Prediction of Shock-Refractory Ventricular Fibrillation During Resuscitation of Out-of-Hospital Cardiac Arrest.

BACKGROUND: Out-of-hospital cardiac arrest due to shock-refractory ventricular fibrillation (VF) is associated with relatively poor survival. The ability to predict refractory VF (requiring ≥3 shocks) in advance of repeated shock failure could enable preemptive targeted interventions aimed at improving outcome, such as earlier administration of antiarrhythmics, reconsideration of epinephrine use or dosage, changes in shock delivery strategy, or expedited invasive treatments. METHODS: We conducted a cohort study of VF out-of-hospital cardiac arrest to develop an ECG-based algorithm to predict patients with refractory VF. Patients with available defibrillator recordings were randomized 80%/20% into training/test groups. A random forest classifier applied to 3-s ECG segments immediately before and 1 minute after the initial shock during cardiopulmonary resuscitation was used to predict the need for ≥3 shocks based on singular value decompositions of ECG wavelet transforms. Performance was quantified by area under the receiver operating characteristic curve. RESULTS: Of 1376 patients with VF out-of-hospital cardiac arrest, 311 (23%) were female, 864 (63%) experienced refractory VF, and 591 (43%) achieved functional neurological survival. Total shock count was associated with decreasing likelihood of functional neurological survival, with a relative risk of 0.95 (95% CI, 0.93-0.97) for each successive shock (P<0.001). In the 275 test patients, the area under the receiver operating characteristic curve for predicting refractory VF was 0.85 (95% CI, 0.79-0.89), with specificity of 91%, sensitivity of 63%, and a positive likelihood ratio of 6.7. CONCLUSIONS: A machine learning algorithm using ECGs surrounding the initial shock predicts patients likely to experience refractory VF, and could enable rescuers to preemptively target interventions to potentially improve resuscitation outcome.

Age, sex, and survival following ventricular fibrillation cardiac arrest: A mechanistic evaluation of the ECG waveform.

BACKGROUND: Studies of outcome differences by sex in out-of-hospital cardiac arrest (OHCA) have produced mixed results that may depend on age, a potential surrogate for menopausal status. OBJECTIVE: We used quantitative measures of ventricular fibrillation (VF) waveforms - indicators of the myocardium's physiology - to assess whether survival differences according to sex and age group may be mediated via a biologic mechanism. METHODS: We conducted a cohort study of VF-OHCA in a metropolitan EMS system. We used multivariable logistic regression to assess the association of survival to hospital discharge with sex and age group (<55, ≥55 years). We determined the proportion of outcome difference mediated by VF waveform measures: VitalityScore and amplitude spectrum area (AMSA). RESULTS: Among 1526 VF-OHCA patients, the average age was 62 years, and 29% were female. Overall, younger women were more likely to survive than younger men (survival 67% vs 54%, p = 0.02), while survival among older women and older men did not differ (40% vs 44%, p = 0.3). Adjusting for Utstein characteristics, women <55 compared to men <55 had greater odds of survival to hospital discharge (OR = 1.93, 95% CI 1.23-3.09), an association not observed between the ≥55 groups. Waveform measures were more favorable among women and mediated some of the beneficial association between female sex and survival among those <55 years: 47% for VitalityScore and 25% for AMSA. CONCLUSIONS: Women <55 years were more likely to survive than men <55 years following VF-OHCA. The biologic mechanism represented by VF waveform mediated some, though not all, of the outcome difference.

Instantaneous amplitude: Association of ventricular fibrillation waveform measures at time of shock with outcome in out-of-hospital cardiac arrest.

BACKGROUND: Prompt defibrillation is key to successful resuscitation from ventricular fibrillation out-of-hospital cardiac arrest (VF-OHCA). Preliminary evidence suggests that the timing of shock relative to the amplitude of the VF ECG waveform may affect the likelihood of resuscitation. We investigated whether the VF waveform amplitude at the time of shock (instantaneous amplitude) predicts outcome independent of other validated waveform measures. METHODS: We conducted a retrospective study of VF-OHCA patients ≥18 old. We evaluated three VF waveform measures for each shock: instantaneous amplitude at the time of shock, and maximum amplitude and amplitude spectrum area (AMSA) over a 3-s window preceding the shock. Linear mixed-effects modeling was used to determine whether instantaneous amplitude was associated with shock-specific return of organized rhythm (ROR) or return of spontaneous circulation (ROSC) independent of maximum amplitude or AMSA. RESULTS: The 566 eligible patients received 1513 shocks, resulting in ROR of 62.0% (938/1513) and ROSC of 22.3% (337/1513). In unadjusted regression, an interquartile increase in instantaneous amplitude was associated with ROR (Odds ratio [OR] [95% confidence interval] = 1.27 [1.11-1.45]) and ROSC (OR = 1.27 [1.14-1.42]). However, instantaneous amplitude was not associated with ROR (OR = 1.13 [0.97-1.30]) after accounting for maximum amplitude, nor with ROR (OR = 1.00 [0.87-1.15]) or ROSC (OR = 1.05 [0.93-1.18]) after accounting for AMSA. By contrast, AMSA and maximum amplitude remained independently associated with ROR and ROSC. CONCLUSIONS: We did not observe an independent association between instantaneous amplitude and shock-specific outcomes. Efforts to time shock to the maximal amplitude of the VF waveform are unlikely to affect resuscitation outcome.

Continuous assessment of ventricular fibrillation prognostic status during CPR: Implications for resuscitation.

BACKGROUND: Ventricular fibrillation (VF) waveform measures reflect myocardial physiologic status. Continuous assessment of VF prognosis using such measures could guide resuscitation, but has not been possible due to CPR artifact in the ECG. A recently-validated VF measure (termed VitalityScore), which estimates the probability (0-100%) of return-of-rhythm (ROR) after shock, can assess VF during CPR, suggesting potential for continuous application during resuscitation. OBJECTIVE: We evaluated VF using VitalityScore to characterize VF prognostic status continuously during resuscitation. METHODS: We characterized VF using VitalityScore during 60 seconds of CPR and 10 seconds of subsequent pre-shock CPR interruption in patients with out-of-hospital VF arrest. VitalityScore utility was quantified using area under the receiver operating characteristic curve (AUC). VitalityScore trends over time were estimated using mixed-effects models, and associations between trends and ROR were evaluated using logistic models. A sensitivity analysis characterized VF during protracted (100-second) periods of CPR. RESULTS: We evaluated 724 VF episodes among 434 patients. After an initial decline from 0-8 seconds following VF onset, VitalityScore increased slightly during CPR from 8-60 seconds (slope: 0.18%/min). During the first 10 seconds of subsequent pre-shock CPR interruption, VitalityScore declined (slope: -14%/min). VitalityScore predicted ROR throughout CPR with AUCs 0.73-0.75. Individual VitalityScore trends during 8-60 seconds of CPR were marginally associated with subsequent ROR (adjusted odds ratio for interquartile slope change (OR) = 1.10, p = 0.21), and became significant with protracted (100 seconds) CPR duration (OR = 1.28, p = 0.006). CONCLUSION: VF prognostic status can be continuously evaluated during resuscitation, a development that could translate to patient-specific resuscitation strategies.

Investigating the Airway Opening Index during cardiopulmonary resuscitation.

INTRODUCTION: Chest compressions during CPR induce oscillations in capnography (ETCO2) waveforms. Studies suggest ETCO2 oscillation characteristics are associated with intrathoracic airflow dependent on airway patency. Oscillations can be quantified by the Airway Opening Index (AOI). We sought to evaluate multiple methods of computing AOI and their association with return of spontaneous circulation (ROSC). METHODS: We conducted a retrospective study of 307 out-of-hospital cardiac arrest (OHCA) cases in Seattle, WA during 2019. ETCO2 and chest impedance waveforms were annotated for the presence of intubation and CPR. We developed four methods for computing AOI based on peak ETCO2 and the oscillations in ETCO2 during chest compressions (ΔETCO2). We examined the feasibility of automating ΔETCO2 and AOI calculation and evaluated differences in AOI across the methods using nonparametric testing (α = 0.05). RESULTS: Median [interquartile range] AOI across all cases using Methods 1-4 was 28.0 % [17.9-45.5 %], 20.6 % [13.0-36.6 %], 18.3 % [11.4-30.4 %], and 22.4 % [12.8-38.5 %], respectively (p < 0.001). Cases with ROSC had a higher median AOI than those without ROSC across all methods, though not statistically significant. Cases with ROSC had a significantly higher median [interquartile range] ΔETCO2 of 7.3 mmHg [4.5-13.6 mmHg] compared to those without ROSC (4.8 mmHg [2.6-9.1 mmHg], p < 0.001). CONCLUSION: We calculated AOI using four proposed methods resulting in significantly different AOI. Additionally, AOI and ΔETCO2 were larger in cases achieving ROSC. Further investigation is required to characterize AOI's ability to predict OHCA outcomes, and whether this information can improve resuscitation care.

A method for continuous rhythm classification and early detection of ventricular fibrillation during CPR.

AIM: We developed a method which continuously classifies the ECG rhythm during CPR in order to guide clinical care. METHODS: We conducted a retrospective study of 432 patients treated following out-of-hospital cardiac arrest. Continuous ECG sequences from two-minute CPR cycles were extracted from defibrillator recordings and further divided into five-second clips. We developed an algorithm using wavelet analysis, hidden semi-Markov modeling, and random forest classification. The algorithm classifies individual clips as asystole, organized rhythm, ventricular fibrillation, or Inconclusive while integrating information from previous clips. Classifications were compared to manual annotations to estimate accuracy in an independent validation dataset. Continuous sequences were classified as shockable, non-shockable, or Inconclusive; classifications were used to compute shock sensitivity and specificity. RESULTS: Of 432 patient-cases, 290 were used for development and 142 for validation. In the 12,294 validation ECG clips during CPR, accuracies were 0.88 (95% CI 0.85-0.91) for asystole, 0.98 (95% CI 0.98-0.99) for organized rhythm, and 0.97 (95% CI 0.96-0.97) for ventricular fibrillation, with 43% classified as Inconclusive. Of 457 continuous sequences, shock sensitivity was 0.90 (95% CI 0.86-0.93), shock specificity was 0.98 (95% CI 0.93-0.99), and 7% were Inconclusive. Median delay to ventricular fibrillation recognition was 10 (IQR 5-32) seconds. CONCLUSION: A novel algorithm continuously classified the primary resuscitation rhythms-asystole, organized rhythms, and ventricular fibrillation-with 88-98% accuracy, enabling accurate shock advisory guidance during most two-minute CPR cycles. Additional investigation is required to understand how algorithm implementation could affect rescuer actions and clinical outcomes.

A pilot evaluation of respiratory mechanics during prehospital manual ventilation.

INTRODUCTION: Respiratory mechanics, such as tidal volume (VT) and inspiratory pressures, may affect outcome in hospitalized patients with respiratory failure. Little is known about respiratory mechanics in the prehospital setting. METHODS: In this prospective, pilot investigation of patients receiving prehospital advanced airway placement, paramedics applied a device to measure respiratory mechanics. We evaluated tidal volume (VT) per predicted body weight (VTPBW) to determine the proportion of breaths within the lung-protective range of 4-10 mL/kg per PBW overall, according to ventilation bag volume (large versus small) and cardiac arrest status (active CPR, post-ROSC, non-arrest). RESULTS: Over 16-months, 7371 post-intubation breaths were measured in 54 patients, 32 patients with cardiac arrest and 22 with other conditions. Paramedics ventilated 19 patients with a small bag and 35 patients with a large bag. Overall, mean VT was 435 mL (95% CI 403, 467); VTPBW was 7.0 mL/kg (95% CI 6.4, 7.6) with 75% within the lung-protective range. Mean VTPBW and peak pressure differed according to arrest status (absolute difference -0.36 mL/kg and 32 cmH2O for active CPR compared to post-ROSC), though not according to bag size. CONCLUSIONS: We observed that measuring respiratory mechanics in the prehospital setting was feasible. Tidal volumes were generally delivered within a safe range. Respiratory mechanics varied most significantly with active CPR with lower VTPBW and higher peak pressures, though did not seem to be affected by bag size. Future work might examine the relationship between respiratory mechanics and outcomes, which may identify opportunities to improve clinical outcomes.

Machine learning and feature engineering for predicting pulse presence during chest compressions.

Current resuscitation protocols require pausing chest compressions during cardiopulmonary resuscitation (CPR) to check for a pulse. However, pausing CPR when a patient is pulseless can worsen patient outcomes. Our objective was to design and evaluate an ECG-based algorithm that predicts pulse presence with or without CPR. We evaluated 383 patients being treated for out-of-hospital cardiac arrest with real-time ECG, impedance and audio recordings. Paired ECG segments having an organized rhythm immediately preceding a pulse check (during CPR) and during the pulse check (without CPR) were extracted. Patients were randomly divided into 60% training and 40% test groups. From training data, we developed an algorithm to predict the clinical pulse presence based on the wavelet transform of the bandpass-filtered ECG. Principal component analysis was used to reduce dimensionality, and we then trained a linear discriminant model using three principal component modes as input features. Overall, 38% (351/912) of checks had a spontaneous pulse. AUCs for predicting pulse presence with and without CPR on test data were 0.84 (95% CI (0.80, 0.88)) and 0.89 (95% CI (0.86, 0.92)), respectively. This ECG-based algorithm demonstrates potential to improve resuscitation by predicting the presence of a spontaneous pulse without pausing CPR with moderate accuracy.

Insights From the Ventricular Fibrillation Waveform Into the Mechanism of Survival Benefit From Bystander Cardiopulmonary Resuscitation.

Background The mechanism by which bystander cardiopulmonary resuscitation (CPR) improves survival following out-of-hospital cardiac arrest is unclear. We hypothesized that ventricular fibrillation (VF) waveform measures, as surrogates of myocardial physiology, mediate the relationship between bystander CPR and survival. Methods and Results We performed a retrospective cohort study of adult, bystander-witnessed patients with out-of-hospital cardiac arrest with an initial rhythm of VF who were treated by a metropolitan emergency medical services system from 2005 to 2018. Patient, resuscitation, and outcome variables were extracted from emergency medical services and hospital records. A total of 3 VF waveform measures (amplitude spectrum area, peak frequency, and median peak amplitude) were computed from a 3-second ECG segment before the initial shock. Multivariable logistic regression estimated the association between bystander CPR and survival to hospital discharge adjusted for Utstein elements. Causal mediation analysis quantified the proportion of survival benefit that was mediated by each VF waveform measure. Of 1069 patients, survival to hospital discharge was significantly higher among the 814 patients who received bystander CPR than those who did not (0.52 versus 0.43, respectively; P<0.01). The multivariable-adjusted odds ratio for bystander CPR and survival was 1.6 (95% CI, 1.2, 2.1), and each VF waveform measure attenuated this association. Depending on the specific waveform measure, the proportion of mediation varied: 53% for amplitude spectrum area, 31% for peak frequency, and 29% for median peak amplitude. Conclusions Bystander CPR correlated with more robust initial VF waveform measures, which in turn mediated up to one-half of the survival benefit associated with bystander CPR. These results provide insight into the biological mechanism of bystander CPR in VF out-of-hospital cardiac arrest.

A method to predict ventricular fibrillation shock outcome during chest compressions.

BACKGROUND: Out-of-hospital ventricular fibrillation (VF) cardiac arrest is a leading cause of death. Quantitative analysis of the VF electrocardiogram (ECG) can predict patient outcomes and could potentially enable a patient-specific, guided approach to resuscitation. However, VF analysis during resuscitation is confounded by cardiopulmonary resuscitation (CPR) artifact in the ECG, challenging continuous application to guide therapy throughout resuscitation. We therefore sought to design a method to predict VF shock outcomes during CPR. METHODS: Study data included 4577 5-s VF segments collected during and without CPR prior to defibrillation attempts in N = 1151 arrest patients. Using training data (460 patients), an algorithm was designed to predict the VF shock outcomes of defibrillation success (return of organized ventricular rhythm) and functional survival (Cerebral Performance Category 1-2). The algorithm was designed with variable-frequency notch filters to reduce CPR artifact in the ECG based on real-time chest compression rate. Ten ECG features and three dichotomous patient characteristics were developed to predict outcomes. These variables were combined using support vector machines and logistic regression. Algorithm performance was evaluated by area under the receiver operating characteristic curve (AUC) to predict outcomes in validation data (691 patients). RESULTS: AUC (95% Confidence Interval) for predicting defibrillation success was 0.74 (0.71-0.77) during CPR and 0.77 (0.74-0.79) without CPR. AUC for predicting functional survival was 0.75 (0.72-0.78) during CPR and 0.76 (0.74-0.79) without CPR. CONCLUSION: A novel algorithm predicted defibrillation success and functional survival during ongoing CPR following VF arrest, providing a potential proof-of-concept towards real-time guidance of resuscitation therapy.

A Method to Detect Presence of Chest Compressions During Resuscitation Using Transthoracic Impedance.

OBJECTIVE: Interruptions in chest compressions during treatment of out-of-hospital cardiac arrest are associated with lower likelihood of successful resuscitation. Real-time automated detection of chest compressions may improve CPR administration during resuscitation, and could facilitate application of next-generation ECG algorithms that employ different parameters depending on compression state. In contrast to accelerometer sensors, transthoracic impedance (TTI) is commonly acquired by defibrillators. We sought to develop and evaluate the performance of a TTI-based algorithm to automatically detect chest compressions. METHODS: Five-second TTI segments were collected from patients with out-of-hospital cardiac arrest treated by one of four defibrillator models. Segments with and without chest compressions were collected prior to each of the first four defibrillation shocks (when available) from each case. Patients were divided randomly into 40% training and 60% validation groups. From the training segments, we identified spectral and time-domain features of the TTI associated with compressions. We used logistic regression to predict compression state from these features. Performance was measured by sensitivity and specificity in the validation set. The relationship between performance and TTI segment length was also evaluated. RESULTS: The algorithm was trained using 1859 segments from 460 training patients. Validation sensitivity and specificity were >98% using 2727 segments from 691 validation patients. Validation performance was significantly reduced using segments shorter than 3.2 s. CONCLUSIONS: A novel method can reliably detect the presence of chest compressions using TTI. These results suggest potential to provide real-time feedback in order to improve CPR performance or facilitate next-generation ECG rhythm algorithms during resuscitation.

Electrocardiogram-based pulse prediction during cardiopulmonary resuscitation.

OBJECTIVE: Resuscitation requires CPR interruptions every 2 min to assess rhythm and pulse status. We developed a method to predict real-time pulse status in organized rhythm ECG segments with and without CPR artifact. METHODS: The study cohort included patients who received attempted resuscitation following ventricular fibrillation arrest. Using audio-supplemented defibrillator recordings, we annotated CPR, rhythm, and pulse status at each two-minute rhythm/pulse check. Paired ECG segments with and without CPR were extracted at each rhythm/pulse check. Using one-third of cases for training and two-thirds for validation, we developed three wavelet-based ECG features and combined them with a logistic model to predict pulse status. Predictive performances of each individual ECG feature and the combined logistic model were measured by the area under the receiver operator characteristic curve (AUC) in the validation cases with and without CPR. RESULTS: There were 238 cases and 911 ECG segment pairs. Among 319 organized rhythm segments in the validation set, AUC for pulse prediction during CPR ranged from 0.67 to 0.79 for the individual ECG features. The logistic model was more predictive than any individual feature (AUC 0.84, 95% CI 0.80-0.89, p < 0.05 for each comparison) and performed similarly regardless of CPR (p = 0.2 for difference). CONCLUSION: ECG features extracted by wavelet analysis predicted pulse status with moderate accuracy among organized rhythm segments with and without CPR. Further study is required to understand how real-time pulse prediction during CPR could help direct care while limiting CPR interruption.

Ventricular Fibrillation Waveform Analysis During Chest Compressions to Predict Survival From Cardiac Arrest.

BACKGROUND: Quantitative measures of the ventricular fibrillation (VF) ECG waveform can assess myocardial physiology and predict cardiac arrest outcomes, making these measures a candidate to help guide resuscitation. Chest compressions are typically paused for waveform measure calculation because compressions cause ECG artifact. However, such pauses contradict resuscitation guideline recommendations to minimize cardiopulmonary resuscitation interruptions. We evaluated a comprehensive group of VF measures with and without ongoing compressions to determine their performance under both conditions for predicting functionally-intact survival, the study's primary outcome. METHODS: Five-second VF ECG segments were collected with and without chest compressions before 2755 defibrillation shocks from 1151 out-of-hospital cardiac arrest patients. Twenty-four individual measures and 3 combination measures were implemented. Measures were optimized to predict functionally-intact survival (Cerebral Performance Category score ≤2) using 460 training cases, and their performance evaluated using 691 independent test cases. RESULTS: Measures predicted functionally-intact survival on test data with an area under the receiver operating characteristic curve ranging from 0.56 to 0.75 (median, 0.73) without chest compressions and from 0.53 to 0.75 (median, 0.69) with compressions ( P<0.001 for difference). Of all measures evaluated, the support vector machine model ranked highest both without chest compressions (area under the receiver operating characteristic curve, 0.75; 95% CI, 0.73-0.78) and with compressions (area under the receiver operating characteristic curve, 0.75; 95% CI, 0.72-0.78; P=0.75 for difference). CONCLUSIONS: VF waveform measures predict functionally-intact survival when calculated during chest compressions, but prognostic performance is generally reduced compared with compression-free analysis. However, support vector machine models exhibited similar performance with and without compressions while also achieving the highest area under the receiver operating characteristic curve. Such machine learning models may, therefore, offer means to guide resuscitation during uninterrupted cardiopulmonary resuscitation.

Rhythm profiles and survival after out-of-hospital ventricular fibrillation cardiac arrest.

OBJECTIVE: Treatment: protocols for cardiac arrest rely upon rhythm analyses performed at two-minute intervals, neglecting possible rhythm changes during the intervening period of CPR. Our objective was to describe rhythm profiles (patterns of rhythm transitions during two-minute CPR cycles) following attempted defibrillation and to assess their relationship to survival. METHODS: The study included out-of-hospital cardiac arrest cases presenting with ventricular fibrillation from 2011 to 2015. The rhythm sequence was annotated during two-minute CPR cycles after the first and second shocks of each case, and the rhythm profile of each sequence was classified. We calculated absolute survival differences among rhythm profiles with the same rhythm at the two-minute check. RESULTS: Of 569 rhythm sequences after the first shock, 46% included a rhythm transition. Overall survival was 47%, and survival proportion varied by rhythm at the two-minute check: ventricular fibrillation (46%), organized (58%) and asystole (20%). Survival was similar between profiles which ended with an organized rhythm at the two-minute check. Likewise, survival was similar between profiles with asystole at the two-minute check. However, in patients with ventricular fibrillation at the two-minute check, survival was twice as high in those with a transient organized rhythm (69%) compared to constant ventricular fibrillation (32%) or transient asystole (28%). CONCLUSION: Rhythm transitions are common after attempted defibrillation. Among patients with ventricular fibrillation at the subsequent two-minute check, transient organized rhythm during the preceding two-minute CPR cycle was associated with favorable survival, suggesting distinct physiologies that could serve as the basis for different treatment strategies.

Ventricular fibrillation waveform measures combined with prior shock outcome predict defibrillation success during cardiopulmonary resuscitation.

AIM: Amplitude Spectrum Area (AMSA) and Median Slope (MS) are ventricular fibrillation (VF) waveform measures that predict defibrillation shock success. Cardiopulmonary resuscitation (CPR) obscures electrocardiograms and must be paused for analysis. Studies suggest waveform measures better predict subsequent shock success when combined with prior shock success. We determined whether this relationship applies during CPR. METHODS: AMSA and MS were calculated from 5-second pre-shock segments with and without CPR, and compared to logistic models combining each measure with prior return of organized rhythm (ROR). RESULTS: VF segments from 692 patients were analyzed during CPR before 1372 shocks and without CPR before 1283 shocks. Combining waveform measures with prior ROR increased areas under receiver operating characteristic curves for AMSA/MS with CPR (0.66/0.68 to 0.73/0.74, p<0.001) and without CPR (0.71/0.72 to 0.76/0.76, p<0.001). CONCLUSIONS: Prior ROR improves prediction of shock success during CPR, and may enable waveform measure calculation without chest compression pauses.