AKINCT06596798
Deep Learning Model and Risk Factors for Tacrolimus-related Acute Kidney Injury
An observational study of AKI, run by Qianfoshan Hospital at 1 site in China.
Trial summary
In this study, the investigators aim to develop a risk prediction model for acute kidney injury (AKI) in hospitalized patients using the calcineurin inhibitor tacrolimus. This will be achieved by mining electronic medical record data and employing explainable deep learning methods. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- AKI
- How many people
- 1,200 participants
- Where
- 1 siteChina
- Study dates
- 1 Sep 2024to 30 Dec 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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
- Healthy volunteers accepted
- Adult, Older adult
Study population: This project is a multicenter study involving hospitalized patients who received tacrolimus treatment and underwent therapeutic drug monitoring at three medical centers from January 2020 to December 2023: Shandong University First Affiliated Hospital, Binzhou Medical University Affiliated Hospital, and Jinan First People\'s Hospital. The diagnosis and staging of acute kidney injury (AKI) in this study follow the relevant diagnostic criteria outlined in the 2012 KDIGO Clinical Practice Guidelines for AKI. AKI is defined by meeting at least one of the following conditions: (1) an increase in serum creatinine (SCr) \> 26.5 mmol/L (\>0.3 mg/dL) within 48 hours, or an increase in SCr to \>1.5 to 1.9 times the baseline value within a continuous 7-day period; (2) a urine output of \<0.5 mL/(kg·h) for 6 to 12 hours.
Who may be able to join
- Use of tacrolimus during hospitalization, with standardized therapeutic drug monitoring
- Age of 18 years or older at the time of admission
- Length of hospital stay ≥ hours
- At least two serum creatinine level tests conducted during the hospital stay
Who cannot join
- Stage 5 chronic kidney disease prior to admission
- Incomplete clinical data
- Serum creatinine levels consistently below 40 mmol/L during hospitalization
Trial locations and contacts
1 site in 1 country.
China1 site
The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital
Jinan, Shandong, 250014
About this study
Official title: Research on the Risk Warning Model and Prevention Strategies for Acute Kidney Injury Associated With Tacrolimus Based on Explainable Deep Neural Networks and Therapeutic Drug Monitoring
In this study, the investigators aim to develop a risk prediction model for acute kidney injury (AKI) in hospitalized patients using the calcineurin inhibitor tacrolimus. This will be achieved by mining electronic medical record data and employing explainable deep learning methods. The model will provide clinical decision support for timely intervention and treatment. Compared to traditional machine learning models, deep neural networks can extract more nuanced features from complex medical data and perform more precise pattern recognition, thereby enhancing prediction accuracy and reliability. By constructing a predictive tool based on explainable deep learning models, the investigators will better assess the association between the use of calcineurin inhibitors and AKI, explore targeted prevention strategies, and offer more precise predictions and intervention guidance to clinicians. Additionally, this research has significant socio-economic benefits and application potential. By reducing the incidence of AKI, the investigators can lower patient hospitalization duration and re-treatment costs, conserve medical resources, and improve patient quality of life. Preventive healthcare not only alleviates the physical and psychological burden on patients but also reduces the strain on the healthcare system, enhances healthcare efficiency, and promotes the rational allocation of medical resources.
- Conditions
- AKI
- Keywords
- Acute kidney injury, Therapeutic Drug Monitoring, deep learning
- Medical subject headings
- Acute Kidney Injury
What is being measured
The primary outcome is the main question the study is designed to answer.
AKI
Acute kidney injury occurred after the patient took tacrolimus during hospitalization
Measured: From January 2020 to December 2023
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Sep 2024
- Study start1 Sep 2024
- Primary completion1 Sep 2026 (estimated)
- Study completion30 Dec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for AKIStudies of the same condition, open and under way.
- More trials run by Qianfoshan 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
- Other
- Time perspective
- Retrospective
- Enrolment
- 1,200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Qianfoshan Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Xiao Li，MD, Qianfoshan Hospital
- Investigators
- Xiao Lii, Principal investigator, Qianfoshan Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06596798
- Sponsor's protocol ID
- LCYX-LX-20240102
- Status last verified
- Sep 2024
- First submitted
- 8 Sep 2024
- Last updated
- 19 Sep 2024
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 19 Sep 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Research on the Risk Warning Model and Prevention Strategies for Acute Kidney Injury Associated With Tacrolimus Based on Explainable Deep Neural Networks and Therapeutic Drug Monitoring
- ClinicalTrials.gov ID
- NCT06596798
- Acronym / sponsor ID
- LCYX-LX-20240102
- Conditions
- AKI
- Keywords
- Acute kidney injury, Therapeutic Drug Monitoring, deep learning
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 1,200 participants (estimated)
- Lead sponsor
- Qianfoshan HospitalUniversity, hospital or other
- Study start
- 1 Sep 2024 (actual)
- Primary completion
- 1 Sep 2026 (estimated)
- Study completion
- 30 Dec 2026 (estimated)
- First posted
- 19 Sep 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.
In this study, the investigators aim to develop a risk prediction model for acute kidney injury (AKI) in hospitalized patients using the calcineurin inhibitor tacrolimus. This will be achieved by mining electronic medical record data and employing explainable deep learning methods. The model will provide clinical decision support for timely intervention and treatment. Compared to traditional machine learning models, deep neural networks can extract more nuanced features from complex medical data and perform more precise pattern recognition, thereby enhancing prediction accuracy and reliability. By constructing a predictive tool based on explainable deep learning models, the investigators will better assess the association between the use of calcineurin inhibitors and AKI, explore targeted prevention strategies, and offer more precise predictions and intervention guidance to clinicians. Additionally, this research has significant socio-economic benefits and application potential. By reducing the incidence of AKI, the investigators can lower patient hospitalization duration and re-treatment costs, conserve medical resources, and improve patient quality of life. Preventive healthcare not only alleviates the physical and psychological burden on patients but also reduces the strain on the healthcare system, enhances healthcare efficiency, and promotes the rational allocation of medical resources.
Study design
- Observational model
- Other
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- This project is a multicenter study involving hospitalized patients who received tacrolimus treatment and underwent therapeutic drug monitoring at three medical centers from January 2020 to December 2023: Shandong University First Affiliated Hospital, Binzhou Medical University Affiliated Hospital, and Jinan First People\'s Hospital. The diagnosis and staging of acute kidney injury (AKI) in this study follow the relevant diagnostic criteria outlined in the 2012 KDIGO Clinical Practice Guidelines for AKI. AKI is defined by meeting at least one of the following conditions: (1) an increase in serum creatinine (SCr) \> 26.5 mmol/L (\>0.3 mg/dL) within 48 hours, or an increase in SCr to \>1.5 to 1.9 times the baseline value within a continuous 7-day period; (2) a urine output of \<0.5 mL/(kg·h) for 6 to 12 hours.
Key inclusion criteria (4 listed)
- Use of tacrolimus during hospitalization, with standardized therapeutic drug monitoring
- Age of 18 years or older at the time of admission
- Length of hospital stay ≥ hours
- At least two serum creatinine level tests conducted during the hospital stay
Key exclusion criteria (3 listed)
- Stage 5 chronic kidney disease prior to admission
- Incomplete clinical data
- Serum creatinine levels consistently below 40 mmol/L during hospitalization
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
AKI
Time frame: From January 2020 to December 2023
Acute kidney injury occurred after the patient took tacrolimus during hospitalization
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 19 Sep 2024.
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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.
Xiao Lii principal investigator
Qianfoshan Hospital
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 AKI, run by Qianfoshan 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 4 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: under way, not looking for participants, as of the sponsor's last update on 19 Sep 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?
This study is still under way. Results are not available in the source record yet.
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?
AKI (time frame: From January 2020 to December 2023). 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 19 Sep 2024.