A Human Clinical Study to Collect Calibration and Performance Data for the RBA-1 and KBS-1
试验速览
- 阶段
- 不适用
- 入组人数
- 1,000
- 试验地点
- 1
- 主要终点
- Analyte validation
研究概览
简要总结
The purpose of the Kaligia Biosciences KBS Systems 1.0(b) analyte monitoring device is to measure the blood analyte levels in patients. The KBS Systems 1.0(b) device avoids the common practice of accessing the vein to draw blood for conventional laboratory analysis. Instead, the KBS Systems 1.0(b) device uses Raman Spectroscopy to acheive the measurement of various blood analytes through the use of only approximately 40µl of blood. Such a small volume of blood can be sampled via a finger prick procedure rather than needing a larger volume of blood sampled via a venipuncture. The spectra contain information of all the molecules present in the blood (RBCs, hemoglobin, glucose, sodium, potassium, etc.). From these spectra, the system is able to analyze the blood and provide results in a matter of minutes, rather than hours or even, in some cases, days.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Only
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Adults > 18 yrs of age.
- •Willingness and ability to provide informed consent
- •Hospital patients with a physician-prescribed laboratory blood test
排除标准
- •People with clotting factor disorders and/or currently taking anticoagulation medication.
- •Has any other medical condition that, in the opinion of the Investigator, would interfere with the person's participation in this study (i.e. double arm amputee).
- •Any skin abnormalities or tattoos located on the palm(s) of the hand(s). The left palm is preferred for this study; however, the right palm can be used if the left palm is excluded.
- •Parkinson's disease, dyskinesia, tremors, or similar that may make it challenging for said person to hold their hand on the device in a still and stable manner.
结局指标
主要结局
Analyte validation
时间窗: 6 months
CLIA laboratory analyte values will be compared to device analyte readings and inform machine learning of device software learning to achieve device calibration and validation of readings.
次要结局
未报告次要终点