Wael Alqarawi1, Omar Dewidar2, Rafik Tadros3, Jason D Roberts4, Christian Steinberg5, Ciorsti J MacIntyre6, Zachary W M Laksman7, Martin S Green8, Girish Nair9, George Wells2, Andrew D Krahn10. 1. Department of Cardiac Sciences, College of Medicine, King Saud University, Riyadh, Saudi Arabia; School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada; Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada. 2. School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada. 3. Cardiovascular Genetics Center, Montreal Heart Institute, Faculty of Medicine, Université de Montréal, Montréal, Québec, Canada. 4. Section of Cardiac Electrophysiology, Division of Cardiology, Department of Medicine, Western University, London, Ontario, Canada. 5. Institut Universitaire de Cardiologie et Pneumologie de Québec, Laval University, Québec city, Québec, Canada. 6. Dalhousie University and Queen Elizabeth II Health Sciences Centre, Halifax, Nova Scotia, Canada. 7. Center for Cardiovascular Innovation, Division of Cardiology, University of British Columbia, Vancouver, British Columbia, Canada. 8. Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada. 9. School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada; Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada. 10. Center for Cardiovascular Innovation, Division of Cardiology, University of British Columbia, Vancouver, British Columbia, Canada. Electronic address: akrahn@mail.ubc.ca.
Abstract
BACKGROUND: Idiopathic ventricular fibrillation (IVF) is diagnosed in patients with apparently unexplained cardiac arrest (UCA) after varying degrees of evaluation. This is largely due to the lack of a standardized approach to UCA. OBJECTIVE: We sought to develop an evidence-based diagnostic algorithm for IVF by systematically examining the yield of diagnostic testing in UCA probands. METHODS: Studies reporting the yield of diagnostic testing in UCA were identified in MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, and conference abstracts. Their methodological quality was assessed by the National Institutes of Health quality assessment tool. Meta-analyses were performed using the random effects model. RESULTS: A total of 21 studies were included. The pooled comprehensive diagnostic testing yield was 43% (95% confidence interval 39%-48%). A lower yield was seen when only definite diagnoses based on the prespecified criteria were used (32% vs 47%; P = .15). Epinephrine challenge, Holter monitoring, and family screening were associated with low yield (<5%), whereas cardiac magnetic resonance imaging, exercise treadmill test, and sodium-channel blocker challenge were associated with high yield (≥5%). Coronary spasm provocation, electrophysiology study, and systematic genetic testing were reported to be abnormal in a high proportion of UCA probands (>10%). CONCLUSION: We developed a stepwise algorithm for UCA evaluation and criteria to assess the strength of IVF diagnosis on the basis of the diagnostic yield of UCA testing.
BACKGROUND: Idiopathic ventricular fibrillation (IVF) is diagnosed in patients with apparently unexplained cardiac arrest (UCA) after varying degrees of evaluation. This is largely due to the lack of a standardized approach to UCA. OBJECTIVE: We sought to develop an evidence-based diagnostic algorithm for IVF by systematically examining the yield of diagnostic testing in UCA probands. METHODS: Studies reporting the yield of diagnostic testing in UCA were identified in MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, and conference abstracts. Their methodological quality was assessed by the National Institutes of Health quality assessment tool. Meta-analyses were performed using the random effects model. RESULTS: A total of 21 studies were included. The pooled comprehensive diagnostic testing yield was 43% (95% confidence interval 39%-48%). A lower yield was seen when only definite diagnoses based on the prespecified criteria were used (32% vs 47%; P = .15). Epinephrine challenge, Holter monitoring, and family screening were associated with low yield (<5%), whereas cardiac magnetic resonance imaging, exercise treadmill test, and sodium-channel blocker challenge were associated with high yield (≥5%). Coronary spasm provocation, electrophysiology study, and systematic genetic testing were reported to be abnormal in a high proportion of UCA probands (>10%). CONCLUSION: We developed a stepwise algorithm for UCA evaluation and criteria to assess the strength of IVF diagnosis on the basis of the diagnostic yield of UCA testing.
Authors: Steffany Grondin; Brianna Davies; Julia Cadrin-Tourigny; Christian Steinberg; Christopher C Cheung; Paloma Jorda; Jeffrey S Healey; Martin S Green; Shubhayan Sanatani; Wael Alqarawi; Paul Angaran; Laura Arbour; Pavel Antiperovitch; Habib Khan; Richard Leather; Peter G Guerra; Lena Rivard; Christopher S Simpson; Martin Gardner; Ciorsti MacIntyre; Colette Seifer; Anne Fournier; Jacqueline Joza; Michael H Gollob; Guillaume Lettre; Mario Talajic; Zachary W Laksman; Jason D Roberts; Andrew D Krahn; Rafik Tadros Journal: Eur Heart J Date: 2022-08-21 Impact factor: 35.855
Authors: Lisa M Verheul; Sanne A Groeneveld; Feddo P Kirkels; Paul G A Volders; Arco J Teske; Maarten J Cramer; Marco Guglielmo; Rutger J Hassink Journal: J Clin Med Date: 2022-08-10 Impact factor: 4.964