The Role of Drones in Out-of-Hospital Cardiac Arrest: A Scoping Review.

Drones may be able to deliver automated external defibrillators (AEDs) directly to bystanders of out-of-hospital cardiac arrest (OHCA) events, improving survival outcomes by facilitating early defibrillation. We aimed to provide an overview of the available literature on the role and impact of drones in AED delivery in OHCA. We conducted this scoping review using the PRISMA-ScR and Arksey and O'Malley framework, and systematically searched five bibliographical databases (Medline, EMBASE, Cochrane CENTRAL, PsychInfo and Scopus) from inception until 28 February 2022. After excluding duplicate articles, title/abstract screening followed by full text review was conducted by three independent authors. Data from the included articles were abstracted and analysed, with a focus on potential time savings of drone networks in delivering AEDs in OHCA, and factors that influence its implementation. Out of the 26 included studies, 23 conducted simulations or physical trials to optimise drone network configuration and evaluate time savings from drone delivery of AEDs, compared to the current emergency medical services (EMS), along with 1 prospective trial conducted in Sweden and 2 qualitative studies. Improvements in response times varied across the studies, with greater time savings in rural areas. However, emergency call to AED attachment time was not reduced in the sole prospective study and a South Korean study that accounted for weather and topography. With growing interest in drones and their potential use in AED delivery spurring new research in the field, our included studies demonstrate the potential advantages of unmanned aerial vehicle (UAV) network implementation in controlled environments to deliver AEDs faster than current EMS. However, for these time savings to translate to reduced times to defibrillation and improvement in OHCA outcomes, careful evaluation and addressing of real-world delays, challenges, and barriers to drone use in AED delivery is required.

Management of Out-of-Hospital Cardiac Arrest during COVID-19: A Tale of Two Cities.

Variations in the impact of the COVID-19 pandemic on out-of-hospital cardiac arrest (OHCA) have been reported. We aimed to, using population-based registries, compare community response, Emergency Medical Services (EMS) interventions and outcomes of adult, EMS-treated, non-traumatic OHCA in Singapore and metropolitan Atlanta, before and during the pandemic. Associations of OHCA characteristics, pre-hospital interventions and pandemic with survival to hospital discharge were analyzed using logistic regression. There were 2084 cases during the pandemic (17 weeks from the first confirmed COVID-19 case) and 1900 in the pre-pandemic period (corresponding weeks in 2019). Compared to Atlanta, OHCAs in Singapore were older, received more bystander interventions (cardiopulmonary resuscitation (CPR): 65.0% vs. 41.4%; automated external defibrillator application: 28.6% vs. 10.1%), yet had lower survival (5.6% vs. 8.1%). Compared to the pre-pandemic period, OHCAs in Singapore and Atlanta occurred more at home (adjusted odds ratio (aOR) 2.05 and 2.03, respectively) and were transported less to hospitals (aOR 0.59 and 0.36, respectively) during the pandemic. Singapore reported more witnessed OHCAs (aOR 1.96) yet less bystander CPR (aOR 0.81) during pandemic, but not Atlanta (p < 0.05). The impact of COVID-19 on OHCA outcomes did not differ between cities. Changes in OHCA characteristics and management during the pandemic, and differences between Singapore and Atlanta were likely the result of systemic and sociocultural factors.

Rapid Detection of SARS-CoV-2 Variants by Molecular Clamping Technology Based RT-qPCR (preprint)

Given the challenges that fast-changing SARS-CoV-2 variants have caused in terms of rapid spread and reduced vaccine efficacy, a rapid and cost-effective assay that can detect new and emerging variants is greatly needed worldwide. We have successfully applied the xenonucleic acid-based molecular-clamping technology to develop a multiplex RT-qPCR assay for SARS-CoV-2 multivariant detection. The assay was tested on 649 nasopharyngeal swab samples that were collected from California and Ohio. The assay was able to correctly identify all 36 Delta variant samples as it accurately detected D614G, T478K and L452R mutations. In addition, the assay was able to correctly identify all 34 Omicron samples by detecting K417N, T478K, N501Y and D614G mutations. This technique reliably detects a variety of variants and has an analytical sensitivity of 100 copies/mL. In conclusion, this novel assay can serve as a rapid and cost-effective tool to facilitate large-scale detection of SARS-CoV-2 variants.

Variation in community and ambulance care processes for out-of-hospital cardiac arrest during the COVID-19 pandemic: a systematic review and meta-analysis.

Bystander cardiopulmonary resuscitation (BCPR), early defibrillation and timely treatment by emergency medical services (EMS) can double the chance of survival from out-of-hospital sudden cardiac arrest (OHCA). We investigated the effect of the COVID-19 pandemic on the pre-hospital chain of survival. We searched five bibliographical databases for articles that compared prehospital OHCA care processes during and before the COVID-19 pandemic. Random effects meta-analyses were conducted, and meta-regression with mixed-effect models and subgroup analyses were conducted where appropriate. The search yielded 966 articles; 20 articles were included in our analysis. OHCA at home was more common during the pandemic (OR 1.38, 95% CI 1.11-1.71, p = 0.0069). BCPR did not differ during and before the COVID-19 pandemic (OR 0.94, 95% CI 0.80-1.11, p = 0.4631), although bystander defibrillation was significantly lower during the COVID-19 pandemic (OR 0.65, 95% CI 0.48-0.88, p = 0.0107). EMS call-to-arrival time was significantly higher during the COVID-19 pandemic (SMD 0.27, 95% CI 0.13-0.40, p = 0.0006). Resuscitation duration did not differ significantly between pandemic and pre-pandemic timeframes. The COVID-19 pandemic significantly affected prehospital processes for OHCA. These findings may inform future interventions, particularly to consider interventions to increase BCPR and improve the pre-hospital chain of survival.

Impact of the COVID-19 pandemic on the epidemiology of out-of-hospital cardiac arrest: a systematic review and meta-analysis.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has significantly influenced epidemiology, yet its impact on out-of-hospital cardiac arrest (OHCA) remains unclear. We aimed to evaluate the impact of the pandemic on the incidence and case fatality rate (CFR) of OHCA. We also evaluated the impact on intermediate outcomes and clinical characteristics. METHODS: PubMed, EMBASE, Web of Science, Scopus, and Cochrane Library databases were searched from inception to May 3, 2021. Studies were included if they compared OHCA processes and outcomes between the pandemic and historical control time periods. Meta-analyses were performed for primary outcomes [annual incidence, mortality, and case fatality rate (CFR)], secondary outcomes [field termination of resuscitation (TOR), return of spontaneous circulation (ROSC), survival to hospital admission, and survival to hospital discharge], and clinical characteristics (shockable rhythm and etiologies). This study was registered in the International Prospective Register of Systematic Reviews (PROSPERO) (CRD42021253879). RESULTS: The COVID-19 pandemic was associated with a 39.5% increase in pooled annual OHCA incidence (p < 0.001). Pooled CFR was increased by 2.65% (p < 0.001), with a pooled odds ratio (OR) of 1.95 for mortality [95% confidence interval (95%CI) 1.51-2.51]. There was increased field TOR (OR = 2.46, 95%CI 1.62-3.74). There were decreased ROSC (OR = 0.65, 95%CI 0.55-0.77), survival to hospital admission (OR = 0.65, 95%CI 0.48-0.89), and survival to discharge (OR = 0.52, 95%CI 0.40-0.69). There was decreased shockable rhythm (OR = 0.73, 95%CI 0.60-0.88) and increased asphyxial etiology of OHCA (OR = 1.17, 95%CI 1.02-1.33). CONCLUSION: Compared to the pre-pandemic period, the COVID-19 pandemic period was significantly associated with increased OHCA incidence and worse outcomes.

A Descriptive Analysis of the Impact of COVID-19 on Emergency Department Attendance and Visit Characteristics in Singapore

Background: The coronavirus disease 2019 (COVID-19) has impacted the utilisation of Emergency Department (ED) services worldwide. This study aims to describe the changes in attendance at a single ED and corresponding patient visit characteristics before and during the COVID-19 period. Methods: In a single-centre retrospective cohort study, we used descriptive statistics to compare ED attendance, patient demographics and visit characteristics during the COVID-19 period (1 January–28 June 2020) and its corresponding historical period in 2019 (2 January–30 June 2019). Results: The mean ED attendance decreased from 342 visits/day in the pre-COVID-19 period to 297 visits/day in the COVID-19 period. This was accompanied by a decline in presentations in nearly every ICD-10-CM diagnosis category except for respiratory-related diseases. Notably, we observed reductions in visits by critically ill patients and severe disease presentations during the COVID-19 period. We also noted a shift in the ED patient case-mix from 'Non-fever’cases to 'Fever’cases, likely giving rise to two distinct trough-to-peak visit patterns during the pre-Circuit Breaker and Circuit Breaker period. Conclusions: This descriptive study revealed distinct ED visit trends across different time periods. The COVID-19 pandemic caused a reduction in ED attendances amongst patients with low-acuity conditions and those with highest priority for emergency care. This raises concern about treatment-seeking delays and the possible impact on health outcomes. The downward trend in low-acuity presentations also presents learning opportunities for ED crowd management planning in a post-COVID-19 era.

Impact of the COVID-19 pandemic on the epidemiology of out-of-hospital cardiac arrest: A systematic review and meta-analysis (preprint)

Background: The coronavirus disease 2019 (COVID-19) pandemic has significantly influenced epidemiology, yet its impact on out-of-hospital cardiac arrest (OHCA) remains unclear. We aimed to evaluate the impact of the pandemic on the incidence and case fatality rate (CFR) of OHCA. We also evaluated the impact on intermediate outcomes and clinical characteristics. Methods PubMed, EMBASE, Web of Science, Scopus, and Cochrane Library databases were searched from inception to May 3, 2021. Studies were included if they compared OHCA processes and outcomes between the pandemic and historical control time periods. Meta-analyses were performed for primary outcomes (annual incidence, mortality, and case fatality rate [CFR]), secondary outcomes (field termination of resuscitation [TOR], return of spontaneous circulation [ROSC]), survival to hospital admission, and survival to hospital discharge), and clinical characteristics (shockable rhythm and etiologies). This study was registered in the International Prospective Register of Systematic Reviews (PROSPERO) (CRD42021253879). Results The COVID-19 pandemic was associated with a 39.5% increase in pooled annual OHCA incidence (p 

Artificial Intelligence Applications for COVID-19 in Intensive Care and Emergency Settings: A Systematic Review.

Background: Little is known about the role of artificial intelligence (AI) as a decisive technology in the clinical management of COVID-19 patients. We aimed to systematically review and critically appraise the current evidence on AI applications for COVID-19 in intensive care and emergency settings. Methods: We systematically searched PubMed, Embase, Scopus, CINAHL, IEEE Xplore, and ACM Digital Library databases from inception to 1 October 2020, without language restrictions. We included peer-reviewed original studies that applied AI for COVID-19 patients, healthcare workers, or health systems in intensive care, emergency, or prehospital settings. We assessed predictive modelling studies and critically appraised the methodology and key findings of all other studies. Results: Of fourteen eligible studies, eleven developed prognostic or diagnostic AI predictive models, all of which were assessed to be at high risk of bias. Common pitfalls included inadequate sample sizes, poor handling of missing data, failure to account for censored participants, and weak validation of models. Conclusions: Current AI applications for COVID-19 are not ready for deployment in acute care settings, given their limited scope and poor quality. Our findings underscore the need for improvements to facilitate safe and effective clinical adoption of AI applications, for and beyond the COVID-19 pandemic.

Impact of COVID-19 on Out-of-Hospital Cardiac Arrest in Singapore.

This study aimed to evaluate the impact of the Coronavirus Disease 2019 (COVID-19) pandemic on out-of-hospital cardiac arrest (OHCA) in Singapore. We used data from the Singapore Civil Defence Force to compare the incidence, characteristics and outcomes of all Emergency Medical Services (EMS)-attended adult OHCA during the pandemic (January-May 2020) and pre-pandemic (January-May 2018 and 2019) periods. Pre-hospital return of spontaneous circulation (ROSC) was the primary outcome. Binary logistic regression was used to calculate the adjusted odds ratios (aOR) for the characteristics of OHCA. Of the 3893 OHCA patients (median age 72 years, 63.7% males), 1400 occurred during the pandemic period and 2493 during the pre-pandemic period. Compared with the pre-pandemic period, OHCAs during the pandemic period more likely occurred at home (aOR: 1.48; 95% CI: 1.24-1.75) and were witnessed (aOR: 1.71; 95% CI: 1.49-1.97). They received less bystander CPR (aOR: 0.70; 95% CI: 0.61-0.81) despite 65% of witnessed arrests by a family member, and waited longer for EMS (OR ≥ 10 min: 1.71, 95% CI 1.46-2.00). Pre-hospital ROSC was less likely during the pandemic period (aOR: 0.67; 95% CI: 0.53-0.84). The pandemic saw increased OHCA incidence and worse outcomes in Singapore, likely indirect effects of COVID-19.

Artificial Intelligence Applications for COVID-19 in Intensive Care and Emergency Settings: A Systematic Review (preprint)

BackgroundLittle is known about the role of artificial intelligence (AI) as a decisive technology in the clinical management of COVID-19 patients. We aimed to systematically review and critically appraise the current evidence on AI applications for COVID-19 in intensive care and emergency settings, focusing on methods, reporting standards, and clinical utility. MethodsWe systematically searched PubMed, Embase, Scopus, CINAHL, IEEE Xplore, and ACM Digital Library databases from inception to 1 October 2020, without language restrictions. We included peer-reviewed original studies that applied AI for COVID-19 patients, healthcare workers, or health systems in intensive care, emergency or prehospital settings. We assessed predictive modelling studies using PROBAST (prediction model risk of bias assessment tool) and a modified TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) statement for AI. We critically appraised the methodology and key findings of all other studies. ResultsOf fourteen eligible studies, eleven developed prognostic or diagnostic AI predictive models, all of which were assessed to be at high risk of bias. Common pitfalls included inadequate sample sizes, poor handling of missing data, failure to account for censored participants, and weak validation of models. Studies had low adherence to reporting guidelines, with particularly poor reporting on model calibration and blinding of outcome and predictor assessment. Of the remaining three studies, two evaluated the prognostic utility of deep learning-based lung segmentation software and one studied an AI-based system for resource optimisation in the ICU. These studies had similar issues in methodology, validation, and reporting. ConclusionsCurrent AI applications for COVID-19 are not ready for deployment in acute care settings, given their limited scope and poor quality. Our findings underscore the need for improvements to facilitate safe and effective clinical adoption of AI applications, for and beyond the COVID-19 pandemic.

Coronavirus disease 2019 (COVID-19): an evidence map of medical literature.

BACKGROUND: Since the beginning of the COVID-19 outbreak in December 2019, a substantial body of COVID-19 medical literature has been generated. As of June 2020, gaps and longitudinal trends in the COVID-19 medical literature remain unidentified, despite potential benefits for research prioritisation and policy setting in both the COVID-19 pandemic and future large-scale public health crises. METHODS: In this paper, we searched PubMed and Embase for medical literature on COVID-19 between 1 January and 24 March 2020. We characterised the growth of the early COVID-19 medical literature using evidence maps and bibliometric analyses to elicit cross-sectional and longitudinal trends and systematically identify gaps. RESULTS: The early COVID-19 medical literature originated primarily from Asia and focused mainly on clinical features and diagnosis of the disease. Many areas of potential research remain underexplored, such as mental health, the use of novel technologies and artificial intelligence, pathophysiology of COVID-19 within different body systems, and indirect effects of COVID-19 on the care of non-COVID-19 patients. Few articles involved research collaboration at the international level (24.7%). The median submission-to-publication duration was 8 days (interquartile range: 4-16). CONCLUSIONS: Although in its early phase, COVID-19 research has generated a large volume of publications. However, there are still knowledge gaps yet to be filled and areas for improvement for the global research community. Our analysis of early COVID-19 research may be valuable in informing research prioritisation and policy planning both in the current COVID-19 pandemic and similar global health crises.

Coronavirus Disease 2019 (COVID-19): An Evidence Map of Medical Literature (preprint)

Since the beginning of the COVID-19 outbreak in December 2019, a substantial body of COVID-19 medical literature has been generated. As of May 2020, gaps in the existing literature remain unidentified and, hence, unaddressed. In this paper, we summarise the medical literature on COVID-19 between 1 January and 24 March 2020 using evidence maps and bibliometric analysis in order to systematically identify gaps and propose areas for valuable future research. The examined COVID-19 medical literature originated primarily from Asia and focussed mainly on clinical features and diagnosis of the disease. Many areas of potential research remain underexplored, such as mental health research, the use of novel technologies and artificial intelligence, research on the pathophysiology of COVID-19 within different body systems, and research on indirect effects of COVID-19 on the care of non-COVID-19 patients. Research collaboration at the international level was limited although improvements may aid global containment efforts.