PhenotypingNCT07085208
Derivation and Validation of Hemodynamic Phenotypes of Cardiac Surgery
An observational study of Phenotyping and Machine Learning, run by Nanjing First Hospital, Nanjing Medical University.
Trial summary
Background \& Objective: Cardiac surgery patients differ significantly in their health conditions and how they react during operations. Standard risk assessments before surgery often miss the real-time changes happening inside a patient's body during the procedure, which can affect their recovery. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Phenotyping, Machine Learning, Cardiac Surgery
- What is being tested?
- Unsupervised Machine Learning for Clinical Phenotyping
- Type of treatment
- Surgery or procedure
- How many people
- 10,847 participants
- Where
- No sites listed yet
- Study dates
- 1 Apr 2016to 31 Dec 2024
- 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
- Adult, Older adult
Study population: All patients aged 18 years or older who underwent cardiac surgery with CPB were identified from each database.
Who may be able to join
- Patients aged 18 years or older
- Patients who underwent cardiac surgery with cardiopulmonary bypass
Who cannot join
- Incomplete information on surgical procedures,
- With History of prior cardiac surgery or underwent second surgery during the same hospitalization
- Insufficient valid perioperative vital sign monitoring data
About this study
Official title: Derivation and Verification of Hemodynamic Clinical Subphenotypes in Patients Undergoing Cardiac Surgery Under Unsupervised Machine Learning
Background \& Objective:
Cardiac surgery patients differ significantly in their health conditions and how they react during operations. Standard risk assessments before surgery often miss the real-time changes happening inside a patient's body during the procedure, which can affect their recovery. Therefore, researchers conducted this study to find different groups (phenotypes) of patients who face varying risks for poor outcomes. They did this by using advanced computer learning techniques to analyze a lot of detailed health information collected both before and during surgery.
Read more
Methods:
This was a study that looked back at patient records from several hospitals. Researchers gathered a large amount of patient information from before surgery, including their basic health details and lab results. They also collected very detailed measurements of patients' vital signs taken during surgery, noting how these changed over time. Then, a computer program that can find patterns without being told what to look for (unsupervised hierarchical clustering) was used to sort patients into distinct groups based on this combined data.
Clinical Relevance:
This study expects to show that using data to identify patient groups can reveal differences that traditional methods miss. These new patient groups, which are based on how their blood flow and vital signs behave, offer a new way to understand risks in real-time. This could help doctors to predict problems more accurately and create personalized care plans for each patient around the time of surgery, which has great potential for practical use in hospitals.
- Conditions
- Phenotyping, Machine Learning, Cardiac Surgery, Hemodynamic Parameters
- Being tested
- Unsupervised Machine Learning for Clinical Phenotyping
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Unsupervised Machine Learning for Clinical PhenotypingProcedure or surgery
This is a data-driven study that uses an unsupervised machine learning algorithm to perform clustering on patient multimodal features. These features include: preoperative demographics, comorbidities, and laboratory data; surgical information; and high-resolution intraoperative data, most notably continuous vital sign trajectories.
What is being measured
The primary outcome is the main question the study is designed to answer.
Acute organ dysfunction
including postoparative acute liver failure and acute kidney injury (up to 7 days postoperative), and acute kidney disease(up to 90 days postoperative)
Measured: Within 7 days post-surgery for acute liver failure and acute kidney inkury, and 90 days for postoperative acute kidney disease
Secondary outcomes (2)
Total LOS and ICU-LOS
Length of hospital stay and length of ICU stay
Measured: up to 90 days post-surgery
In-hospital mortality
All-cause in-hospital mortality
Measured: up to 90 days postoperative, from the end of surgery until patient discharge
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted25 Jul 2025
- Study start1 Apr 2016
- Primary completion31 Aug 2024
- Study completion31 Dec 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for PhenotypingStudies of the same condition, open and under way.
- More trials run by Nanjing First Hospital, Nanjing Medical UniversityThe sponsor's other registered studies.
- 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
- Retrospective
- Enrolment
- 10,847 participants (actual)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07085208
- Sponsor's protocol ID
- KY20250224-KS-04
- Status last verified
- Feb 2025
- First submitted
- 15 Jul 2025
- Last updated
- 25 Jul 2025
- Results posted
- No
Study at a glance
From the registry record, last updated by the sponsor on 25 Jul 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Derivation and Verification of Hemodynamic Clinical Subphenotypes in Patients Undergoing Cardiac Surgery Under Unsupervised Machine Learning
- ClinicalTrials.gov ID
- NCT07085208
- Acronym / sponsor ID
- KY20250224-KS-04
- Conditions
- Phenotyping, Machine Learning, Cardiac Surgery, Hemodynamic Parameters
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 10,847 participants (actual)
- Lead sponsor
- Nanjing First Hospital, Nanjing Medical UniversityUniversity, hospital or other
- Study start
- 1 Apr 2016 (actual)
- Primary completion
- 31 Aug 2024 (actual)
- Study completion
- 31 Dec 2024 (actual)
- First posted
- 25 Jul 2025 (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.
Background \& Objective:
Cardiac surgery patients differ significantly in their health conditions and how they react during operations. Standard risk assessments before surgery often miss the real-time changes happening inside a patient's body during the procedure, which can affect their recovery. Therefore, researchers conducted this study to find different groups (phenotypes) of patients who face varying risks for poor outcomes. They did this by using advanced computer learning techniques to analyze a lot of detailed health information collected both before and during surgery.
Methods:
This was a study that looked back at patient records from several hospitals. Researchers gathered a large amount of patient information from before surgery, including their basic health details and lab results. They also collected very detailed measurements of patients' vital signs taken during surgery, noting how these changed over time. Then, a computer program that can find patterns without being told what to look for (unsupervised hierarchical clustering) was used to sort patients into distinct groups based on this combined data.
Clinical Relevance:
This study expects to show that using data to identify patient groups can reveal differences that traditional methods miss. These new patient groups, which are based on how their blood flow and vital signs behave, offer a new way to understand risks in real-time. This could help doctors to predict problems more accurately and create personalized care plans for each patient around the time of surgery, which has great potential for practical use in hospitals.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- All patients aged 18 years or older who underwent cardiac surgery with CPB were identified from each database.
Key inclusion criteria (2 listed)
- Patients aged 18 years or older
- Patients who underwent cardiac surgery with cardiopulmonary bypass
Key exclusion criteria (3 listed)
- Incomplete information on surgical procedures,
- With History of prior cardiac surgery or underwent second surgery during the same hospitalization
- Insufficient valid perioperative vital sign monitoring data
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 |
|---|---|---|
| Unsupervised Machine Learning for Clinical PhenotypingThis is a data-driven study that uses an unsupervised machine learning algorithm to perform clustering on patient multimodal features. These features include: preoperative demographics, comorbidities, and laboratory data; surgical information; and high-resolution intraoperative data, most notably continuous vital sign trajectories. | Procedure or surgery | – |
Primary outcome
Acute organ dysfunction
Time frame: Within 7 days post-surgery for acute liver failure and acute kidney inkury, and 90 days for postoperative acute kidney disease
including postoparative acute liver failure and acute kidney injury (up to 7 days postoperative), and acute kidney disease(up to 90 days postoperative)
2 secondary outcomes
Total LOS and ICU-LOS
Time frame: up to 90 days post-surgery
In-hospital mortality
Time frame: up to 90 days postoperative, from the end of surgery until patient discharge
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 25 Jul 2025.
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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.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
An observational study of Phenotyping and Machine Learning, run by Nanjing First Hospital, Nanjing Medical University.
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 3 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: finished, as of the sponsor's last update on 25 Jul 2025. 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Acute organ dysfunction (time frame: Within 7 days post-surgery for acute liver failure and acute kidney inkury, and 90 days for postoperative acute kidney disease). 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 25 Jul 2025.