment is greater in patients who received revascularisation and worse in those with UA. Also, the degree of agreement varies between states. As the linked data and the ICD codes are being used more often in research to support the evidence-based policies and practice, more attention is needed in testing and improving the accuracy of the ICD-10 codes as well as the ICD-11 codes that are soon to be introduced. Acknowledgement/Funding KH is funded by Heart Foundation Postdoctoral Fellowship. SNAPSHOT data linkage project was funded by the NSW Heart Foundation CVRN Project Grant