Brittany M Bogle

Assessment of knowledge and attitudes regarding automated external defibrillators and cardiopulmonary resuscitation among American University students

Aim We sought to quantify knowledge and attitudes regarding automated external defibrillators (AEDs) and cardiopulmonary resuscitation (CPR) among university students. We also aimed to determine awareness of the location of an actual AED on campus. Methods We performed an online survey of undergraduate and graduate students at a mid-sized, private university that has 37 AEDs located throughout its two campuses. Results 267 students responded to the survey. Almost all respondents could identify CPR (98.5%) and an AED (88.4%) from images, but only 46.1% and 18.4%, respectively, could indicate the basic mechanism of CPR and AEDs. About a quarter (28.1%) of respondents were comfortable using an AED without assistance, compared with 65.5% when offered assistance. Of those who did not feel comfortable, 87.7% indicated that they were ‘afraid of doing something wrong.’ One out of 6 (17.6%) respondents knew that a student centre had an AED, and only 2% could recall its precise location within the building. Most (66.3%) respondents indicated they would look for an AED near fire extinguishers, followed by the entrance of a building (19.6%). Conclusions This study found that most students at an American university can identify CPR and AEDs, but do not understand their basic mechanisms of action or are willing to perform CPR or use AEDs unassisted. Recent CPR/AED training and 9-1-1 assistance increases comfort. The most common fear reported was incorrect CPR or AED use. Almost all students could not recall where an AED was located in a student centre.

Lifetime Risk for Sudden Cardiac Death in the Community.

Background Sudden cardiac death (SCD) is a leading cause of death in the United States and often occurs without previous cardiac symptoms. Lifetime risk for SCD and the influence of established risk factors on lifetime risks for SCD have not been estimated previously. Methods and Results We followed Framingham Heart Study participants who were free of cardiovascular disease before their earliest examination. SCD was defined as death attributed to coronary heart disease within 1 hour of symptom onset without another probable cause of death, as adjudicated by a panel of 3 physicians. Lifetime risk for SCD was estimated to 85 years of age for men and women, with death attributed to other causes as the competing risk, and stratified by risk factor levels. We followed 2294 men and 2785 women for 160 396 person‐years; 375 experienced SCD. At 45 years of age, lifetime risks were 10.9% (95% CI, 9.4–12.5) for men and 2.8% (95% CI, 2.1–3.5) for women. Greater aggregate burden of established risk factors was associated with a higher lifetime risk for SCD. Categorizing men and women solely by blood pressure levels resulted in a clear stratification of lifetime risk curves. Conclusions We present the first lifetime risk estimates for SCD. Greater aggregate risk factor burden, or blood pressure level alone, is associated with higher lifetime risks for SCD. This high risk of premature death attributed to SCD (approximately 1 in 9 men and 1 in 30 women) should serve as a motivator of public health efforts in preventing and responding to SCD.

A Simple Community-Based Risk-Prediction Score for Sudden Cardiac Death

BACKGROUNDnAlthough sudden cardiac death is a leading cause of death in the United States, most victims of sudden cardiac death are not identified as at risk prior to death. We sought to derive and validate a population-based risk score that predicts sudden cardiac death.nnnMETHODSnThe Atherosclerosis Risk in Communities (ARIC) Study recorded clinical measures from men and women aged 45-64 years at baseline; 11,335 white and 3780 black participants were included in this analysis. Participants were followed over 10 years and sudden cardiac death was physician adjudicated. Cox proportional hazards models were used to derive race-specific equations to estimate the 10-year sudden cardiac death risk. Covariates for the risk score were selected from available demographic and clinical variables. Utility was assessed by calculating discrimination (Harrells C-index) and calibration (Hosmer-Lemeshow chi-squared test). The white-specific equation was validated among 5626 Framingham Heart Study participants.nnnRESULTSnDuring 10 years follow-up among ARIC participants (mean age 54.4 years, 52.4% women), 145 participants experienced sudden cardiac death; the majority occurred in the highest quintile of predicted risk. Model covariates included age, sex, total cholesterol, lipid-lowering and hypertension medication use, blood pressure, smoking status, diabetes, and body mass index. The score yielded very good internal discrimination (white-specific C-index 0.82; 95% confidence interval [CI], 0.78-0.85; black-specific C-index 0.75; 95% CI, 0.68-0.82) and very good external discrimination among Framingham participants (C-index 0.82; 95% CI, 0.79-0.86). Calibration plots indicated excellent calibration in ARIC (white-specific chi-squared 5.3, Pxa0=xa0.82; black-specific chi-squared 4.1, Pxa0=xa0.77), and a simple recalibration led to excellent fit within Framingham (chi-squared 2.1, P = 0.99).nnnCONCLUSIONSnThe proposed risk scores may be used to identify those at risk for sudden cardiac death within 10 years and particularly classify those at highest risk who may merit further screening.

Regional Evaluation of the Severity-Based Stroke Triage Algorithm for Emergency Medical Services Using Discrete Event Simulation

Background and Purpose— The Severity-Based Stroke Triage Algorithm for Emergency Medical Services endorses routing patients with suspected large vessel occlusion acute ischemic strokes directly to endovascular stroke centers (ESCs). We sought to evaluate different specifications of this algorithm within a region. Methods— We developed a discrete event simulation environment to model patients with suspected stroke transported according to algorithm specifications, which varied by stroke severity screen and permissible additional transport time for routing patients to ESCs. We simulated King County, Washington, and Mecklenburg County, North Carolina, distributing patients geographically into census tracts. Transport time to the nearest hospital and ESC was estimated using traffic-based travel times. We assessed undertriage, overtriage, transport time, and the number-needed-to-route, defined as the number of patients enduring additional transport to route one large vessel occlusion patient to an ESC. Results— Undertriage was higher and overtriage was lower in King County compared with Mecklenburg County for each specification. Overtriage variation was primarily driven by screen (eg, 13%–55% in Mecklenburg County and 10%–40% in King County). Transportation time specifications beyond 20 minutes increased overtriage and decreased undertriage in King County but not Mecklenburg County. A low- versus high-specificity screen routed 3.7× more patients to ESCs. Emergency medical services spent nearly twice the time routing patients to ESCs in King County compared with Mecklenburg County. Conclusions— Our results demonstrate how discrete event simulation can facilitate informed decision making to optimize emergency medical services stroke severity-based triage algorithms. This is the first step toward developing a mature simulation to predict patient outcomes.

Lipid Profiles in Out-of-Hospital Sudden Unexpected Death

Objective To determine the association between serum lipid measurements and the occurrence of out-of-hospital sudden unexpected death (OHSUD). Patients and Methods We compared 139 OHSUD cases (43 female patients [30.9%]) and 968 controls (539 female patients [55.7%]) from Wake County, North Carolina, from March 1, 2013, through February 28, 2015. Individuals were included if they were aged 18 to 64 years and had lipid measurements in the 5 years before their death (cases) or the most recent health care encounter (controls). Covariates were abstracted from medical records for all subjects, and those with triglyceride (TG) levels greater than 400 mg/dL (to convert to mmol/L, multiply by 0.0259) were excluded for low-density lipoprotein (LDL)–related analyses. Results By linear regression using age- and sex-adjusted models, cases of OHSUD had lower adjusted mean total cholesterol (170.3±52.2 mg/dL vs 188.9±39.7 mg/dL; P<.001), LDL cholesterol (90.9±39.6 mg/dL vs 109.6±35.2 mg/dL; P<.001), and non–high-density lipoprotein (HDL) (121.6±49.8 mg/dL vs 134.3±39.6 mg/dL; P<.001) levels and a higher adjusted TG/HDL-C ratio (4.7±7 vs 3±2.7; P<.001) than did controls. By logistic regression using age- and sex-adjusted models, the odds of OHSUD were elevated per unit increase in TG/HDL-C ratio (1.08; 95% CI, 1.03-1.12). Conclusion Out-of-hospital sudden unexpected death cases had more favorable levels of total cholesterol, LDL cholesterol, and non-HDL, possibly indicating a lack of association between traditional lipid cardiovascular risk factors and sudden unexpected death. A comparatively elevated TG/HDL-C ratio in cases may corroborate an evolving hypothesis of how vasoactive and prothrombotic remnant-like lipoprotein particles contribute to sudden unexpected death.

Vital exhaustion and sudden cardiac death in the Atherosclerosis Risk in Communities Study

Objective Vital exhaustion (VE), a construct defined as lack of energy, increased fatigue and irritability, and feelings of demoralisation, has been associated with cardiovascular events. We sought to examine the relation between VE and sudden cardiac death (SCD) in the Atherosclerosis Risk in Communities (ARIC) Study. Methods The ARIC Study is a predominately biracial cohort of men and women, aged 45–64 at baseline, initiated in 1987 through random sampling in four US communities. VE was measured using the Maastricht questionnaire between 1990 and 1992 among 13u2009923 individuals. Cox proportional hazards models were used to examine the hazard of out-of-hospital SCD across tertiles of VE scores. Results Through 2012, 457 SCD cases, defined as a sudden pulseless condition presumed due to a ventricular tachyarrhythmia in a previously stable individual, were identified in ARIC by physician record review. Adjusting for age, sex and race/centre, participants in the highest VE tertile had an increased risk of SCD (HR 1.48, 95%u2009CI 1.17 to 1.87), but these findings did not remain significant after adjustment for established cardiovascular disease risk factors (HR 0.94, 95%u2009CI 0.73 to 1.20). Conclusions Among participants of the ARIC study, VE was not associated with an increased risk for SCD after adjustment for cardiovascular risk factors.

Delivery of Automated External Defibrillators (AED) by Drones: Implications for Emergency Cardiac Care

Purpose of ReviewOut-of-hospital cardiac arrest (OHCA) remains a significant health problem in the USA and only 8.6% of victims survive with good neurological function, despite advances in emergency cardiac care. The likelihood of OHCA survival decreases by 10% for every minute without resuscitation.Recent FindingsAutomatic external defibrillators (AEDs) have the potential to save lives yet public access defibrillators are underutilized (<u20092% of the time) because they are difficult to locate and rarely available in homes or residential areas, where the majority (70%) of OHCA occur. Even when AEDs are within close proximity (within 100xa0m), they are not used 40% of the time.SummaryUnmanned aerial vehicles, or drones, have the potential to deliver AEDs to a bystander and augment emergency medical service (EMS) care. We review the use of drones in medicine, what is currently known, and clinical implications for advancing emergency cardiac care.

SUDDEN CARDIAC DEATH RISK DISTRIBUTION IN THE UNITED STATES POPULATION: RESULTS FROM THE NATIONAL HEALTH AND NUTRITION EXAMINATION SURVEY (NHANES), 2005-2012

Background: Sudden cardiac death (SCD) accounts for more than half of all deaths from cardiovascular disease (CVD) and is the first manifestation of heart disease in 50% of these individuals. We sought to determine the distribution of SCD risk in the general US population.nnMethods: We recently

A Moment Matching Approach for Generating Synthetic Data

Synthetic data are becoming increasingly important mechanisms for sharing data among collaborators and with the public. Multiple methods for the generation of synthetic data have been proposed, but many have short comings with respect to maintaining the statistical properties of the original data. We propose a new method for fully synthetic data generation that leverages linear and integer mathematical programming models in order to match the moments of the original data in the synthetic data. This method has no inherent disclosure risk and does not require parametric or distributional assumptions. We demonstrate this methodology using the Framingham Heart Study. Existing synthetic data methods that use chained equations were compared with our approach. We fit Cox proportional hazards, logistic regression, and nonparametric models to synthetic data and compared with models fitted to the original data. True coverage, the proportion of synthetic data parameter confidence intervals that include the original datas parameter estimate, was 100% for parametric models when up to four moments were matched, and consistently outperformed the chained equations approach. The area under the curve and accuracy of the nonparametric models trained on synthetic data marginally differed when tested on the full original data. Models were also trained on synthetic data and a partition of original data and were tested on a held-out portion of original data. Fourth-order moment matched synthetic data outperformed others with respect to fitted parametric models but did not always outperform other methods with fitted nonparametric models. No single synthetic data method consistently outperformed others when assessing the performance of nonparametric models. The performance of fourth-order moment matched synthetic data in fitting parametric models suggests its use in these cases. Our empirical results also suggest that the performance of synthetic data generation techniques, including the moment matching approach, is less stable for use with nonparametric models. The benefits of the moment matching approach should be weighed against additional computational costs. In summary, our results demonstrate that the introduced moment matching approach may be considered as an alternative to existing synthetic data generation methods.