The ability of machine learning algorithms to predict defibrillation success during cardiac arrest: A systematic review.

OBJECTIVE: To evaluate the existing knowledge on the effectiveness of machine learning (ML) algorithms inpredicting defibrillation success during in- and out-of-hospital cardiac arrest. METHODS: MEDLINE, Embase, CINAHL and Scopus were searched from inception to August 30, 2022. Studies were included that utilized ML algorithms for prediction of successful defibrillation, observed as return of spontaneous circulation (ROSC), survival to hospital or discharge, or neurological status at discharge.Studies were excluded if involving a trauma, an unknown underlying rhythm, an implanted cardiac defibrillator or if focused on the prediction or onset of cardiac arrest. Risk of bias was assessed using the PROBAST tool. RESULTS: There were 2399 studies identified, of which 107 full text articles were reviewed and 15 observational studies (n = 5680) were included for final analysis. 29 ECG waveform features were fed into 15 different ML combinations. The best performing ML model had an accuracy of 98.6 (98.5 - 98.7)%, with 4 second ECG intervals. An algorithm incorporating end-tidal CO2 reported an accuracy of 83.3% (no CI reported). Meta-analysis was not performed due to heterogeneity in study design, ROSC definitions, and characteristics. CONCLUSION: Machine learning algorithms, specifically Neural Networks, have been shown to have potential to predict defibrillation success for cardiac arrest with high sensitivity and specificity.Due to heterogeneity, inconsistent reporting, and high risk of bias, it is difficult to conclude which, if any, algorithm is optimal. Further clinical studies with standardized reporting of patient characteristics, outcomes, and appropriate algorithm validation are still required to elucidate this. PROSPERO 2020 CRD42020148912.

Responding to Cardiac Arrest in the Community in the Digital Age.

Sudden cardiac arrest (SCA) is a common event, affecting almost 400,000 individuals annually in North America. Initiation of cardiopulmonary resuscitation (CPR) and early defibrillation using an automated external defibrillator (AED) are critical for survival, yet many bystanders are reluctant to intervene. Digital technologies, including mobile devices, social media, and crowdsourcing might help play a role to improve survival from SCA. In this article we review the current digital tools and strategies available to increase rates of bystander recognition of SCA, prompt immediate activation of emergency medical services (EMS), initiate high-quality CPR, and to locate, retrieve, and operate AEDs. Smartphones can help to educate and connect bystanders with EMS dispatchers, through text messaging or video calling, to encourage the initiation of CPR and retrieval of the closest AED. Wearable devices and household smart speakers could play a future role in continuous vital signs monitoring in individuals at risk of lethal arrhythmias and send an alert to either chosen contacts or EMS. Machine learning algorithms and mathematical modelling might aid EMS dispatchers with better recognition of SCA as well as policymakers with where to best place AEDs for optimal accessibility. There are challenges with the use of digital tech, including the need for government regulation and issues with data ownership, accessibility, and interoperability. Future research will include smart cities, e-linkages, new technologies, and using social media for mass education. Together or in combination, these emerging digital technologies might represent the next leap forward in SCA survival.

Healthcare costs and resource utilization associated with treatment of out-of-hospital cardiac arrest.

BACKGROUND: The management of out-of-hospital cardiac arrest (OHCA) patients requires the coordination of prehospital, in-hospital and post-discharge teams. Data reporting a comprehensive analysis of all costs associated with treating OHCA are scarce. We aimed to describe the total costs (and their components) related to the management of OHCA patients. PATIENT AND METHODS: We performed an analysis on a merged database of the Toronto Regional RescuNet Epistry database (prehospital data) and administrative population-based databases in Ontario. All non-traumatic OHCA patients over 18 years of age treated by the EMS between January 1, 2006, and March 31, 2014, were included in this study. The primary outcome was per patient longitudinal cumulative healthcare costs, from time of collapse to a maximum follow-up until death or 30 days after the event. We included all available cost sectors, from the perspective of the health system payer. We used multivariable generalized linear models with a logarithmic link and a gamma distribution to determine predictors of healthcare costs. RESULTS: 25,826/44,637 patients were treated by EMS services for an OHCA (mostly male 64.4%, mean age 70.1). 11,727 (45%) were pronounced dead on scene, 8359 (32%) died in the emergency department, 3640 (14%) were admitted to hospital but died before day-30, and 2100 (8.1%) were still alive at day-30. Total cost was $690 [interquartile range (IQR) $308, $1742] per patient; ranging from $290 [IQR $188, $390] for patients who were pronounced on scene to $39,216 [IQR 21,802, 62,093] for patients who were still alive at day-30. In-hospital costs accounted for 93% of total costs. After adjustment for age and gender, rate of patient survival was the main driver of total costs: the rate ratio was 3.88 (95% confidence interval 3.80, 3.95), 49.46 and 148.89 for patients who died in the ED, patients who died after the ED but within 30 days, and patients who were still alive at day-30 compared to patients who were pronounced dead on scene, respectively. Factors independently associated with costs were the number of prehospital teams (rate ratio (RR) 5.50 [5.32, 5.67] for being treated by 4 teams vs. 1), the need for hospital transfer (RR 2.38 [2.01, 2.82]), coronary angiography (RR 1.43 [1.27, 1.62]) and targeted temperature management (RR 1.25 [1.09, 1.44]). CONCLUSION: Survival is the main driver of total costs of treating OHCA patients in a large Canadian health system. Inpatient costs accounted for the majority of the total costs; potentially modifiable factors include the number of prehospital teams that arrive to the scene of the arrest and the need for between-hospital transfers after successful resuscitation.

Challenges in a large mixed drug overdose patient.

A 39-year-old homeless man was found confused and incoherent after ingesting an estimated total of 200 tablets of various medications. He presented to the emergency department with delirium, tachycardia, clonus and hyperthermia of 38.0°C. His condition worsened rapidly with his temperature rising to 39.9°C despite active cooling. The patient was subsequently sedated, intubated, paralysed and admitted to the intensive care unit, where he remained for 38 days. His initial presentation of a large mixed drug overdose manifested as serotonin syndrome, which had a protracted course complicated by ethanol withdrawal. He eventually stabilised and was transferred to the general ward, and was subsequently discharged from hospital on day 47 with continuing psychiatric care. This case demonstrates the challenges and considerations in diagnosis and management of large mixed drug overdoses where multiple toxidromes overlap.