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BackgroundThe PulsePoint Respond app is a novel system that can be implemented in emergency dispatch centers to crowdsource basic life support (BLS) for patients with cardiac arrest and facilitate bystander cardiopulmonary resuscitation (CPR) and automated external defibrillator use while first responders are en route. ObjectiveThe aim of this study was to conduct a North American survey to evaluate the public perception of the above-mentioned strategy, including acceptability and willingness to respond to alerts. MethodsWe designed a Web-based survey administered by IPSOS Reid, an established external polling vendor. Sampling was designed to ensure broad representation using recent census statistics. ResultsA total of 2415 survey responses were analyzed (1106 from Canada and 1309 from the United States). It was found that 98.37% (1088/1106) of Canadians and 96% (1259/1309) of Americans had no objections to PulsePoint being implemented in their community; 84.27% (932/1106) of Canadians and 55.61% (728/1309) of Americans said they would download the app to become a potential responder to cardiac arrest, respectively. Among Canadians, those who said they were likely to download PulsePoint were also more likely to have ever had CPR training (OR 1.7, 95% CI 1.2-2.4; P=.002); however, this was not true of American respondents (OR 1.0, 95% CI 0.79-1.3; P=.88). When asked to imagine themselves as a cardiac arrest victim, 95.39% (1055/1106) of Canadians and 92.44% (1210/1309) of Americans had no objections to receiving crowdsourced help in a public setting; 88.79% (982/1106) of Canadians and 84.87% (1111/1309) of Americans also had no objections to receiving help in a private setting, respectively. The most common concern identified with respect to PulsePoint implementation was a responder’s lack of ability, training, or access to proper equipment in a public setting. ConclusionsThe North American public finds the concept of crowdsourcing BLS for out-of-hospital cardiac arrest to be acceptable. It demonstrates willingness to respond to PulsePoint CPR notifications and to accept help from others alerted by the app if they themselves suffered a cardiac arrest.

INTRODUCTION: The COVID-19 pandemic has led to closures of non-essential businesses and buildings. The impact of such closures on automated external defibrillator (AED) accessibility compared to changes in foot traffic levels is unknown. METHODS: We identified all publicly available online AED registries in Canada last updated May 1, 2019 or later. We mapped AED locations to location types and classified each location type as completely inaccessible, partially inaccessible, or unaffected based on government-issued closure orders as of May 1, 2020. Using location and mobility data from Googles COVID-19 Community Mobility Reports, we identified the change in foot traffic levels between February 15-May 1, 2020 (excluding April 10-12) compared to the baseline of January 3-February 1, 2020, and determined the discrepancy between foot traffic levels and AED accessibility. RESULTS: We identified four provincial and two municipal AED registries containing a total of 5848 AEDs. Of those, we estimated that 69.9% were completely inaccessible, 18.8% were partially inaccessible, and 11.3% were unaffected. Parks, retail and recreation locations, and workplaces experienced the greatest reduction in AED accessibility. The greatest discrepancies between foot traffic levels and AED accessibility occurred in parks, retail and recreation locations, and transit stations. CONCLUSION: A majority of AEDs became inaccessible during the COVID-19 pandemic due to government-mandated closures. In a substantial number of locations across Canada, the reduction in AED accessibility was far greater than the reduction in foot traffic.

Background Mathematical optimization of automated external defibrillator (AED) placement may improve AED accessibility and out-of-hospital cardiac arrest (OHCA) outcomes compared with American Heart Association (AHA) and European Resuscitation Council (ERC) placement guidelines. We conducted an in silico trial (simulated prospective cohort study) comparing mathematically optimized placements with placements derived from current AHA and ERC guidelines, which recommend placement in locations where OHCAs are usually witnessed. Methods and Results We identified all public OHCAs of presumed cardiac cause from 2008 to 2016 in Copenhagen, Denmark. For the control, we computationally simulated placing 24/7-accessible AEDs at every unique, public, witnessed OHCA location at monthly intervals over the study period. The intervention consisted of an equal number of simulated AEDs placements, deployed monthly, at mathematically optimized locations, using a model that analyzed historical OHCAs before that month. For each approach, we calculated the number of OHCAs in the study period that occurred within a 100-m route distance based on Copenhagens road network of an available AED after it was placed (OHCA coverage). Estimated impact on bystander defibrillation and 30-day survival was calculated by multivariate logistic regression. The control scenario involved 393 AEDs at historical, public, witnessed OHCA locations, covering 15.8% of the 653 public OHCAs from 2008 to 2016. The optimized locations provided significantly higher coverage (24.2%; P

Background Time to treatment is critical for survival from sudden cardiac arrest. Every minute delay in defibrillation results in a 7% to 10% reduction in survival. This is particularly problematic in rural and remote regions, where emergency medical service response is prolonged and automated external defibrillators (AEDs) are often not available. Our primary objective was to examine the feasibility of a novel AED drone delivery method for rural and remote sudden cardiac arrest. A secondary objective was to compare response times between AED drone delivery and ambulance to mock sudden cardiac arrest resuscitations. Methods and Results We conducted 6 simulations in 2 rural communities in southern Ontario, Canada. In the first 2 simulations, the drone and ambulance were dispatched from the same paramedic base. In simulations 3 and 4, the drone and ambulance were dispatched from separate paramedic bases; and in simulations 5 and 6, the drone was dispatched from an optimized location. During each simulation, a mock call was placed to 911 and a single AED drone and an ambulance were simultaneously dispatched to a predetermined destination. On scene, trained first responders retrieved the AED from the drone and initiated resuscitative efforts on a mannequin until paramedics arrived. No difficulties were encountered during drone activation by dispatch, ascent, landing, or bystander retrieval of the AED from the drone. During simulations 1 and 2, the distance to the scene was 6.6 km. For simulations 3 and 4, the ambulance response distance increased to 8.8 km while drone remained at 6.6 km; and in simulations 5 and 6, the ambulance response distance was 20 km compared with 9 km for the drone. During each flight, the AED drone arrived on scene before the ambulance, between 1.8 and 8.0 minutes faster. Conclusions This study suggests AED drone delivery is feasible, with the potential for improvements in response time during simulated sudden cardiac arrest scenarios. Further research is required to determine the appropriate system configuration for AED drone delivery in an integrated emergency medical service system as well as optimal strategies to simplify bystander application of a drone-delivered AED.

Objective  Sinonasal undifferentiated carcinoma (SNUC) is an aggressive neoplasm, with conflicting existing literature regarding prognosis and treatment due to the rarity of disease. Characterization of optimal SNUC management is necessary for improved outcomes. Study Design  Case series with planned data collection and analysis. Setting  Hospital of the University of Pennsylvania and Pennsylvania Hospital. Participants  Patients with pathologically confirmed SNUC treated within a 15-year period were identified, and records were obtained and evaluated for several demographic characteristics. Main Outcomes Measures  Disease-specific survival from diagnosis was the primary endpoint, while disease recurrence was a secondary endpoint of the study. Results  Twenty-seven patients with established SNUC were included in this cohort, with a median age of 55 years. Eighty-five percent of patients were surgically treated, and 85% of patients presented with stage IV disease. Two-year disease-specific survival was 66% and 5-year disease-specific survival was 46%. Ninety-six percent of patients received both chemotherapy and radiation as adjuvant treatment. Nodal disease at presentation and disease recurrence both significantly decreased patient survival ( p

Objective  Sinonasal undifferentiated carcinoma (SNUC) is an aggressive neoplasm, with conflicting existing literature regarding prognosis and treatment due to the rarity of disease. Characterization of optimal SNUC management is necessary for improved outcomes. Study Design  Case series with planned data collection and analysis. Setting  Hospital of the University of Pennsylvania and Pennsylvania Hospital. Participants  Patients with pathologically confirmed SNUC treated within a 15-year period were identified, and records were obtained and evaluated for several demographic characteristics. Main Outcomes Measures  Disease-specific survival from diagnosis was the primary endpoint, while disease recurrence was a secondary endpoint of the study. Results  Twenty-seven patients with established SNUC were included in this cohort, with a median age of 55 years. Eighty-five percent of patients were surgically treated, and 85% of patients presented with stage IV disease. Two-year disease-specific survival was 66% and 5-year disease-specific survival was 46%. Ninety-six percent of patients received both chemotherapy and radiation as adjuvant treatment. Nodal disease at presentation and disease recurrence both significantly decreased patient survival ( p

The sequential use of signaling pathways is essential for the guidance of pluripotent progenitors into diverse cell fates. Here, we show that Shp2 exclusively mediates FGF but not PDGF signaling in the neural crest to control lacrimal gland development. In addition to preventing p53-independent apoptosis and promoting the migration of Sox10-expressing neural crests, Shp2 is also required for expression of the homeodomain transcription factor Alx4, which directly controls Fgf10 expression in the periocular mesenchyme that is necessary for lacrimal gland induction. We show that Alx4 binds an Fgf10 intronic element conserved in terrestrial but not aquatic animals, underlying the evolutionary emergence of the lacrimal gland system in response to an airy environment. Inactivation of ALX4/Alx4 causes lacrimal gland aplasia in both human and mouse. These results reveal a key role of Alx4 in mediating FGF-Shp2-FGF signaling in the neural crest for lacrimal gland development.

BACKGROUND: Public access defibrillation programs can improve survival after out-of-hospital cardiac arrest, but automated external defibrillators (AEDs) are rarely available for bystander use at the scene. Drones are an emerging technology that can deliver an AED to the scene of an out-of-hospital cardiac arrest for bystander use. We hypothesize that a drone network designed with the aid of a mathematical model combining both optimization and queuing can reduce the time to AED arrival. METHODS: We applied our model to 53 702 out-of-hospital cardiac arrests that occurred in the 8 regions of the Toronto Regional RescuNET between January 1, 2006, and December 31, 2014. Our primary analysis quantified the drone network size required to deliver an AED 1, 2, or 3 minutes faster than historical median 911 response times for each region independently. A secondary analysis quantified the reduction in drone resources required if RescuNET was treated as a large coordinated region. RESULTS: The region-specific analysis determined that 81 bases and 100 drones would be required to deliver an AED ahead of median 911 response times by 3 minutes. In the most urban region, the 90th percentile of the AED arrival time was reduced by 6 minutes and 43 seconds relative to historical 911 response times in the region. In the most rural region, the 90th percentile was reduced by 10 minutes and 34 seconds. A single coordinated drone network across all regions required 39.5% fewer bases and 30.0% fewer drones to achieve similar AED delivery times. CONCLUSIONS: An optimized drone network designed with the aid of a novel mathematical model can substantially reduce the AED delivery time to an out-of-hospital cardiac arrest event.

BACKGROUND: Efforts to guide automated external defibrillator placement for out-of-hospital cardiac arrest (OHCA) treatment have focused on identifying broadly defined location categories without considering hours of operation. Broad location categories may be composed of many businesses with varying accessibility. Identifying specific locations for automated external defibrillator deployment incorporating operating hours and time of OHCA occurrence may improve automated external defibrillator accessibility. We aim to identify specific businesses and municipal locations that maximize OHCA coverage on the basis of spatiotemporal assessment of OHCA risk in the immediate vicinity of franchise locations. METHODS: This study was a retrospective population-based cohort study using data from the Toronto Regional RescuNET Epistry cardiac arrest database. We identified all nontraumatic public OHCAs occurring in Toronto, ON, Canada, from January 2007 through December 2015. We identified 41 unique businesses and municipal location types with ≥20 locations in Toronto from the YellowPages, Canadian Franchise Association, and the City of Toronto Open Data Portal. We obtained their geographic coordinates and hours of operation from Web sites, by phone, or in person. We determined the number of OHCAs that occurred within 100 m of each location when it was open (spatiotemporal coverage) for Toronto overall and downtown. The businesses and municipal locations were then ranked by spatiotemporal OHCA coverage. To evaluate temporal stability of the rankings, we calculated intraclass correlation of the annual coverage values. RESULTS: There were 2654 nontraumatic public OHCAs. Tim Hortons ranked first in Toronto, covering 286 OHCAs. Starbucks ranked first in downtown, covering 110 OHCAs. Coffee shops and bank machines from the 5 largest Canadian banks occupied 8 of the top 10 spots in both Toronto and downtown. The rankings exhibited high temporal stability with intraclass correlation values of 0.88 (95% confidence interval, 0.83-0.93) in Toronto and 0.79 (95% confidence interval, 0.71-0.86) in downtown. CONCLUSIONS: We identified and ranked businesses and municipal locations by spatiotemporal OHCA risk in their immediate vicinity. This approach may help policy makers and funders to identify and prioritize potential partnerships for automated external defibrillator deployment in public-access defibrillator programs.

BACKGROUND: There is preliminary evidence that patients with chronic rhinosinusitis (CRS) and comorbid obstructive sleep apnea (OSA) have reduced quality-of-life (QOL) improvements after functional endoscopic sinus surgery (FESS) compared with patients without OSA. The effect of OSA severity on QOL improvement after FESS is unknown. OBJECTIVES: To better characterize the QOL improvement after FESS for patients with comorbid OSA and to assess whether QOL improvement is dependent on OSA severity. METHODS: This multi-institution, retrospective cohort study evaluated adult patients with CRS who underwent FESS between 2007 and 2015. Preoperative, 1-month, 3-month, 6-month, and 1-year postoperative 22-Item Sino-Nasal Outcome Test scores were used to evaluate QOL. We compared patients without OSA with patients with stratified OSA based on the preoperative apnea-hypopnea index. A multilevel, mixed-effects linear regression model was used for the analysis. RESULTS: Of 480 participants, 83 (17%) had OSA, and 47 of these patients had polysomnography results available for review. Both patients with OSA and patients without OSA reported significant QOL improvement after surgery (p < 0.0001) relative to baseline. In the unadjusted model, the subjects with OSA demonstrated a statistically worse outcome in 22-Item Sino-Nasal Outcome Test scores at each time point (2.4 points higher per time point, p = 0.006). When controlling for covariates, the adjusted model showed no difference in QOL outcome based on OSA status (p = 0.114). When stratified by OSA disease severity, the adjusted model showed no difference in the QOL outcome. CONCLUSIONS: Patients with CRS and comorbid OSA had worse QOL outcomes after FESS; however, when controlling for patient factors, there was no difference in QOL outcome. OSA disease severity did not seem to predict QOL improvement after FESS.

BACKGROUND: Immediate access to an automated external defibrillator (AED) increases the chance of survival for out-of-hospital cardiac arrest (OHCA). Current deployment usually considers spatial AED access, assuming AEDs are available 24 h a day. OBJECTIVES: The goal of this study was to develop an optimization model for AED deployment, accounting for spatial and temporal accessibility, to evaluate if OHCA coverage would improve compared with deployment based on spatial accessibility alone. METHODS: This study was a retrospective population-based cohort trial using data from the Toronto Regional RescuNET Epistry cardiac arrest database. We identified all nontraumatic public location OHCAs in Toronto, Ontario, Canada (January 2006 through August 2014) and obtained a list of registered AEDs (March 2015) from Toronto Paramedic Services. Coverage loss due to limited temporal access was quantified by comparing the number of OHCAs that occurred within 100 meters of a registered AED (assumed coverage 24 h per day, 7 days per week) with the number that occurred both within 100 meters of a registered AED and when the AED was available (actual coverage). A spatiotemporal optimization model was then developed that determined AED locations to maximize OHCA actual coverage and overcome the reported coverage loss. The coverage gain between the spatiotemporal model and a spatial-only model was computed by using 10-fold cross-validation. RESULTS: A total of 2,440 nontraumatic public OHCAs and 737 registered AED locations were identified. A total of 451 OHCAs were covered by registered AEDs under assumed coverage 24 h per day, 7 days per week, and 354 OHCAs under actual coverage, representing a coverage loss of 21.5% (p < 0.001). Using the spatiotemporal model to optimize AED deployment, a 25.3% relative increase in actual coverage was achieved compared with the spatial-only approach (p < 0.001). CONCLUSIONS: One in 5 OHCAs occurred near an inaccessible AED at the time of the OHCA. Potential AED use was significantly improved with a spatiotemporal optimization model guiding deployment.