Unlabelled: Quantitative ECG Analysis.
Introduction: Optimal atrial tachyarrhythmia management is facilitated by accurate electrocardiogram interpretation, yet typical atrial flutter (AFl) may present without sawtooth F-waves or RR regularity, and atrial fibrillation (AF) may be difficult to separate from atypical AFl or rapid focal atrial tachycardia (AT). We analyzed whether improved diagnostic accuracy using a validated analysis tool significantly impacts costs and patient care.
Methods And Results: We performed a prospective, blinded, multicenter study using a novel quantitative computerized algorithm to identify atrial tachyarrhythmia mechanism from the surface ECG in patients referred for electrophysiology study (EPS). In 122 consecutive patients (age 60 ± 12 years) referred for EPS, 91 sustained atrial tachyarrhythmias were studied. ECGs were also interpreted by 9 physicians from 3 specialties for comparison and to allow healthcare system modeling. Diagnostic accuracy was compared to the diagnosis at EPS. A Markov model was used to estimate the impact of improved arrhythmia diagnosis. We found 13% of typical AFl ECGs had neither sawtooth flutter waves nor RR regularity, and were misdiagnosed by the majority of clinicians (0/6 correctly diagnosed by consensus visual interpretation) but correctly by quantitative analysis in 83% (5/6, P = 0.03). AF diagnosis was also improved through use of the algorithm (92%) versus visual interpretation (primary care: 76%, P < 0.01). Economically, we found that these improvements in diagnostic accuracy resulted in an average cost-savings of $1,303 and 0.007 quality-adjusted-life-years per patient.
Conclusions: Typical AFl and AF are frequently misdiagnosed using visual criteria. Quantitative analysis improves diagnostic accuracy and results in improved healthcare costs and patient outcomes.
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http://dx.doi.org/10.1111/j.1540-8167.2010.01809.x | DOI Listing |
Scand J Med Sci Sports
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School of Physical Education, Shanghai University of Sport, Shanghai, China.
Long-term training enables professional athletes to develop concentrated and efficient neural network organizations for specific tasks. This study used functional near-infrared spectroscopy to investigate task performance, brain functional characteristics, and their relationships in footballers during sport-specific motor-cognitive processes. Twenty-four footballers (athlete group, with 18 remaining of good signal quality) and 20 non-footballers (control group, with 16 remaining) completed four tasks: a single task (trigger buttons corresponding to the appearance direction of teammates with kicking actions), an N-back direction task, a dual task, and an N-back digit task.
View Article and Find Full Text PDFHistopathology
December 2024
University of Virginia Health System, Charlottesville, VA, USA.
The resurgence of measles, syphilis, and HIV presents a significant threat to global health, especially in the wake of the COVID-19 pandemic. These three infections involve lymph nodes and have unique pathologic findings in lymph nodes. We explore the pathological and clinical characteristics of these infections, focusing on their involvement of lymph nodes and their pathologic diagnosis in lymph node specimens.
View Article and Find Full Text PDFTransl Neurosci
January 2024
Merivale High School, 1755 Merivale Rd, Nepean, ON K2G 1E2, Canada.
The limitation of artificial intelligence (AI) large language models to diagnose diseases from the perspective of patient safety remains underexplored and potential challenges, such as diagnostic errors and legal challenges, need to be addressed. To demonstrate the limitations of AI, we used ChatGPT-3.5 developed by OpenAI, as a tool for medical diagnosis using text-based case reports of multiple sclerosis (MS), which was selected as a prototypic disease.
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Department of Ophthalmology, The Second Affiliated Hospital of Chengdu Medical College, Nuclear Industry 416 Hospital, Chengdu, Sichuan, China.
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Ann Clin Epidemiol
October 2024
Center of Medical Statistics, Minato-Ku, Tokyo, Japan.
Background: Large electronic databases have been widely used in recent years; however, they can be susceptible to bias due to incomplete information. To address this, validation studies have been conducted to assess the accuracy of disease diagnoses defined in databases. However, such studies may be constrained by potential misclassification in references and the interdependence between diagnoses from the same data source.
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