Thiruganasambandamoorthy, Venkatesh

Background: In the emergency setting, many diagnostic pathways incorporate change in high-sensitivity cardiac troponin (hs-cTn) concentrations (i.e., the delta) to classify patients as low-risk (rule-out) or high-risk (rule-in) for possible myocardial infarction (MI). However, the impact of analytical variation on the delta for correct classification is unknown, especially at concentrations below and around the 99th percentile. Our objective was to assess the impact of delta variation for correct risk classification across the European Society of Cardiology (ESC 0/1 h and 0/2 h), the High-STEACS, and the common change criteria (3C) pathways. Methods: A yearlong accuracy study for hs-cTnT was performed where laboratories across Canada tested three patient-based samples (level 1 target value = 6 ng/L, level 2 target value = 9 ng/L, level 3 target value = 12 ng/L) monthly across 41 different analyzers. The assigned low-delta between levels 1 and 2 was 3 ng/L (i.e., 9 − 6 = 3 ng/L) and the assigned high-delta between levels 1 and 3 was 6 ng/L (i.e., 12 − 6 = 6 ng/L). The low- and high-deltas for each analyzer were determined monthly from the measured values, with the difference calculated from the assigned deltas. The obtained deltas were then assessed via the different pathways on correct classification (i.e., percent correct with 95% confidence intervals, CI) and using non-parametric analyses. Results: The median (interquartile range) difference between the measured versus assigned low-delta (n = 436) and high-delta (n = 439) was −1 ng/L (−1 to 0). The correct classification differed among the pathways. The ESC 0/1 h pathway yielded the lowest percentage of correct classification at 35.3% (95% CI: 30.8 to 40.0) for the low-delta and 90.0% (95% CI: 86.8 to 92.6) for the high-delta. The 3C and ESC 0/2 h pathways yielded higher and equivalent estimates on correct classification: 95.2% (95% CI: 92.7 to 97.0) for the low-delta and 98.2% (95% CI: 96.4 to 99.2) for the high-delta. The High-STEACS pathway yielded 99.5% (95% CI: 98.4 to 99.9) of correct classifications for the high-delta but only 36.2% (95% CI: 31.7 to 40.9) for the low-delta. Conclusions: Analytical variation will impact risk classification for MI when using hs-cTn deltas alone per the pathways. The 3C and ESC 0/2 h pathways have <5% misclassification when using deltas for hs-cTnT in this dataset. Additional studies with different hs-cTnI assays at concentrations below and near the 99th percentile are warranted to confirm these findings.

Abstract Aims Syncope remains a diagnostic challenge despite advancements in testing and treatment. Cardiac syncope is an independent predictor of mortality and can be difficult to distinguish from other causes of transient loss of consciousness (TLOC). This paper explores whether artificial intelligence (AI) can improve the evaluation and management of patients with syncope. Methods and results We conducted a literature review and incorporated the opinions of experts in the fields of syncope and AI. The cause of TLOC is often unclear, hospitalization criteria are ambiguous, diagnostic tests are frequently non-informative, and assessments are costly. Patients are left with unanswered questions and limited guidance. Artificial intelligence (AI) has the potential to optimize syncope evaluation by processing large data sets, detecting imperceptible patterns, and assisting clinicians. However, AI has limitations, including errors, lack of human empathy, and uncertain clinical utility. Liability issues further complicate its integration. We present three viewpoints: (i) AI is crucial for advancing syncope management; (ii) AI can enhance the patient experience; and (iii) AI in syncope care is inevitable. Conclusion Artificial intelligence may improve syncope diagnosis and management, particularly through machine learning–based test interpretation and wearable device data. However, it has yet to surpass human clinical judgment in complex decision-making. Current challenges include gaps in understanding syncope mechanisms, AI interpretability, generalizability, and clinical integration. Standardized diagnostic approaches, real-world validation, and curated data sets are essential for progress. Artificial intelligence may enhance efficiency and communication but raises concerns regarding confidentiality, bias, inequities, and legal implications.

Background and Objectives: Knowledge of the incidence and time frames of the adverse events of patients presenting syncope at the ED is essential for developing effective management strategies. The aim of the present study was to perform a meta-analysis of the incidence and time frames of adverse events of syncope patients. Materials and Methods: We combined individual patients’ data from prospective observational studies including adult patients who presented syncope at the ED. We assessed the pooled rate of adverse events at 24 h, 72 h, 7–10 days, 1 month and 1 year after ED evaluation. Results: We included nine studies that enrolled 12,269 patients. The mean age varied between 53 and 73 years, with 42% to 57% females. The pooled rate of adverse events was 5.1% (95% CI 3.4% to 7.7%) at 24 h, 7.0% (95% CI 4.9% to 9.9%) at 72 h, 8.4% (95% CI 6.2% to 11.3%) at 7–10 days, 10.3% (95% CI 7.8% to 13.3%) at 1 month and 21.3% (95% CI 15.8% to 28.0%) at 1 year. The pooled death rate was 0.2% (95% CI 0.1% to 0.5%) at 24 h, 0.3% (95% CI 0.1% to 0.7%) at 72 h, 0.5% (95% CI 0.3% to 0.9%) at 7–10 days, 1% (95% CI 0.6% to 1.7%) at 1 month and 5.9% (95% CI 4.5% to 7.7%) at 1 year. The most common adverse event was arrhythmia, for which its rate was 3.1% (95% CI 2.0% to 4.9%) at 24 h, 4.8% (95% CI 3.5% to 6.7%) at 72 h, 5.8% (95% CI 4.2% to 7.9%) at 7–10 days, 6.9% (95% CI 5.3% to 9.1%) at 1 month and 9.9% (95% CI 5.5% to 17) at 1 year. Ventricular arrhythmia was rare. Conclusions: The risk of death or life-threatening adverse event is rare in patients presenting syncope at the ED. The most common adverse events are brady and supraventricular arrhythmias, which occur during the first 3 days. Prolonged ECG monitoring in the ED in a short stay unit with ECG monitoring facilities may, therefore, be beneficial.

The aims of this study were (1) to identify research publications studying noninvasive electrocardiogram (ECG) monitoring devices, (2) to define and categorize current technology in noninvasive ECG recording, and (3) to discuss desirable noninvasive recording features for personalized syncope evaluation to guide technological advancement and future studies. We performed a systematic review of the literature that assessed noninvasive ECG-monitoring devices, regardless of the reason for monitoring. We performed an Internet search and corresponded with syncope experts and companies to help identify further eligible products. We extracted information about included studies and device features. We found 173 relevant papers. The main reasons for ECG monitoring were atrial fibrillation (n = 45), coronary artery disease (n = 10), syncope (n = 8), palpitations (n = 8), other cardiac diseases (n = 67), and technological aspects of monitoring (n = 35). We identified 198 devices: 5 hospital telemetry devices, 12 patches, 46 event recorders, 70 Holter monitors, 23 external loop recorders, 20 mobile cardiac outpatient telemetries, and 22 multifunctional devices. The features of each device were very heterogeneous. There are a large number of ECG-monitoring devices with different features available in the market. Our findings may help clinicians select the appropriate device for their patients. Since there are only a few published articles analyzing their usefulness in syncope patients, further research might improve their use in this clinical setting.