Automated cardiac arrest detection using a photoplethysmography wristband: algorithm development and validation in patients with induced circulatory arrest in the DETECT-1 study.

BACKGROUND: Unwitnessed out-of-hospital cardiac arrest is associated with low survival chances because of the delayed activation of the emergency medical system in most cases. Automated cardiac arrest detection and alarming using biosensor technology would offer a potential solution to provide early help. We developed and validated an algorithm for automated circulatory arrest detection using wrist-derived photoplethysmography from patients with induced circulatory arrests. METHODS: In this prospective multicentre study in three university medical centres in the Netherlands, adult patients (aged 18 years or older) in whom short-lasting circulatory arrest was induced as part of routine practice (transcatheter aortic valve implantation, defibrillation testing, or ventricular tachycardia induction) were eligible for inclusion. Exclusion criteria were a known bilateral significant subclavian artery stenosis or medical issues interfering with the wearing of the wristband. After providing informed consent, patients were equipped with a photoplethysmography wristband during the procedure. Invasive arterial blood pressure and electrocardiography were continuously monitored as the reference standard. Development of the photoplethysmography algorithm was based on three consecutive training cohorts. For each cohort, patients were consecutively enrolled. When a total of 50 patients with at least one event of circulatory arrest were enrolled, that cohort was closed. Validation was performed on the fourth set of included patients. The primary outcome was sensitivity for the detection of circulatory arrest. FINDINGS: Of 306 patients enrolled between March 14, 2022, and April 21, 2023, 291 patients were included in the data analysis. In the development phase (n=205), the first training set yielded a sensitivity for circulatory arrest detection of 100% (95% CI 94-100) and four false positive alarms; the second training set yielded a sensitivity of 100% (94-100), with six false positive alarms; and the third training set yielded a sensitivity of 100% (94-100), with two false positive alarms. In the validation phase (n=86), the sensitivity for circulatory arrest detection was 98% (92-100) and 11 false positive circulatory arrest alarms. The positive predictive value was 90% (95% CI 82-94). INTERPRETATION: The automated detection of induced circulatory arrests using wrist-derived photoplethysmography is feasible with good sensitivity and low false positives. These promising findings warrant further development of this wearable technology to enable automated cardiac arrest detection and alarming in a home setting. FUNDING: Dutch Heart Foundation (Hartstichting).

Relationship between stress hyperglycaemic ratio and incidence of in-hospital cardiac arrest in patients with acute coronary syndrome: a retrospective cohort study.

BACKGROUND: The stress hyperglycaemic ratio (SHR), a new marker that reflects the true hyperglycaemic state of patients with acute coronary syndrome (ACS), is strongly associated with adverse clinical outcomes in these patients. Studies on the relationship between the SHR and in-hospital cardiac arrest (IHCA) incidence are limited. This study elucidated the relationship between the SHR and incidence of IHCA in patients with ACS. METHODS: In total, 1,939 patients with ACS who underwent percutaneous coronary intervention (PCI) at the Affiliated Hospital of Zunyi Medical University were included. They were divided into three groups according to the SHR: group T1 (SHR ≤ 0.838, N = 646), group T2 (0.838< SHR ≤ 1.140, N = 646), and group T3 (SHR3 > 1.140, N = 647). The primary endpoint was IHCA incidence. RESULTS: The overall IHCA incidence was 4.1% (N = 80). After adjusting for covariates, SHR was significantly associated with IHCA incidence in patients with ACS who underwent PCI (odds ratio [OR] = 2.6800; 95% confidence interval [CI] = 1.6200-4.4300; p<0.001), and compared with the T1 group, the T3 group had an increased IHCA risk (OR = 2.1800; 95% CI = 1.2100-3.9300; p = 0.0090). In subgroup analyses, after adjusting for covariates, patients with ST-segment elevation myocardial infarction (STEMI) (OR = 3.0700; 95% CI = 1.4100-6.6600; p = 0.0050) and non-STEMI (NSTEMI) (OR = 2.9900; 95% CI = 1.1000-8.1100; p = 0.0310) were at an increased IHCA risk. After adjusting for covariates, IHCA risk was higher in patients with diabetes mellitus (DM) (OR = 2.5900; 95% CI = 1.4200-4.7300; p = 0.0020) and those without DM (non-DM) (OR = 3.3000; 95% CI = 1.2700-8.5800; p = 0.0140); patients with DM in the T3 group had an increased IHCA risk compared with those in the T1 group (OR = 2.4200; 95% CI = 1.0800-5.4300; p = 0.0320). The restriction cubic spline (RCS) analyses revealed a dose-response relationship between IHCA incidence and SHR, with an increased IHCA risk when SHR was higher than 1.773. Adding SHR to the baseline risk model improved the predictive value of IHCA in patients with ACS treated with PCI (net reclassification improvement [NRI]: 0.0734 [0.0058-0.1409], p = 0.0332; integrated discrimination improvement [IDI]: 0.0218 [0.0063-0.0374], p = 0.0060). CONCLUSIONS: In patients with ACS treated with PCI, the SHR was significantly associated with the incidence of IHCA. The SHR may be a useful predictor of the incidence of IHCA in patients with ACS. The addition of the SHR to the baseline risk model had an incremental effect on the predictive value of IHCA in patients with ACS treated with PCI.

Misinformation on resuscitation and first aid as an uncontrolled problem that demands close attention: a brief scoping review.

OBJECTIVES: Misinformation is currently recognised by the World Health Organization as an apparent threat to public health. This study aimed to provide an outline of published evidence on misinformation related to the potentially life-saving interventions - first aid and cardiopulmonary resuscitation (CPR). STUDY DESIGN: A scoping review. METHODS: The review was conducted in accordance with the PRISMA Extension for Scoping Reviews. English-language publications describing original studies that evaluated the quality of publicly available information on first aid and/or CPR were included without limitations to the year of publication. RESULTS: Forty-four original studies published between 1982 and 2023 were reviewed. Annual number of publications varied from 0 to 6. The studies have focused on the evaluation of information concerning initial care of cardiac arrest, choking, heart attack, poisoning, burns, and other emergencies. Forty three studies (97.7 %) have reported varying frequencies of misinformation, when public sources, including websites, YouTube videos, and modern artificial intelligence-based chatbots, omitted life-saving instructions on first aid or CPR or contained incorrect information that contradicted relevant international guidelines. Eleven studies (25.0 %) have also revealed potentially harmful advice, which, if followed by an unsuspecting person, may cause direct injury or death of a victim. CONCLUSIONS: Misinformation concerning CPR and first aid cannot be ignored and demands close attention from relevant stakeholders to mitigate its harmful impacts. More studies are urgently needed to determine optimal methods for detecting and measuring misinformation, to understand mechanisms that drive its spread, and to develop effective measures to correct and prevent misinformation.

Impact on clinical outcome of ventricular arrhythmias in patients undergoing transcatheter aortic valve implantation.

BACKGROUND: Transcatheter aortic valve implantation (TAVI) has become a largely used treatment for severe aortic stenosis. There are limited data, however, about predictors of long-term prognosis in this population. In this study, we assessed whether ventricular arrhythmias may predict clinical outcomes in patients undergoing TAVI. METHODS AND RESULTS: We performed a 24 h ECG Holter monitoring in 267 patients who underwent TAVI for severe aortic stenosis within 30 days from a successful procedure. The occurrence of frequent premature ventricular complexes (PVCs; ≥30/h), polymorphic PVCs and nonsustained ventricular tachycardia (NSVT) was obtained for each patient. Clinical outcome was obtained for 228 patients (85%), for an average follow-up of 3.5 years (range 1.0-8.6). Cardiovascular events (CVEs; cardiovascular death or resuscitated cardiac arrest) occurred in 26 patients (11.4%) and 63 patients died (27.6%). Frequent PVCs but not polymorphic PVCs and NSVT were found to be associated with CVEs at univariate analysis. Frequent PVCs were indeed found in 12 patients with (46.2%) and 35 without (17.3%) CVEs [hazard ratio 2.30; 95% confidence interval (CI) 1.03-5.09; P  = 0.04], whereas polymorphic PVCs were found in 11 (42.3%) and 54 (26.7%) patients of the two groups, respectively (hazard ratio 1.44; 95% CI 0.64-3.25; P  = 0.38), and NSVT in 9 (34.6%) and 43 patients of the two groups, respectively (hazard ratio 1.18; 95% CI 0.48-2.87; P  = 0.72). Frequent PVCs, however, were not significantly associated with CVEs at multivariate Cox regression analysis (hazard ratio 1.53; 95% CI 0.37-6.30; P  = 0.56). Both frequent PVCs, polymorphic PVCs and NSVT showed no significant association with mortality. CONCLUSION: In our study, the detection of frequent PVCs at Holter monitoring after TAVI was a predictor of CVEs (cardiovascular death/cardiac arrest), but this association was lost in multivariable analysis.

Ensemble Learning Method for In-Hospital Cardiac Arrest Prediction.

Cardiac arrest prediction for multivariate time series data have been developed and obtained high precision performance. However, these algorithms still did not achieved high sensitivity and suffer from a high false-alarm. Therefore, we propose a ensemble approach for prediction satisfying precision-recall result compared than other machine learning methods. As a result, our proposed method obtained an overall area under precision-recall curve of 46.7%. It is possible to more accurately respond rapidly cardiac arrest event.

The predictive value of highly malignant EEG patterns after cardiac arrest: evaluation of the ERC-ESICM recommendations.

PURPOSE: The 2021 guidelines endorsed by the European Resuscitation Council (ERC) and the European Society of Intensive Care Medicine (ESICM) recommend using highly malignant electroencephalogram (EEG) patterns (HMEP; suppression or burst-suppression) at > 24 h after cardiac arrest (CA) in combination with at least one other concordant predictor to prognosticate poor neurological outcome. We evaluated the prognostic accuracy of HMEP in a large multicentre cohort and investigated the added value of absent EEG reactivity. METHODS: This is a pre-planned prognostic substudy of the Targeted Temperature Management trial 2. The presence of HMEP and background reactivity to external stimuli on EEG recorded > 24 h after CA was prospectively reported. Poor outcome was measured at 6 months and defined as a modified Rankin Scale score of 4-6. Prognostication was multimodal, and withdrawal of life-sustaining therapy (WLST) was not allowed before 96 h after CA. RESULTS: 845 patients at 59 sites were included. Of these, 579 (69%) had poor outcome, including 304 (36%) with WLST due to poor neurological prognosis. EEG was recorded at a median of 71 h (interquartile range [IQR] 52-93) after CA. HMEP at > 24 h from CA had 50% [95% confidence interval [CI] 46-54] sensitivity and 93% [90-96] specificity to predict poor outcome. Specificity was similar (93%) in 541 patients without WLST. When HMEP were unreactive, specificity improved to 97% [94-99] (p = 0.008). CONCLUSION: The specificity of the ERC-ESICM-recommended EEG patterns for predicting poor outcome after CA exceeds 90% but is lower than in previous studies, suggesting that large-scale implementation may reduce their accuracy. Combining HMEP with an unreactive EEG background significantly improved specificity. As in other prognostication studies, a self-fulfilling prophecy bias may have contributed to observed results.

Mortality trends, disparities, and social vulnerability in cardiac arrest mortality in the young: A cross-sectional analysis.

BACKGROUND: Cardiac arrest (CA) is a leading cause of death in the United States (US). Social determinants of health may impact CA outcomes. We aimed to assess mortality trends, disparities, and the influence of the social vulnerability index (SVI) on CA outcomes in the young. METHODS: We conducted a cross-sectional analysis of age-adjusted mortality rates (AAMRs) related to CA in the United States from the Years 1999 to 2020 in individuals aged 35 years and younger. Data were obtained from death certificates and analyzed using log-linear regression models. We examined disparities in mortality rates based on demographic variables. We also explored the impact of the SVI on CA mortality. RESULTS: A total of 4792 CA deaths in the young were identified. Overall AAMR decreased from 0.20 in 1999 to 0.14 in 2020 with an average annual percentage change of -1.3% (p = .001). Black (AAMR: 0.30) and male populations (AAMR: 0.14) had higher AAMR compared with White (AAMR: 0.11) and female (AAMR: 0.11) populations, respectively. Nonmetropolitan (AAMR: 0.29) and Southern (AAMR: 0.26) regions were also impacted by higher AAMR compared with metropolitan (AAMR: 0.11) and other US census regions, respectively. A higher SVI was associated with greater mortality risks related to CA (risk ratio: 1.82 [95% CI, 1.77-1.87]). CONCLUSIONS: Our analysis of CA in the young revealed disparities based on demographics, with a decline in AAMR from 1999 to 2020. There is a correlation between a higher SVI and increased CA mortality risk, highlighting the importance of targeted interventions to address these disparities effectively.

Critical Care Management of Patients After Cardiac Arrest: A Scientific Statement From the American Heart Association and Neurocritical Care Society.

The critical care management of patients after cardiac arrest is burdened by a lack of high-quality clinical studies and the resultant lack of high-certainty evidence. This results in limited practice guideline recommendations, which may lead to uncertainty and variability in management. Critical care management is crucial in patients after cardiac arrest and affects outcome. Although guidelines address some relevant topics (including temperature control and neurological prognostication of comatose survivors, 2 topics for which there are more robust clinical studies), many important subject areas have limited or nonexistent clinical studies, leading to the absence of guidelines or low-certainty evidence. The American Heart Association Emergency Cardiovascular Care Committee and the Neurocritical Care Society collaborated to address this gap by organizing an expert consensus panel and conference. Twenty-four experienced practitioners (including physicians, nurses, pharmacists, and a respiratory therapist) from multiple medical specialties, levels, institutions, and countries made up the panel. Topics were identified and prioritized by the panel and arranged by organ system to facilitate discussion, debate, and consensus building. Statements related to postarrest management were generated, and 80% agreement was required to approve a statement. Voting was anonymous and web based. Topics addressed include neurological, cardiac, pulmonary, hematological, infectious, gastrointestinal, endocrine, and general critical care management. Areas of uncertainty, areas for which no consensus was reached, and future research directions are also included. Until high-quality studies that inform practice guidelines in these areas are available, the expert panel consensus statements that are provided can advise clinicians on the critical care management of patients after cardiac arrest.

2023 American Heart Association Focused Update on Adult Advanced Cardiovascular Life Support: An Update to the American Heart Association Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care.

Cardiac arrest is common and deadly, affecting up to 700 000 people in the United States annually. Advanced cardiac life support measures are commonly used to improve outcomes. This "2023 American Heart Association Focused Update on Adult Advanced Cardiovascular Life Support" summarizes the most recent published evidence for and recommendations on the use of medications, temperature management, percutaneous coronary angiography, extracorporeal cardiopulmonary resuscitation, and seizure management in this population. We discuss the lack of data in recent cardiac arrest literature that limits our ability to evaluate diversity, equity, and inclusion in this population. Last, we consider how the cardiac arrest population may make up an important pool of organ donors for those awaiting organ transplantation.

Clinical Management of Brugada Syndrome: Commentary From the Experts.

Although there is consensus on the management of patients with Brugada Syndrome with high risk for sudden cardiac arrest, asymptomatic or intermediate-risk patients present clinical management challenges. This document explores the management opinions of experts throughout the world for patients with Brugada Syndrome who do not fit guideline recommendations. Four real-world clinical scenarios were presented with commentary from small expert groups for each case. All authors voted on case-specific questions to evaluate the level of consensus among the entire group in nuanced diagnostic and management decisions relevant to each case. Points of agreement, points of controversy, and gaps in knowledge are highlighted.

Explainable Artificial Intelligence Warning Model Using an Ensemble Approach for In-Hospital Cardiac Arrest Prediction: Retrospective Cohort Study.

BACKGROUND: Cardiac arrest (CA) is the leading cause of death in critically ill patients. Clinical research has shown that early identification of CA reduces mortality. Algorithms capable of predicting CA with high sensitivity have been developed using multivariate time series data. However, these algorithms suffer from a high rate of false alarms, and their results are not clinically interpretable. OBJECTIVE: We propose an ensemble approach using multiresolution statistical features and cosine similarity-based features for the timely prediction of CA. Furthermore, this approach provides clinically interpretable results that can be adopted by clinicians. METHODS: Patients were retrospectively analyzed using data from the Medical Information Mart for Intensive Care-IV database and the eICU Collaborative Research Database. Based on the multivariate vital signs of a 24-hour time window for adults diagnosed with heart failure, we extracted multiresolution statistical and cosine similarity-based features. These features were used to construct and develop gradient boosting decision trees. Therefore, we adopted cost-sensitive learning as a solution. Then, 10-fold cross-validation was performed to check the consistency of the model performance, and the Shapley additive explanation algorithm was used to capture the overall interpretability of the proposed model. Next, external validation using the eICU Collaborative Research Database was performed to check the generalization ability. RESULTS: The proposed method yielded an overall area under the receiver operating characteristic curve (AUROC) of 0.86 and area under the precision-recall curve (AUPRC) of 0.58. In terms of the timely prediction of CA, the proposed model achieved an AUROC above 0.80 for predicting CA events up to 6 hours in advance. The proposed method simultaneously improved precision and sensitivity to increase the AUPRC, which reduced the number of false alarms while maintaining high sensitivity. This result indicates that the predictive performance of the proposed model is superior to the performances of the models reported in previous studies. Next, we demonstrated the effect of feature importance on the clinical interpretability of the proposed method and inferred the effect between the non-CA and CA groups. Finally, external validation was performed using the eICU Collaborative Research Database, and an AUROC of 0.74 and AUPRC of 0.44 were obtained in a general intensive care unit population. CONCLUSIONS: The proposed framework can provide clinicians with more accurate CA prediction results and reduce false alarm rates through internal and external validation. In addition, clinically interpretable prediction results can facilitate clinician understanding. Furthermore, the similarity of vital sign changes can provide insights into temporal pattern changes in CA prediction in patients with heart failure-related diagnoses. Therefore, our system is sufficiently feasible for routine clinical use. In addition, regarding the proposed CA prediction system, a clinically mature application has been developed and verified in the future digital health field.