These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


Pubmed for Handhelds

PUBMED FOR HANDHELDS

Search MEDLINE/PubMed

  • Title: Predicting defibrillation success.
    Author: Strohmenger HU.
    Journal: Curr Opin Crit Care; 2008 Jun; 14(3):311-6. PubMed ID: 18467892.
    Abstract:
    PURPOSE OF REVIEW: Ventricular fibrillation is the primary rhythm in many cardiac arrest patients. Since the late 1980s, the surface electrocardiogram of ventricular fibrillation has been subjected to analysis to obtain reliable information about the likelihood of successful countershock and to estimate the duration of cardiac arrest. Considerable efforts were made in the past 2 years to further improve the predictive power of rescue shock measures. RECENT FINDINGS: In a retrospective clinical study, ventricular fibrillation single feature analysis was not able to reliably estimate duration between cardiac arrest onset and initial electrocardiogram. Combining ventricular fibrillation features in the time and frequency domain by employing neural networks did not further improve the best single feature prediction power taken from higher ventricular fibrillation frequency bands. Cardioversion outcome prediction based on the wavelet technique increased the specificity up to 66% at the 95% sensitivity level. SUMMARY: Recent results question the ventricular fibrillation feature analysis as a reliable tool to estimate the duration of human cardiac arrest. Animal and clinical studies confirmed that ventricular fibrillation waveform analysis contains information to reliably predict the countershock success rate and further improved countershock outcome prediction. Prospective clinical studies are highly warranted to demonstrate that ventricular fibrillation waveform analysis definitely improves survival after cardiac arrest.
    [Abstract] [Full Text] [Related] [New Search]