Mapping drug-class associations with sudden cardiac arrest in the outpatient setting: an exploratory population-based case-control analysis.

Introduction: AHA recommends that automated external defibrillators (AEDs) be made available in public areas with high likelihood of a sudden cardiac arrest (SCA). Only 18 US states have regulations requiring AEDs in schools and the legislation varies. There is no comprehensive evaluation of the epidemiology of sudden death in schools. School nurses are often the only healthcare providers within a school. New York State (NYS) enrolls almost 3.5 million students with New York City (NYC) being the largest school system in the US; legislation requires AEDs in all public but not in private schools. Purpose: Our aims were threefold: to gain epidemiologic data on SCAs in NYS schools, to evaluate the availability and utilization of AEDs, and to identify factors affecting deployment of AEDs. Methods: Electronic surveys were sent to school nurse members of the NYS Association of School Nurses and NYC school nurses. We also conducted structured interviews with a representative sample of NYS school nurses. Results: Nurses representing 750 public schools and 116 private schools responded, a response rate of 36.4%. There were 71 SCA events, with majority affecting adults on school grounds 41 (58%). AEDs were deployed in 59 (73%). Short term survival occurred in 50 (69%) with 40 (56%) returning to regular activity. While not required to have AEDs available, most private schools had them 69(60%). There were 21 (30%) SCA events in private schools with 8 (11%) occurring in private schools with no AEDs. Of these, 6 (75%) had a negative outcome. Interviews revealed that staff attitude, fear, and training adequacy were factors influencing AED utilization. Conclusions: AEDs in schools improve survival outcomes. There has been no comprehensive evaluation of SCA events in US schools and no uniformity in AED legislation among states. While focus is on students, NYS data suggests that attention should also encompass adult SCAs in schools. Comparisons of AED utilization and SCA outcomes in states with and without AED legislation will be of interest, and may have health policy implications.

AP-452680-2 GEOSPATIAL ANALYSIS OF BYSTANDER-INITIATED AUTOMATED EXTERNAL DEFIBRILLATOR UTILIZATION IN A LARGE US COMMUNITY

Improving Youth Sports Safety: Implementing an Emergency Action Plan for Sudden Cardiac Arrest

Unexpected shift in circadian and septadian variation of sudden cardiac arrest: the Oregon Sudden Unexpected Death Study

A sudden cardiac arrest (SCA) occurs when the heart abruptly stops beating; because of the nature of SCA, capturing data in the out-of-hospital setting from actual bystander response is difficult. Current technologies such as virtual reality (VR) allow the creation of scenarios programmed for heightened realism. No studies have used an immersive VR system to observe lay bystander response. We sought to characterize lay bystander response to an unannounced simulated VR SCA event during a multisensory scenario. Using a VR wearable device combined with a cardiopulmonary resuscitation (CPR) recording manikin, we created a 3-minute multisensory SCA scenario that allowed for the observation of lay bystander response. Subjects were unaware of the nature of the emergency event but were told to respond how they would to an emergency situation. Subject's ability to proceed through the American Heart Association's Chain of Survival and their CPR quality were recorded. Frequencies and percentages were calculated using descriptive statistics. Between June 2016 and June 2017, 119 lay subjects were enrolled. Of those, 92% asked for 911 to be called, 81% attempted CPR, 13% requested an automated external defibrillator (AED), and 6% used the AED; 82% stated that they felt as if they were at a real SCA event. Cardiopulmonary resuscitation data were collected (n = 81), the mean CC rate was 93.5 ± 22.4 cpm, and the mean CC depth was 38.4 ± 13.8 mm. In our unannounced, immersive VR SCA observational study of lay bystanders, most subjects attempted CPR, although the majority did not use an AED.

The application of machine learning in health care often necessitates the use of hierarchical codes such as the International Classification of Diseases (ICD) and Anatomical Therapeutic Chemical (ATC) systems. These codes classify diseases and medications, respectively, thereby forming extensive data dimensions. Unsupervised feature selection tackles the "curse of dimensionality" and helps to improve the accuracy and performance of supervised learning models by reducing the number of irrelevant or redundant features and avoiding overfitting. Techniques for unsupervised feature selection, such as filter, wrapper, and embedded methods, are implemented to select the most important features with the most intrinsic information. However, they face challenges due to the sheer volume of ICD and ATC codes and the hierarchical structures of these systems. The objective of this study was to compare several unsupervised feature selection methods for ICD and ATC code databases of patients with coronary artery disease in different aspects of performance and complexity and select the best set of features representing these patients. We compared several unsupervised feature selection methods for 2 ICD and 1 ATC code databases of 51,506 patients with coronary artery disease in Alberta, Canada. Specifically, we used the Laplacian score, unsupervised feature selection for multicluster data, autoencoder-inspired unsupervised feature selection, principal feature analysis, and concrete autoencoders with and without ICD or ATC tree weight adjustment to select the 100 best features from over 9000 ICD and 2000 ATC codes. We assessed the selected features based on their ability to reconstruct the initial feature space and predict 90-day mortality following discharge. We also compared the complexity of the selected features by mean code level in the ICD or ATC tree and the interpretability of the features in the mortality prediction task using Shapley analysis. In feature space reconstruction and mortality prediction, the concrete autoencoder-based methods outperformed other techniques. Particularly, a weight-adjusted concrete autoencoder variant demonstrated improved reconstruction accuracy and significant predictive performance enhancement, confirmed by DeLong and McNemar tests (P<.05). Concrete autoencoders preferred more general codes, and they consistently reconstructed all features accurately. Additionally, features selected by weight-adjusted concrete autoencoders yielded higher Shapley values in mortality prediction than most alternatives. This study scrutinized 5 feature selection methods in ICD and ATC code data sets in an unsupervised context. Our findings underscore the superiority of the concrete autoencoder method in selecting salient features that represent the entire data set, offering a potential asset for subsequent machine learning research. We also present a novel weight adjustment approach for the concrete autoencoders specifically tailored for ICD and ATC code data sets to enhance the generalizability and interpretability of the selected features.

β-Adrenoceptor blockers (β-blockers) are one of the oldest classes of cardiovascular drugs still in use. Several short- and long-term clinical outcomes studies have demonstrated their effectiveness and safety for the treatment of hypertension, coronary artery disease, myocardial infarction, heart failure and sudden cardiac death. Due to their safety and efficacy, β-blockers have been recommended by several national and international committees as first-line therapy of hypertension. However, despite their proven benefits, their use as first-line treatment for hypertension has come under criticism lately. Because of these recent developments, several authors have recommended that β-blockers no longer be used as first-line therapy for hypertension. In this review, evidence-based information will be presented, which will demonstrate that β-blockers are an effective and safe class of antihypertensive and cardiovascular drugs for most patients with the exception of black and elderly hypertensive patients in whom the β-blockers are less effective compared to other classes of drugs. In addition, evidence will be presented from several major meta-analyses that β-blockers are equally effective in reducing blood pressure and cardiovascular complications. This review will also discuss differences in the mechanism of action of older and newer (third-generation) β-blockers and provide evidence that newer agents should be preferred over the older ones in the treatment of hypertensive patients with certain comorbidities.

Introduction: Every year about 500,000 people in Europe are victims of cardiac arrest.Cardiopulmonary resuscitation (CPR) implemented by lay persons in outpatient settings increased facing the survival rate for 2-3 times, if applicable in outpatient settings.Application of CPR measures within the golden minutes in Europe, may save the lives of about 100 000 persons per year.Objective: To investigate application of cardiopulmonary resuscitation (CPR) measures within the golden minutes in Europe. Aim:The main Purpose of this paper is to present the treated cases with cardiac arrest in the Emergency Centre of the University Clinical Centre of Kosovo and (CUCK). Methods:The material is taken from the University Clinical Center of Kosovo -Emergency Centre in Pristina, during the two (2) year period (2010)(2011).The Collected date belong to the patients with cardiac arrest have been recorded in the patients' log book protocol at the Emergency Clinic. Results and conclusions:During the 2010 to 2011 in the Emergency Center of the CUCK in Pristina have been treated a total of 269 patients with cardiac arrest, of whom 159 or 59.1% have been treated in 2010, and 110 patients or 40.9% in 2011.Of the 269 patients treated in the Emergency Centre, 93 or 34.6% have exited lethally in the Emergency Centre, and 176 or 65.4% have been transferred to other Clinics.In the total number of patients with cardiac arrest, males have dominated with 186 cases, or 69.1%.The average age of patients included in the survey was 56.7year old (SD ± 16.0 years.Of the 269 patients with Cardiac arrest, defibrillation has been applied for 93 or 34.6% of patients.In the outpatient settings defibrillation has been applied for 3 or 3.2% of patients.Patients were defibrillated with application of one to four shocks.Of 27 cases with who have survived cardiac arrest, none of them have suffered cardiac arrest at home, 3 or 11.1% of them have suffered cardiac arrest on the street, and 24 or 88.9% of them have suffered cardiac arrest in the hospital.5 out of 27 patients survived have ended with neurological impairment.Conclusions: Cardiac arrest cases were present during all days of the week, but frequently most reported cases have been on Monday with 32.0% of cases, and on Friday with 24.5% of cases.All survivors from cardiac arrest have received appropriate medical assistance within 10 minutes from attack,which implies that if cardiac arrest occurs near an institution health care (with an opportunity to provide the emergent health care) the rate of survival is higher.

Prediction of drug’s Anatomical Therapeutic Chemical (ATC) code by integrating drug–domain network

Risk Factors of Peri-Intubation Cardiac Arrest in Critically Ill Patients Presenting to the Emergency Department of a Low-Income Country: A Case-Control Study.

Safety Alerts: An Observational Study in Portugal

BackgroundPeople with chronic kidney disease (CKD) have high levels of co-morbidity and polypharmacy placing them at increased risk of prescribing-related harm. Tools for assessing prescribing safety in the general population using prescribing safety indicators (PSIs) have been established. However, people with CKD pose different prescribing challenges to people without kidney disease. Therefore, PSIs designed for use in the general population may not include all PSIs relevant to a CKD population.The aim of this study was to systematically collate a library of PSIs relevant to people with CKD.MethodsA systematic literature search identified papers reporting PSIs. CKD-specific PSIs were extracted and categorised by Anatomical Therapeutic Chemical (ATC) classification codes. Duplicate PSIs were removed to create a final list of CKD-specific PSIs.ResultsNine thousand, eight hundred fifty-two papers were identified by the systematic literature search, of which 511 proceeded to full text screening and 196 papers were identified as reporting PSIs. Following categorisation by ATC code and duplicate removal, 841 unique PSIs formed the final set of CKD-specific PSIs. The five ATC drug classes containing the largest proportion of CKD-specific PSIs were: Cardiovascular system (26%); Nervous system (13.4%); Blood and blood forming organs (12.4%); Alimentary and metabolism (12%); and Anti-infectives for systemic use (11.3%).ConclusionCKD-specific PSIs could be used alone or alongside general PSIs to assess the safety and quality of prescribing within a CKD population.

Pharmacoepidemiologic studies, which promote rational drug use and improve health outcomes, often require Anatomical Therapeutic Chemical Classification System (ATC) drug classification within real-world data (RWD) sources. Existing classification tools are expensive, brittle, or have restrictive terms of service, and lack context that may inform classification itself. This study sought to establish large language models (LLMs) as an assisting technology in the drug classification task. This included developing artificial intelligence prompts that reason about drugs using RWD and showing that the resulting accuracy, efficiency, and effectiveness are favorable to alternative methods. A prompt was constructed to classify aspirin as either an analgesic or antithrombotic and evaluated within 12,294 anonymized daily dose strings from a polychronic population residing in the United States and Canada. The patients used a smart medication dispenser called "spencer" and consented to the use of their data for research. The LLM prompt requested that the best and next-best second-level ATC code be returned, and grading was performed on a 3-point scale. After success in a pilot sample of 20, an inference sample of 200 was taken without replacement. Finite population inference was carried out on the proportion of outputs receiving 1 of the top 2 grades. As a benchmark, Google's Programmable Search Engine was used to query the drug name plus "ATC code" followed by regex-based extraction of ATC codes. All imperfect results were reviewed. The population consisted of 12,294 daily dose strings from 86.26% (2908/3371) patients residing in Canada and 13.73% (463/3371) residing in the United States. A prompt using the chain-of-thought reasoning was able to distinguish between aspirin's analgesic versus antithrombotic therapeutic uses and performed well in the pilot sample. In the inferential sample, 87.5% (175/200) were graded as perfect, 5% (10/200) had a minor issue, and 7.5% (15/200) had a major issue. The estimate of the proportion of at least mostly correct classification was 92.5% (185/200, 80% CI 90.1%-94.9%). For the search-based algorithm, 82.5% (165/200) were deemed acceptable. The chain-of-thought reasoning was most helpful with supplements (eg, folic acid) when high doses indicated antianemic preparations. The problem formulation of daily dose inputs and multiple ATC outputs was sometimes incompatible with the drug (eg, pregabalin, calcitriol, and methotrexate). GPT-4o offers cost-effective drug classification from RWD without violating any terms of service. Using a chain-of-thought prompting technique, GPT-4o can reason about drug dosages that affect the class. The wide accessibility of LLMs gives every research team the ability to classify drugs at scale, a key prerequisite of pharmacoepidemiologic research.

Introduction. In the event of sudden cardiac arrest (SCA) early intervention provided by a layperson can be life-saving. Teaching first aid in primary school may increase the lifelong ability and motivation of young people to take action in an emergency. Objective. The aim of this article is to report a training experience on BLSD (Basic Life Support and Defibrillation) designed for a group of pupils in an Italian primary school, with assessment of its effectiveness at a distance. Methods. The assessment was carried out using a multiple choice questionnaire on a sample of 130 pupils aged 11-12, 62 trained in BLSD and 68 as a control group. The trained group also performed an emergency simulation to assess their learning of practical skills. Results. Using the t test, significant differences emerged in the questionnaire scores between the case-control group. The results of the skill test were positive, even for the most difficult manoeuvres such as opening airways, assessing breathing, or using an AED (Automated External Defibrillator). Conclusion. Although there are still some open questions regarding the ability to retain these skills in the medium/long term, the study shows that life-saving manoeuvres can be effectively taught to primary school pupils.

Introduction: Both individually as well as in composite risk scores, specific markers on the 12-lead ECG are associated with increased sudden cardiac arrest (SCA) risk. Atrial fibrillation (AF), the most common arrhythmia, has also been associated with increased risk of SCA. However, patients with AF were mostly excluded from these SCA association studies due to the complexity of their ECG features. Hypothesis: Specific markers on the 12-lead ECG can also predict SCA risk in individuals with AF. Methods: We conducted a case-control study utilizing SCA cases from a large community-based study in the US Northwest (catchment population ~ 1 million; 2002-2020). We included subjects aged ≥18 with detailed lifetime medical records and archived ECG prior and unrelated to the SCA event. Controls with AF ECGs and no history of SCA were recruited from the same area. For validation, we selected SCA cases from a separate community-based study in southern California (catchment population ~ 850,000; 2015-2023), with controls obtained from the same region. ECG variables that were statistically significant in univariable analysis were used to develop an ECG-based score for SCA risk prediction (ECG Risk Score, AF ERS). Results: In the discovery population (447 SCA cases and 138 controls, mean age 74.9±11.3 years, 73.5% male), SCA cases had longer QTc (481.5 vs. 471.4 ms; p=0.06) and Tpeak-Tend intervals (87.7 vs. 81.9 ms; p=0.009), higher LVH prevalence (20.4% vs. 9.4%; p=0.003), delayed QRS transition (54.7% vs. 36.8%; p&lt;0.001), and QRS-T angle &gt;90° (55.7% vs. 47.4%; p=0.09). The AF ERS (range 0-5) was developed by adding 1 point for each of these factors: prolonged QTc, QRS-T angle &gt;90°, Tpeak-Tend &gt;89 ms, LVH, and delayed QRS transition zone. After adjusting for demographic characteristics and comorbidities in a multivariable model ORs for SCA increased from 2.6 (95% CI: 1.0–6.7) for AF ERS=2 to 5.1 (95% CI: 1.5–9.1) for AF ERS≥3 compared to the reference group (AF ERS = 0; Figure) . The validation cohort comprised 315 SCA cases and 138 controls, mean age 77.7±11.4 years, 64.5% male. The AF ERS score consistently showed significantly higher adjusted ORs for SCA across all AF ERS groups compared to the reference group. Conclusion: SCA risk can be predicted successfully from AF ECGs, offering an opportunity to enhance risk stratification and prevention in this important subgroup of individuals. Further research is needed to validate the AF ERS in larger and more diverse populations.