Presence Of Cardiopulmonary Resuscitation Research Articles

Overview of Machine Learning and Deep Learning Approaches for Detecting Shockable Rhythms in AED in the Absence or Presence of CPR

Sudden Cardiac Arrest (SCA) is one of the leading causes of death worldwide. Therefore, timely and accurate detection of such arrests and immediate defibrillation support for the victim is critical. An automated external defibrillator (AED) is a medical device that diagnoses the rhythms and provides electric shocks to SCA patients to restore normal heart rhythms. Machine learning and deep learning-based approaches are popular in AEDs for detecting shockable rhythms and automating defibrillation. There are some works in the literature for reviewing various machine learning (ML) and deep learning (DL) algorithms for shockable ECG signals in AED. Starting in 2017 and beyond, different DL algorithms were proposed for the AED. This paper provides an overview of AED, including its circuit diagram and application to SCA patients. It also presents the most up-to-date ML and DL approaches for detecting shockable rhythms in AEDs without cardiopulmonary resuscitation (CPR) or during CPR. It also provides a performance comparison of these approaches and discusses other researchers’ results that lay the foundation for researchers to delve in-depth. Furthermore, the research gaps and recommendations for future research provided in this review paper will be helpful to the researchers, scientists, and engineers in conducting further research in this critical field.

Outcome of out-of-hospital cardiopulmonary arrest in children: A multicenter cohort study.

Kurt F, Kendirli T, Gündüz RC, Kesici S, Akça H, Şahin Ş, Kalkan G, Derbent M, Tuygun N, Ödek Ç, Gültekin-Keser A, Oğuz S, Polat E, Derinöz O, Tekin D, Tekşam Ö, Bayrakcı B, Suskan E. Outcome of out-of-hospital cardiopulmonary arrest in children: A multicenter cohort study. Turk J Pediatr 2018; 60: 488-496. The aim of this study was to evaluate the demographic characteristics of children who experienced out-of-hospital cardiopulmonary arrest (CPA), and to assess the impact of the bystander cardiopulmonary resuscitation (CPR) on the survival rate of witnessed arrests and the effects of the arrest and CPR durations on the neurological outcomes. This multicenter, retrospective study included a total of 182 patients who underwent CPR for out-of-hospital CPA between January 2008 and December 2012 at six centers in Ankara, Turkey. The median [interquartile range (IQR)] age was 22 (5-54) months; 60.4% of the patients were males, and 44% were younger than one year of age. The witnessed arrest rate was 75.8% (138/182) and the rate of bystander CPR was 13.9% (13/93). In these patients the rate of the return of spontaneous circulation (ROSC) was higher (76.9%). Following resuscitation in the patients for whom the spontaneous circulation was able to be returned, the median (IQR) duration of arrest was 5 (1- 15) min, while it was 15 (5-40) min for the remaining patients (p < 0.001). The ROSC rate was 94.9% in patients who underwent CPR for less than 20 min and 22% in patients requiring CPR longer than 20 min (p < 0.001). Survival to hospital discharge was 14.3%. Of these patients, 57.7% experienced neurological disability. The short duration of an arrest and the presence of CPR are both critical for survival. We suggest that a witness to the CPA, performing early and efficient CPR, yields better results.

A Review of the Performance of Artifact Filtering Algorithms for Cardiopulmonary Resuscitation

Various filtering strategies have been adopted and investigated to suppress the cardiopulmonary resuscitation (CPR) artifact. In this article, two types of artifact removal methods are reviewed: one is the method that removes CPR artifact using only ECG signals, and the other is the method with additional reference signals, such as acceleration, compression depth and transthoracic impedance. After filtering, the signal-to-noise ratio is improved from 0 dB to greater than 2.8 dB, the sensitivity is increased to > 90% as recommended by the American Heart Association, whereas the specificity was far from the recommended 95%, which is considered to be the major drawback of the available artifact removal methods. The overall performance of the adaptive filtering methods with additional reference signal outperforms the methods using only ECG signals. Further research should focus on the refinement of artifact filtering methods and the improvement of shock advice algorithms with the presence of CPR.

Outcome of Patients with Ruptured Abdominal Aortic Aneurysm after Cardiopulmonary Resuscitation

Purpose : Ruptured aneurysm of the abdominal aorta (RAAA) is a condition associated with high mortality rate. If Cardiopulmonary Resuscitation (CPR) is required, outcome is considered even worse. The aim of this study was to assess the effect of CPR on 30-day mortality of RAAA patients. Furthermore the Hardman index was evaluated. Methods : 109 patients with RAAA during a 5 year period (1001–1005) were analysed retrospectively. 30-day mortality, the presence of CPR and Hardman risk factors were recorded. The presence of CPR and the Hardman index were related to clinical outcome.Results : 104 patients were included in our analysis. Eighteen patients received CPR. Overall 30-day mortality was 40%. Patients receiving CPR had a higher mortality rate than patients who did not (89% vs. 30%, p < 0.0001). Patients receiving CPR prior to surgery had a mortality rate of 100% (n = 11). In patients with a Hardman Index of < 1, 1 and > 3 the 30-day mortality was respectively 15%, 47% and 81%.Conclusion : Requirement of CPR has a detrimental effect on RAAA-patient outcome. Patients receiving CPR prior to surgery have no survival chance. We advocate that surgery in these patients should not be undertaken. Hardman Index has a predictive value concerning 30-day mortality.