Background: The bystander CPR (CPR) rate in Birmingham, Alabama is just 15.5%, contributing to one of the lowest out-of-hospital cardiac arrest (OHCA) survival rates in the United States. The utilization of telecommunicator CPR (T-CPR) in Birmingham is unknown. We aimed to evaluate existing T-CPR performance and compare local metrics to recently published American Heart Association (AHA) T-CPR guidelines. Methods: We retrospectively reviewed all 9-1-1 audio recordings for adult (≥18 years) non-traumatic Emergency Medical Services (EMS)-treated OHCA in Birmingham during 2023. EMS-witnessed events or those occurring within healthcare or correctional facilities were excluded. T-CPR metrics were manually extracted and compared to the AHA T-CPR benchmarks using descriptive statistics. Results: Among 236 included OHCA cases, 94 (39.8%) were correctly recognized by telecommunicators (AHA goal: >75%). Of cases recognizable by AHA definitions, 50.0% were identified correctly by telecommunicators (AHA goal: >95%), with a median recognition time of 60 s (AHA goal: <90 s). T-CPR instructions were provided to 72.7% of recognizable cases (AHA goal: >75%), with a median time to first chest compression of 172 s (AHA goal: <150 s). When T-CPR instructions were offered to callers who were willing and able to perform CPR, chest compressions were initiated in 97.9% of cases. Conclusion: Despite low rates of telecommunicator recognition of OHCA and T-CPR instruction in Birmingham, nearly all callers who received T-CPR instructions began chest compressions. Targeted improvements in T-CPR implementation represent a high-impact opportunity to increase CPR rates in Birmingham and other communities with low bystander engagement.

BACKGROUND: Automated external defibrillators (AEDs) are rarely used by bystanders in Birmingham, Alabama. We sought to characterize AED availability, accessibility, and usability in the highest-risk out-of-hospital cardiac arrest neighborhoods of Birmingham. METHODS: We conducted a descriptive observational study from June 12 to August 27, 2025, in 3 Birmingham neighborhoods with the highest relative out-of-hospital cardiac arrest risk. Potential public AED sites, defined as properties other than single-family residences, were identified using Google Maps landmark descriptions. Sites were contacted by telephone, email, or in person to confirm AED presence, and device-specific data were collected in person. Outcomes included AED availability (presence of ≥1 AED), accessibility (public access and hours), and usability (ability to power on with pads). At-risk devices contained expired batteries and pads. Observed and effective AED densities were compared with the recommended 5 AEDs per square mile. Observed AED density was defined as AEDs per square mile, regardless of accessibility hours. Effective AED density was defined as AEDs per square mile adjusted for weekly hours of accessibility. RESULTS: A total of 287 potential AED locations were identified, and 284 (99%) were contacted and provided data. Fifty-six AEDs were reported at 43 locations (15.1%). Of these, 19 (33.9%) were not publicly accessible. Among the 37 publicly accessible AEDs, none were available 24 hours per day. Thirty-five (94.6%) were found in usable condition, and 5 (13.5%) were classified as at risk. Two (5.4%) were inoperable. The observed density of publicly usable AEDs was 13.5 AEDs per square mile, with an effective density of 3.7 AEDs per square mile due to limited hours of access. CONCLUSIONS: Despite exceeding the recommended AED density, limited public accessibility and maintenance issues substantially reduce effective AED coverage in Birmingham. Reporting effective AED density, which accounts for hours of access, reveals gaps between device presence and real-world accessibility.

BACKGROUND:Venous thromboembolism (VTE) is associated with approximately 100 000 deaths annually in the United States (U.S.). Progress in the prevention, diagnosis, treatment, and recovery from VTE depends on research funding. The National Institutes of Health (NIH), the world's largest funder of biomedical research, does not currently report VTE-specific funding in its annual Categorical Spending Report. OBJECTIVES:This study aimed to provide a descriptive analysis of NIH funding for VTE research over the past decade. METHODS:We conducted a search of the NIH Research Portfolio Online Reporting Tools Expenditures and Results database from 2015 to 2024 using a string of VTE-related search terms. Grants were categorized as VTE research (yes/no) using a large language model prompted with predefined classification criteria. We tabulated annual NIH funding amounts, the number of VTE-related grants, and the number of unique principal investigators. For 2023, VTE research investment was compared with that for heart disease and stroke, the leading causes of vascular mortality in the U.S. RESULTS:The search yielded 2130 grants with complete data, of which 1114 were classified as VTE research. When excluding renewal awards, 490 unique VTE grants were identified. Total inflation-adjusted NIH funding for VTE research was $42 million in 2015, peaked at $73 million in 2021, and totaled $67.1 million in 2024. In 2023, NIH funding per annual deaths was $2765 for heart disease, $2724 for stroke, and $639 for VTE. CONCLUSION:NIH investment in VTE research has increased over the past decade, but remains disproportionately low relative to other major causes of vascular mortality in the U.S.

Background:Care in the hospital, post-arrest care, is a crucial component of out-of-hospital cardiac arrest (OHCA) management, but current OHCA databases contain limited post-arrest care data. To address this, we present the methodology for assembling a multicenter, real-world post-arrest care research registry using the Trinetx database. Methods:We queried the Trinetx research database (01/01/2000-02/19/2025), a federated database of electronic health record data from over 100 healthcare organisations in the US and internationally, to identify OHCAs from ICD codes for cardiac arrest related to emergency department (ED) visits. To define our cohort of patients eligible to receive post-arrest care, we identified OHCAs that survived to admission based on having a temporally associated inpatient encounter (based on encounter type/Current Procedure Terminology codes) or an ED visit lasting >24 h. We defined survival to discharge as 1) having an encounter after the hospitalisation and 2) having a death date after the end of the hospitalisation. We report patient characteristics and the number of clinical variables available in each data table for the cohort. Results:We identified 222,868 OHCAs and included 88,753 patients (39.8%) who survived to admission. The median age was 65, 60.3% were male, and 59.8% were White. Survival to discharge rate was 48.5%. The database contained 188,038,385 clinical data points: 40,868,707 vital signs, 52,145,594 labs, 17,781,035 procedures, 72,575,389 medication administrations, and 4,667,660 diagnoses. Conclusion:Using Trinetx real-world data, it is possible to create a multicentre, OHCA post-arrest care database with a substantial number of clinical variables, enabling novel post-arrest care research.

OBJECTIVES:To evaluate the agreement between bystander cardiopulmonary resuscitation (B-CPR) documented by emergency medical services (EMS) personnel in the Birmingham Cardiac Arrest Registry to Enhance Survival (CARES) and B-CPR identified through 9-1-1 audio review. METHODS:We conducted a retrospective observational analysis of adult non-traumatic out-of-hospital cardiac arrest (OHCA) cases in Birmingham from January 1 to December 31, 2023. We excluded EMS-witnessed events, those in nursing homes, health care facilities, jails/prisons, or involving patients who were conscious during the 9-1-1 call. The provision of B-CPR was classified as "yes" or "no" in CARES based on EMS documentation and compared to B-CPR status determined through review of the corresponding 9-1-1 audio by a single reviewer. Agreement between sources was assessed using percent agreement, Cohen's kappa, Gwet's AC, and McNemar's test. RESULTS:Of 236 total cases, EMS documented a B-CPR rate of 12.3% whereas audio review indicated a B-CPR rate of 27.5%. Concordant classification occurred in 180 (76.3%) cases: 19 cases where both sources indicated B-CPR was performed and 161 where both indicated it was not. Discrepancies occurred in 56 cases (23.7%), including 46 instances where 9-1-1 audio identified B-CPR but EMS did not, and 10 where EMS documented B-CPR but audio review did not. Among the 46 audio-confirmed cases not captured by EMS, most involved B-CPR that ended before EMS arrival (e.g., B-CPR was discontinued by the caller), and 7 appeared to be EMS misclassifications. In the 10 cases where EMS documented B-CPR but audio did not, all involved calls that ended prior to EMS arrival without recognition of OHCA or B-CPR instruction. Overall agreement was fair to moderate: Cohen's kappa = 0.28 [95%CI 0.15, 0.42], Gwet's AC1 = 0.65 [95%CI 0.56, 0.75]), and McNemar's test showed significant asymmetry in classification, p < 0.001. CONCLUSIONS:The provision of B-CPR differed in nearly 25% of OHCA cases when comparing EMS documentation with 9-1-1 audio review. Most discrepancies resulted from early termination of B-CPR by the caller prior to EMS arrival, while a smaller proportion appeared to reflect EMS misclassification. These findings underscore the importance of sustained telecommunicator CPR instruction through EMS arrival at the patient's side.

BACKGROUND:Identifying high-risk locations for out-of-hospital cardiac arrest (OHCA) is essential for targeted community interventions. We aimed to use the Social Vulnerability Index (SVI) and geospatial mapping of OHCA data to locate high-risk neighborhoods in Birmingham, Alabama, for community training programs. METHODS:A retrospective observational analysis was conducted using nontraumatic OHCA cases in adults (≥18 years) in Birmingham from January 1, 2020 to December 31, 2023. Census tracts served as proxies for neighborhoods. A 5-step process identified high-risk tracts: (1) OHCA incidents were geocoded in ArcGIS and assigned a geographic identifier by census tract; (2) SVI data were merged with each record; (3) the Getis-Ord GI* statistic identified a hot spot with 99% confidence; (4) tracts with an SVI >90th percentile within the hot spot were flagged; and (5) excess relative risk rates were calculated and stratified by quintiles. The primary outcome was high-risk census tracts defined by hot spot location, high SVI, and high relative risk. RESULTS:A total of 966 OHCA cases from 115 census tracts were included. Hot spot analysis identified 30 tracts (26.1%) with high-risk characteristics. Within these, 17 tracts had an SVI >90th percentile, and 10 tracts had an excess relative risk in the top quintile. Hot spot tracts had a higher proportion of Black residents and lower cardiopulmonary resuscitation rates. No significant difference in survival outcomes were observed; however, the overall neurologically intact survival was 1.2%. CONCLUSIONS:Multiple neighborhoods in Birmingham exhibit extreme levels of social vulnerability and excess relative risk of OHCA, making them ideal candidates for community training initiatives.

Objective: To compare the performance of three artificial intelligence (AI) classification strategies against manually classified National Institutes of Health (NIH) cardiac arrest (CA) grants, with the goal of developing a publicly available tool to track CA research funding in the United States. Methods: Three AI strategies-traditional machine learning (ML), large language model (LLM) zero-shot learning, and LLM few-shot learning- were compared to manually categorized CA grant abstracts from NIH RePORTER (2007-2021). Traditional ML used a regularized logistic regression model trained on embedding vectors generated by OpenAI's text-embedding-3-small model. Zero-shot learning, using GPT-4o-mini, classified grants based on task descriptions without labeled examples. Few-shot learning included six example grants. Models were evaluated on a balanced 20% holdout test set using accuracy, precision (positive predictive value), recall (sensitivity), and F1 score (harmonic mean of precision and recall). Results: Out of 1,505 grants categorized, 378 (25%) were identified as CA research, yielding 302 grants in the holdout test set, 76 of which were CA research. The few-shot approach performed best, achieving the highest accuracy (0.90) and the best balance of precision and recall (F1 score 0.82). In contrast, traditional ML had the lowest accuracy (0.87) and the highest precision (0.89) but suffered from poor recall, with approximately 2.5 times more false negatives than either generative approach. The zero-shot approach outperformed traditional ML in accuracy (0.88) and recall (0.86) but had lower precision (0.72). Conclusion: AI can rapidly identify CA grants with excellent accuracy and very good precision and recall, making it a promising tool for tracking research funding.

In these 2025 Advanced Life Support Guidelines, the American Heart Association provides comprehensive recommendations for the resuscitation and management of adults experiencing cardiac arrest, respiratory arrest, and life-threatening cardiovascular emergencies. Based on structured evidence reviews and the latest clinical research, these guidelines offer evidence-based strategies to optimize survival and patient outcomes. The 2025 guidelines provide guidance for the treatment of cardiac arrest, including ventricular fibrillation, pulseless ventricular tachycardia, asystole, and pulseless electrical activity, as well as peri-arrest conditions such as atrial fibrillation and flutter with rapid ventricular response. Recommendations are made for defibrillation, electrical cardioversion, advanced airway management, drug therapies, and intravenous access. Additionally, guidelines are provided for the use of double sequential defibrillation, head-up cardiopulmonary resuscitation, and point-of-care ultrasound in the advanced life support setting. Termination of resuscitation rules have been refined to guide decision-making based on the emergency medical services professional's scope of practice. Finally, these guidelines also underscore the importance of identifying causative versus secondary arrhythmias to inform the appropriate timing of therapeutic strategies.

BACKGROUND:Little is known about antidysrhythmic administration disparities for out-of-hospital cardiac arrest (OHCA). OBJECTIVES:We evaluated the association between combined lower-income and minoritized communities with antidysrhythmic administration for OHCA. METHODS:We studied the 2018-2021 National Emergency Medical Services Information System encounters, linked to census data. We included adult OHCAs with a shockable rhythm. We used encounter ZIP Code data to calculate household income quartiles (Q1-highest to Q4-lowest). We created combined income and race/ethnicity strata, yielding 6 cohorts and 2 ordered groups (1-4a [Black] and 1-4b [Hispanic] with 1 and 2 shared between them): 1) Q1 income/>70% White, 2) Q2 income/50%-70% White, 3a) Q3 Income/50%-70% Black, 4a) Q4 Income/>70% Black, 3b) Q3 Income/50%-70% Hispanic, and 4b) Q4 income/>70% Hispanic. We evaluated the association of combined income and race/ethnicity groups to administration of an antidysrhythmic, with cohort 1 as the reference. RESULTS:We included 61,437 OHCAs. Compared to Q1 income/>70% White (33.5%), Q2 income/50-70% White had higher odds of antidysrhythmic administration (36.0%, aOR 1.15 [1.1-1.2]). However, all other groups had lower odds of antidysrhythmic administration (Q3 income/50-70% Black - 28.1%, aOR 0.8 [0.7-0.9]; Q4 income/>70% Black - 29.6%, aOR 0.9 [0.8-0.95]; Q3 income/50-70% Hispanic - 31.1%, aOR 0.9 [0.8-0.99]; Q4 income/>70% Hispanic - 23.0%, aOR 0.6 [0.6-0.7]). Using ordinal regression, decreasing income and increasing Black race (aOR 0.95[0.9-0.97]) as well as decreasing income and increasing Hispanic ethnicity (aOR 0.9 [0.9-0.95]) in a community were associated with decreased odds of antidysrhythmic administration CONCLUSION: Decreasing household income and increasing minoritized race/ethnicity were associated with decreased odds of antidysrhythmic administration.

ObjectiveTo calculate disability-adjusted life years (DALY) and labor productivity loss due to drug overdose out-of-hospital cardiac arrest (DO-OHCA) and compare its contribution to the burden of disease and economic impact of all-cause nontraumatic out-of-hospital cardiac arrest (OHCA) in the US.MethodsWe performed a retrospective observational cohort analysis of all adult (age >= 18 years) nontraumatic emergency medical services-treated OHCA events, including those due to DO-OHCA, from the national Cardiac Arrest Registry to Enhance Survival (CARES) database from January 1, 2017 and December 31, 2020. The main outcome measures of interest were disability-adjusted life years, annual, and lifetime labor productivity loss over the 4-year study period. The findings for the study population were extrapolated to a national level using the CARES population catchment and U.S. population estimates by year.ResultsA total of 378,088 adult OHCA events, including 23,252 DO-OHCA (6.2%) met study inclusion criteria. The DO-OHCA DALY increased from 156,707 in 2017 to 265,692 in 2020. Per year, DO-OHCA contributed to 11.4%, 12.0%, 10.5%, and 11.4% of all OHCA DALY lost from 2017-2020, respectively. The mean annual and lifetime productivity losses for all OHCA were stable over time (annual: $47K in 2017 to $50K in 2020; lifetime: $647K in 2017 to $692K in 2020). The CARES population catchment increased by 39.8% over the study period (102.6 M in 2017 to 143.4 M in 2020). For DO-OHCA, the mean annual productivity loss was approximately 30% higher than non-DO-OHCA ($64K vs. $49K in 2020, respectively). The mean lifetime productivity loss for DO-OHCA was 2.5 times higher than non-DO-OHCA ($1.6 M vs. $630K in 2020, respectively).ConclusionsThe DALY due to DO-OHCA has increased over time with expansion of the CARES dataset, but its relative contribution to total OHCA DALY (all non-traumatic etiologies) remained fairly stable. The DO-OHCAs represent approximately 6% of all adult non-traumatic EMS-treated OHCA events but has a disproportionately greater economic impact. Continued efforts to reduce DO-OHCA through public health initiatives are warranted to lessen the societal impact of OHCA in the U.S.

Background Given increases in drug overdose‐associated mortality, there is interest in better understanding of drug overdose out‐of‐hospital cardiac arrest (OHCA). A comparison between overdose‐attributable OHCA and nonoverdose‐attributable OHCA will inform public health measures. Methods and Results We analyzed data from 2017 to 2021 in the Cardiac Arrest Registry to Enhance Survival (CARES), comparing overdose‐attributable OHCA (OD‐OHCA) with OHCA from other nontraumatic causes (non‐OD‐OHCA). Arrests involving patients <18 years, health care facility residents, patients with cancer diagnoses, and patients with select missing data were excluded. Our main outcome of interest was survival with good neurological outcome, defined as Cerebral Performance Category score 1 or 2. From a data set with 537 100 entries, 29 500 OD‐OHCA cases and 338 073 non‐OD‐OHCA cases met inclusion criteria. OD‐OHCA cases involved younger patients with fewer comorbidities, were less likely to be witnessed, and less likely to present with a shockable rhythm. Unadjusted survival to hospital discharge with Cerebral Performance Category score =1 or 2 was significantly higher in the OD‐OHCA cohort (OD: 15.2% versus non‐OD: 6.9%). Adjusted results showed comparable survival with Cerebral Performance Category score =1 or 2 when the first monitored arrest rhythm was shockable (OD: 28.9% versus non‐OD: 23.5%, P=0.087) but significantly higher survival rates with Cerebral Performance Category score =1 or 2 for OD‐OHCA when the first monitored arrest rhythm was nonshockable (OD: 9.6% versus non‐OD: 3.1%, P<0.001). Conclusions Among patients presenting with nonshockable rhythms, OD‐OHCA is associated with significantly better outcomes. Further research should explore cardiac arrest causes, and public health efforts should attempt to reduce the burden from drug overdoses.

Introduction. While racial NIH funding disparities have been identified, little is known about the link between community demographics of institutions and NIH funding. We sought to evaluate the association between institution zip code characteristics and NIH funding. Methods. We linked the 2011-2021 NIH RePORTER database to Census data. We calculated the funding to each institution and stratified institutions into funding quartiles. We defined out independent variables as institution ZIP code level race/ethnicity (White, Black, and Hispanic), and socioeconomic status (household income, high school graduation rate, and unemployment rate). We used ordinal regression models to evaluate the association between institution ZIP code characteristics and grant funding quartile. Results. We included 731,548 grants (US$271,495,839,744) from 3,971 ZIP codes. The funding amounts in millions of U.S. dollars for the funding quartiles were fourth - 0.25, third - 1.1, second - 3.8, first - 43.5. Using ordinal regression, we found an association between increasing unemployment rate (OR = 1.03 [1.02, 1.05]), increasing high school graduation rate (OR = 3.6 [1.6, 8.4]), decreasing proportion of White people (OR = 0.4 [0.3, 0.5]), increasing proportion of Black people (OR = 1.3 [0.9, 1.8]), and increasing proportion of Hispanic/Latine people (OR = 2.5 [1.7, 3.5]) and higher grant funding quartiles. We found no association between household income and grant funding quartile. Conclusion. We found ZIP code demographics to be inadequate for evaluating NIH funding disparities, and the association between institution ZIP code demographics and investigator demographics is unclear. To evaluate and improve grant funding disparities, better grant recipient data accessibility and transparency are needed.