Near-infrared VisionNVIMNCT04399811
Near-infrared Vision for Microcirculatory Status
An observational study of Near-infrared Vision and Machine Learning, run by Shanghai Zhongshan Hospital.
Trial summary
The investigators aimed to combine the image of near-infrared vision and machine learning method to evaluate the microcirculatory status of critical ill patients. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Near-infrared Vision, Machine Learning, Microcirculatory Status
- What is being tested?
- Near-infrared Vision Photograph
- How many people
- 2,000 participants
- Where
- No sites listed yet
- Study dates
- 17 May 2020to 31 Dec 2023
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: General critical ill patients who required evaluation of perfussion.
Who may be able to join
- Age≥18 years;
- Patients who were transfered to our ICU.
Who cannot join
- Abnormalities of lower limbs arteries
Trial locations and contacts
No sites are listed yet.
About this study
Official title: Machine Learning-Based Near-infrared Vision to Evaluate the Microcirculatory of Critical Ill Patients: A Prospective Observational Study
The investigators aimed to combine the image of near-infrared vision and machine learning method to evaluate the microcirculatory status of critical ill patients.
Read the detailed description
The heat distribution of body is determined by the circulatory status. The investigators plan to the near-infrared vision to collect heat distribution information of limbs. Then, the machine learning method will be performed to recognize the subtle differences between images. Due to lack of golden standard of microcirculatory status, indirect parameters (such as lactate clearance, capillary refill time) and clinical outcomes will be recorded to evaluate the performance of maching learning model.
- Being tested
- Near-infrared Vision Photograph
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Near-infrared Vision PhotographDiagnostic test
The near-infrared image will be recorded (by a infrared camera connected to a laptop) for each enrolled patient
What is being measured
The primary outcome is the main question the study is designed to answer.
Hospital mortality
The rate of patients who died during hospital stay.
Measured: From date of admission to our ICU until the date of hospital discharge or date of death from any cause, whichever came first, assessed up to 2 months.
Secondary outcomes (2)
Lacteta clearance rate
The percent change of lactate between lactates measured in two time points. This parameter was usually used to guide resuscitation in septic shock.
Measured: When the near-infrared image is taken for a patient, the blood gas analysis will be performed immediately to get the value of lactate. After 2-hours, another blood gas analysis will be conducted to get the second value of lactete.
Capillary refill time (CRT)
A noninvasive parameter of peripheral perfusion status. CRT was measured by applying firm pressure to the ventral surface of the right index finger distal phalanx with a glass microscope slide. The pressure was increased until the skin was blank and then maintained for 10 seconds. The time for return of the normal skin color was registered with a chronometer, and a refill time greater than 3 seconds was defined as abnormal.(Hernández 2019.JAMA)
Measured: When the near-infrared image is taken for a patient, the capillary refill time will be measured immediately.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 May 2020
- Study start17 May 2020 (estimated)
- Primary completion31 Dec 2023 (estimated)
- Study completion31 Dec 2023 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Near-infrared VisionStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 2,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Shanghai Zhongshan Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04399811
- Sponsor's protocol ID
- B2020-057R
- Acronym
- NVIM
- Status last verified
- May 2020
- First submitted
- 16 May 2020
- Last updated
- 22 May 2020
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Our institutional ethics commitee did nor approve to share data.
Study at a glance
From the registry record, last updated by the sponsor on 22 May 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Machine Learning-Based Near-infrared Vision to Evaluate the Microcirculatory of Critical Ill Patients: A Prospective Observational Study
- ClinicalTrials.gov ID
- NCT04399811
- Acronym / sponsor ID
- NVIM / B2020-057R
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 2,000 participants (estimated)
- Lead sponsor
- Shanghai Zhongshan HospitalUniversity, hospital or other
- Study start
- 17 May 2020 (estimated)
- Primary completion
- 31 Dec 2023 (estimated)
- Study completion
- 31 Dec 2023 (estimated)
- First posted
- 22 May 2020 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
The investigators aimed to combine the image of near-infrared vision and machine learning method to evaluate the microcirculatory status of critical ill patients.
Detailed description
The heat distribution of body is determined by the circulatory status. The investigators plan to the near-infrared vision to collect heat distribution information of limbs. Then, the machine learning method will be performed to recognize the subtle differences between images. Due to lack of golden standard of microcirculatory status, indirect parameters (such as lactate clearance, capillary refill time) and clinical outcomes will be recorded to evaluate the performance of maching learning model.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- General critical ill patients who required evaluation of perfussion.
Key inclusion criteria (2 listed)
- Age≥18 years;
- Patients who were transfered to our ICU.
Key exclusion criteria (1 listed)
- Abnormalities of lower limbs arteries
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Near-infrared Vision PhotographThe near-infrared image will be recorded (by a infrared camera connected to a laptop) for each enrolled patient | Diagnostic test | – |
Primary outcome
Hospital mortality
Time frame: From date of admission to our ICU until the date of hospital discharge or date of death from any cause, whichever came first, assessed up to 2 months.
The rate of patients who died during hospital stay.
2 secondary outcomes
Lacteta clearance rate
Time frame: When the near-infrared image is taken for a patient, the blood gas analysis will be performed immediately to get the value of lactate. After 2-hours, another blood gas analysis will be conducted to get the second value of lactete.
Capillary refill time (CRT)
Time frame: When the near-infrared image is taken for a patient, the capillary refill time will be measured immediately.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 22 May 2020.
- Individual participant dataSharing planned: No. Our institutional ethics commitee did nor approve to share data.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Questions about this trial
What is this clinical trial studying?
An observational study of Near-infrared Vision and Machine Learning, run by Shanghai Zhongshan Hospital.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 22 May 2020. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Zhe Luo (+8613916127028, luo.zhe@zs-hospital.sh.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Hospital mortality (time frame: From date of admission to our ICU until the date of hospital discharge or date of death from any cause, whichever came first, assessed up to 2 months.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 22 May 2020.