RESUMO
The automatic detection of pulse during out-of-hospital cardiac arrest (OHCA) is necessary for the early recognition of the arrest and the detection of return of spontaneous circulation (end of the arrest). The only signal available in every single defibrillator and valid for the detection of pulse is the electrocardiogram (ECG). In this study we propose two deep neural network (DNN) architectures to detect pulse using short ECG segments (5 s), i.e., to classify the rhythm into pulseless electrical activity (PEA) or pulse-generating rhythm (PR). A total of 3914 5-s ECG segments, 2372 PR and 1542 PEA, were extracted from 279 OHCA episodes. Data were partitioned patient-wise into training (80%) and test (20%) sets. The first DNN architecture was a fully convolutional neural network, and the second architecture added a recurrent layer to learn temporal dependencies. Both DNN architectures were tuned using Bayesian optimization, and the results for the test set were compared to state-of-the art PR/PEA discrimination algorithms based on machine learning and hand crafted features. The PR/PEA classifiers were evaluated in terms of sensitivity (Se) for PR, specificity (Sp) for PEA, and the balanced accuracy (BAC), the average of Se and Sp. The Se/Sp/BAC of the DNN architectures were 94.1%/92.9%/93.5% for the first one, and 95.5%/91.6%/93.5% for the second one. Both architectures improved the performance of state of the art methods by more than 1.5 points in BAC.
RESUMO
There is no reliable automated non-invasive solution for monitoring circulation and guiding treatment in prehospital emergency medicine. Cardiac output (CO) monitoring might provide a solution, but CO monitors are not feasible/practical in the prehospital setting. Non-invasive ballistocardiography (BCG) measures heart contractility and tracks CO changes. This study analyzed the feasibility of estimating CO using morphological features extracted from BCG signals. In 20 healthy subjects ECG, carotid/abdominal BCG, and invasive arterial blood pressure based CO were recorded. BCG signals were adaptively processed to isolate the circulatory component from carotid (CCc) and abdominal (CCa) BCG. Then, 66 features were computed on a beat-to-beat basis to characterize amplitude/duration/area/length of the fluctuation in CCc and CCa. Subjects' data were split into development set (75%) to select the best feature subset with which to build a machine learning model to estimate CO and validation set (25%) to evaluate model's performance. The model showed a mean absolute error, percentage error and 95% limits of agreement of 0.83 L/min, 30.2% and - 2.18-1.89 L/min respectively in the validation set. BCG showed potential to reliably estimate/track CO. This method is a promising first step towards an automated, non-invasive and reliable CO estimator that may be tested in prehospital emergencies.
Assuntos
Balistocardiografia , Sistema Cardiovascular , Humanos , Estudos de Viabilidade , Voluntários Saudáveis , Débito Cardíaco/fisiologia , Frequência Cardíaca/fisiologiaRESUMO
BACKGROUND: This study aimed to evaluate three prehospital early warning scores (EWSs): RTS, MGAP and MREMS, to predict short-term mortality in acute life-threatening trauma and injury/illness by comparing United States (US) and Spanish cohorts. METHODS: A total of 8,854 patients, 8,598/256 survivors/nonsurvivors, comprised the unified cohort. Datasets were randomly divided into training and test sets. Training sets were used to analyse the discriminative power of the scores in terms of the area under the curve (AUC), and the score performance was assessed in the test set in terms of sensitivity (SE), specificity (SP), accuracy (ACC) and balanced accuracy (BAC). RESULTS: The three scores showed great discriminative power with AUCs>0.90, and no significant differences between cohorts were found. In the test set, RTS/MREMS/MGAP showed SE/SP/ACC/BAC values of 86.0/89.9/89.6/87.1%, 91.0/86.9/87.5/88.5%, and 87.7/82.9/83.4/85.2%, respectively. CONCLUSIONS: All EWSs showed excellent ability to predict the risk of short-term mortality, independent of the country.
Assuntos
Serviços Médicos de Emergência , Ferimentos e Lesões , Humanos , Feminino , Masculino , Pessoa de Meia-Idade , Estados Unidos/epidemiologia , Adulto , Ferimentos e Lesões/mortalidade , Espanha/epidemiologia , Serviços Médicos de Emergência/normas , Idoso , Estudos de Coortes , Escore de Alerta PrecoceRESUMO
OBJECTIVES: Filtering the cardiopulmonary resuscitation (CPR) artifact has been a major approach to minimizing interruptions to CPR for rhythm analysis. However, the effects of these filters on interruptions to CPR have not been evaluated. This study presents the first methodology for directly quantifying the effects of filtering on the uninterrupted CPR time. METHODS: A total of 241 shockable and 634 nonshockable out-of-hospital cardiac arrest records (median duration, 150 seconds) from 248 patients were analyzed. Filtering and rhythm analysis were commenced after 1 minute of CPR, and the end point for CPR was established at the time of the first shock diagnosis. Kaplan-Meier curves were used to compute the probability of interrupting CPR as a function of time. The probabilities of delivering 2 minutes of uninterrupted CPR for the shockable and nonshockable rhythms were compared with the 2-minute cycles of uninterrupted CPR recommended by the guidelines. RESULTS: For the nonshockable rhythms, the probabilities of delivering at least 2 and 3 minutes of uninterrupted CPR were 58% (95% confidence interval, 54%-62%) and 48% (44%-52%), respectively. These are the probabilities of reducing and substantially reducing the frequency of CPR interruptions for rhythm analysis. For the shockable rhythms, the probability of avoiding unnecessary CPR prolongation beyond 2 minutes was 100% (99%-100%). CONCLUSIONS: Filtering reduces the frequency of CPR interruptions for rhythm analysis in less than 60% of nonshockable rhythms. New strategies to increase the probability of prolonging CPR for nonshockable rhythms should be defined and evaluated using the methodology proposed in this study.
Assuntos
Reanimação Cardiopulmonar/estatística & dados numéricos , Cardioversão Elétrica/estatística & dados numéricos , Eletrocardiografia , Fidelidade a Diretrizes/estatística & dados numéricos , Massagem Cardíaca , Humanos , Estimativa de Kaplan-Meier , Parada Cardíaca Extra-Hospitalar/terapia , Estudos Prospectivos , Fatores de TempoRESUMO
OBJECTIVE: Ventilation control is important during resuscitation from out-of-hospital cardiac arrest (OHCA). We compared different methods for calculating ventilation rates (VR) during OHCA. METHODS: We analyzed data from the Pragmatic Airway Resuscitation Trial, identifying ventilations through capnogram recordings. We determined VR by: 1) counting the number of breaths within a time epoch ("counted" VR), and 2) calculating the mean of the inverse of measured time between breaths within a time epoch ("measured" VR). We repeated the VR estimates using different time epochs (10, 20, 30, 60 sec). We defined hypo- and hyperventilation as VR <6 and >12 breaths/min, respectively. We assessed differences in estimated hypo- and hyperventilation with each VR measurement technique. RESULTS: Of 3,004 patients, data were available for 1,010. With the counted method, total hypoventilation increased with longer time epochs ([10-s epoch: 75 sec hypoventilation] to [60-s epoch: 97 sec hypoventilation]). However, with the measured method, total hypoventilation decreased with longer time epochs ([10-s epoch: 223 sec hypoventilation] to [60-s epoch: 150 sec hypoventilation]). With the counted method, the total duration of hyperventilation decreased with longer time epochs ([10-s epochs: 35 sec hyperventilation] to [60-s epoch: 0 sec hyperventilation]). With the measured method, total hyperventilation decreased with longer time epochs ([10-s epoch: 78 sec hyperventilation] to [60-s epoch: 0 sec hyperventilation]). Differences between the measured and counted estimates were smallest with a 60-s time epoch. CONCLUSIONS: Quantifications of hypo- and hyperventilation vary with the applied measurement methods. Measurement methods are important when characterizing ventilation rates in OHCA.
Assuntos
Reanimação Cardiopulmonar , Parada Cardíaca Extra-Hospitalar , Humanos , Reanimação Cardiopulmonar/métodos , Parada Cardíaca Extra-Hospitalar/terapia , Hiperventilação/etiologia , HipoventilaçãoRESUMO
BACKGROUND: We sought to describe ventilation rates during out-of-hospital cardiac arrest (OHCA) resuscitation and their associations with airway management strategy and outcomes. METHODS: We analyzed continuous end-tidal carbon dioxide capnography data from adult OHCA enrolled in the Pragmatic Airway Resuscitation Trial (PART). Using automated signal processing techniques, we determined continuous ventilation rates for consecutive 10-second epochs after airway insertion. We defined hypoventilation as a ventilation rate < 6 breaths/min. We defined hyperventilation as a ventilation rate > 12 breaths/min. We compared differences in total and percentage post-airway hyper- and hypoventilation between airway interventions (laryngeal tube (LT) vs. endotracheal intubation (ETI)). We also determined associations between hypo-/hyperventilation and OHCA outcomes (ROSC, 72-hour survival, hospital survival, hospital survival with favorable neurologic status). RESULTS: Adequate post-airway capnography were available for 1,010 (LT n = 714, ETI n = 296) of 3,004 patients. Median ventilation rates were: LT 8.0 (IQR 6.5-9.6) breaths/min, ETI 7.9 (6.5-9.7) breaths/min. Total duration and percentage of post-airway time with hypoventilation were similar between LT and ETI: median 1.8 vs. 1.7 minutes, p = 0.94; median 10.5% vs. 11.5%, p = 0.60. Total duration and percentage of post-airway time with hyperventilation were similar between LT and ETI: median 0.4 vs. 0.4 minutes, p = 0.91; median 2.1% vs. 1.9%, p = 0.99. Hypo- and hyperventilation exhibited limited associations with OHCA outcomes. CONCLUSION: In the PART Trial, EMS personnel delivered post-airway ventilations at rates satisfying international guidelines, with only limited hypo- or hyperventilation. Hypo- and hyperventilation durations did not differ between airway management strategy and exhibited uncertain associations with OCHA outcomes.
Assuntos
Reanimação Cardiopulmonar , Serviços Médicos de Emergência , Parada Cardíaca Extra-Hospitalar , Adulto , Manuseio das Vias Aéreas/métodos , Reanimação Cardiopulmonar/métodos , Humanos , Hiperventilação/etiologia , Hipoventilação/etiologia , Intubação Intratraqueal/métodos , Parada Cardíaca Extra-Hospitalar/terapiaRESUMO
BACKGROUND: Out-of-hospital cardiac arrest (OHCA) data debriefing and clinical research often require the retrospective analysis of large datasets containing defibrillator files from different vendors and clinical annotations by the emergency medical services. AIM: To introduce and evaluate a methodology to automatically extract cardiopulmonary resuscitation (CPR) quality data in a uniform and systematic way from OHCA datasets from multiple heterogeneous sources. METHODS: A dataset of 2236 OHCA cases from multiple defibrillator models and manufacturers was analyzed. Chest compressions were automatically identified using the thoracic impedance and compression depth signals. Device event time-stamps and clinical annotations were used to set the start and end of the analysis interval, and to identify periods with spontaneous circulation. A manual audit of the automatic annotations was conducted and used as gold standard. Chest compression fraction (CCF), rate (CCR) and interruption ratio were computed as CPR quality variables. The unsigned error between the automated procedure and the gold standard was calculated. RESULTS: Full-episode median errors below 2% in CCF, 1 min-1 in CCR, and 1.5% in interruption ratio, were measured for all signals and devices. The proportion of cases with large errors (>10% in CCF and interruption ratio, and >10 min-1 in CCR) was below 10%. Errors were lower for shorter sub-intervals of interest, like the airway insertion interval. CONCLUSIONS: An automated methodology was validated to accurately compute CPR metrics in large and heterogeneous OHCA datasets. Automated processing of defibrillator files and the associated clinical annotations enables the aggregation and analysis of CPR data from multiple sources.
Assuntos
Reanimação Cardiopulmonar , Serviços Médicos de Emergência , Parada Cardíaca Extra-Hospitalar , Humanos , Parada Cardíaca Extra-Hospitalar/terapia , Estudos Retrospectivos , TóraxRESUMO
BACKGROUND: Chest compression (CC) quality is associated with improved out-of-hospital cardiopulmonary arrest (OHCA) outcomes. Airway management efforts may adversely influence CC quality. We sought to compare the effects of initial laryngeal tube (LT) and initial endotracheal intubation (ETI) airway management strategies upon chest compression fraction (CCF), rate and interruptions in the Pragmatic Airway Resuscitation Trial (PART). METHODS: We analyzed CPR process files collected from adult OHCA enrolled in PART. We used automated signal processing techniques and a graphical user interface to calculate CC quality measures and defined interruptions as pauses in chest compressions longer than 3â¯s. We determined CC fraction, rate and interruptions (number and total duration) for the entire resuscitation and compared differences between LT and ETI using t-tests. We repeated the analysis stratified by time before, during and after airway insertion as well as by successive 3-min time segments. We also compared CC quality between single vs. multiple airway insertion attempts, as well as between bag-valve-mask (BVM-only) vs. ETI or LT. RESULTS: Of 3004 patients enrolled in PART, CPR process data were available for 1996 (1001 LT, 995 ETI). Mean CPR analysis duration were: LT 22.6⯱â¯10.8â¯min vs. ETI 25.3⯱â¯11.3â¯min (pâ¯<â¯0.001). Mean CC fraction (LT 88% vs. ETI 87%, pâ¯=â¯0.05) and rate (LT 114 vs. ETI 114 compressions per minute (cpm), pâ¯=â¯0.59) were similar between LT and ETI. Median number of CC interruptions were: LT 11 vs. ETI 12 (pâ¯=â¯0.001). Total CC interruption duration was lower for LT than ETI (LT 160 vs. ETI 181â¯s, pâ¯=â¯0.002); this difference was larger before airway insertion (LT 56 vs. ETI 78â¯s, pâ¯<â¯0.001). There were no differences in CC quality when stratified by 3-min time epochs. CONCLUSION: In the PART trial, compared with ETI, LT was associated with shorter total CC interruption duration but not other CC quality measures. CC quality may be associated with OHCA airway management.
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Reanimação Cardiopulmonar , Serviços Médicos de Emergência , Parada Cardíaca Extra-Hospitalar , Adulto , Manuseio das Vias Aéreas , Humanos , Intubação Intratraqueal , Parada Cardíaca Extra-Hospitalar/terapiaRESUMO
Sudden cardiac arrest is one of the leading causes of death in the industrialized world. Pulse detection is essential for the recognition of the arrest and the recognition of return of spontaneous circulation during therapy, and it is therefore crucial for the survival of the patient. This paper introduces the first method based exclusively on the ECG for the automatic detection of pulse during cardiopulmonary resuscitation. Random forest classifier is used to efficiently combine up to nine features from the time, frequency, slope, and regularity analysis of the ECG. Data from 191 cardiac arrest patients was used, and 1177 ECG segments were processed, 796 with pulse and 381 without pulse. A leave-one-patient out cross validation approach was used to train and test the algorithm. The statistical distributions of sensitivity (SE) and specificity (SP) for pulse detection were estimated using 500 patient-wise bootstrap partitions. The mean (std) SE/SP for nine-feature classifier was 88.4 (1.8) %/89.7 (1.4) %, respectively. The designed algorithm only requires 4-s-long ECG segments and could be integrated in any commercial automated external defibrillator. The method permits to detect the presence of pulse accurately, minimizing interruptions in cardiopulmonary resuscitation therapy, and could contribute to improve survival from cardiac arrest.
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Parada Cardíaca/fisiopatologia , Frequência Cardíaca/fisiologia , Algoritmos , Reanimação Cardiopulmonar/métodos , Eletrocardiografia/métodos , Humanos , Sensibilidade e EspecificidadeRESUMO
Monitoring ventilation rate is key to improve the quality of cardiopulmonary resuscitation (CPR) and increase the probability of survival in the event of an out-of-hospital cardiac arrest (OHCA). Ventilations produce discernible fluctuations in the thoracic impedance signal recorded by defibrillators. Impedance-based detection of ventilations during CPR is challenging due to chest compression artifacts. This study presents a method for an accurate detection of ventilations when chest compressions are delivered using a piston-driven mechanical device. Data from 223 OHCA patients were analyzed and 399 analysis segments totaling 3101 minutes of mechanical CPR were extracted. A total of 18327 ventilations were annotated using concurrent capnogram recordings. An adaptive least mean squares filter was used to remove compression artifacts. Potential ventilations were detected using a greedy peak detector, and the ventilation waveform was characterized using 8 waveform features. These features were used in a logistic regression classifier to discriminate true ventilations from false positives produced by the greedy peak detector. The classifier was trained and tested using patient wise 10-fold cross validation (CV), and 100 random CV partitions were created to statistically characterize the performance metrics. The peak detector presented a sensitivity (Se) of 99.30%, but a positive predictive value (PPV) of 54.43%. The best classifier configuration used 6 features and improved the mean (sd) Se and PPV of the detector to 93.20% (0.06) and 94.43% (0.04), respectively. When used to measure per minute ventilation rates for feedback to the rescuer, the mean (sd) absolute error in ventilation rate was 0.61 (1.64) min-1. The first impedance-based method to accurately detect ventilations and give feedback on ventilation rate during mechanical CPR has been demonstrated.
Assuntos
Reanimação Cardiopulmonar , Parada Cardíaca Extra-Hospitalar , Desfibriladores , Impedância Elétrica , Humanos , VentilaçãoRESUMO
Pulse detection during out-of-hospital cardiac arrest remains challenging for both novel and expert rescuers because current methods are inaccurate and time-consuming. There is still a need to develop automatic methods for pulse detection, where the most challenging scenario is the discrimination between pulsed rhythms (PR, pulse) and pulseless electrical activity (PEA, no pulse). Thoracic impedance (TI) acquired through defibrillation pads has been proven useful for detecting pulse as it shows small fluctuations with every heart beat. In this study we analyse the use of deep learning techniques to detect pulse using only the TI signal. The proposed neural network, composed by convolutional and recurrent layers, outperformed state of the art methods, and achieved a balanced accuracy of 90% for segments as short as 3 s.
Assuntos
Impedância Elétrica , Redes Neurais de Computação , Parada Cardíaca Extra-Hospitalar/diagnóstico , Pulso Arterial , Reanimação Cardiopulmonar , HumanosRESUMO
BACKGROUND: Automated detection of return of spontaneous circulation (ROSC) is still an unsolved problem during cardiac arrest. Current guidelines recommend the use of capnography, but most automatic methods are based on the analysis of the ECG and thoracic impedance (TI) signals. This study analysed the added value of EtCO2 for discriminating pulsed (PR) and pulseless (PEA) rhythms and its potential to detect ROSC. MATERIALS AND METHODS: A total of 426 out-of-hospital cardiac arrest cases, 117 with ROSC and 309 without ROSC, were analysed. First, EtCO2 values were compared for ROSC and no ROSC cases. Second, 5098 artefact free 3-s long segments were automatically extracted and labelled as PR (3639) or PEA (1459) using the instant of ROSC annotated by the clinician on scene as gold standard. Machine learning classifiers were designed using features obtained from the ECG, TI and the EtCO2 value. Third, the cases were retrospectively analysed using the classifier to discriminate cases with and without ROSC. RESULTS: EtCO2 values increased significantly from 41â¯mmHg 3-min before ROSC to 57â¯mmHg 1-min after ROSC, and EtCO2 was significantly larger for PR than for PEA, 46â¯mmHg/20â¯mmHg (pâ¯<â¯0.05). Adding EtCO2 to the machine learning models increased their area under the curve (AUC) by over 2 percentage points. The combination of ECG, TI and EtCO2 had an AUC for the detection of pulse of 0.92. Finally, the retrospective analysis showed a sensitivity and specificity of 96.6% and 94.5% for the detection of ROSC and no-ROSC cases, respectively. CONCLUSION: Adding EtCO2 improves the performance of automatic algorithms for pulse detection based on ECG and TI. These algorithms can be used to identify pulse on site, and to retrospectively identify cases with ROSC.
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Capnografia/métodos , Cardiografia de Impedância/métodos , Reanimação Cardiopulmonar/métodos , Eletrocardiografia/métodos , Determinação da Frequência Cardíaca/métodos , Parada Cardíaca Extra-Hospitalar , Idoso , Feminino , Humanos , Aprendizado de Máquina , Masculino , Pessoa de Meia-Idade , Monitorização Fisiológica/métodos , Parada Cardíaca Extra-Hospitalar/sangue , Parada Cardíaca Extra-Hospitalar/diagnóstico , Parada Cardíaca Extra-Hospitalar/terapia , Reprodutibilidade dos Testes , Sensibilidade e EspecificidadeRESUMO
GOAL: Accurate shock decision methods during piston-driven cardiopulmonary resuscitation (CPR) would contribute to improve therapy and increase cardiac arrest survival rates. The best current methods are computationally demanding, and their accuracy could be improved. The objective of this work was to introduce a computationally efficient algorithm for shock decision during piston-driven CPR with increased accuracy. METHODS: The study dataset contains 201 shockable and 844 nonshockable ECG segments from 230 cardiac arrest patients treated with the LUCAS-2 mechanical CPR device. Compression artifacts were removed using the state-of-the-art adaptive filters, and shock/no-shock discrimination features were extracted from the stationary wavelet transform analysis of the filtered ECG, and fed to a support vector machine (SVM) classifier. Quasi-stratified patient wise nested cross-validation was used for feature selection and SVM hyperparameter optimization. The procedure was repeated 50 times to statistically characterize the results. RESULTS: Best results were obtained for a six-feature classifier with mean (standard deviation) sensitivity, specificity, and total accuracy of 97.5 (0.4), 98.2 (0.4), and 98.1 (0.3), respectively. The algorithm presented a five-fold reduction in computational demands when compared to the best available methods, while improving their balanced accuracy by 3 points. CONCLUSIONS: The accuracy of the best available methods was improved while drastically reducing the computational demands. SIGNIFICANCE: An efficient and accurate method for shock decisions during mechanical CPR is now available to improve therapy and contribute to increase cardiac arrest survival.
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Reanimação Cardiopulmonar/métodos , Sistemas de Apoio a Decisões Clínicas , Eletrocardiografia/métodos , Parada Cardíaca/terapia , Máquina de Vetores de Suporte , Parada Cardíaca/fisiopatologia , Humanos , Análise de OndaletasRESUMO
GOAL: An accurate rhythm analysis during cardiopulmonary resuscitation (CPR) would contribute to increase the survival from out-of-hospital cardiac arrest. Piston-driven mechanical compression devices are frequently used to deliver CPR. The objective of this paper was to design a method to accurately diagnose the rhythm during compressions delivered by a piston-driven device. METHODS: Data was gathered from 230 out-of-hospital cardiac arrest patients treated with the LUCAS 2 mechanical CPR device. The dataset comprised 201 shockable and 844 nonshockable ECG segments, whereof 270 were asystole (AS) and 574 organized rhythm (OR). A multistage algorithm (MSA) was designed, which included two artifact filters based on a recursive least squares algorithm, a rhythm analysis algorithm from a commercial defibrillator, and an ECG-slope-based rhythm classifier. Data was partitioned randomly and patient-wise into training (60%) and test (40%) for optimization and validation, and statistically meaningful results were obtained repeating the process 500 times. RESULTS: The mean (standard deviation) sensitivity (SE) for shockable rhythms, specificity (SP) for nonshockable rhythms, and the total accuracy of the MSA solution were: 91.7 (6.0), 98.1 (1.1), and 96.9 (0.9), respectively. The SP for AS and OR were 98.0 (1.7) and 98.1 (1.4), respectively. CONCLUSIONS: The SE/SP were above the 90%/95% values recommended by the American Heart Association for shockable and nonshockable rhythms other than sinus rhythm, respectively. SIGNIFICANCE: It is possible to accurately diagnose the rhythm during mechanical chest compressions and the results considerably improve those obtained by previous algorithms.
Assuntos
Algoritmos , Reanimação Cardiopulmonar/métodos , Eletrocardiografia/classificação , Processamento de Sinais Assistido por Computador , Artefatos , Humanos , Parada Cardíaca Extra-Hospitalar/fisiopatologia , Parada Cardíaca Extra-Hospitalar/terapia , Sensibilidade e EspecificidadeRESUMO
AIM: To evaluate the performance of a state-of-the-art cardiopulmonary resuscitation (CPR) artefact suppression method by assessing to what extent the filtered electrocardiogram (ECG) can be correctly diagnosed by emergency medicine doctors. METHODS: A total of 819 ECG segments were used. Each segment contained two consecutive 10â¯s intervals, an artefact free interval and an interval corrupted by CPR artefacts. Each ECG segment was digitally processed to remove CPR artefacts using an adaptive filter. Each ECG segment was split into artefact-free and filtered intervals, randomly reordered for dissociation, and independently offered to four reviewers for rhythm annotation. The rhythm annotations of the artefact-free intervals were considered as the gold standard against which the rhythm annotations of the filtered intervals were evaluated. For the filtered intervals, the rater agreement (κ, Kappa score) with the gold standard, the sensitivity and the specificity were computed individually for each reviewer, and jointly through the majority decision of the pool of reviewers (DPR). These results were also compared to those obtained using a commercial shock advisory algorithm (SAA). RESULTS: The agreement between each reviewer and the gold standard was moderate ranging between κâ¯=â¯0.41-0.64. The sensitivities and specificities ranged between 64.3-95.0%, and 70.0-95.9%, respectively. The agreement for the DPR was substantial with κâ¯=â¯0.64 (0.62-0.66), a sensitivity of 90.6%, and a specificity of 85.6%. For the SAA, the agreement was fair with κâ¯=â¯0.33 (0.31-0.35), a sensitivity of 90.3%, and a specificity of 66.4%. CONCLUSION: Clinicians outperformed the SAA, but specificities remained below the specifications recommended by the American Heart Association. Visual assessment of the filtered ECG by clinicians is not reliable enough, and varies greatly among clinicians. Results considerably improve by considering the consensus decision of a pool of clinicians.
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Reanimação Cardiopulmonar/métodos , Eletrocardiografia/métodos , Parada Cardíaca Extra-Hospitalar , Fibrilação Ventricular , Algoritmos , Artefatos , Desfibriladores/estatística & dados numéricos , Estudos de Viabilidade , Massagem Cardíaca , Humanos , Parada Cardíaca Extra-Hospitalar/diagnóstico , Parada Cardíaca Extra-Hospitalar/fisiopatologia , Sensibilidade e Especificidade , Fibrilação Ventricular/diagnóstico , Fibrilação Ventricular/fisiopatologiaRESUMO
AIM: The rates of chest compressions (CCs) and ventilations are both important metrics to monitor the quality of cardiopulmonary resuscitation (CPR). Capnography permits monitoring ventilation, but the CCs provided during CPR corrupt the capnogram and compromise the accuracy of automatic ventilation detectors. The aim of this study was to evaluate the feasibility of an automatic algorithm based on the capnogram to detect ventilations and provide feedback on ventilation rate during CPR, specifically addressing intervals where CCs are delivered. METHODS: The dataset used to develop and test the algorithm contained in-hospital and out-of-hospital cardiac arrest episodes. The method relies on adaptive thresholding to detect ventilations in the first derivative of the capnogram. The performance of the detector was reported in terms of sensitivity (SE) and Positive Predictive Value (PPV). The overall performance was reported in terms of the rate error and errors in the hyperventilation alarms. Results were given separately for the intervals with CCs. RESULTS: A total of 83 episodes were considered, resulting in 4880min and 46,740 ventilations (8741 during CCs). The method showed an overall SE/PPV above 99% and 97% respectively, even in intervals with CCs. The error for the ventilation rate was below 1.8min-1 in any group, and >99% of the ventilation alarms were correctly detected. CONCLUSION: A method to provide accurate feedback on ventilation rate using only the capnogram is proposed. Its accuracy was proven even in intervals where canpography signal was severely corrupted by CCs. This algorithm could be integrated into monitor/defibrillators to provide reliable feedback on ventilation rate during CPR.
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Algoritmos , Capnografia/métodos , Reanimação Cardiopulmonar , Parada Cardíaca , Hiperventilação , Ventilação Pulmonar/fisiologia , Reanimação Cardiopulmonar/efeitos adversos , Reanimação Cardiopulmonar/métodos , Precisão da Medição Dimensional , Estudos de Viabilidade , Parada Cardíaca/diagnóstico , Parada Cardíaca/fisiopatologia , Parada Cardíaca/terapia , Humanos , Hiperventilação/etiologia , Hiperventilação/prevenção & controle , Monitorização Fisiológica , Valor Preditivo dos Testes , Sensibilidade e Especificidade , Processamento de Sinais Assistido por ComputadorRESUMO
AIM: To develop and evaluate a method to detect circulation in the presence of organized rhythms (ORs) during resuscitation using signals acquired by defibrillation pads. METHODS: Segments containing electrocardiogram (ECG) and thoracic impedance (TI) signals free of artifacts were used. The ECG corresponded to ORs classified as pulseless electrical activity (PEA) or pulse-generating rhythm (PR). A first dataset containing 1091 segments was split into training and test sets to develop and validate the circulation detector. The method processed ECG and TI to obtain the impedance circulation component (ICC). Morphological features were extracted from ECG and ICC, and combined into a classifier to discriminate between PEA and PR. The performance of the method was evaluated in terms of sensitivity (PR) and specificity (PEA). A second dataset (86 segments from different patients) was used to assess two application of the method: confirmation of arrest by recognizing absence of circulation during ORs and detection of return of spontaneous circulation (ROSC) during resuscitation. In both cases, time to confirmation of arrest/ROSC was determined. RESULTS: The method showed a sensitivity/specificity of 92.1%/90.3% and 92.2%/91.9% for training and test sets respectively. The method confirmed cardiac arrest with a specificity of 93.3% with a median delay of 0s after the first OR annotation. ROSC was detected with a sensitivity of 94.4% with a median delay of 57s from ROSC onset. CONCLUSION: The method showed good performance, and can be reliably used to distinguish perfusing from non-perfusing ORs.
Assuntos
Circulação Sanguínea , Reanimação Cardiopulmonar , Desfibriladores , Impedância Elétrica , Eletrocardiografia , Parada Cardíaca/diagnóstico , Parada Cardíaca/terapia , Parada Cardíaca/fisiopatologia , Humanos , TóraxRESUMO
AIM: To determine the accuracy and reliability of the thoracic impedance (TI) signal to assess cardiopulmonary resuscitation (CPR) quality metrics. METHODS: A dataset of 63 out-of-hospital cardiac arrest episodes containing the compression depth (CD), capnography and TI signals was used. We developed a chest compression (CC) and ventilation detector based on the TI signal. TI shows fluctuations due to CCs and ventilations. A decision algorithm classified the local maxima as CCs or ventilations. Seven CPR quality metrics were computed: mean CC-rate, fraction of minutes with inadequate CC-rate, chest compression fraction, mean ventilation rate, fraction of minutes with hyperventilation, instantaneous CC-rate and instantaneous ventilation rate. The CD and capnography signals were accepted as the gold standard for CC and ventilation detection respectively. The accuracy of the detector was evaluated in terms of sensitivity and positive predictive value (PPV). Distributions for each metric computed from the TI and from the gold standard were calculated and tested for normality using one sample Kolmogorov-Smirnov test. For normal and not normal distributions, two sample t-test and Mann-Whitney U test respectively were applied to test for equal means and medians respectively. Bland-Altman plots were represented for each metric to analyze the level of agreement between values obtained from the TI and gold standard. RESULTS: The CC/ventilation detector had a median sensitivity/PPV of 97.2%/97.7% for CCs and 92.2%/81.0% for ventilations respectively. Distributions for all the metrics showed equal means or medians, and agreements >95% between metrics and gold standard was achieved for most of the episodes in the test set, except for the instantaneous ventilation rate. CONCLUSION: With our data, the TI can be reliably used to measure all the CPR quality metrics proposed in this study, except for the instantaneous ventilation rate.
Assuntos
Algoritmos , Cardiografia de Impedância/métodos , Reanimação Cardiopulmonar/normas , Parada Cardíaca Extra-Hospitalar/terapia , Impedância Elétrica , Humanos , Parada Cardíaca Extra-Hospitalar/fisiopatologia , Reprodutibilidade dos TestesRESUMO
BACKGROUND: Quality of cardiopulmonary resuscitation (CPR) is an important determinant of survival from cardiac arrest. The use of feedback devices is encouraged by current resuscitation guidelines as it helps rescuers to improve quality of CPR performance. AIM: To determine the feasibility of a generic algorithm for feedback related to chest compression (CC) rate using the transthoracic impedance (TTI) signal recorded through the defibrillation pads. METHODS: We analysed 180 episodes collected equally from three different emergency services, each one using a unique defibrillator model. The new algorithm computed the CC-rate every 2s by analysing the TTI signal in the frequency domain. The obtained CC-rate values were compared with the gold standard, computed using the compression force or the ECG and TTI signals when the force was not recorded. The accuracy of the CC-rate, the proportion of alarms of inadequate CC-rate, chest compression fraction (CCF) and the mean CC-rate per episode were calculated. RESULTS: Intervals with CCs were detected with a mean sensitivity and a mean positive predictive value per episode of 96.3% and 97.0%, respectively. Estimated CC-rate had an error below 10% in 95.8% of the time. Mean percentage of accurate alarms per episode was 98.2%. No statistical differences were found between the gold standard and the estimated values for any of the computed metrics. CONCLUSION: We developed an accurate algorithm to calculate and provide feedback on CC-rate using the TTI signal. This could be integrated into automated external defibrillators and help improve the quality of CPR in basic-life-support settings.
Assuntos
Algoritmos , Cardiografia de Impedância/métodos , Reanimação Cardiopulmonar , Serviços Médicos de Emergência/normas , Retroalimentação , Parada Cardíaca/terapia , Reanimação Cardiopulmonar/instrumentação , Reanimação Cardiopulmonar/métodos , Reanimação Cardiopulmonar/normas , Desfibriladores , Eletrocardiografia/métodos , Estudos de Viabilidade , Humanos , Valor Preditivo dos Testes , Pressão , Garantia da Qualidade dos Cuidados de Saúde/métodos , Melhoria de Qualidade , Reprodutibilidade dos TestesRESUMO
Quality of cardiopulmonary resuscitation (CPR) improves through the use of CPR feedback devices. Most feedback devices integrate the acceleration twice to estimate compression depth. However, they use additional sensors or processing techniques to compensate for large displacement drifts caused by integration. This study introduces an accelerometer-based method that avoids integration by using spectral techniques on short duration acceleration intervals. We used a manikin placed on a hard surface, a sternal triaxial accelerometer, and a photoelectric distance sensor (gold standard). Twenty volunteers provided 60 s of continuous compressions to test various rates (80-140 min(-1)), depths (3-5 cm), and accelerometer misalignment conditions. A total of 320 records with 35312 compressions were analysed. The global root-mean-square errors in rate and depth were below 1.5 min(-1) and 2 mm for analysis intervals between 2 and 5 s. For 3 s analysis intervals the 95% levels of agreement between the method and the gold standard were within -1.64-1.67 min(-1) and -1.69-1.72 mm, respectively. Accurate feedback on chest compression rate and depth is feasible applying spectral techniques to the acceleration. The method avoids additional techniques to compensate for the integration displacement drift, improving accuracy, and simplifying current accelerometer-based devices.