Resuscitation plus · 2022
Mechanical life support algorithm developed by simulation for inpatient emergency management of recipients of implantable left ventricular assist devices.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
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.
Akhtar Waqas, Gamble Brigitte, Kiff Kristine, Wypych-Zych Agnieszka, Raj Binu, Takata Junko, Gil Fernando Riesgo, Hurtado Ana, Rosenberg Alex, Bowles Christopher T
Аннотация
BACKGROUND: Published guidance concerning emergency management of left ventricular assist device (LVAD) recipients is both limited and lacking in consensus which increases the risk of delayed and/or inappropriate actions.
METHODS: In our specialist tertiary referral centre we developed, by iteration, a novel in-hospital resuscitation algorithm for LVAD emergencies which we validated through simulation and assessment of our multi-disciplinary team. A Mechanical Life Support course was established to provide theoretical and practical education combined with simulation to consolidate knowledge and confidence in algorithm use. We assessed these measures using confidence scoring, a key performance indicator (the time taken to restart LVAD function) and a multiple-choice question (MCQ) examination.
RESULTS: The mean baseline staff confidence score in management of LVAD emergencies was 2.4 ± 1.2 out of a maximum of 5 (n = 29). After training with simulation, mean confidence score increased to 3.5 ± 0.8 (n = 13).Clinical personnel who were provided with the novel resuscitation algorithm were able to reduce time taken to restart LVAD function from a mean value of 49 ± 8.2 seconds (pre-training) to 20.4 ± 5 seconds (post-training) (n = 42, p < 0.0001).The Mechanical Life Support course increased mean confidence from 2.5 ± 1.2 to 4 ± 0.6 (n = 44, p < 0.0001) and mean MCQ score from 18.7 ± 3.4 to 22.8 ± 2.6, out of a maximum of 28 (n = 44, p < 0.0001).
CONCLUSION: We present a simplified LVAD Advanced Life Support algorithm to aid the crucial first minutes of resuscitation where basic interventions are likely to be critical in assuring good patient outcomes.
METHODS: In our specialist tertiary referral centre we developed, by iteration, a novel in-hospital resuscitation algorithm for LVAD emergencies which we validated through simulation and assessment of our multi-disciplinary team. A Mechanical Life Support course was established to provide theoretical and practical education combined with simulation to consolidate knowledge and confidence in algorithm use. We assessed these measures using confidence scoring, a key performance indicator (the time taken to restart LVAD function) and a multiple-choice question (MCQ) examination.
RESULTS: The mean baseline staff confidence score in management of LVAD emergencies was 2.4 ± 1.2 out of a maximum of 5 (n = 29). After training with simulation, mean confidence score increased to 3.5 ± 0.8 (n = 13).Clinical personnel who were provided with the novel resuscitation algorithm were able to reduce time taken to restart LVAD function from a mean value of 49 ± 8.2 seconds (pre-training) to 20.4 ± 5 seconds (post-training) (n = 42, p < 0.0001).The Mechanical Life Support course increased mean confidence from 2.5 ± 1.2 to 4 ± 0.6 (n = 44, p < 0.0001) and mean MCQ score from 18.7 ± 3.4 to 22.8 ± 2.6, out of a maximum of 28 (n = 44, p < 0.0001).
CONCLUSION: We present a simplified LVAD Advanced Life Support algorithm to aid the crucial first minutes of resuscitation where basic interventions are likely to be critical in assuring good patient outcomes.