Chest compression pauses during defibrillation attempts

Deakin, Charles D.; Koster, Rudolph W.

doi:10.1097/mcc.0000000000000310

Cited by 12 publications

(7 citation statements)

References 31 publications

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“…Therefore, the AED performance goals on artifact-free ECGs are demanding (>90% for VFs, >75% for VTs, >95% for NSh, and >99% for normal sinus rhythms (NSR)), as set by the American Heart Association (AHA) in the early 1997 [4]. Besides, the hands-off pauses in chest compressions required for artifact-free ECG analysis in AEDs should be shortened, considering that 5 s to 10 s delay of the shock after stopping chest compressions reduces the probability of the defibrillation success and survival [5,6]. Therefore, effective strategies for early shock decision have been reported within the AED setting, such as early starting of the ECG analysis at the end of chest compressions [7] or during ventilation pauses [8]; as well as short ECG analysis durations, varying across studies from 2 s to 10 s [3,7,[9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Fully Convolutional Deep Neural Networks with Optimized Hyperparameters for Detection of Shockable and Non-Shockable Rhythms

Krasteva

Ménétré

Didon

et al. 2020

Sensors

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Deep neural networks (DNN) are state-of-the-art machine learning algorithms that can be learned to self-extract significant features of the electrocardiogram (ECG) and can generally provide high-output diagnostic accuracy if subjected to robust training and optimization on large datasets at high computational cost. So far, limited research and optimization of DNNs in shock advisory systems is found on large ECG arrhythmia databases from out-of-hospital cardiac arrests (OHCA). The objective of this study is to optimize the hyperparameters (HPs) of deep convolutional neural networks (CNN) for detection of shockable (Sh) and nonshockable (NSh) rhythms, and to validate the best HP settings for short and long analysis durations (2–10 s). Large numbers of (Sh + NSh) ECG samples were used for training (720 + 3170) and validation (739 + 5921) from Holters and defibrillators in OHCA. An end-to-end deep CNN architecture was implemented with one-lead raw ECG input layer (5 s, 125 Hz, 2.5 uV/LSB), configurable number of 5 to 23 hidden layers and output layer with diagnostic probability p ∈ [0: Sh,1: NSh]. The hidden layers contain N convolutional blocks × 3 layers (Conv1D (filters = Fi, kernel size = Ki), max-pooling (pool size = 2), dropout (rate = 0.3)), one global max-pooling and one dense layer. Random search optimization of HPs = {N, Fi, Ki}, i = 1, … N in a large grid of N = [1, 2, … 7], Fi = [5;50], Ki = [5;100] was performed. During training, the model with maximal balanced accuracy BAC = (Sensitivity + Specificity)/2 over 400 epochs was stored. The optimization principle is based on finding the common HPs space of a few top-ranked models and prediction of a robust HP setting by their median value. The optimal models for 1–7 CNN layers were trained with different learning rates LR = [10−5; 10−2] and the best model was finally validated on 2–10 s analysis durations. A number of 4216 random search models were trained. The optimal models with more than three convolutional layers did not exhibit substantial differences in performance BAC = (99.31–99.5%). Among them, the best model was found with {N = 5, Fi = {20, 15, 15, 10, 5}, Ki = {10, 10, 10, 10, 10}, 7521 trainable parameters} with maximal validation performance for 5-s analysis (BAC = 99.5%, Se = 99.6%, Sp = 99.4%) and tolerable drop in performance (<2% points) for very short 2-s analysis (BAC = 98.2%, Se = 97.6%, Sp = 98.7%). DNN application in future-generation shock advisory systems can improve the detection performance of Sh and NSh rhythms and can considerably shorten the analysis duration complying with resuscitation guidelines for minimal hands-off pauses.

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Section: Introductionmentioning

confidence: 99%

Fully Convolutional Deep Neural Networks with Optimized Hyperparameters for Detection of Shockable and Non-Shockable Rhythms

Krasteva

Ménétré

Didon

et al. 2020

Sensors

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“…Despite obvious weaknesses in the studies reviewed, the risk of prolonged pauses in chest compressions during FoCUS does seem plausible in both hospital and prehospital settings. This is of significant concern when considering their importance to the survivability of OHCA (Deakin and Koster, 2016).…”

Section: Discussionmentioning

confidence: 99%

“…It has been recognised that intra-arrest ultrasound can detract from other essential elements of care, especially high-quality chest compressions (Gardner et al, 2017). Deakin and Koster (2016) highlight the significance of chest compression pauses being associated with decreased chances of survival. Recently the European Society of Cardiology produced a position statement on handheld ultrasound devices (HUD) for FoCUS (Cardim et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Focused cardiac ultrasound in out-of-hospital cardiac arrest: a literature review

Brown

Quinn

2021

Journal of Paramedic Practice

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Focused cardiac ultrasound (FoCUS) is emerging in emergency medical systems, particularly in the context of prognostication in out-of-hospital cardiac arrest. However, FoCUS has not been formally incorporated into UK guidelines because of a lack of evidence. Furthermore, concerns have been raised that FoCUS can distract people from providing other essential and evidenced elements of care. This broad literature search aims to shed light on the practice of FoCUS in cardiac arrest by reviewing articles related to in-hospital and out-of-hospital practice. The findings are conspicuous by the lack of high-quality studies, particularly regarding prognostication. Association between ultrasound findings and outcome are asserted, as is the feasibility of paramedic use of FoCUS, although the evidence is from small and non-randomised studies and subject to bias.

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“…It is increasingly acknowledged that intra-arrest ultrasound can detract from other essential elements of care, especially high-quality chest compressions (Gardner et al, 2017). Pauses in chest compressions are associated with decreased chances of survival (Deakin and Koster, 2016). Recently the European Society of Cardiology published a position statement on HUDs for FoCUS in which the potential for prehospital use is acknowledged, while outlining limitations in making quantifiable assertions with HUDs (Cardim et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Focused ultrasound in out-of-hospital cardiac arrest by advanced paramedics

Brown

Fothergill

McIntyre

et al. 2021

Journal of Paramedic Practice

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Background: This study describes and evaluates advanced paramedic practitioner (APP) use of focused cardiac ultrasound (FoCUS) in out-of-hospital cardiac arrest (OHCA), and relates ultrasound findings with decisions to terminate resuscitation. The authors report characteristics of patients who do/do not undergo a FoCUS examination by APPs, ultrasound probe positions used and whether FoCUS findings were associated with decisions to terminate resuscitation or to convey patients to an emergency department (ED) with ongoing resuscitation. Method: A retrospective, observational cohort study of all adult medical OHCA patients attended by APPs in Greater London during 2018 was carried out using data from emergency medical services (EMS) and APP databases. Results: Twenty-eight APPs attended 1444 OHCA patients in 2018, of whom 744 underwent FoCUS. The subcostal probe position was used most frequently (74%), followed by the parasternal long axis (19%), with significantly smaller use of the parasternal short axis and apical windows. Absence of spontaneous cardiac motion (SCM) was associated with resuscitation being terminated (333 out of 391; 85%) and the presence of SCM was associated with conveyance to the emergency department (213 out of 264 patients; 80%). All decisions to terminate resuscitation were within the APP scope of practice. Conclusion: The authors believe this is the largest prehospital study involving FoCUS in OHCA. An association between FoCUS findings and decisions made to either convey patients to hospital or terminate resuscitation was found. The SC window was most used and ROLE decisions were deemed to be in accordance with local guidance and practice.

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Chest compression pauses during defibrillation attempts

Abstract: It is important to avoid any unnecessary pause in chest compressions before and after a defibrillation shock. Pauses should be kept to an absolute minimum, preferably to less than 10 s.

Cited by 12 publications

References 31 publications

Fully Convolutional Deep Neural Networks with Optimized Hyperparameters for Detection of Shockable and Non-Shockable Rhythms

Fully Convolutional Deep Neural Networks with Optimized Hyperparameters for Detection of Shockable and Non-Shockable Rhythms

Focused cardiac ultrasound in out-of-hospital cardiac arrest: a literature review

Focused ultrasound in out-of-hospital cardiac arrest by advanced paramedics

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