To Assess the Accuracy of the eZscan Study in the Screening for Diabetic Nephropathy
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.
|Study Design:||Observational Model: Case Control
Time Perspective: Cross-Sectional
|Official Title:||A Phase II, Open-Label, Cross-Sectional, Study to Compare eZscan, With Standard Methods of Screening for Diabetic Nephropathy, As a Tool for Detection of Type 2 Diabetic Nephropathy|
- The optimal eZscan unit to detect the presence of diabetic nephropathy as defined by eGFR and ACR using ROC analysis, sensitivity and specificity values. [ Time Frame: 9 months ] [ Designated as safety issue: No ]
- 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. [ Time Frame: 9 months ] [ Designated as safety issue: No ]
|Study Start Date:||January 2009|
|Study Completion Date:||October 2009|
|Primary Completion Date:||October 2009 (Final data collection date for primary outcome measure)|
Patients with overt diabetic nephropathy as evidenced by ACR greater than or equal to 30mg/mmol on urinalysis and eGFR greater than or equal to 15ml/min/1.73m2 and less than 60ml/min/1.73m2
Patients without diabetic nephropathy as defined by the absence of albuminuria (defined by a random spot urinary ACR <2.5 mg/mmol in women or ACR<3.5 mg/mmol in men)and eGFR greater or equal to 90 ml/min/1.73m2
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.
- Male or female aged between 21 and 75 years (inclusive).
- Has confirmed type 2 diabetes mellitus
- With or without diabetic nephropathy based on recent complication screening
- Written informed consent given
- Has amputation of arm or leg
- Uses beta blockers or drugs known to affect the sympathetic nervous system
- Has an electrical implantable device (pacemaker, defibrillator)
- Known to have sensitivity to nickel or any other standard electrodes
- Sufferers from epilepsy or seizures
- Patients on renal replacement therapy
- Patients with chronic kidney disease due to known non-diabetes causes e.g. renal stone or obstructive uropathy.
- Patients confirmed to have urinary tract infection on the day of assessment.
The optimal eZscan unit to detect the presence of diabetic nephropathy as defined by eGFR and ACR using ROC analysis, sensitivity and specificity values.
- 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.
Please refer to this study by its ClinicalTrials.gov identifier: NCT01163591
|China, Hong Kong|
|Diabetes Mellitus and Endocrine Centre, Prince of Wales Hospital|
|Shatin, Hong Kong, China|
|Principal Investigator:||Risa Ozaki, MBChB, MRCP||Chinese University of Hong Kong|