2020 Korean Guidelines for Cardiopulmonary Resuscitation. Part 3. Adult basic life support

Ventricle tracking in transesophageal echocardiography (TEE) images during cardiopulmonary resuscitation (CPR) using deep learning and monogenic filtering

High-quality cardiopulmonary resuscitation (CPR) is the most important factor in promoting resuscitation outcomes; therefore, monitoring the quality of CPR is strongly recommended in current CPR guidelines. Recently, transesophageal echocardiography (TEE) has been proposed as a potential real-time feedback modality because physicians can obtain clear echocardiographic images without interfering with CPR. The quality of CPR would be optimized if the myocardial ejection fraction (EF) could be calculated in real-time during CPR. We conducted a study to derive a protocol to detect systole and diastole automatically and calculate EF using TEE images acquired from patients with cardiac arrest. The data were supplemented using thin-plate spline transformation to solve the problem of insufficient data. The deep learning model was constructed based on ResUNet +  + , and a monogenic filtering method was applied to clarify the ventricular boundary. The performance of the model to which the monogenic filter was added and the existing model was compared. The left ventricle was segmented in the ME LAX view, and the left and right ventricles were segmented in the ME four-chamber view. In most of the results, the performance of the model to which the monogenic filter was added was high, and the difference was very small in some cases; but the performance of the existing model was high. Through this learned model, the effect of CPR can be quantitatively analyzed by segmenting the ventricle and quantitatively analyzing the degree of contraction of the ventricle during systole and diastole.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13534-023-00293-9.

The Influence of Cardiac Arrest Floor-Level Location within a Building on Survival Outcomes

This nationwide, population-based observational study investigated the association between the floor level of out-of-hospital cardiac arrest (OHCA) incidence and survival outcomes in South Korea, notable for its significant high-rise apartment living. Data were collected retrospectively from OHCA patients through the South Korean Out-of-Hospital Cardiac Arrest Surveillance database. The study incorporated cases that included the OHCA's building floor information. The primary outcome assessed was survival to discharge, analyzed using multivariate logistic regression, and the secondary outcome was favorable neurological outcome. Among 36,977 patients, a total of 29,729 patients were included, and 1680 patients were survivors. A weak yet significant correlation between floor level and hospital arrival time was observed. Interestingly, elevated survival rates were noted among patients from higher floors despite extended emergency medical service response times. Multivariate analysis identified age, witnessed OHCA, shockable rhythm, and prehospital return of spontaneous circulation (ROSC) as primary determinants of survival to discharge. The floor level's impact on survival was less substantial than anticipated, suggesting residential emergency response enhancements should prioritize witness interventions, shockable rhythm management, and prehospital ROSC rates. The study underscores the importance of bespoke emergency response strategies in high-rise buildings, particularly in urban areas, and the potential of digital technologies to optimize response times and survival outcomes.

Development of an automatic device performing chest compression and external defibrillation: An animal-based pilot study

BACKGROUND

Automatic chest compression devices (ACCDs) can promote high-quality cardiopulmonary resuscitation (CPR) and are widely used worldwide. Early application of automated external defibrillators (AEDs) along with high-quality CPR is crucial for favorable outcomes in patients with cardiac arrest. Here, we developed an automated CPR (A-CPR) apparatus that combines ACCD and AED and evaluated its performance in a pilot animal-based study.

METHODS

Eleven pigs (n = 5, A-CPR group; n = 6, ACCD CPR and AED [conventional CPR (C-CPR)] group) were enrolled in this study. After 2 min observation without any treatment following ventricular fibrillation induction, CPR with a 30:2 compression/ventilation ratio was performed for 6 min, mimicking basic life support (BLS). A-CPR or C-CPR was applied immediately after BLS, and resuscitation including chest compression and defibrillation, was performed following a voice prompt from the A-CPR device or AED. Hemodynamic parameters, including aortic pressure, right atrial pressure, coronary perfusion pressure, carotid blood flow, and end-tidal carbon dioxide, were monitored during resuscitation. Time variables, including time to start rhythm analysis, time to charge, time to defibrillate, and time to subsequent chest compression, were also measured.

RESULTS

There were no differences in baseline characteristics, except for arterial carbon dioxide pressure (39 in A-CPR vs. 33 in C-CPR, p = 0.034), between the two groups. There were no differences in hemodynamic parameters between the groups. However, time to charge (28.9 ± 5.6 s, A-CPR group; 47.2 ± 12.4 s, C-CPR group), time to defibrillate (29.1 ± 7.2 s, A-CPR group; 50.5 ± 12.3 s, C-CPR group), and time to subsequent chest compression (32.4 ± 6.3 s, A-CPR group; 56.3 ± 10.7 s, C-CPR group) were shorter in the A-CPR group than in the C-CPR group (p = 0.015, 0.034 and 0.02 respectively).

CONCLUSIONS

A-CPR can provide effective chest compressions and defibrillation, thereby shortening the time required for defibrillation.

Modification of termination of resuscitation rule with compression time interval in South Korea

This study aimed to validate the predictive performance of the termination of resuscitation (TOR) rule and examine the compression time interval (CTI) as a criterion for modifying the rule. This retrospective observational study analyzed adult out-of-hospital cardiac arrest (OHCA) patients attended by emergency medical service (EMS) providers in mixed urban-rural areas in Korea in 2020 and 2021. We evaluated the predictive performance of basic life support (BLS) and the Korean Cardiac Arrest Research Consortium (KoCARC) TOR rule using the false-positive rate (FPR) and positive predictive value (PPV). We modified the age cutoff criterion and examined the CTI as a new criterion. According to the TOR rule, 1827 OHCA patients were classified into two groups. The predictive performance of the BLS TOR rule had an FPR of 11.7% (95% confidence interval (CI): 5.9-17.5) and PPV of 98.4% (97.6-99.2) for mortality, and an FPR of 3.6% (0.0-7.8) and PPV of 78.6% (75.9-81.3) for poor neurological outcomes at hospital discharge. The predictive performance of the KoCARC TOR rule had an FPR of 5.0% (1.1-8.9) and PPV of 98.9% (98.0-99.8) for mortality, and an FPR of 3.7% (0.0-7.8) and PPV of 50.0% (45.7-54.3) for poor neurological outcomes at hospital discharge. The modified cutoff value for age was 68 years, with an area under the receiver operating characteristic curve over 0.7. In the group that met the BLS TOR rule, the cutoff of the CTI for death was not determined and was 21 min for poor neurological outcomes. In the group that met the KoCARC TOR rule, the cutoff of the CTI for death and poor neurological outcomes at the time of hospital discharge was 25 min and 21 min, respectively. The BLS TOR and KoCARC TOR rules showed inappropriate predictive performance for mortality and poor neurological outcomes. However, the predictive performance of the TOR rule could be supplemented by modifying the age criterion and adding the CTI criterion of the KoCARC.

Left ventricle segmentation in transesophageal echocardiography images using a deep neural network

PURPOSE

There has been little progress in research on the best anatomical position for effective chest compressions and cardiac function during cardiopulmonary resuscitation (CPR). This study aimed to divide the left ventricle (LV) into segments to determine the best position for effective chest compressions using the LV systolic function seen during CPR.

METHODS

We used transesophageal echocardiography images acquired during CPR. A deep neural network with an attention mechanism and a residual feature aggregation module were applied to the images to segment the LV. The results were compared between the proposed model and U-Net.

RESULTS

The results of the proposed model showed higher performance in most metrics when compared to U-Net: dice coefficient (0.899±0.017 vs. 0.792±0.027, p<0.05); intersection of union (0.822±0.026 vs. 0.668±0.034, p<0.05); recall (0.904±0.023 vs. 0.757±0.037, p<0.05); precision (0.901±0.021 vs. 0.859±0.034, p>0.05). There was a significant difference between the proposed model and U-Net.

CONCLUSION

Compared to U-Net, the proposed model showed better performance for all metrics. This model would allow us to evaluate the systolic function of the heart during CPR in greater detail by segmenting the LV more accurately.

Optimal Landmark for Chest Compressions during Cardiopulmonary Resuscitation Derived from a Chest Computed Tomography in Arms-Down Position

Compressions at the left ventricle increase rate of return of spontaneous circulation. This study aimed to identify the landmark of the point of maximal left ventricular diameter on the sternum (LVmax) by using chest computed tomography (CCT) in the arms-down position, which was similar to an actual cardiac arrest patient. A retrospective study was conducted between September 2014 and November 2020. We included adult patients who underwent CCT in an arms-down position and measured the rescuer’s hand. We measured the distance from the sternal notch to LVmax (DLVmax), to the lower half of sternum (DLH), and to the point of maximal force of hand, which placed the lowest palmar margin of the rescuer’s reference hand at the xiphisternal junction. Thirty-nine patients were included. The LVmax was located below the lower half of the sternum; DLVmax and DLH were 12.6 and 10.0 cm, respectively (p < 0.001). Distance from the sternal notch to the point of maximal force of the left hand, with the ulnar border located at the xiphisternal junction, was close to DLVmax; 11.3 and 12.6 cm, respectively (p = 0.076). In conclusion, LVmax was located below the lower half of the sternum, which is recommended by current guidelines.