Background: The COVID-19 pandemic has strained healthcare systems and how best to address post-COVID health needs is uncertain. Here we describe the post-COVID symptoms of 675 patients followed up using a virtual review pathway, stratified by severity of acute COVID infection.
Methods: COVID-19 survivors completed an online/telephone questionnaire of symptoms after 12+ weeks and a chest X-ray. Dependent on findings at virtual review, patients were provided information leaflets, attended for investigations and/or were reviewed face-to-face. Outcomes were compared between patients following high-risk and low-risk admissions for COVID pneumonia, and community referrals.
Results: Patients reviewed after hospitalisation for COVID pneumonia had a median of two ongoing physical health symptoms post-COVID. The most common was fatigue (50.3% of high-risk patients). Symptom burden did not vary significantly by severity of hospitalised COVID pneumonia but was highest in community referrals. Symptoms suggestive of depression, anxiety and post-traumatic stress disorder were common (depression occurred in 24.9% of high-risk patients). Asynchronous virtual review facilitated triage of patients at highest need of face-to-face review.
Conclusion: Many patients continue to have a significant burden of post-COVID symptoms irrespective of severity of initial pneumonia. How best to assess and manage long COVID will be of major importance over the next few years.
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http://dx.doi.org/10.7861/clinmed.2021-0037 | DOI Listing |
Diabetes Ther
January 2025
Departamento de Endocrinología y Metabolismo, Unidad de Investigación en Enfermedades Metabolicas, Instituto Nacional de Ciencias Médicas y Nutrición, Salvador Zubirán, Mexico City, Mexico.
Introduction: Young adulthood is well documented as being a particularly challenging area of type 1 diabetes (T1D) healthcare. Many young adults with T1D (YAT1D) are distracted from effective disease self-management; T1D healthcare service engagement can be problematic and inconsistent, and high rates of unplanned healthcare contacts prevail. Video conferencing use can facilitate services to be flexible and responsive.
View Article and Find Full Text PDFRadiol Phys Technol
January 2025
Department of Radiological Sciences, Graduate School of Human Health Sciences, Tokyo Metropolitan University, 7-2-10 Higashi-ogu, Arakawa, Tokyo, 116-8551, Japan.
In plain radiography, scattered X-ray correction processing (Virtual Grid: VG) is used to estimate and correct scattered rays in images. We developed an objective evaluation system for bedside chest X-ray images using VG and investigated its usefulness. First, we trained the blind/referenceless image spatial quality evaluator (BRISQUE) on 200 images obtained by portable chest radiography.
View Article and Find Full Text PDFDisabil Rehabil
January 2025
Postgraduate Program in Rehabilitation Sciences, Universidade Nove de Julho (UNINOVE), São Paulo, SP, Brazil.
Purpose: 1) To identify outcome measures used in support programs designed to enhance functioning in autistic children and adolescents, and 2) To map the content of these measures to the domains of the International Classification of Functioning, Disability and Health (ICF).
Methods: A systematic review was conducted. Searches were performed in Medline/PubMed, EMBASE and Virtual Health Library databases, with no restrictions imposed regarding language or year of publication.
Mil Med
January 2025
Division of Gynecologic Oncology, Department of Gynecologic Surgery & Obstetrics, Tripler Army Medical Center, Honolulu, HI 96859, USA.
Endometrial cancer is the most prevalent gynecologic cancer in the United States and has rising incidence and mortality. Endometrial intraepithelial neoplasia or atypical endometrial hyperplasia (EIN-AEH), a precancerous neoplasm, is surgically managed with hysterectomy in patients who have completed childbearing because of risk of progression to cancer. Concurrent endometrial carcinoma (EC) is also present on hysterectomy specimens in up to 50% of cases.
View Article and Find Full Text PDFClin Transl Sci
January 2025
Global Biometrics and Data Management, Pfizer Research and Development, New York, New York, USA.
The pharmaceutical industry constantly strives to improve drug development processes to reduce costs, increase efficiencies, and enhance therapeutic outcomes for patients. Model-Informed Drug Development (MIDD) uses mathematical models to simulate intricate processes involved in drug absorption, distribution, metabolism, and excretion, as well as pharmacokinetics and pharmacodynamics. Artificial intelligence (AI), encompassing techniques such as machine learning, deep learning, and Generative AI, offers powerful tools and algorithms to efficiently identify meaningful patterns, correlations, and drug-target interactions from big data, enabling more accurate predictions and novel hypothesis generation.
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