COntinuous Signs Monitoring In Covid-19 Patients
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 48
- 试验地点
- 2
- 主要终点
- Development of an AI model to predict clinically relevant outcomes for ward-based patients with COVID-19 monitored for up to 20 days. Metrics to be employed depend on the algorithm used but include, Log-Loss, precision and/or recall and confusion matrix.
研究概览
简要总结
This is a pilot study to assess whether artificial intelligence (AI) combined with continuous vital signs monitoring from wearable sensors can predict clinically relevant outcomes in patients with suspected or confirmed Covid-19 infection on general medical wards.
详细描述
Adult patients on general medical wards with COVID-19 infection considered to be at high risk of deterioration will be asked to wear vital signs sensors for the duration of their hospital stay. These sensors are an established method of recording patient vital signs and are CE marked. Patients enrolled in the study will continue to receive routine medical care as directed by their treating team.
All data recorded from the wearable sensors in this study will be analysed in conjunction with routine data collected during the patient's treatment. Several models will be created using deep learning AI techniques with the aim of reliably predicting several important clinical outcomes. The study will identify whether continuous monitoring alone can improve identification of deteriorating patients compared to traditional vital signs and if the addition of AI technology / algorithms can provide even earlier identification.
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
盲法说明
The treating team on the ward will be blinded to the observations recorded by the wearable vital signs sensors
入排标准
- 年龄范围
- 16 Years 至 —（Child, Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Participants are eligible to be included in the study only if all of the following criteria apply:
- •Adult (aged 16 years or older), hospital inpatients
- •Suspected or confirmed COVID-19 infection (nasopharyngeal swab sent or planned):
- •Positive nasopharyngeal swab during this admission OR
- •Nasopharyngeal swab pending during this admission and the treating team suspect COVID-19 OR
- •Negative nasopharyngeal swab during this admission but the treating team continue to suspect COVID-19 OR
- •Positive nasopharyngeal swab in the last 7 days
- •Emergency admission to hospital within the last 72 hours and/or a positive nasopharyngeal test within the last 72 hours taken from a patient who was already an inpatient at the time the swab was taken.
- •Symptoms consistent with COVID-19 infection at the time of admission or when swab taken: cough, shortness of breath, alteration to sense of taste or smell, fevers or other symptoms in keeping with COVID-19 in the opinion of the study team.
- •For full active treatment (including escalation to critical care)
- •The patient is at risk of deterioration (as evidenced by a requirement for supplementary oxygen)
排除标准
- •Participants are excluded from the study if any of the following criteria apply:
- •Patients unable to give informed consent.
- •Patients with a life expectancy of <24hours.
- •Known allergy or history of contact dermatitis to medical adhesives.
- •Patients with pacemakers, implantable defibrillators or neurostimulators.
- •Patients with an arterio-venous fistula in either arm.
研究组 & 干预措施
Wearable monitors - Isansys Patient Status Engine
All patients will wear the continuous vital sign monitoring sensors.
干预措施: Continuous vital sign monitoring - Isansys Patient Status Engine (Device)
Wearable monitors - Isansys Patient Status Engine
All patients will wear the continuous vital sign monitoring sensors.
干预措施: Machine Learning/AI Algorithm (Other)
结局指标
主要结局
Development of an AI model to predict clinically relevant outcomes for ward-based patients with COVID-19 monitored for up to 20 days. Metrics to be employed depend on the algorithm used but include, Log-Loss, precision and/or recall and confusion matrix.
时间窗: 1 year
次要结局
- Performance of the wearable vital signs sensor as measured by the percentage of possible data capture that is actually obtained(1 year)
- Look for evidence of circadian disruption in the vital signs of the enrolled patients.(1 year)