Aims: Trends in characteristics, management, and survival in out-of-hospital cardiac arrest (OHCA) and in-hospital cardiac arrest (IHCA) were studied in the Swedish Cardiopulmonary Resuscitation Registry (SCRR). Methods and results: The SCRR was used to study 106 296 cases of OHCA (1990–2020) and 30 032 cases of IHCA (2004–20) in whom resuscitation was attempted. In OHCA, survival increased from 5.7% in 1990 to 10.1% in 2011 and remained unchanged thereafter. Odds ratios [ORs, 95% confidence interval (CI)] for survival in 2017–20 vs. 1990–93 were 2.17 (1.93–2.43) overall, 2.36 (2.07–2.71) for men, and 1.67 (1.34–2.10) for women. Survival increased for all aetiologies, except trauma, suffocation, and drowning. OR for cardiac aetiology in 2017–20 vs. 1990–93 was 0.45 (0.42–0.48). Bystander cardiopulmonary resuscitation increased from 30.9% to 82.2%. Shockable rhythm decreased from 39.5% in 1990 to 17.4% in 2020. Use of targeted temperature management decreased from 42.1% (2010) to 18.2% (2020). In IHCA, OR for survival in 2017–20 vs. 2004–07 was 1.18 (1.06–1.31), showing a non-linear trend with probability of survival increasing by 46.6% during 2011–20. Myocardial ischaemia or infarction as aetiology decreased during 2004–20 from 67.4% to 28.3% [OR 0.30 (0.27–0.34)]. Shockable rhythm decreased from 37.4% to 23.0% [OR 0.57 (0.51–0.64)]. Approximately 90% of survivors (IHCA and OHCA) had no or mild neurological sequelae. Conclusion: Survival increased 2.2-fold in OHCA during 1990–2020 but without any improvement in the final decade, and 1.2-fold in IHCA during 2004–20, with rapid improvement the last decade. Cardiac aetiology and shockable rhythms were halved. Neurological outcome has not improved.

Early defibrillation is critical for the chance of survival in out-of-hospital cardiac arrest (OHCA). Drones, used to deliver automated external defibrillators (AEDs), may shorten time to defibrillation, but this has never been evaluated in real-life emergencies. The aim of this study was to investigate the feasibility of AED delivery by drones in real-life cases of OHCA. In this prospective clinical trial, three AED-equipped drones were placed within controlled airspace in Sweden, covering approximately 80 000 inhabitants (125 km²). Drones were integrated in the emergency medical services for automated deployment in beyond-visual-line-of-sight flights: (i) test flights from 1 June to 30 September 2020 and (ii) consecutive real-life suspected OHCAs. Primary outcome was the proportion of successful AED deliveries when drones were dispatched in cases of suspected OHCA. Among secondary outcomes was the proportion of cases where AED drones arrived prior to ambulance and time benefit vs. ambulance. Totally, 14 cases were eligible for dispatch during the study period in which AED drones took off in 12 alerts to suspected OHCA, with a median distance to location of 3.1 km [interquartile range (IQR) 2.8–3.4). AED delivery was feasible within 9 m (IQR 7.5–10.5) from the location and successful in 11 alerts (92%). AED drones arrived prior to ambulances in 64%, with a median time benefit of 01:52 min (IQR 01:35–04:54) when drone arrived first. In an additional 61 test flights, the AED delivery success rate was 90% (55/61). In this pilot study, we have shown that AEDs can be carried by drones to real-life cases of OHCA with a successful AED delivery rate of 92%. There was a time benefit as compared to emergency medical services in cases where the drone arrived first. However, further improvements are needed to increase dispatch rate and time benefits. ClinicalTrials.gov Identifier: NCT04415398. The dispatch centre alerts emergency medical services and a drone equipped with an automated external defibrillator for response to suspected out-of-hospital cardiac arrest. Data are presented with interquartile range for time benefit (min:s) and distance to the location (kilometres to the site and metres to the object when winching down the automated external defibrillator).

Aim: To study the characteristics and outcome among cardiac arrest cases with COVID-19 and differences between the pre-pandemic and the pandemic period in out-of-hospital cardiac arrest (OHCA) and in-hospital cardiac arrest (IHCA). Method and results: We included all patients reported to the Swedish Registry for Cardiopulmonary Resuscitation from 1 January to 20 July 2020. We defined 16 March 2020 as the start of the pandemic. We assessed overall and 30-day mortality using Cox regression and logistic regression, respectively. We studied 1946 cases of OHCA and 1080 cases of IHCA during the entire period. During the pandemic, 88 (10.0%) of OHCAs and 72 (16.1%) of IHCAs had ongoing COVID-19. With regards to OHCA during the pandemic, the odds ratio for 30-day mortality in COVID-19-positive cases, compared with COVID-19-negative cases, was 3.40 [95% confidence interval (CI) 1.31–11.64]; the corresponding hazard ratio was 1.45 (95% CI 1.13–1.85). Adjusted 30-day survival was 4.7% for patients with COVID-19, 9.8% for patients without COVID-19, and 7.6% in the pre-pandemic period. With regards to IHCA during the pandemic, the odds ratio for COVID-19-positive cases, compared with COVID-19-negative cases, was 2.27 (95% CI 1.27–4.24); the corresponding hazard ratio was 1.48 (95% CI 1.09–2.01). Adjusted 30-day survival was 23.1% in COVID-19-positive cases, 39.5% in patients without COVID-19, and 36.4% in the pre-pandemic period. Conclusion: During the pandemic phase, COVID-19 was involved in at least 10% of all OHCAs and 16% of IHCAs, and, among COVID-19 cases, 30-day mortality was increased 3.4-fold in OHCA and 2.3-fold in IHCA.

Fast recognition of out-of-hospital cardiac arrest (OHCA) by dispatchers might increase survival. The aim of this observational study of emergency calls was to (1) examine whether a machine learning framework (ML) can increase the proportion of calls recognizing OHCA within the first minute compared with dispatchers, (2) present the performance of ML with different false positive rate (FPR) settings, (3) examine call characteristics influencing OHCA recognition. ML can be configured with different FPR settings, i.e., more or less inclined to suspect an OHCA depending on the predefined setting. ML OHCA recognition within the first minute is evaluated with a 1.5 FPR as the primary endpoint, and other FPR settings as secondary endpoints. ML was exposed to a random sample of emergency calls from 2018. Voice logs were manually audited to evaluate dispatchers time to recognition. Of 851 OHCA calls, the ML recognized 36% (n = 305) within 1 min compared with 25% (n = 213) by dispatchers. The recognition rate at any time during the call was 86% for ML and 84% for dispatchers, with a median time to recognition of 72 versus 94 s. OHCA recognized by both ML and dispatcher showed a 28 s mean difference in favour of ML (P < 0.001). ML with higher FPR settings reduced recognition times. ML recognized a higher proportion of OHCA within the first minute compared with dispatchers and has the potential to be a supportive tool during emergency calls. The optimal FPR settings need to be evaluated in a prospective study.

Early defibrillation is essential for increasing the chance of survival in out-of-hospital-cardiac-arrest (OHCA). Automated external defibrillator (AED)-equipped drones have a substantial potential to shorten times to defibrillation in OHCA patients. However, optimal locations for drone deployment are unknown. Our aims were to find areas of high incidence of OHCA on a national level for placement of AED-drones, and to quantify the number of drones needed to reach 50, 80, 90 and 100% of the target population within eight minutes. This is a retrospective observational study of OHCAs reported to the Swedish Registry for Cardiopulmonary Resuscitation between 2010-2018. Spatial analyses of optimal drone placement were performed using geographical information system (GIS)-analyses covering high-incidence areas (>100 OHCAs in 2010-2018) and response times. 39,246 OHCAs were included. To reach all OHCAs in high-incidence areas with AEDs delivered by drone or ambulance within eight minutes, 61 drone systems would be needed, resulting in overall OHCA coverage of 58.2%, and median timesaving of 05:01 (min:sec) [IQR 03:22-06:19]. To reach 50% of the historically reported OHCAs in <8 min, 21 drone systems would be needed; for 80%, 366; for 90%, 784, and for 100%, 2408. At a national level, GIS-analyses can identify high incidence areas of OHCA and serve as tools to quantify the need of AED-equipped drones. Use of only a small number of drone systems can increase national coverage of OHCA substantially. Prospective real-life studies are needed to evaluate theoretically optimized suggestions for drone placement.