Diagnostic accuracy, clinical characteristics, and prognostic differences of patients with acute myocarditis according to inclusion criteria
Roman Roy, Antonio Cannatà, Mohammad Al-Agil, Emma Ferone, António Jordán, Brian To-Dang, Matthew Sadler, Aamir Shamsi, Mohammad Albarjas, Susan Piper, Mauro Giacca, Ajay M. Shah, Theresa A. McDonagh, Daniel I. Bromage, Paul A. Scott
Abstract
INTRODUCTION: The diagnosis of acute myocarditis (AM) is complex due to its heterogeneity and typically is defined by either Electronic Healthcare Records (EHRs) or advanced imaging and endomyocardial biopsy, but there is no consensus. We aimed to investigate the diagnostic accuracy of these approaches for AM. METHODS: Data on ICD 10th Revision(ICD-10) codes corresponding to AM were collected from two hospitals and compared to cardiac magnetic resonance (CMR)-confirmed or clinically suspected (CS)-AM cases with respect to diagnostic accuracy, clinical characteristics, and all-cause mortality. Next, we performed a review of published AM studies according to inclusion criteria. RESULTS: We identified 291 unique admissions with ICD-10 codes corresponding to AM in the first three diagnostic positions. The positive predictive value of ICD-10 codes for CMR-confirmed or CS-AM was 36%, and patients with CMR-confirmed or CS-AM had a lower all-cause mortality than those with a refuted diagnosis (P = 0.019). Using an unstructured approach, patients with CMR-confirmed and CS-AM had similar demographics, comorbidity profiles and survival over a median follow-up of 52 months (P = 0.72). Our review of the literature confirmed our findings. Outcomes for patients included in studies using CMR-confirmed criteria were favourable compared to studies with endomyocardial biopsy-confirmed AM cases. CONCLUSION: ICD-10 codes have poor accuracy in identification of AM cases and should be used with caution in clinical research. There are important differences in management and outcomes of patients according to the selection criteria used to diagnose AM. Potential selection biases must be considered when interpreting AM cohorts and requires standardization of inclusion criteria for AM studies.