Correlation between end-tidal carbon dioxide and the degree of compression of heart cavities measured by transthoracic echocardiography during cardiopulmonary resuscitation for out-of-hospital cardiac arrest.
ABSTRACT: BACKGROUND:The concept of personalized cardiopulmonary resuscitation (CPR) requires a parameter that reflects its hemodynamic efficiency. While intra-arrest ultrasound is increasingly implemented into the advanced life support, we realized a pre-hospital clinical study to evaluate whether the degree of compression of the right ventricle (RV) and left ventricle (LV) induced by chest compressions during CPR for out-of-hospital cardiac arrest (OHCA) and measured by transthoracic echocardiography correlates with the levels of end-tidal carbon dioxide (EtCO2) measured at the time of echocardiographic investigation. METHODS:Thirty consecutive patients resuscitated for OHCA were included in the study. Transthoracic echocardiography was performed from a subcostal view during ongoing chest compressions in all of them. This was repeated three times during CPR in each patient, and EtCO2 levels were registered. From each investigation, a video loop was recorded. Afterwards, maximal and minimal diameters of LV and RV were obtained from the recorded loops and the compression index of LV (LVCI) and RV (RVCI) was calculated as (maximal?-?minimal/maximal diameter)?×?100. Maximal compression index (CImax) defined as the value of LVCI or RVCI, whichever was greater was also assessed. Correlations between EtCO2 and LVCI, RVCI, and CImax were expressed as Spearman's correlation coefficient (r). RESULTS:Evaluable echocardiographic records were found in 18 patients, and a total of 52 measurements of all parameters were obtained. Chest compressions induced significant compressions of all observed cardiac cavities (LVCI?=?20.6?±?13.8%, RVCI?=?34.5?±?21.6%, CImax?=?37.4?±?20.2%). We identified positive correlation of EtCO2 with LVCI (r?=?0.672, p??20?mmHg with 100% sensitivity and specificity. CONCLUSIONS:Evaluable echocardiographic records were reached in most of the patients. EtCO2 positively correlated with all parameters under consideration, while the strongest correlation was found between CImax and EtCO2. Therefore, CImax is a candidate parameter for the guidance of hemodynamic-directed CPR. TRIAL REGISTRATION:ClinicalTrial.gov, NCT03852225 . Registered 21 February 2019 - Retrospectively registered.
Project description:AIM:Current resuscitation guidelines recommend waveform capnography as an indirect indicator of perfusion during cardiopulmonary resuscitation (CPR). Chest compressions (CCs) and ventilations during CPR have opposing effects on the exhaled carbon dioxide (CO2) concentration, which need to be better characterized. The purpose of this study was to model the impact of ventilations in the exhaled CO2 measured from capnograms collected during out-of-hospital cardiac arrest (OHCA) resuscitation. METHODS:We retrospectively analyzed OHCA monitor-defibrillator files with concurrent capnogram, compression depth, transthoracic impedance and ECG signals. Segments with CC pauses, two or more ventilations, and with no pulse-generating rhythm were selected. Thus, only ventilations should have caused the decrease in CO2 concentration. The variation in the exhaled CO2 concentration with each ventilation was modeled with an exponential decay function using non-linear-least-squares curve fitting. RESULTS:Out of the original 1002 OHCA dataset (one per patient), 377 episodes had the required signals, and 196 segments from 96 patients met the inclusion criteria. Airway type was endotracheal tube in 64.8% of the segments, supraglottic King LT-D™ in 30.1%, and unknown in 5.1%. Median (IQR) decay factor of the exhaled CO2 concentration was 10.0% (7.8 - 12.9) with R2 = 0.98(0.95 - 0.99). Differences in decay factor with airway type were not statistically significant (p = 0.17). From these results, we propose a model for estimating the contribution of CCs to the end-tidal CO2 level between consecutive ventilations and for estimating the end-tidal CO2 variation as a function of ventilation rate. CONCLUSION:We have modeled the decrease in exhaled CO2 concentration with ventilations during chest compression pauses in CPR. This finding allowed us to hypothesize a mathematical model for explaining the effect of chest compressions on ETCO2 compensating for the influence of ventilation rate during CPR. However, further work is required to confirm the validity of this model during ongoing chest compressions.
Project description:Out-of-hospital cardiac arrest (OHCA) is recognized as a global mortality challenge, and digital strategies could contribute to increase the chance of survival. In this paper, we investigate if cardiopulmonary resuscitation (CPR) quality measurement using smartphone video analysis in real-time is feasible for a range of conditions. With the use of a web-connected smartphone application which utilizes the smartphone camera, we detect inactivity and chest compressions and measure chest compression rate with real-time feedback to both the caller who performs chest compressions and over the web to the dispatcher who coaches the caller on chest compressions. The application estimates compression rate with 0.5?s update interval, time to first stable compression rate (TFSCR), active compression time (TC), hands-off time (TWC), average compression rate (ACR), and total number of compressions (NC). Four experiments were performed to test the accuracy of the calculated chest compression rate under different conditions, and a fifth experiment was done to test the accuracy of the CPR summary parameters TFSCR, TC, TWC, ACR, and NC. Average compression rate detection error was 2.7 compressions per minute (±5.0?cpm), the calculated chest compression rate was within ±10?cpm in 98% (±5.5) of the time, and the average error of the summary CPR parameters was 4.5% (±3.6). The results show that real-time chest compression quality measurement by smartphone camera in simulated cardiac arrest is feasible under the conditions tested.
Project description:When a cardiac arrest occurs, it is necessary to perform cardiopulmonary resuscitation (CPR) as soon as possible. This requires maintaining the pressure depth at 5 cm at a rate of 100 cpm. For CPR machines, which are frequently used in ambulances, the return of spontaneous circulation (ROSC) is not superior to that of manual CPR, although CPR machines can maintain the compression rate and reciprocal distance of the compression plate more accurately. When the thoracic cavity is deformed due to repeated chest compressions, CPR machines must be adjusted. It is necessary to develop a method for measuring whether adequate CPR is achieved using CPR machines. CPR was performed on two pigs with a CPR machine, commencing 1 minute after the heart was stopped. Four CPR modes were used, with compression rates of 60 or 100 cpm and compression depths of 3 or 5 cm. The CPR machine was equipped with a load cell for measuring compression force, and a potentiometer for measuring compression depth. The measurement results obtained from the sensor were used to calculate the frequency components. The compression force and depth data were used to calculate the mechanical power of the CPR machine and mechanical impedance of the thoracic cavity. Changes in end-tidal carbon dioxide (ETCO2), coronary perfusion pressure (CPP), carotid blood flow (CBF), and right atrial pressure (RAP) were measured during performance of CPR; change in RAP refers to variation therein with chest compressions. Continuous CPR in both animals resulted in deformation of the chest cavity and a steady decline in impedance. The correlation between CPR power and change in RAP was 0.78, and that between compression force and CBF was 0.64. Impedance was not correlated with blood pressure or CBF. When the condition of the animal deteriorated due to cardiac arrest, the CPP decreased and ETCO2 increased. The CPR power and RAP varied according to the CPR mode rather than the condition of the animal. Measuring the CPR machine power does not require a separate procedure, such as catheter intubation, so should be suitable as an index of the quality of CPR in emergency situations.
Project description:Chest compressions (CC) during cardiopulmonary resuscitation are not sufficiently effective in many circumstances. Mechanical CC could be more effective than manual CC, but there are no studies comparing both techniques in children. The objective of this study was to compare the effectiveness of manual and mechanical chest compressions with Thumper device in a pediatric cardiac arrest animal model.An experimental model of asphyxial cardiac arrest (CA) in 50 piglets (mean weight 9.6 kg) was used. Animals were randomized to receive either manual CC or mechanical CC using a pediatric piston chest compressions device (Life-Stat®, Michigan Instruments). Mean arterial pressure (MAP), arterial blood gases and end-tidal CO2 (etCO2) values were measured at 3, 9, 18 and 24 minutes after the beginning of resuscitation.There were no significant differences in MAP, DAP, arterial blood gases and etCO2 between chest compression techniques during CPR. Survival rate was higher in the manual CC (15 of 30 = 50%) than in the mechanical CC group (3 of 20 = 15%) p = 0.016. In the mechanical CC group there was a non significant higher incidence of haemorrhage through the endotracheal tube (45% vs 20%, p = 0.114).In a pediatric animal model of cardiac arrest, mechanical piston chest compressions produced lower survival rates than manual chest compressions, without any differences in hemodynamic and respiratory parameters.
Project description:The use of real-time feedback systems to guide rescuers during cardiopulmonary resuscitation (CPR) significantly contributes to improve adherence to published resuscitation guidelines. Recently, we designed a novel method for computing depth and rate of chest compressions relying solely on the spectral analysis of chest acceleration. That method was extensively tested in a simulated manikin scenario. The purpose of this study is to report the results of this method as tested in human out-of-hospital cardiac arrest (OHCA) cases.The algorithm was evaluated retrospectively with seventy five OHCA episodes recorded by monitor-defibrillators equipped with a CPR feedback device. The acceleration signal and the compression signal computed by the CPR feedback device were stored in each episode. The algorithm was continuously applied to the acceleration signals. The depth and rate values estimated every 2-s from the acceleration data were compared to the reference values obtained from the compression signal. The performance of the algorithm was assesed in terms of the sensitivity and positive predictive value (PPV) for detecting compressions and in terms of its accuracy through the analysis of measurement error.The algorithm reported a global sensitivity and PPV of 99.98% and 99.79%, respectively. The median (P75) unsigned error in depth and rate was 0.9 (1.7) mm and 1.0 (1.7) cpm, respectively. In 95% of the analyzed 2-s windows the error was below 3.5 mm and 3.1 cpm, respectively.The CPR feedback algorithm proved to be reliable and accurate when tested retrospectively with human OHCA episodes. A new CPR feedback device based on this algorithm could be helpful in the resuscitation field.
Project description:Feedback on chest compressions and ventilations during cardiopulmonary resuscitation (CPR) is important to improve survival from out-of-hospital cardiac arrest (OHCA). The thoracic impedance signal acquired by monitor-defibrillators during treatment can be used to provide feedback on ventilations, but chest compression components prevent accurate detection of ventilations. This study introduces the first method for accurate ventilation detection using the impedance while chest compressions are concurrently delivered by a mechanical CPR device. A total of 423 OHCA patients treated with mechanical CPR were included, 761 analysis intervals were selected which in total comprised 5 884 minutes and contained 34 864 ventilations. Ground truth ventilations were determined using the expired CO 2 channel. The method uses adaptive signal processing to obtain the impedance ventilation waveform. Then, 14 features were calculated from the ventilation waveform and fed to a random forest (RF) classifier to discriminate false positive detections from actual ventilations. The RF feature importance was used to determine the best feature subset for the classifier. The method was trained and tested using stratified 10-fold cross validation (CV) partitions. The training/test process was repeated 20 times to statistically characterize the results. The best ventilation detector had a median (interdecile range, IDR) F 1-score of 96.32 (96.26-96.37). When used to provide feedback in 1-min intervals, the median (IDR) error and relative error in ventilation rate were 0.002 (-0.334-0.572) min-1 and 0.05 (-3.71-9.08)%, respectively. An accurate ventilation detector during mechanical CPR was demonstrated. The algorithm could be introduced in current equipment for feedback on ventilation rate and quality, and it could contribute to improve OHCA survival rates.
Project description:New chest compression detection technology allows for the recording and graphical depiction of clinical cardiopulmonary resuscitation (CPR) chest compressions. The authors sought to determine the inter-rater reliability of chest compression pattern classifications by human raters. Agreement with automated chest compression classification was also evaluated by computer analysis.This was an analysis of chest compression patterns from cardiac arrest patients enrolled in the ongoing Resuscitation Outcomes Consortium (ROC) Continuous Chest Compressions Trial. Thirty CPR process files from patients in the trial were selected. Using written guidelines, research coordinators from each of eight participating ROC sites classified each chest compression pattern as 30:2 chest compressions, continuous chest compressions (CCC), or indeterminate. A computer algorithm for automated chest compression classification was also developed for each case. Inter-rater agreement between manual classifications was tested using Fleiss's kappa. The criterion standard was defined as the classification assigned by the majority of manual raters. Agreement between the automated classification and the criterion standard manual classifications was also tested.The majority of the eight raters classified 12 chest compression patterns as 30:2, 12 as CCC, and six as indeterminate. Inter-rater agreement between manual classifications of chest compression patterns was ? = 0.62 (95% confidence interval [CI] = 0.49 to 0.74). The automated computer algorithm classified chest compression patterns as 30:2 (n = 15), CCC (n = 12), and indeterminate (n = 3). Agreement between automated and criterion standard manual classifications was ? = 0.84 (95% CI = 0.59 to 0.95).In this study, good inter-rater agreement in the manual classification of CPR chest compression patterns was observed. Automated classification showed strong agreement with human ratings. These observations support the consistency of manual CPR pattern classification as well as the use of automated approaches to chest compression pattern analysis.
Project description:BACKGROUND:Based on laboratory cardiopulmonary resuscitation (CPR) investigations and limited adult data, the American Heart Association Consensus Statement on CPR Quality recommends titrating CPR performance to achieve end-tidal carbon dioxide (ETCO2) >20?mmHg. AIMS:We prospectively evaluated whether ETCO2?>?20?mmHg during CPR was associated with survival to hospital discharge. METHODS:Children ?37 weeks gestation in Collaborative Pediatric Critical Care Research Network intensive care units with chest compressions for ?1?min and ETCO2 monitoring prior to and during CPR between July 1, 2013 and June 31, 2016 were included. ETCO2 and Utstein-style cardiac arrest data were collected. Multivariable Poisson regression models with robust error estimates were used to estimate relative risk of outcomes. RESULTS:Blinded investigators analyzed ETCO2 waveforms from 43 children. During CPR, the median ETCO2 was 23?mmHg [quartiles, 16 and 28?mmHg], median ventilation rate was 29 breaths/min [quartiles, 24 and 35 breaths/min], and median duration of CPR was 5?min [quartiles, 2 and 16?min]. Return of spontaneous circulation occurred after 71% of CPR events and 37% of patients survived to hospital discharge. For children with mean ETCO2 during CPR?>?20?mmHg, the adjusted relative risk for survival was 0.92 (0.41, 2.08), p?=?0.84. The median mean ETCO2 among children who survived to hospital discharge was 20?mmHg [quartiles; 15, 28?mmHg] versus 23?mmHg [16, 28?mmHg] among non-survivors. CONCLUSION:Mean ETCO2?>?20?mmHg during pediatric in-hospital CPR was not associated with survival to hospital discharge, and ETCO2 was not different in survivors versus non-survivors.
Project description:AIM:High-quality chest compressions is challenging for bystanders and first responders to out-of-hospital cardiac arrest (OHCA). Long compression pauses and compression rates higher than recommended are common and detrimental to survival. Our aim was to design a simple and low computational cost algorithm for feedback on compression rate using the transthoracic impedance (TI) acquired by automated external defibrillators (AEDs). METHODS:ECG and TI signals from AED recordings of 242 OHCA patients treated by basic life support (BLS) ambulances were retrospectively analyzed. Beginning and end of chest compression series and each individual compression were annotated. The algorithm computed a biased estimate of the autocorrelation of the TI signal in consecutive non-overlapping 2-s analysis windows to detect the presence of chest compressions and estimate compression rate. RESULTS:A total of 237 episodes were included in the study, with a median (IQR) duration of 10 (6-16) min. The algorithm performed with a global sensitivity in the detection of chest compressions of 98.7%, positive predictive value of 98.7%, specificity of 97.1%, and negative predictive value of 97.1% (validation subset including 207 episodes). The unsigned error in the estimation of compression rate was 1.7 (1.3-2.9) compressions per minute. CONCLUSION:Our algorithm is accurate and robust for real-time guidance on chest compression rate using AEDs. The algorithm is simple and easy to implement with minimal software modifications. Deployment of AEDs with this capability could potentially contribute to enhancing the quality of chest compressions in the first minutes from collapse.
Project description:Background:The Nuss procedure temporarily places intrathoracic bars for repair of pectus excavatum (PE). The bars may impact excursion and compliance of the anterior chest wall while in place. Effective chest compressions during cardiopulmonary resuscitation (CPR) require depressing the anterior chest wall enough to compress the heart between sternum and spine. We assessed the force required to perform the American Heart Association's recommended chest compression depth after Nuss repair. Methods:A lumped element elastic model was developed to simulate the relationship between chest compression forces and displacement with focus on the amount of force required to achieve a depth of 5 cm in the presence of 1-3 Nuss bars. Literature review was conducted for evidence supporting potential use of active abdominal compressions and decompression (AACD) as an alternative method of CPR. Results:The presence of bars notably lowered compression depth by a minimum of 69% compared to a chest without bar(s). The model also demonstrated a dramatic increase (minimum of 226%) in compressive forces required to achieve recommended 5 cm depth. Literature review suggests AACD could be an alternative CPR in patients with Nuss bar(s). Conclusions:In our model, Nuss bars limited the ability to perform chest compressions due to increased force required to achieve a 5 cm compression. The greater the number of Nuss bars present the greater the force required. This may prevent effective CPR. Use of active abdominal compressions and decompressions should be studied further as an alternative resuscitation modality for patients after the Nuss procedure.