COVID-19 Advanced Respiratory Physiology (CARP) Study
试验速览
- 阶段
- 不适用
- 入组人数
- 150
- 试验地点
- 1
- 主要终点
- Machine-learning model development
研究概览
简要总结
The anticipated second wave of COVID-19 cases will present healthcare system challenges, including requirement to monitor large numbers of patients for deteriorating respiratory failure. Rising respiratory rate can identify deterioration requiring escalation of care. However constant monitoring of respiratory rate can be challenging outwith critical care units due to feasibility and inaccuracy of intermittent measurements.
Wearable biosensors which allows for remote patient monitoring of RR is therefore attractive, particularly when combined in a dashboard with clinical summary data. This would establish source data and infrastructure for the training and validation of machine-learning models, with decision support risk-predictions prioritising alerts and clinician reviews.
详细描述
Altair medical has developed a pre-commercial investigational wearable (chest-worn) biosensor which can measure continuous respiratory rate and respiratory events. This sensor has been verified to have good correlation with reference impedance plethysmography data.
Inclusion criteria:
All inpatients in the QEUH with respiratory failure from any cause.
Exclusion criteria:
Lack of capacity to consent
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Inclusion criteria
- •Adult patients with respiratory failure of any cause requiring hospital admission, oxygen therapy and routine NEWS-2 physiology observations.
- •CARP detailed sub-study Adult patients with respiratory failure who are suitable for opportunistically acquired serial detailed physiology measurements taken alongside routine clinical care by study team.
- •CARP follow-up remote-monitoring sub-study Adult patients with respiratory failure who have provided informed consent and have a smartphone to connect to Fitbit and Lenus accounts for postdischarge wearable device data capture.
排除标准
- •Exclusion criteria • Lack of capacity or inability to comprehend informed consent.
结局指标
主要结局
Machine-learning model development
时间窗: 1 year
Developing risk-predictions for clinically significant deteriorations
次要结局
未报告次要终点