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Diabetes mellitus (DM) is a metabolic disorder commonly encountered by the healthcare professionals. Diabetic nephropathy is one of its complications, which is becoming the most common cause of end-stage renal failure in Hong Kong. As of March 31, 2000, a total of 1026 patients with diabetes were on renal replacement therapy and the number is steadily increasing. According to ADA guidelines, screening for diabetic nephropathy should be performed on an annual basis to assess urine albumin excretion rate. Serum creatinine should also be measured in all diabetic patients regardless of the degree of urine albumin excretion rate. Timed urinary collection can be a cumbersome procedure for patients and a simpler and fast test that maintains reasonable sensitivity is called for. A tool that is non-invasive and able to identify patients with early nephropathy changes would be valuable.
The skin has been found to have the potential to provide an important non-invasive route for diagnostic monitoring of human subjects for a wide range of applications. eZscan® technology is a patented active electrophysiological technology which uses low level DC-inducing reverse iontophoresis, together with chronoamperometry, to evaluate the behaviour of the tissues in specific locations of the body. This non invasive test is a potential tool for the screening for diabetic nephropathy.
The aim of this study is to compare eZscan with the standard methods of screening for diabetic nephropathy in patients with type 2 diabetes mellitus.
Full description
Patients with type 2 diabetes mellitus with and without diabetic nephropathy will be identified from clinical records and approached for their interest in participating in the study. Written informed consent will be obtained from patients who qualify according to the eligibility criteria and agree to join the study.
Inclusion criteria:
Exclusion criteria:
Primary endpoint:
The optimal eZscan unit to detect the presence of diabetic nephropathy as defined by eGFR and ACR using ROC analysis, sensitivity and specificity values.
Other analysis:
A prediction algorithm using age, sex, body mass index and eZcan score will be developed to predict eGFR as continuous and categorical variables using Cox regression analysis.
Students t test and analysis of variance will be used to compare the eZcan values between patients with and those without diabetic nephropathy with age and sex adjustment Frequency of adverse events will also be listed.
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Exclusion criteria
100 participants in 2 patient groups
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Data sourced from clinicaltrials.gov
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