Background: Head-up tilt table test (HUTT) is widely used for the investigation of syncope and presyncope. Time required for positive response has a probable relationship to the sensitivity of neural reflex mechanism underlying syncope.
Methods: We evaluated patients with history of syncope. Group A included patients with recurrent syncope defined as two or more episodes of syncope and group B included those with one episode of syncope. Time required for the test to become positive in both the groups was analyzed. Patients with positive HUTT were followed for 1 year.
Results: Of 80 patients, 68 eligible study patients were divided into group A (32.35%) and group B (67.65%). HUTT was positive in 41 patients (positivity rate = 60.29%). Positivity rate was 100% in group A and 41.3% in group B. Median time interval for positive response in groups A and B were 12.5 and 30.0 minutes, respectively. An inverse association was found between recurrent syncope and time interval for positive response (R = - 0.282, P = 0.002). Time interval of less than 19.5 minutes for positive response had 94.5% sensitivity and 97.89% specificity for recurrent syncope. On follow-up, positive association was found between time interval for positive response less than 19.5 minutes and future recurrence of syncope (odds ratio = 22.75, 95% confidence interval = 4.37-118.34, P = 0.000).
Conclusion: HUTT time interval of less than 19.5 minutes for positive response predicted future recurrence of syncope. Hence, it may serve as an important predictor of future recurrence of syncope.
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http://dx.doi.org/10.1111/pace.13219 | DOI Listing |
Blood Adv
January 2025
Ente Ospedaliero Cantonale, Switzerland.
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Department I of Internal Medicine and German CLL Study Group; Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf (CIO ABCD); University of Cologne, Faculty of Medicine and University Hos, Cologne, Germany.
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To develop a method leveraging hospital-based surveillance to estimate influenza-related hospitalizations by state, age, and month as a means of enhancing current US influenza burden estimation efforts. Using data from the Influenza Hospitalization Surveillance Network (FluSurv-NET), we extrapolated monthly FluSurv-NET hospitalization rates after adjusting for testing practices and diagnostic test sensitivities to non-FluSurv-NET states. We used a Poisson zero-inflated model with an overdispersion parameter within the Bayesian hierarchical framework and accounted for uncertainty and variability between states and across time.
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January 2025
Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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