Predicting ROSC in out-of-hospital cardiac arrest using expiratory carbon dioxide concentration: Is trend-detection instead of absolute threshold values the key?

Brinkrolf, Peter; Borowski, Matthias; Metelmann, Camilla; Lukas, Roman-Patrik; Pidde-Küllenberg, Laura; Böhn, Andreas

doi:10.1016/j.resuscitation.2017.11.040

Cited by 23 publications

(20 citation statements)

References 26 publications

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“…As the timing of TOR is difficult to decide, a clear pathophysiological rationale is crucial to effectively supporting the TOR decision. For example, established and validated expiratory carbon dioxide cut-off values can be used to predict ROSC after OHCA, although their sensitivity and specificity are suboptimal (sensitivity: 73.9%, specificity: 58.4%, AUC: 63.5) 18 . Thus, there is a clear need for reliable biomarkers that can guide physicians in making the TOR decision.…”

Section: Discussionmentioning

confidence: 99%

Quantitative pupillometry and neuron-specific enolase independently predict return of spontaneous circulation following cardiogenic out-of-hospital cardiac arrest: a prospective pilot study

Yokobori

Wang

Yang

et al. 2018

Sci Rep

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This study aimed to identify neurological and pathophysiological factors that predicted return of spontaneous circulation (ROSC) among patients with out-of-hospital cardiac arrest (OHCA). This prospective 1-year observational study evaluated patients with cardiogenic OHCA who were admitted to a tertiary medical center, Nippon Medical School Hospital. Physiological and neurological examinations were performed at admission for quantitative infrared pupillometry (measured with NPi-200, NeurOptics, CA, USA), arterial blood gas, and blood chemistry. Simultaneous blood samples were also collected to determine levels of neuron-specific enolase (NSE), S-100b, phosphorylated neurofilament heavy subunit, and interleukin-6. In-hospital standard advanced cardiac life support was performed for 30 minutes.The ROSC (n = 26) and non-ROSC (n = 26) groups were compared, which a revealed significantly higher pupillary light reflex ratio, which was defined as the percent change between maximum pupil diameter before light stimuli and minimum pupil diameter after light stimuli, in the ROSC group (median: 1.3% [interquartile range (IQR): 0.0–2.0%] vs. non-ROSC: (median: 0%), (Cut-off: 0.63%). Furthermore, NSE provided the great sensitivity and specificity for predicting ROSC, with an area under the receiver operating characteristic curve of 0.86, which was created by plotting sensitivity and 1-specificity. Multivariable logistic regression analyses revealed that the independent predictors of ROSC were maximum pupillary diameter (odds ratio: 0.25, 95% confidence interval: 0.07–0.94, P = 0.04) and NSE at admission (odds ratio: 0.96, 95% confidence interval: 0.93–0.99, P = 0.04). Pupillary diameter was also significantly correlated with NSE concentrations (r = 0.31, P = 0.027). Conclusively, the strongest predictors of ROSC among patients with OHCA were accurate pupillary diameter and a neuronal biomarker, NSE. Quantitative pupillometry may help guide the decision to terminate resuscitation in emergency departments using a neuropathological rationale. Further large-scale studies are needed.

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Section: Discussionmentioning

confidence: 99%

Quantitative pupillometry and neuron-specific enolase independently predict return of spontaneous circulation following cardiogenic out-of-hospital cardiac arrest: a prospective pilot study

Yokobori

Wang

Yang

et al. 2018

Sci Rep

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“…According to current advanced life support (ALS) guidelines [5,6], monitoring ETCO 2 level during resuscitation is beneficial for several reasons including: supervision of cardiopulmonary resuscitation (CPR) quality [7][8][9][10], prediction of patient's outcome [11][12][13][14][15], and early recognition of return of spontaneous circulation (ROSC) [16][17][18][19]. The relationship between ETCO 2 and defibrillation effectiveness could also be useful to guide defibrillation during CPR [20].…”

Section: Introductionmentioning

confidence: 99%

“…Accurate detection of ROSC using waveform capnography in pre-hospital ALS settings remains a challenge. Existing methods are based on the detection of sudden increases in the level of ETCO 2 [16][17][18][19]. However, the obtained results generally lack sensitivity or specificity.…”

Section: Introductionmentioning

confidence: 99%

Assessment of the evolution of end-tidal carbon dioxide within chest compression pauses to detect restoration of spontaneous circulation

et al. 2021

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Background Measurement of end-tidal CO2 (ETCO2) can help to monitor circulation during cardiopulmonary resuscitation (CPR). However, early detection of restoration of spontaneous circulation (ROSC) during CPR using waveform capnography remains a challenge. The aim of the study was to investigate if the assessment of ETCO2 variation during chest compression pauses could allow for ROSC detection. We hypothesized that a decay in ETCO2 during a compression pause indicates no ROSC while a constant or increasing ETCO2 indicates ROSC. Methods We conducted a retrospective analysis of adult out-of-hospital cardiac arrest (OHCA) episodes treated by the advanced life support (ALS). Continuous chest compressions and ventilations were provided manually. Segments of capnography signal during pauses in chest compressions were selected, including at least three ventilations and with durations less than 20 s. Segments were classified as ROSC or non-ROSC according to case chart annotation and examination of the ECG and transthoracic impedance signals. The percentage variation of ETCO2 between consecutive ventilations was computed and its average value, ΔETavg, was used as a single feature to discriminate between ROSC and non-ROSC segments. Results A total of 384 segments (130 ROSC, 254 non-ROSC) from 205 OHCA patients (30.7% female, median age 66) were analyzed. Median (IQR) duration was 16.3 (12.9,18.1) s. ΔETavg was 0.0 (-0.7, 0.9)% for ROSC segments and -11.0 (-14.1, -8.0)% for non-ROSC segments (p < 0.0001). Best performance for ROSC detection yielded a sensitivity of 95.4% (95% CI: 90.1%, 98.1%) and a specificity of 94.9% (91.4%, 97.1%) for all ventilations in the segment. For the first 2 ventilations, duration was 7.7 (6.0, 10.2) s, and sensitivity and specificity were 90.0% (83.5%, 94.2%) and 89.4 (84.9%, 92.6%), respectively. Our method allowed for ROSC detection during the first compression pause in 95.4% of the patients. Conclusion Average percent variation of ETCO2 during pauses in chest compressions allowed for ROSC discrimination. This metric could help confirm ROSC during compression pauses in ALS settings.

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“…Acceleration signals measured on the common carotid artery from in-hospital cardiac arrest patients [22] or acquired from accelerometer-based CPR feedback devices in a porcine model of cardiac arrest [23] have also been used to develop pulse detectors. An abrupt rise of end tidal CO 2 (EtCO 2 ) has recently been reported as a specific but non-sensitive predictor of ROSC in OHCA [24], [25]. Several studies have used the ECG [26]- [28] or thoracic impedance (TI) [29], [30] to detect pulse.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Framework for Pulse Detection During Out-of-Hospital Cardiac Arrest

et al. 2020

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The availability of an automatic pulse detection during out-of-hospital cardiac arrest (OHCA) would allow the rapid identification of cardiac arrest and the prompt detection of return of spontaneous circulation. The aim of this study was to develop a reliable pulse detection algorithm using the electrocardiogram (ECG) and thoracic impedance (TI), the signals available in most defibrilators. The dataset used in the study consisted of 1140 ECG and TI segments from 187 OHCA patients, whereof 792 were labelled as pulse generating rhythm (PR) and 348 as pulseless electrical activity (PEA) by a pool of experts in OHCA. First, an adaptive filtering scheme was used to extract the impedance circulation component and its first derivative from the TI. Then, the wavelet decomposition of the ECG was carried out to obtain the different subband components and the denoised ECG. Pulse/no-pulse (PR/PEA) discrimination features were extracted from those signals and fed into a support vector machine (SVM) classifier that made the pulse/no-pulse decision. A quasi-stratified and patient wise nested cross validation procedure was used to select the best feature subset and to tune the SVM hyperparameters. This procedure was repeated 50 times to estimate the statistical distributions of the performance metrics of the method. The optimal solution consisted in a five feature classifier that yielded a mean (standard deviation) sensitivity, specificity, balanced accuracy and total accuracy of 92.4% (0.7), 93.0% (0.8), 92.7% (0.5) and 92.6% (0.5), respectively. When compared to available methods, our solution presented an improvement in balanced accuracy of at least 2.5 points. A reliable pulse detection algorithm for OHCA using the signals available in defibrillators was acomplished.INDEX TERMS Machine learning, adaptive filtering, stationary wavelet transform (SWT), support vector machine (SVM), out-of-hospital cardiac arrest (OHCA), thoracic impedance, electrocardiogram (ECG), pulse detection.

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Predicting ROSC in out-of-hospital cardiac arrest using expiratory carbon dioxide concentration: Is trend-detection instead of absolute threshold values the key?

Cited by 23 publications

References 26 publications

Quantitative pupillometry and neuron-specific enolase independently predict return of spontaneous circulation following cardiogenic out-of-hospital cardiac arrest: a prospective pilot study

Quantitative pupillometry and neuron-specific enolase independently predict return of spontaneous circulation following cardiogenic out-of-hospital cardiac arrest: a prospective pilot study

Assessment of the evolution of end-tidal carbon dioxide within chest compression pauses to detect restoration of spontaneous circulation

A Machine Learning Framework for Pulse Detection During Out-of-Hospital Cardiac Arrest

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