Causes of Perioperative Cardiac Arrest: Mnemonic, Classification, Monitoring, and Actions

Perioperative cardiac arrest (POCA) is a catastrophic complication that requires immediate recognition and correction of the underlying cause to improve patient outcomes. While the hypoxia, hypovolemia, hydrogen ions (acidosis), hypo-/hyperkalemia, and hypothermia (Hs) and toxins, tamponade (cardiac), tension pneumothorax, thrombosis (pulmonary), and thrombosis (coronary) (Ts) mnemonic is a valuable tool for rapid differential diagnosis, it does not cover all possible causes leading to POCA. To address this limitation, we propose using the preload-contractility-afterload-rate and rhythm (PCARR) construct to categorize POCA, which is comprehensive, systemic, and physiologically logical. We provide evidence for each component in the PCARR construct and emphasize that it complements the Hs and Ts mnemonic rather than replacing it. Furthermore, we discuss the significance of utilizing monitored variables such as electrocardiography, pulse oxygen saturation, end-tidal carbon dioxide, and blood pressure to identify clues to the underlying cause of POCA. To aid in investigating POCA causes, we suggest the Anesthetic care, Surgery, Echocardiography, Relevant Check and History (A-SERCH) list of actions. We recommend combining the Hs and Ts mnemonic, the PCARR construct, monitoring, and the A-SERCH list of actions in a rational manner to investigate POCA causes. These proposals require real-world testing to assess their feasibility.

Association between the triglyceride-glucose index and in-hospital major adverse cardiovascular events in patients with acute coronary syndrome: results from the Improving Care for Cardiovascular Disease in China (CCC)-Acute Coronary Syndrome project

OBJECTIVE

Although the TyG index is a reliable predictor of insulin resistance (IR) and cardiovascular disease, its effectiveness in predicting major adverse cardiac events in hospitalized acute coronary syndrome (ACS) patients has not been validated in large-scale studies. In this study, we aimed to explore the association between the TyG index and the occurrence of MACEs during hospitalization.

METHODS

We recruited ACS patients from the CCC-ACS (Improving Cardiovascular Care in China-ACS) database and calculated the TyG index using the formula ln(fasting triglyceride [mg/dL] × fasting glucose [mg/dL]/2). These patients were classified into four groups based on quartiles of the TyG index. The primary endpoint was the occurrence of MACEs during hospitalization, encompassing all-cause mortality, cardiac arrest, myocardial infarction (MI), and stroke. We performed Cox proportional hazards regression analysis to clarify the correlation between the TyG index and the risk of in-hospital MACEs among patients diagnosed with ACS. Additionally, we explored this relationship across various subgroups.

RESULTS

A total of 101,113 patients were ultimately included, and 2759 in-hospital MACEs were recorded, with 1554 (49.1%) cases of all-cause mortality, 601 (21.8%) cases of cardiac arrest, 251 (9.1%) cases of MI, and 353 (12.8%) cases of stroke. After adjusting for confounders, patients in TyG index quartile groups 3 and 4 showed increased risks of in-hospital MACEs compared to those in quartile group 1 [HR = 1.253, 95% CI 1.121-1.400 and HR = 1.604, 95% CI 1.437-1.791, respectively; p value for trend < 0.001], especially in patients with STEMI or renal insufficiency. Moreover, we found interactions between the TyG index and age, sex, diabetes status, renal insufficiency status, and previous PCI (all p values for interactions < 0.05).

CONCLUSIONS

In patients with ACS, the TyG index was an independent predictor of in-hospital MACEs. Special vigilance should be exercised in females, elderly individuals, and patients with renal insufficiency.

Excessive pickle consumption: beware of adrenal crisis

Adrenal insufficiency (AI) is one of the most life-threatening disorders resulting from adrenal cortex dysfunction. Symptoms and signs of AI are often nonspecific, and the diagnosis can be missed and lead to the development of AI with severe hypotension and hypovolemic shock. We report the case of a 13-year-old child admitted for cardiac arrest following severe hypovolemic shock. The patient initially presented with isolated mild abdominal pain and vomiting together with unexplained hyponatremia. He was discharged after an initial short hospitalization with rehydration but with persistent hyponatremia. After discharge, he had persistent refractory vomiting, finally leading to severe dehydration and extreme asthenia. He was admitted to pediatric intensive care after prolonged hypovolemic cardiac arrest with severe anoxic encephalopathy leading to brain death. After re-interviewing, the child's parents reported that he had experienced polydipsia, a pronounced taste for salt with excessive consumption of pickles lasting for months, and a darkened skin since their last vacation 6 months earlier. A diagnosis of autoimmune Addison's disease was made. Primary AI is a rare life-threatening disease that can lead to hypovolemic shock. The clinical symptoms and laboratory findings are nonspecific, and the diagnosis should be suspected in the presence of unexplained collapse, hypotension, vomiting, or diarrhea, especially in the case of hyponatremia.

Extracorporeal Life Support Organization Center of Excellence recognition is associated with improved failure to rescue after cardiac arrest

OBJECTIVE

The influence of Extracorporeal Life Support Organization (ELSO) center of excellence (CoE) recognition on failure to rescue after cardiac surgery is unknown. We hypothesized that ELSO CoE would be associated with improved failure to rescue.

METHODS

Patients undergoing a Society of Thoracic Surgeons index operation in a regional collaborative (2011-2021) were included. Patients were stratified by whether or not their operation was performed at an ELSO CoE. Hierarchical logistic regression analyzed the association between ELSO CoE recognition and failure to rescue.

RESULTS

A total of 43,641 patients were included across 17 centers. In total, 807 developed cardiac arrest with 444 (55%) experiencing failure to rescue after cardiac arrest. Three centers received ELSO CoE recognition, and accounted for 4238 patients (9.71%). Before adjustment, operative mortality was equivalent between ELSO CoE and non-ELSO CoE centers (2.08% vs 2.36%; P = .25), as was the rate of any complication (34.5% vs 33.8%; P = .35) and cardiac arrest (1.49% vs 1.89%; P = .07). After adjustment, patients undergoing surgery at an ELSO CoE facility were observed to have 44% decreased odds of failure to rescue after cardiac arrest, relative to patients at non-ELSO CoE facility (odds ratio, 0.56; 95% CI, 0.316-0.993; P = .047).

CONCLUSIONS

ELSO CoE status is associated with improved failure to rescue following cardiac arrest for patients undergoing cardiac surgery. These findings highlight the important role that comprehensive quality programs serve in improving perioperative outcomes in cardiac surgery.

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.

Long-term survival after cardiac arrest in patients undergoing emergent coronary angiography

AIM

To determine long-term survival of patients after cardiac arrest undergoing emergent coronary angiography and therapeutic hypothermia.

METHODS

We analysed data from patients treated within the regional STEMI Network from January 2015 to December 2020. The primary endpoint was all-cause mortality at median follow-up. Secondary endpoints were periprocedural complications (arrhythmias, pulmonary edema, cardiogenic shock, mechanical complication, stent thrombosis, reinfarction, bleeding) and 6-month all-cause death. A landmark analysis was performed, studying two time periods; 0-6 months and beyond 6 months.

RESULTS

From a total of 24,125 patients in the regional STEMI network, 494 patients who suffered from cardiac arrest were included and divided into two groups: treated with (n = 119) and without therapeutic hypothermia (n = 375). At median follow-up (16.0 [0.2-33.3] months), there was no difference in the adjusted mortality rate between groups (51.3 % with hypothermia vs 48.0 % without hypothermia; HRadj1.08 95%CI [0.77-1.53]; p = 0.659). There was a higher frequency of bleeding in the hypothermia group (6.7 % vs 1.1 %; ORadj 7.99 95%CI [2.05-31.2]; p = 0.002), without difference for the rest of periprocedural complications. At 6-month follow-up, adjusted all-cause mortality rate was similar between groups (46.2 % with hypothermia vs 44.5 % without hypothermia; HRadj1.02 95%CI [0.71-1.47]; p = 0.900). Also, no differences were observed in the adjusted mortality rate between 6 months and median follow-up (9.4 % with hypothermia vs 6.3 % without hypothermia; HRadj2.02 95%CI [0.69-5.92]; p = 0.200).

CONCLUSIONS

In a large cohort of patients with cardiac arrest within a regional STEMI network, those treated with therapeutic hypothermia did not improve long-term survival compared to those without hypothermia.

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.

EEG Pattern With Spectral Analysis Can Prognosticate Good and Poor Neurologic Outcomes After Cardiac Arrest

PURPOSE

To investigate the prognostic value of a simple stratification system of electroencephalographical (EEG) patterns and spectral types for patients after cardiac arrest.

METHODS

In this prospectively enrolled cohort, using manually selected EEG segments, patients after cardiac arrest were stratified into five independent EEG patterns (based on background continuity and burden of highly epileptiform discharges) and four independent power spectral types (based on the presence of frequency components). The primary outcome is cerebral performance category (CPC) at discharge. Results from multimodal prognostication testing were included for comparison.

RESULTS

Of a total of 72 patients, 6 had CPC 1-2 by discharge, all of whom had mostly continuous EEG background without highly epileptiform activity at day 3. However, for the same EEG background pattern at day 3, 19 patients were discharged at CPC 3 and 15 patients at CPC 4-5. After adding spectral analysis, overall sensitivity for predicting good outcomes (CPC 1-2) was 83.3% (95% confidence interval 35.9% to 99.6%) and specificity was 97.0% (89.5% to 99.6%). In this cohort, standard prognostication testing all yielded 100% specificity but low sensitivity, with imaging being the most sensitive at 54.1% (36.9% to 70.5%).

CONCLUSIONS

Adding spectral analysis to qualitative EEG analysis may further improve the diagnostic accuracy of EEG and may aid developing novel measures linked to good outcomes in postcardiac arrest coma.

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.

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.

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.

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.

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.

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.

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.

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.

Association between accelerometer-derived physical activity and incident cardiac arrest

AIMS

Studies on objectively measured physical activity (PA) have investigated acute cardiovascular outcomes but not cardiac arrest (CA). Our study aimed to investigate the dose-response relationship between accelerometer-measured PA and CA by intensity of PA.

METHODS AND RESULTS

This prospective cohort study included 98 893 UK Biobank participants whose PA data were measured using wrist-worn accelerometers. Total PA volume was measured using the average overall acceleration. Minutes per week of light PA (LPA), moderate PA (MPA), and vigorous PA (VPA) were recorded. The incident CA was identified using diagnostic codes linked to hospital encounters and death records. Cox proportional hazard models with restricted cubic splines were used to study the associations, including sex differences. During the follow-up period (median: 7.31 years; interquartile range: 6.78-7.82 years), 282 incident CAs (0.39 per 1000 person-years) occurred. Total PA was inversely related to CA risk. The CA risk decreased sharply until the time spent in MPA or VPA reached ∼360 min or 20 min per week, respectively, after which it was relatively flat. The LPA was not associated with CA risk. Subgroup analyses showed a more pronounced association between PA and a reduced risk of CA in women compared to men.

CONCLUSION

Accelerometer-measured PA, particularly MPA and VPA, was associated with a lower CA risk. Furthermore, a stronger association was observed in women than men.

The International Cardiac Arrest Research Consortium Electroencephalography Database

OBJECTIVES

To develop the International Cardiac Arrest Research (I-CARE), a harmonized multicenter clinical and electroencephalography database for acute hypoxic-ischemic brain injury research involving patients with cardiac arrest.

DESIGN

Multicenter cohort, partly prospective and partly retrospective.

SETTING

Seven academic or teaching hospitals from the United States and Europe.

PATIENTS

Individuals 16 years old or older who were comatose after return of spontaneous circulation following a cardiac arrest who had continuous electroencephalography monitoring were included.

INTERVENTIONS

Not applicable.

MEASUREMENTS AND MAIN RESULTS

Clinical and electroencephalography data were harmonized and stored in a common Waveform Database-compatible format. Automated spike frequency, background continuity, and artifact detection on electroencephalography were calculated with 10-second resolution and summarized hourly. Neurologic outcome was determined at 3-6 months using the best Cerebral Performance Category (CPC) scale. This database includes clinical data and 56,676 hours (3.9 terabytes) of continuous electroencephalography data for 1,020 patients. Most patients died ( n = 603, 59%), 48 (5%) had severe neurologic disability (CPC 3 or 4), and 369 (36%) had good functional recovery (CPC 1-2). There is significant variability in mean electroencephalography recording duration depending on the neurologic outcome (range, 53-102 hr for CPC 1 and CPC 4, respectively). Epileptiform activity averaging 1 Hz or more in frequency for at least 1 hour was seen in 258 patients (25%) (19% for CPC 1-2 and 29% for CPC 3-5). Burst suppression was observed for at least 1 hour in 207 (56%) and 635 (97%) patients with CPC 1-2 and CPC 3-5, respectively.

CONCLUSIONS

The I-CARE consortium electroencephalography database provides a comprehensive real-world clinical and electroencephalography dataset for neurophysiology research of comatose patients after cardiac arrest. This dataset covers the spectrum of abnormal electroencephalography patterns after cardiac arrest, including epileptiform patterns and those in the ictal-interictal continuum.

Electrical-mechanical dynamical coupling between electrocardiographic and photoplethysmographic signals during cardiopulmonary resuscitation

BACKGROUND AND OBJECTIVE

Cardiac arrest (CA) remains a significant cause of death and disability. High-quality cardiopulmonary resuscitation (CPR) can improve the survival rate of CA. A challenging issue is to find physiological indicators for screening and evaluating the cardiovascular function associated with CPR. This study aimed to investigate the electrical-mechanical dynamic coupling between electrocardiographic (ECG) and photoplethysmographic (PPG) signals for indicating cardiovascular function in the progress of CPR.

METHOD

The ECG and PPG signals were simultaneously collected from a porcine CA model (n = 10) induced by ventricular fibrillation, and were further divided into four periods: Baseline, CA, CPR, and recovery of spontaneous circulation (ROSC). Recurrence quantitative analysis (RQA) was applied to examine the nonlinear dynamics of the ECG and PPG signals individually, and cross recurrence quantitative analysis (CRQA) was used to examine the ECG-PPG dynamical coupling.

RESULTS

The CA influenced the dynamic patterns of electrical and mechanical activities and the electrical-mechanical coupling, which can be observed from the reduced entropy (ENTR) (p < 0.01), reduced determinism (DET) (p < 0.01) and reduced trapping time (TT) (p < 0.01) at CA compared to Baseline. The recurrence rate (RR), ENTR, DET, and TT at CPR were significantly lower than the parameters at ROSC but higher than those at CA.

CONCLUSIONS

The electrical-mechanical dynamical coupling was sensitive to CPR and able to reflect the changes in cardiac function in the process of CPR.

Predicting Risk of Cardiac Arrest in Young Asian Americans: Insights from an Artificial Neural Network Analysis of the Nationwide Cohort

We used the Artificial Neural Network (ANN) model to identify predictors of Sudden Cardiac Arrest (SCA) in a national cohort of young Asian patients in the United States. The National Inpatient Sample (2019) was used to identify young Asians (18-44-year-old) who were hospitalized with SCA. The neural network's predicted criteria for SCA were selected. After eliminating missing data, young Asians (n = 65,413) were randomly divided into training (n = 45,094) and testing (n = 19347) groups. Training data (70%) was used to calibrate ANN while testing data (30%) was utilized to assess the algorithm's accuracy. To determine ANN's performance in predicting SCA, we compared the frequency of incorrect prediction between training and testing data and measured the area under the Receiver Operating Curve (AUC). The 2019 young Asian cohort had 327,065 admissions (median age 32 years; 84.2% female), with SCA accounting for 0.21%. The exact rate of error in predictions vs. tests was shown by training data (0.2% vs 0.2%). In descending order, the normalized importance of predictors to accurately predict SCA in young adults included prior history of cardiac arrest, sex, age, diabetes, anxiety disorders, prior coronary artery bypass grafting, hypertension, congenital heart disease, income, peripheral vascular disease, and cancer. The AUC was 0.821, indicating an excellent ANN model for SCA prediction. Our ANN models performed excellently in revealing the order of important predictors of SCA in young Asian American patients. These findings could have a considerable impact on clinical practice to develop risk prediction models to improve the survival outcome in high-risk patients.

Ventricular Fibrillation Is a Sign of Life

Ventricular fibrillation (VF) is a deadly rhythm problem. With asystole, it represents one of the most extreme emergencies that may engage vital prognosis within only few minutes if appropriated treatment is not instituted. It is learned in all medical schools worldwide that VF is not compatible with consciousness and sustained life. Moreover, at 37°C, and without restauration of cardiac flow, VF may be responsible for severe and most often irreversible brain damage after 3 minutes.

Automated Seizure Detection in Patients with Cardiac Arrest: A Retrospective Review of Ceribell™ Rapid-EEG Recordings

BACKGROUND

In patients with cardiac arrest who remain comatose after return of spontaneous circulation, seizures and other abnormalities on electroencephalogram (EEG) are common. Thus, guidelines recommend urgent initiation of EEG for the evaluation of seizures in this population. Point-of-care EEG systems, such as Ceribell™ Rapid Response EEG (Rapid-EEG), allow for prompt initiation of EEG monitoring, albeit through a reduced-channel montage. Rapid-EEG incorporates an automated seizure detection software (Clarity™) to measure seizure burden in real time and alert clinicians at the bedside when a high seizure burden, consistent with possible status epilepticus, is identified. External validation of Clarity is still needed. Our goal was to evaluate the real-world performance of Clarity for the detection of seizures and status epilepticus in a sample of patients with cardiac arrest.

METHODS

This study was a retrospective review of Rapid-EEG recordings from all the patients who were admitted to the medical intensive care unit at Kent Hospital (Warwick, RI) between 6/1/2021 and 3/18/2022 for management after cardiac arrest and who underwent Rapid-EEG monitoring as part of their routine clinical care (n = 21). Board-certified epileptologists identified events that met criteria for seizures or status epilepticus, as per the 2021 American Clinical Neurophysiology Society's Standardized Critical Care EEG Terminology, and evaluated any seizure burden detections generated by Clarity.

RESULTS

In this study, 4 of 21 patients with cardiac arrest (19.0%) who underwent Rapid-EEG monitoring had multiple electrographic seizures, and 2 of those patients (9.5%) had electrographic status epilepticus within the first 24 h of the study. None of these ictal abnormalities were detected by the Clarity seizure detection system. Clarity showed 0% seizure burden throughout the entirety of all four Rapid-EEG recordings, including the EEG pages that showed definite seizures or status epilepticus.

CONCLUSIONS

The presence of frequent electrographic seizures and/or status epilepticus can go undetected by Clarity. Timely and careful review of all raw Rapid-EEG recordings by a qualified human EEG reader is necessary to guide clinical care, regardless of Clarity seizure burden measurements.

Cardiac arrest in the perioperative period: a consensus guideline for identification, treatment, and prevention from the European Society of Anaesthesiology and Intensive Care and the European Society for Trauma and Emergency Surgery

INTRODUCTION

Cardiac arrest in the operating room is a rare but potentially life-threatening event with mortality rates of more than 50%. Contributing factors are often known, and the event is recognised rapidly as patients are usually under full monitoring. This guideline covers the perioperative period and is complementary to the European Resuscitation Council (ERC) guidelines.

MATERIAL AND METHODS

The European Society of Anaesthesiology and Intensive Care and the European Society for Trauma and Emergency Surgery jointly nominated a panel of experts to develop guidelines for the recognition, treatment and prevention of cardiac arrest in the perioperative period. A literature search was conducted in MEDLINE, EMBASE, CINAHL and the Cochrane Central Register of Controlled Trials. All searches were restricted to publications from 1980 to 2019 inclusive and to the English, French, Italian and Spanish languages. The authors also contributed individual, independent literature searches.

RESULTS

This guideline contains background information and recommendation for the treatment of cardiac arrest in the operating room environment, and addresses controversial topics such as open chest cardiac massage (OCCM), resuscitative endovascular balloon occlusion (REBOA) and resuscitative thoracotomy, pericardiocentesis, needle decompression and thoracostomy.

CONCLUSION

Successful prevention and management of cardiac arrest during anaesthesia and surgery requires anticipation, early recognition and a clear treatment plan. The ready availability of expert staff and equipment must also be taken into consideration. Success not only depends on medical knowledge, technical skills and a well organised team using crew resource management but also on an institutional safety culture embedded in everyday practice through continuous education, training and multidisciplinary co-operation.

Spiked Helmet Sign in the Inferior Leads

This case report describes a patient in their 80s who was admitted to the critical care unit with pulseless electrical activity cardiac arrest.

Using a Clinical Triggers System to Improve Early Recognition of Clinical Changes

PURPOSE

A medical-surgical telemetry unit implemented a clinical triggers system for early recognition of clinical deterioration and bedside management between nurses and providers. The goal was to decrease cardiopulmonary arrest events.

DESCRIPTION OF THE PROJECT

A clinical triggers system was developed to help nurses to identify clinical markers early and advocate for prompt bedside assessment and interventions. When clinical triggers were identified, the nurse notified the provider, who performed a bedside assessment within 15 minutes. If the provider did not respond promptly, the rapid response team was activated.

OUTCOMES

Before intervention, the unit experienced 14 cardiopulmonary arrest events (rate of 1.37 per 1000 patient days). Incidences decreased annually to 5, 4, and 3 events (rates of 0.49, 0.39, and 0.3 per 1000 patient days) during the 3-year implementation period.

CONCLUSIONS

The clinical triggers system was successful in achieving the project objective of decreasing unit cardiopulmonary arrest events through early recognition and response to patient deterioration during the implementation period. The clinical nurse specialist helped nurses to use the clinical triggers system to detect and respond to clinical changes. Nurses were empowered to address concerns and promote patient safety.

Markov model for detection of ECG instability prior to cardiac arrest in single-ventricle patients

OBJECTIVES

Assess the degree of instability in the electrocardiogram (ECG) waveform in patients with single-ventricle physiology before a cardiac arrest and compare them with similar patients who did not experience a cardiac arrest.

METHODS

Retrospective control study in patients with single-ventricle physiology who underwent Norwood, Blalock-Taussig shunt, pulmonary artery band, and aortic arch repair from 2013 to 2018. Electronic medical records were obtained for all included patients. For each subject, 6 h of ECG data were analyzed. In the arrest group, the end of the sixth hour coincides with the cardiac arrest. In the control group, the 6-h windows were randomly selected. We used a Markov chain framework and the likelihood ratio test to measure the degree of ECG instability and to classify the arrest and control groups.

RESULTS

The study dataset consists of 38 cardiac arrest events and 67 control events. Our Markov model was able to classify the arrest and control groups based on the ECG instability with an ROC AUC of 82% at the hour preceding the cardiac arrests.

CONCLUSION

We designed a method using the Markov chain framework to measure the level of instability in the beat-to-beat ECG morphology. Furthermore, we were able to show that the Markov model performed well to distinguish patients in the arrest group compared to the control group.

Investigation of Unexplained Cardiac Arrest: Phenotyping and Genetic Testing

Unexplained cardiac arrest (UCA) is a working diagnosis that should be replaced by a final diagnosis once evaluation is completed. Complete evaluation of UCA should include high-yield tests like cardiac magnetic resonance imaging, exercise treadmill test, and sodium-channel blocker challenge to identify latent causes of UCA. If no clear etiology is revealed after complete evaluation, idiopathic ventricular fibrillation may be diagnosed, and the strength of its diagnosis can be divided into definitive, probable, and possible based on the number of high-yield tests performed. Care should be provided by a multidisciplinary team with expertise in this area.

Kidney-specific biomarkers for predicting acute kidney injury following cardiac arrest

AIM

To evaluate the performance of kidney-specific biomarkers (neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1), and cystatin-C) in early detection of acute kidney injury (AKI) following cardiac arrest (CA) when compared to serum creatinine.

METHODS

Adult CA patients who had kidney-specific biomarkers of AKI collected within 12 h of return of spontaneous circulation (ROSC) were included. The association between renal biomarker levels post-ROSC and the development of KDIGO stage III AKI within 7 days of enrollment were assessed as well as their predictive value of future AKI development, neurological outcomes, and survival to discharge.

RESULTS

Of 153 patients, 54 (35%) developed stage III AKI within 7 days, and 98 (64%) died prior to hospital discharge. Patients who developed stage III AKI, compared to those who did not, had higher median levels of creatinine, NGAL, and cystatin-C (p < 0.001 for all). There was no statistically significant difference in KIM-1 between groups. No biomarker outperformed creatinine in the ability to predict stage III AKI, neurological outcomes, or survival outcomes (p > 0.05 for all). However, NGAL, cystatin-C, and creatinine all performed better than KIM-1 in their ability to predict AKI development (p < 0.01 for all).

CONCLUSION

In post-CA patients, creatinine, NGAL, and cystatin-C (but not KIM-1) measured shortly after ROSC were higher in patients who subsequently developed AKI. No biomarker was statistically superior to creatinine on its own for predicting the development of post-arrest AKI.

Neuroprognostication after cardiac arrest: what the cardiologist should know

Two aspects are a key to mastering prognostication of comatose cardiac arrest survivors: a detailed knowledge about the clinical trajectories of consciousness recovery (or lack thereof) and the ability to correctly interpret the results of multimodal investigations, which include clinical examination, electroencephalography, neuroimaging, evoked potentials, and blood biomarkers. While the very good and the very poor ends of the clinical spectrum typically do not pose diagnostic challenges, the intermediate 'grey zone' of post-cardiac arrest encephalopathy requires cautious interpretation of the available information and sufficiently long clinical observation. Late recovery of coma patients with initially ambiguous diagnostic results is increasingly reported, as are unresponsive patients with various forms of residual consciousness, including so-called cognitive motor dissociation, rendering prognostication of post-anoxic coma highly complex. The aim of this paper is to provide busy clinicians with a high-yield, concise overview of neuroprognostication after cardiac arrest, emphasizing notable developments in the field since 2020.

Cardiac arrest as initial presentation of pheochromocytoma in a young

We reported a case of pheochromocytoma with initial presentation of cardiac arrest. The patient underwent implantable cardioverter defibrillator for primary prevention but subsequently experienced repeated implantable cardioverter defibrillator shocks and syncopal episodes. A mass was found in the adrenal gland by CT, which was confirmed by anatomopathological analysis of the surgical specimen.

The value of a machine learning algorithm to predict adverse short-term outcome during resuscitation of patients with in-hospital cardiac arrest: a retrospective study

Background and importance Guidelines recommend that hospital emergency teams locally validate criteria for termination of cardiopulmonary resuscitation in patients with in-hospital cardiac arrest (IHCA). Objective To determine the value of a machine learning algorithm to predict failure to achieve return of spontaneous circulation (ROSC) and unfavourable functional outcome from IHCA using only data readily available at emergency team arrival. Design Retrospective cohort study. Setting and participants Adults who experienced an IHCA were attended to by the emergency team. Outcome measures and analysis Demographic and clinical data typically available at the arrival of the emergency team were extracted from the institutional IHCA database. In addition, outcome data including the Cerebral Performance Category (CPC) score count at hospital discharge were collected. A model selection procedure for random forests with a hyperparameter search was employed to develop two classification algorithms to predict failure to achieve ROSC and unfavourable (CPC 3-5) functional outcomes. Main results Six hundred thirty patients were included, of which 390 failed to achieve ROSC (61.9%). The final classification model to predict failure to achieve ROSC had an area under the receiver operating characteristic curve of 0.9 [95% confidence interval (CI), 0.89-0.9], a balanced accuracy of 0.77 (95% CI, 0.75-0.79), an F1-score of 0.78 (95% CI, 0.76-0.79), a positive predictive value of 0.88 (0.86-0.91), a negative predictive value of 0.61 (0.6-0.63), a sensitivity of 0.69 (0.66-0.72), and a specificity of 0.84 (0.8-0.88). Five hundred fifty-nine subjects experienced an unfavourable outcome (88.7%). The final classification model to predict unfavourable functional outcomes from IHCA at hospital discharge had an area under the receiver operating characteristic curve of 0.93 (95% CI, 0.92-0.93), a balanced accuracy of 0.59 (95% CI, 0.57-0.61), an F1-score of 0.94 (95% CI, 0.94-0.95), a positive predictive value of 0.91 (0.9-0.91), a negative predictive value of 0.57 (0.48-0.66), a sensitivity of 0.98 (0.97-0.99), and a specificity of 0.2 (0.16-0.24). Conclusion Using data readily available at emergency team arrival, machine learning algorithms had a high predictive power to forecast failure to achieve ROSC and unfavourable functional outcomes from IHCA while cardiopulmonary resuscitation was still ongoing; however, the positive predictive value of both models was not high enough to allow for early termination of resuscitation efforts.