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
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 100
- 试验地点
- 1
- 主要终点
- The optimal eZscan unit to detect the presence of diabetic nephropathy as defined by eGFR and ACR using ROC analysis, sensitivity and specificity values.
研究概览
简要总结
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.
详细描述
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:
- 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
Exclusion criteria:
- 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.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Control
- 时间视角
- Cross Sectional
入排标准
- 年龄范围
- 21 Years 至 75 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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
排除标准
- •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.
结局指标
主要结局
The optimal eZscan unit to detect the presence of diabetic nephropathy as defined by eGFR and ACR using ROC analysis, sensitivity and specificity values.
时间窗: 9 months
次要结局
- 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.(9 months)
研究者
Risa Ozaki
Honorary Clinical Associate Professor
Chinese University of Hong Kong