NCT06223204已完成不适用
GLEAM: Noninvasive Glucose Measurement Using Impedance Tomography - a Pilot Project
适应症
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 16
- 试验地点
- 1
- 主要终点
- Change of the electrical impedance tomography (EIT) signal of the thoracic region across the glycemic trajectory.
研究概览
简要总结
The GLEAM study aims at assessing the potential of electrical impedance tomography (EIT) for noninvasive glucose measurement.
详细描述
Within the GLEAM study, paired samples of EIT and blood glucose measurements will be collected in individuals with type 1 diabetes during standardized euglycemia, hypoglycemia and hyperglycemia. These samples will be used to assess the potential of EIT for noninvasive glucose measurement and/or dysglycemia detection.
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Other
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 60 Years（Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Written, informed consent
- •Type 1 Diabetes mellitus as defined by WHO for at least 6 months
- •Aged 18 - 60 years
- •HbA1c ≤ 9.0 %
- •Insulin treatment with good knowledge of insulin self-management
- •Use of a continuous (CGM) or flash glucose monitoring system (FGM)
- •Native language German or Swiss German
排除标准
- •Incapacity to give informed consent
- •Contraindications to insulin aspart (NovoRapid®)
- •Known allergies to adhesives of the EIT device (e.g., gel electrodes)
- •Pregnancy, breast-feeding or lack of safe contraception
- •Active heart, lung, liver, gastrointestinal, renal or psychiatric disease
- •Patients with implantable electronic devices (e.g., pacemaker or implantable cardioverter defibrillator (ICD)) or thoracic metal implants
- •Epilepsy or history of seizure
- •Active drug or alcohol abuse
- •Chronic neurological or ear-nose-and-throat (ENT) disease influencing voice or history of voice disorder
- •Thoracic or back deformities
- •Body mass index (BMI) >35.0 kg/m2
- •Open wounds, burns, or rashes on the upper thorax
- •Active smoking
- •Medication known to interfere with voice or to induce listlessness (e.g., opioids, benzodiazepines, etc.)
结局指标
主要结局
Change of the electrical impedance tomography (EIT) signal of the thoracic region across the glycemic trajectory.
时间窗: 5 hours
EIT signals will be collected at multiple frequencies between 50 kHz and 1 MHz from the thoracic region in euglycemia, hypoglycemia and hyperglycemia using a multi-channel EIT measurement device.
次要结局
- Change of hypoglycemia symptoms across the glycemic trajectory.(5 hours)
- Performance of a machine learning model to detect dysglycemia from the above-mentioned signals (EIT, symptoms, voice, physiological signals) quantified as sensitivity.(5 hours)
- Voice parameters indicative of dysglycemia(5 hours)
- Performance of a machine learning model to detect dysglycemia from the above-mentioned signals (EIT, symptoms, voice, physiological signals) quantified as area under the receiver operating characteristics curve (AUROC).(5 hours)
- Performance of a machine learning model to detect dysglycemia from the above-mentioned signals (EIT, symptoms, voice, physiological signals) quantified as specificity.(5 hours)
- Performance of the machine learning model to predict glucose values from the above-mentioned signals (EIT, symptoms, voice, physiological signals) quantified as root mean squared error (RMSE).(5 hours)
- Performance of the machine learning model to predict glucose values from the above-mentioned signals (EIT, symptoms, voice, physiological signals) quantified as mean absolute relative difference (MARD).(5 hours)
- Performance of the machine learning model to predict glucose values from the above-mentioned signals (EIT, symptoms, voice, physiological signals) using Bland-Altman plots.(5 hours)
- Performance of the machine learning model to predict glucose values from the above-mentioned signals (EIT, symptoms, voice, physiological signals) using the Clarke Error Grid.(5 hours)
- Change in cognitive performance across the glycemic trajectory.(5 hours)
研究者
研究点 (1)
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