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Morbidity, Multiple clinical trials

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NCT ID: NCT04842396 Completed - Frailty Clinical Trials

Low-volume Cycling Training in Older People With Multimorbidity

Start date: September 1, 2019
Phase: N/A
Study type: Interventional

INTRODUCTION: Physical exercise, when practiced regularly and in adequate doses, is a proven nonpharmacological measure that helps to prevent and reverse noncommunicable diseases, as well as reduce mortality rates from any cause. In general, older adults perform insufficient physical activity and do not meet the doses recommended by the World Health Organization for the improvement of health through physical activity. OBJECTIVE: Our main aim will be to evaluate the effect of a 6-week intervention on health-related outcomes (body composition, hemodynamic and functionality changes) in 24 individuals aged 65 and older with multimorbidity. METHODS AND ANALYSIS: The study was a 2 x 2 randomized controlled trial using a two-group design (exercise vs. control) and two repeated measures (pre- vs. postintervention). The intervention (on the MOTOmed Muvi) will consist of a very low volume (60 minutes per week) of low-to-moderate intensity exercise training to assess body composition evaluation, hemodynamic parameter evaluation and functional evaluation. Participants will be recruited at the Gerontological Complex La Milagrosa (A Coruña, Spain), consisting of a daycare center and a nursing home. For the statistical analysis, nonparametric ANOVA type statistics and mixed models for repeated measures will be used.

NCT ID: NCT04788433 Completed - Covid19 Clinical Trials

Registry of Coronavirus Complications - CORRELATION WITH GLYCOMIC PROFILE in COVID-19 Patients

ROCCO
Start date: March 4, 2021
Phase:
Study type: Observational

COVID infection has resulted in multi-organ injury and may result in cardiovascular, pulmonary, neurological, and muscular damage. It is associated with significant asthenia and the long-term effects of the infection are still unclear, particularly for the development of pain and delayed functional rehabilitation. Glycomics "is the systematic study of the structure of glycans in a given cell type or organism. Glycans are complex oligosaccharides attached to proteins and lipids that regulate a variety of organic processes, including immunity Thus, glycans may influence different moments of the response to the virus and involved in the clinical severity of the disease, but may also change depending on the severity of symptoms and the organic response to SARS-CoV-2 infection. Glycomic data could provide important insights into interindividual differences at the molecular level that directly interact with SARS-CoV-2 and the development of mid- and long-term side effects. The ability to identify early those susceptible to developing COVID-19 infection and at higher risk for COVID-19 with unfavorable outcomes long after infection would help guide therapeutic strategy and provide important guidance for rational health care organization, which is of outmost importance. Long-term outcome data regarding post-COVID patient functional capacity and glycomics will be compared to assess whether there may be differences in protein glycosylation that may predict patient outcome.

NCT ID: NCT04756479 Completed - Covid19 Clinical Trials

Mortality Due to COVID-19 in the COVID-AGEBRU Study

COVID-AGEBRU
Start date: March 1, 2020
Phase:
Study type: Observational

The COVID-Age Brugmann study aims to identify the clinical predictors of mortality risk in older patients admitted to an acute care unit due to COVID-19

NCT ID: NCT04346550 Completed - Pain Clinical Trials

Comparison of Morbidity After Laparoscopic Cholecystectomy for Acutely Inflamed Gall Bladder With and Without Drain

Start date: October 2, 2017
Phase: N/A
Study type: Interventional

The purpose of this study was to compare the frequency of pain and mean hospital stay in patients with and without drain insertion, following laparoscopic cholecystectomy for acutely inflamed gallbladder.

NCT ID: NCT04014010 Completed - Surgery Clinical Trials

Machine Learning Modeling of Intraoperative Hemodynamic Predictors of Postoperative Outcomes

Start date: January 1, 2013
Phase:
Study type: Observational

With population aging and limited resources, strategies to improve outcomes after surgery are ever more important. There is a limited understanding of what ranges of hemodynamic variables under anesthesia are associated with better outcomes. This retrospective cohort study will analyze how hemodynamic variables during surgeries predict mortality, morbidity, Intensive Care Unit admission, length of hospital stay, and hospital readmission. The use of machine learning in a large, broad surgery population dataset could detect new relationships and strategies that may inform current practice, and generate ideas for future research.