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Automated Apnoea Detection in Preterms on Non-invasive Ventilation

2026年8月25日 更新者:King's College Hospital NHS Trust

Prospective Observational Study of Automated Apnoea Detection in Preterm Infants Receiving Non-invasive Respiratory Support

The aim of this study is to monitor the frequency of apnoeas (pauses in breathing) on various methods of non-invasive respiratory support that are detected by an automated machine-learning (ML) model based on diaphragmatic electromyography (dEMG), in infants born at less than 32 weeks of gestation.

Our hypothesis is that the ML algorithm will improve identification of apnoeic episodes and their classification to central or obstructive.

The study will measure outcomes including the number of apnoeic episodes during the monitoring period, their classification to central and obstructive apnoeas and the predictive ability of the machine-learning algorithm to correctly identify and classify these episodes compared to those documented in nursing charts. Correct classification of apnoeic episodes may help identify underlying causes that require specific intervention.

研究概览

地位

尚未招聘

条件

详细说明

When the clinical team identifies an infant is eligible for enrolment to the study and following verbal assent of the attending neonatal consultant, a member of clinical staff will initially approach the parents/ legal guardians of eligible infants and if they agree, a researcher. The parents will be provided with an information sheet about the study. The researchers will answer questions and respond to any concerns in a face-to-face meeting. Written informed consent will be obtained.

Electrical activity of the diaphragm, airway pressure, flow and peripheral oxygen saturation levels will be recorded for a duration of eight hours. Transcutaneous diaphragm EMG (sEMG) will be monitored using three surface electrodes (3M Red Dot Foam monitoring electrode 2228, 3M, United Kingdom) that are placed on the infant's abdomen and sternum. The electrodes are connected to a small battery-operated measuring device (SERA, DEMCON; Makawi Medical Systems, the Netherlands) that amplifies and pre-processes the signals received from the electrodes. The pre-processed signals are sent via a Bluetooth connection to a receiving unit that performs higher level processing to derive the EMG signal and other measurements. These results are communicated via a wired connection to a bedside computer running SERA Graphical User Interface (GUI) software.

Airway pressure and flow signals will be measured by a flow sensor and pressure tube (Sensirion AG, Stäfa, Switzerland) that will be placed between the exit of the SLE6000/ SLE6000N ventilators (Inspiration Healthcare, Croydon, UK) and the tubing that is attached to the patient. This will ensure pressure and flow data are recorded simultaneously with the sEMG signal. These data will also be fed to the SERA measuring device.

An SpO2 cable (SLE uSpO2, Inspiration Healthcare, Croydon, UK) will be connected to the patient and the ventilator for continuous (second by second) recording of SpO2 levels. These data will be time synced using post processing.

Participants will also be connected to the standard bedside monitor (Phillips Intellivue MX750) for the whole duration of the study.

Surface EMG, pressure, flow and SpO2 data will be synchronised. Two researchers will identify all apnoeic episodes and classify them as central, obstructive or noise.

The ML algorithm will also be used to identify and classify apnoeic episodes that occurred during the study duration.

Comparisons will be made between the apnoeic episodes identified the researchers, the ML algorithm and the nursing staff electronic patient records. These records include classification of an episode as apnoea or desaturation or bradycardia, its duration and any actions taken.

研究类型

观察性的

注册 (估计的)

30

联系人和位置

本节提供了进行研究的人员的详细联系信息,以及有关进行该研究的地点的信息。

学习联系方式

参与标准

研究人员寻找符合特定描述的人,称为资格标准。这些标准的一些例子是一个人的一般健康状况或先前的治疗。

资格标准

适合学习的年龄

  • 孩子

接受健康志愿者

不

取样方法

非概率样本

研究人群

Infants admitted and cared for at the Neonatal Intensive Care Unit at King's College Hospital

描述

Inclusion Criteria:

Preterm infants <32 weeks of gestation at birth and up to 36 weeks postmenstrual age, on non-invasive respiratory support including:

  • non-invasive positive pressure ventilation (NIPPV)
  • nasal continuous positive airway pressure (CPAP)
  • heated humidified high flow nasal cannula (HHFNC) oxygen, either as primary or post extubation respiratory support.

Exclusion Criteria:

  • Infants born above 32 weeks of gestation.
  • Infants with known major congenital abnormalities.
  • Infants above 36 weeks postmenstrual age (PMA).
  • Non-English speakers.

学习计划

本节提供研究计划的详细信息,包括研究的设计方式和研究的衡量标准。

研究是如何设计的?

设计细节

研究衡量的是什么?

主要结果指标

结果测量
大体时间
Number of apnoeic episodes correctly identified by the automated machine learning model
大体时间:From enrollment to the end of monitoring at eight hours
From enrollment to the end of monitoring at eight hours

次要结果测量

结果测量
大体时间
The proportion of apnoeas correctly classified as central or obstructive by the automated machine learning model
大体时间:From enrollment to the end of monitoring at eight hours
From enrollment to the end of monitoring at eight hours

合作者和调查者

在这里您可以找到参与这项研究的人员和组织。

调查人员

  • 首席研究员:Anne Greenough, Professor、King's College Hospital NHS Trust

研究记录日期

这些日期跟踪向 ClinicalTrials.gov 提交研究记录和摘要结果的进度。研究记录和报告的结果由国家医学图书馆 (NLM) 审查,以确保它们在发布到公共网站之前符合特定的质量控制标准。

研究主要日期

学习开始 (估计的)

2026年9月7日

初级完成 (估计的)

2027年7月27日

研究完成 (估计的)

2027年7月27日

研究注册日期

首次提交

2026年8月25日

首先提交符合 QC 标准的

2026年8月25日

首次发布 (实际的)

2026年8月31日

研究记录更新

最后更新发布 (实际的)

2026年8月31日

上次提交的符合 QC 标准的更新

2026年8月25日

最后验证

2026年8月1日

更多信息

与本研究相关的术语

计划个人参与者数据 (IPD)

计划共享个人参与者数据 (IPD)?

不

药物和器械信息、研究文件

研究美国 FDA 监管的药品

不

研究美国 FDA 监管的设备产品

不

此信息直接从 clinicaltrials.gov 网站检索,没有任何更改。如果您有任何更改、删除或更新研究详细信息的请求,请联系 register@clinicaltrials.gov. clinicaltrials.gov 上实施更改,我们的网站上也会自动更新.

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