Background: Clinically significant Graves' orbitopathy (GO) develops in about 25% of those with Graves' disease (GD); most cases of GD in the UK are managed by endocrinologists. Despite this, patients report significant delays before a diagnosis of GO is made. Measures to increase awareness of the early signs of GO and establishing a fast-track referral pathway to specialist care should overcome these delays and potentially improve outcomes.
Aims: We aimed to determine whether issuing a "GO early warning card" to all GD patients raises awareness of GO and facilitates early diagnosis, what percentage of cards result in a telephone contact, the number of "false reports" from card carriers and patient perceptions of the cards.
Methods: We designed cards, detailing common GO symptoms and a telephone number for patients developing symptoms. Cards were distributed to 160 GD patients, without known GO, attending four endocrine clinics in the UK (December 2015-March 2016). We recorded telephone contacts over twelve months from when the last card was distributed and gathered patient feedback.
Results: The early warning cards were well received by patients in general. Over twelve months, ten telephone contacts from nine patients, all related to ocular symptoms, were received (6% of cards issued). Nine calls resulted in an additional clinic review (for eight patients), and four diagnoses of GO were made.
Conclusions: This pilot study demonstrates that it is feasible to distribute GO early warning cards in clinic, and that they can be used to facilitate an early diagnosis of GO.
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http://dx.doi.org/10.1111/cen.13438 | DOI Listing |
Front Public Health
January 2025
Ateneo School of Medicine and Public Health, Pasig, Metro Manila, Philippines.
Introduction: As climate change advances, the looming threat of dengue fever, intricately tied to rising temperatures, intensifies, posing a substantial and enduring public health challenge in the Philippines. This study aims to investigate the historical and projected excess dengue disease burden attributable to temperature to help inform climate change policies, and guide resource allocation for strategic climate change and dengue disease interventions.
Methods: The study utilized established temperature-dengue risk functions to estimate the historical dengue burden attributable to increased temperatures.
JAMIA Open
February 2025
Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN 55905, United States.
Objectives: In the general hospital wards, machine learning (ML)-based early warning systems (EWSs) can identify patients at risk of deterioration to facilitate rescue interventions. We assess subpopulation performance of a ML-based EWS on medical and surgical adult patients admitted to general hospital wards.
Materials And Methods: We assessed the scores of an EWS integrated into the electronic health record and calculated every 15 minutes to predict a composite adverse event (AE): all-cause mortality, transfer to intensive care, cardiac arrest, or rapid response team evaluation.
Since late 2021, a panzootic of highly pathogenic H5N1 avian influenza virus has driven significant morbidity and mortality in wild birds, domestic poultry, and mammals. In North America, infections in novel avian and mammalian species suggest the potential for changing ecology and establishment of new animal reservoirs. Outbreaks among domestic birds have persisted despite aggressive culling, necessitating a re-examination of how these outbreaks were sparked and maintained.
View Article and Find Full Text PDFBMC Med Inform Decis Mak
January 2025
Department of Internal Medicine, Singapore General Hospital, Singapore, Singapore.
Sci Rep
January 2025
Department of Emergency, The First Hospital of China Medical University, No. 155, Nanjing North Street, Heping District, Shenyang, 11001, China.
The study aimed to develop and validate a sepsis prediction model using structured electronic medical records (sEMR) and machine learning (ML) methods in emergency triage. The goal was to enhance early sepsis screening by integrating comprehensive triage information beyond vital signs. This retrospective cohort study utilized data from the MIMIC-IV database.
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