Background: More aggressive management may be warranted for patients with acute pulmonary embolism (PE) and the greatest pulmonary vascular obstruction. We hypothesized that a scoring system based on the ECG might identify such patients.
Methods: Consecutive patients investigated for PE at Christchurch Hospital between 1997 and 2002 with high-probability ventilation/perfusion (V/Q) scan findings were studied. The ECG obtained closest to and within 48 h of the scan was scored by two independent observers, and the mean ECG score was calculated. V/Q scan findings were categorized into those with < 30%, 30 to 50%, and > 50% perfusion defect by two independent observers experienced in V/Q interpretation. A consensus score was taken when disagreement occurred.
Results: Two hundred twenty-nine patients were included in the study. The interobserver agreement for ECG score was 0.96 (Cronbach alpha) and V/Q score was 0.55 (kappa). The ECG predicted those with the greatest amount of perfusion defects. Mean ECG score was 2.6 (SD 2.8) in patients with < 30% perfusion defect, 3.2 (SD 2.9) in patients with 30 to 50% perfusion defect, and 5.3 (SD 3.7) in patients with > 50% perfusion defect. The area under the receiver operating characteristic curve for ECG score and those with > 50% perfusion defect was 0.71 (SE 0.04). An ECG score of > or = 3 predicted those with > 50% perfusion defect with a sensitivity of 70% (95% confidence interval [CI], 59 to 81%), and a specificity of 59% (95% CI, 51 to 67%).
Conclusion: An ECG score, simple to derive, predicts those with the greatest percentage of perfusion defect. Using the ECG for management warrants prospective evaluation.
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http://dx.doi.org/10.1378/chest.125.5.1651 | DOI Listing |
Background And Aims: The importance of risk stratification in patients with chest pain extends beyond diagnosis and immediate treatment. This study sought to evaluate the prognostic value of electrocardiogram feature-based machine learning models to risk-stratify all-cause mortality in those with chest pain.
Methods: This was a prospective observational cohort study of consecutive, non-traumatic patients with chest pain.
Endocr Connect
January 2025
C Guimard, Department of Medicine, Clinique Jules Verne, Nantes, France.
Objective: Hypercalcemia is often considered as an emergency because of a potential risk life-threatening arrhythmias or coma. However, there is little evidence, apart from case studies, that hypercalcemia can be immediately life-threatening. The aim of our study was to assess prospectively, if hypercalcemia (Ca ≥ 3 mmol/L) was associated with immediately life-threatening complications.
View Article and Find Full Text PDFHeart Rhythm O2
December 2024
Cardiology Department, Bichat Hospital, Paris, France.
Background: Detection of atrial tachyarrhythmias (ATA) on long-term electrocardiogram (ECG) recordings is a prerequisite to reduce ATA-related adverse events. However, the burden of editing massive ECG data is not sustainable. Deep learning (DL) algorithms provide improved performances on resting ECG databases.
View Article and Find Full Text PDFCirc Cardiovasc Imaging
January 2025
Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC. (P.S., C.H., G.B., A.H., S.H.S., P.S.D., M.A.D.).
Background: Patients with abnormal (positive) exercise electrocardiography, but normal stress echocardiography (+ECG/-Echo), have an increased risk of adverse cardiovascular events compared with patients with a normal (negative) ECG and a normal stress Echo (-ECG/-Echo). However, it is unclear if +ECG/-Echo discordance is associated with a greater burden of subclinical coronary atherosclerosis.
Methods: Project Baseline Health Study participants who underwent a stress Echo and coronary artery calcium (CAC) scan were stratified by stress Echo result: -ECG/-Echo or +ECG/-Echo.
Sensors (Basel)
December 2024
Faculty of Information Science and Technology, Beijing University of Technology, Beijing 100124, China.
With the increasing complexity of urban roads and rising traffic flow, traffic safety has become a critical societal concern. Current research primarily addresses drivers' attention, reaction speed, and perceptual abilities, but comprehensive assessments of cognitive abilities in complex traffic environments are lacking. This study, grounded in cognitive science and neuropsychology, identifies and quantitatively evaluates ten cognitive components related to driving decision-making, execution, and psychological states by analyzing video footage of drivers' actions.
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