View clinical trials related to Neuropathy, Diabetic.
Filter by:Diabetes is a serious and chronic disease that affects more than 347 million people in the world. It is the leading cause of death by age and its prevalence is increasing annually throughout the world. Diabetes is a disorder that manifests itself with elevated blood glucose levels that may be the resultof a deficiency in insulin secretion or action, or a combination of both problems. The "Diabetic foot" includes a number of syndromes in which the interaction of the loss of protective sensation by the presence of sensory neuropathy, the change in pressure points due to motor neuropathy, autonomic dysfunction and decreased Blood flow due to peripheral vascular disease can lead to the appearance of injuries or ulcers induced by minor traumas that go "unnoticed." This situation leads to significant morbidity and a high risk of amputation. It can be prevented with the application of prevention programs, based on the early detection of neuropathy, assessment of associated risk factors, along with the application of a structured program of education and treatment of risk factors. PRIMARY OBJECTIVES: 1- Comparison of ulceration rates, decrease in amputation rates in the target population with intervention: LSCI, thermography and creation of personalized insoles versus the control group with assessment, treatment and follow-up, without the intervention of interest in the study. 2- Correlation between changes in perfusion and temperature detected in combination of LSCI and thermography to predict diabetic foot ulcers and the risk of having ulcers. Study Model: Parallel Assignment 1:1 . Patients with inclusion criteria and without exclusion criteria will be randomized into two groups with Randomization with sequence concealment, centralized in computer support. OxMaR (Oxford Minimization and Randomization) After signing the informed consent, the patients will be divided into two groups. Number of Arms 2 Masking: None (Open Label) A-GROUP WITH LSCI, 3D FOOT CREATOR FOLLOW UP B- GROUP WITHOUT LSCI, 3D FOOT CREATOR FOLLOW UP.
The European Working Group on Sarcopenia in the Elderly1 defines sarcopenia as a disorder of the progressive and generalized musculoskeletal system [1], which is associated with the increase and probability of adverse outcomes including falls, fractures, physical disability, and mortality [2]. what is associated with increased and likelihood of adverse outcomes including falls, fractures, disability physical and mortality [2]. For a long time, sarcopenia was associated with aging, affecting onlyold people. At present and after several research works related to fragility and theaging, it has been identified that the development of sarcopenia begins earlier in life [3], and that there are many contributing causes besides aging [4], [5]. This new knowledge has implications in the intervention of sarcopenia that prevents or delays its development. Sarcopenia is currently considered a muscle disease (muscle failure), based on adverse changes in the muscles of the musculoskeletal system accumulated throughout life, with loss of muscle strength such as main determinant [6], [7]. Sarcopenia has been overlooked in clinical practice, apparently due to to the complexity in determining the variables to be measured, how to measure them, and the values or cut-off points can guide diagnosis and treatment, and how best to assess the effects of therapeutic intervention [8]. In terms economic, the presence of sarcopenia increases the risk of hospitalization and increases the cost of care during hospital admission [9]. Diabetes is the main cause of non-traumatic amputation of the lower limb (MI), being foot ulcers diabetic the cause of 80% of the amputations of people with diabetes[10]. A study conducted by the Chongqing University Hospital showed that sarcopenia is independently related to the foot diabetic and that patients with diabetic foot have a worse prognosis if they suffer from sarcopenia. HYPOTHESIS: The surface electromyography (EMGs) signal recording of the foot musculature, will allow extracting biomarkers that allow monitoring and follow-up of sarcopenia in diabetic patients. MAIN OBJECTIVES: 1- Generate tools based on artificial intelligence (AI) using the database with the biomarkers obtained, in order to analyze the predisposing and triggering risk factors associated with diabetic foot ulcers, according to the IWGDF2. 2- Describe the profile of the diabetic patient in terms of degree of sarcopenia with respect to the population without diabetes in a group of adults. DESIGN: Observational study comparison between cases and controls: a group with the presence of Diabetes Mellitus and another without. SAMPLE: Approximately 16% of diabetic patients will develop an ulcer during their evolution and the Annual incidence is 2-3%, which doubles to 6% in the presence of polyneuropathy. Population of the Department of Health 168,978. Prevalence of diabetes in Spain 7.8%. It is estimated that there are 13,182 in the department people with diabetes. Confidence level 95%, expected frequency of ulcers 6% and confidence limit 9%, it was calculates the sample of 26 patients. 30 patients per group will be recruited. GROUP 1: 30 patients with Diabetes Mellitus. GROUP 2: 30 control patients without Diabetes Mellitus. The period of inclusion of patients is estimated at 5 months. METHOD: the assessment interventions will be carried out in two days. During the first visit, examination to identify risk to the foot: clinical history (PA, comorbidity data, previous injuries to the feet). feet..), examination of the vascular state, examination of loss of protective sensitivity, perception of pressure, skin inspection, inspection of bone/joint structures, physical limitations and level of knowledge of the foot care. During the second visit: diagnostic tests for sarcopenia (bioimpedance and electromyography), arthropometric measurements, malnutrition, dependence and activity marker tests. EXPECTED RESULTS: clarify some aspects related to the sarcopenia-diabetic foot binomial, and isolate risk factors for future prevention, by obtaining biomarkers with EMGs in lower limbs.
Predicting early onset neuropathy in people with type 1 diabetes