Liver TransplantationNCT06534840
Evaluation of Pulmonary Complications in Liver Transplantation Patients Based on Machine Learning
An observational study of Liver Transplantation, run by West China Hospital at 1 site in China.
Trial summary
The main objective of this study is to develop a machine learning model that predicts moderate-severe prediction model of pulmonary complications in liver transplantation patients within 14 postoperative day using a real-world, local preoperative and intraoperative electronic health records, not administrative codes. Read more about this study
- Who is it for?
- Ages 18 to 80 years
- Conditions the study is for
- Liver Transplantation
- How many people
- 400 participants
- Where
- 1 siteChina
- Study dates
- 15 Jul 2024to 15 Dec 2024
- How long will I take part?
- 14 Days
- 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 to 80 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients who underwent liver transplantation from January 1,2016to December 31，2019 were admitted to the study.
Who may be able to join
- Adult patients (age ≥ 18 years)
- Undergoing liver transplantation
Who cannot join
- Re-transplantation
- Multi-organ transplants
- Intra-operative deaths
- severe encephalopathy (West Haven criteria III or IV)
- Incomplete clinical data
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
West China Hospital, Sichuan University
Chengdu, Sichuan, 610041
Chunling Jiang, PhD, 85423593, jiang_chunling@yahoo.com
Yan Xu, PhD, 85423593, jiang_chunling@yahoo.com
About this study
Official title: Establishment and Evaluation of Moderate-severe Prediction Model of Pulmonary Complications in Liver Transplantation Patients Based on Machine Learning Algorithm
The main objective of this study is to develop a machine learning model that predicts moderate-severe prediction model of pulmonary complications in liver transplantation patients within 14 postoperative day using a real-world, local preoperative and intraoperative electronic health records, not administrative codes.
Read the detailed description
Postoperative pulmonary complications can increase the length of hospital stay and medical costs. In particular, moderate to severe pulmonary complications, which often require clinical intervention, once occur, will lead to significantly prolonged postoperative hospitalization or even cause permanent damage or death in severe cases. A number of risk-stratified cation models have been developed to identify patients at increased risk of postoperative pulmonary complications. However, these models were built by using the traditional regression analysis. However, the traditional prediction methods have the disadvantages of limited processing power of nonlinear models and outlier, and relatively single selection variables. The obtained models have poor accuracy, and the quantification degree is not enough, so it is difficult to popularize clinical application. Artificial machine learning can use it by analyzing a large number of specific features in the rich data set to identify and learn to accurately predict the diagnosis and prognosis of diseases, and surpass traditional prediction models in dealing with classification problems. The algorithms are flexible, and it is more and more widely used in clinical practice research. However, there are few reports on machine learning models predicting prognostic models related to postoperative pulmonary complications in liver transplantation patients. Therefore, we aimed to build predictive models using artificial machine learning methods to screen for their risk factors in order to provide early intervention and individualized treatment for high-risk patients.
- Conditions
- Liver Transplantation
What is being measured
The primary outcome is the main question the study is designed to answer.
Prediction of postoperative moderate-to-severe pulmonary complications
IA total of 72 variables are expected to be included, using 6 types of machine learning methods, including decision tree (DT), logistic regression (LR), random forest (, RF), support vector machine (SVM), extreme gradient lift (XGBoost), and gradient lift decision tree (GBDT) to build a moderate postoperative prediction model
Measured: August 2024-December 2024
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted2 Aug 2024
- Study start15 Jul 2024
- Primary completion15 Dec 2024 (estimated)
- Study completion15 Dec 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Liver TransplantationStudies of the same condition, open and under way.
- More trials run by West China HospitalThe 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
- Prospective
- Enrolment
- 400 participants (estimated)
- Target duration
- 14 Days
Sponsor and investigators
- Lead sponsor
- West China Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Chunling Jiang, West China Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06534840
- Sponsor's protocol ID
- 2024HX8193
- Status last verified
- Jul 2024
- First submitted
- 30 Jul 2024
- Last updated
- 2 Aug 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 2 Aug 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Establishment and Evaluation of Moderate-severe Prediction Model of Pulmonary Complications in Liver Transplantation Patients Based on Machine Learning Algorithm
- ClinicalTrials.gov ID
- NCT06534840
- Acronym / sponsor ID
- 2024HX8193
- Conditions
- Liver Transplantation
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 400 participants (estimated)
- Lead sponsor
- West China HospitalUniversity, hospital or other
- Study start
- 15 Jul 2024 (actual)
- Primary completion
- 15 Dec 2024 (estimated)
- Study completion
- 15 Dec 2024 (estimated)
- First posted
- 2 Aug 2024 (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 main objective of this study is to develop a machine learning model that predicts moderate-severe prediction model of pulmonary complications in liver transplantation patients within 14 postoperative day using a real-world, local preoperative and intraoperative electronic health records, not administrative codes.
Detailed description
Postoperative pulmonary complications can increase the length of hospital stay and medical costs. In particular, moderate to severe pulmonary complications, which often require clinical intervention, once occur, will lead to significantly prolonged postoperative hospitalization or even cause permanent damage or death in severe cases. A number of risk-stratified cation models have been developed to identify patients at increased risk of postoperative pulmonary complications. However, these models were built by using the traditional regression analysis. However, the traditional prediction methods have the disadvantages of limited processing power of nonlinear models and outlier, and relatively single selection variables. The obtained models have poor accuracy, and the quantification degree is not enough, so it is difficult to popularize clinical application. Artificial machine learning can use it by analyzing a large number of specific features in the rich data set to identify and learn to accurately predict the diagnosis and prognosis of diseases, and surpass traditional prediction models in dealing with classification problems. The algorithms are flexible, and it is more and more widely used in clinical practice research. However, there are few reports on machine learning models predicting prognostic models related to postoperative pulmonary complications in liver transplantation patients. Therefore, we aimed to build predictive models using artificial machine learning methods to screen for their risk factors in order to provide early intervention and individualized treatment for high-risk patients.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 14 Days
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients who underwent liver transplantation from January 1,2016to December 31，2019 were admitted to the study.
Key inclusion criteria (2 listed)
- Adult patients (age ≥ 18 years)
- Undergoing liver transplantation
Key exclusion criteria (5 listed)
- Re-transplantation
- Multi-organ transplants
- Intra-operative deaths
- severe encephalopathy (West Haven criteria III or IV)
- Incomplete clinical data
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Prediction of postoperative moderate-to-severe pulmonary complications
Time frame: August 2024-December 2024
IA total of 72 variables are expected to be included, using 6 types of machine learning methods, including decision tree (DT), logistic regression (LR), random forest (, RF), support vector machine (SVM), extreme gradient lift (XGBoost), and gradient lift decision tree (GBDT) to build a moderate postoperative prediction model
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 2 Aug 2024.
- Individual participant dataSharing planned: Undecided
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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.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Liver Transplantation, run by West China Hospital at 1 site in China.
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 5 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 2 Aug 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
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 Chun ling Jiang, PhD (+8602885423593, jiang_chunling@yahoo.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Prediction of postoperative moderate-to-severe pulmonary complications (time frame: August 2024-December 2024). 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 2 Aug 2024.