Plain English Summary
Background and study aims
Invasive assisted mechanical ventilation (MV) provides primary life support to the patients without the ability to breathe in the intensive care unit (ICU). A mismatch between the MV and the breathing rate controlled by the brain of the patients results in patient-ventilator asynchrony (PVA), which will lead to a series of adverse clinical outcomes. Automatic detection of PVA is highly necessary for monitoring its occurrence in clinic.
Who can participate?
Patients aged above 18 years, admitted to intensive care unit and on invasive mechanical ventilation
What does the study involve?
From enrolment to the end of their participation, we will pay attention to the incidence of patient-ventilator asynchrony during invasive mechanical ventilation and the outcome when they leave ICU. The total duration of observation is the mechanical ventilation time in ICU and the total duration of follow-up are 28 days.
What are the possible benefits and risks of participating?
Improper mechanical respiratory assistance can lead to a series of poor clinical outcomes. Based on the ventilator waveform monitoring, patient-ventilator asynchrony can be further understood on the prognosis of patients, providing a basis for the understanding and treatment of improper mechanical respiratory assistance in clinical practice. Because this is an observational trial，we will not consider the risks during our observation.
Where is the study run from?
Sir Run Run Shaw Hospital affiliated to medical college of ZheJiang University, China
When is the study starting and how long is it expected to run for?
Nov ember 2019 to November 2020
Who is funding the study?
Sir Run Run Shaw Hospital, school of medicine, Zhejiang University, China
Who is the main contact?
Dr Huiqing Ge
gehq@zju.edu.cn
Jie Ding
2233702918@qq.com
Trial website
Contact information
Type
Scientific
Primary contact
Dr Huiqing Ge
ORCID ID
Contact details
3 QingChun East Road
Jianggan district
HangZhou
310016
China
+86 13588706787
gehq@zju.edu.cn
Type
Scientific
Additional contact
Miss Jie Ding
ORCID ID
Contact details
3 QingChun East Road
Jianggan district
HangZhou
310016
China
+86 18268113280
2233702918@qq.com
Additional identifiers
EudraCT number
Nil known
ClinicalTrials.gov number
Nil known
Protocol/serial number
20190916-16
Study information
Scientific title
The influence of the incidence of patient-ventilator asynchrony on the prognosis of mechanical ventilated patients—analysis based on the deep learning results of ventilator waveforms
Acronym
PVA
Study hypothesis
Mismatch between the MV and the need of the patients results in patient ventilator asynchrony (PVA), which will lead to a series of adverse clinical outcomes. So our observational trial is to learn more about the effects of the incidence of patient-ventilator asynchrony on the prognosis of invasive mechanical ventilated patients
Ethics approval
Approved 16/09/2019, Ethics committee of Sir Run Run Shaw Hospital of Zhejiang University (Zhejiang 310016, China; +86 571 86006811; 594961420@qq.com), ref: 20190916-16
Study design
Cross-sectional cohort study
Primary study design
Observational
Secondary study design
Cross sectional study
Trial setting
Hospitals
Trial type
Other
Patient information sheet
Not available in web format, please use the contact details to request a patient information sheet
Condition
Patients with invasive mechanical ventilation
Intervention
From enrolment to the end of their participation, we will pay attention to the incidence of patient-ventilator asynchrony during invasive mechanical ventilation and the outcome when they leave ICU. The total duration of observation is the mechanical ventilation time in ICU and the total duration of follow-up are 28 days.
Ventilator waveforms will be collected from the adults (> 18 years old) in the ICUs. Data annotation will be performed on a manually screened dataset rather than on the whole one. A self-developed software is used for annotating the waveforms in the screened dataset. We propose a 2-layer long short-term memory (LSTM) network to detect two common types of PVA – double triggering (DT) and ineffective inspiratory effort during expiration (IEE).The labels of “DT”, “IEE”, and “Others” will be given to each breath.
Intervention type
Other
Phase
Drug names
Primary outcome measure
The incidence of PVA during the mechanical ventilation time
Secondary outcome measures
Analysis of the related factors of PVA incidence:
1. Practice models and patterns of mechanical ventilation in patients during the mechanical ventilation time
2. Mechanical ventilation time
3. Mortality at 28 days
Overall trial start date
02/09/2019
Overall trial end date
15/11/2020
Reason abandoned (if study stopped)
Eligibility
Participant inclusion criteria
1. Age above 18 years
2. Admitted to the intensive care unit and on invasive mechanical ventilation
Participant type
Patient
Age group
Adult
Gender
Both
Target number of participants
at least 300
Participant exclusion criteria
Does not meet inclusion criteria
Recruitment start date
15/11/2019
Recruitment end date
15/11/2020
Locations
Countries of recruitment
China
Trial participating centre
Sir Run Run Shaw Hospital affiliated to medical college of ZheJiang University
3 QingChun East Road
Jianggan district
HangZhou
310000
China
Sponsor information
Organisation
Sir Run Run Shaw Hospital, school of medicine, Zhejiang university
Sponsor details
3 QingChun East Road
Jianggan district
HangZhou
310016
China
+86 571 86006811
594961420@qq.com
Sponsor type
Hospital/treatment centre
Website
Funders
Funder type
University/education
Funder name
Sir Run Run Shaw Hospital, school of medicine, Zhejiang university
Alternative name(s)
Funding Body Type
Funding Body Subtype
Location
Results and Publications
Publication and dissemination plan
Planned publication in a high-impact peer-reviewed journal.
IPD sharing statement:
The current data sharing plans for this study are unknown and will be available at a later date
Intention to publish date
15/11/2021
Participant level data
To be made available at a later date
Basic results (scientific)
Publication list
2020 results in https://pubmed.ncbi.nlm.nih.gov/32250853/ (added 05/11/2020)