ArticleJournal of cardiovascular electrophysiology2026
Association of Diagnostic Code Priority With Identification of True Cardiac Arrest in Patients Presenting to the Emergency Department.
Article in Journal of cardiovascular electrophysiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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11 authors.
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Abstract
backgroundSudden cardiac arrest (SCA), a leading cause of mortality, is administratively coded as ventricular tachycardia (VT), ventricular fibrillation (VF), or unspecified SCA. Diagnostic codes are widely used in research, yet their accuracy in reflecting true SCA events is uncertain. The objective of this study is to evaluate the relationship between administrative diagnostic code rank (listing order) and the likelihood of true SCA in patients presenting to the emergency department (ED).
methodsFrom a database of 22 369 patients with VT/VF/SCA, we randomly selected 380 patients for detailed chart review. Diagnostic accuracy was confirmed when patients (1) had sustained a true VT/VF/SCA event during the index hospitalization and (2) when the event was not precipitated by non-cardiac causes.
resultsManual chart review for the 380 patients (age 71 ± 12 years, 53% women) confirmed that 65% experienced true VT/VF/SCA and 55% had an arrest unrelated to non-cardiac causes. Diagnostic accuracy was 100% at priority rank #1 and declined significantly at lower ranks. In the overall larger cohort of 22 369 patients, recurrence of VT/VF/SCA trended 3% lower (p = 0.086) and all-cause mortality was 5% higher (p < 0.001) with each diagnostic priority rank decrease, over a mean follow-up of 1.5 years.
conclusionDiagnostic codes show variable predictive accuracy for true VT/VF/SCA events, with the highest accuracy at priority rank #1. Higher-priority codes identify patients most likely to have true SCA, while lower-priority codes capture sick patients with other in-hospital events. These results have important implications for the interpretation of administrative health datasets.
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