m+Medelevator.RU
← Поиск публикаций
Current opinion in critical care · 2026

Defibrillation in refractory cardiac arrest: knowledge gaps and future priorities.

СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…

История · было → стало

Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.

Babini Giovanni, Cucino Alberto, Ristagno Giuseppe

Аннотация

PURPOSE OF REVIEW: Refractory shockable cardiac arrest remains associated with poor outcomes despite prompt cardiopulmonary resuscitation and repeated defibrillation. This review summarizes recent evidence on ventricular fibrillation (VF) phenotyping, vector-change (VC) defibrillation, double sequential external defibrillation (DSED), waveform analysis, and emerging decision-support approaches.

RECENT FINDINGS: Refractory VF is increasingly recognized as a heterogeneous syndrome rather than a uniform failure of repeated shocks. True shock-refractory or incessant VF, recurrent VF, and mixed phenotypes differ in frequency, prognosis, and potential therapeutic targets. VC defibrillation using an antero-posterior pad configuration may improve defibrillation efficacy in selected patients, whereas DSED represents a more complex intervention whose effectiveness may depend on shock vector, timing, inter-shock interval, and system-level implementation. Physiological and electrical markers, including amplitude spectrum area (AMSA), transthoracic impedance (TTI), and ECG-based predictive models, may enable earlier identification of patients unlikely to respond to continued conventional defibrillation.

SUMMARY: Current evidence does not support a single rigid treatment algorithm for refractory VF. Future research should move beyond shock-count-based escalation and prospectively evaluate physiology-informed, adaptive strategies integrating rhythm phenotype, AMSA, TTI, CPR quality, machine-learning-based prediction, and operational feasibility within pragmatic clinical trials.

ИП Урсолов Владлен Сергеевич

ИНН 500118482352 · ОГРНИП 314500117700049

Реквизиты и контакты