AKINCT06596811
Research on the Risk Warning Model and Prevention Strategies for Acute Kidney Injury Associated With Cyclosporine Based on Explainable Deep Neural Networks and Therapeutic Drug Monitoring
An observational study of AKI and Therapeutic Drug Monitoring (TDM), run by Qianfoshan Hospital at 1 site in China.
Trial summary
In this study, the investigators will focus on hospitalized patients using cyclosporine and develop an acute kidney injury risk prediction model through in-depth analysis of electronic medical record data, employing interpretable 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, Therapeutic Drug Monitoring (TDM)
- 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: Inpatients treated with tacrolimus or cyclosporine and monitored for therapeutic drug concentrations at three medical centers from January 2020 to December 2023, including Shandong First Medical University Affiliated Hospital, Binzhou Medical University Affiliated Hospital, and Jinan First People's Hospital.
Who may be able to join
- During hospitalization, tacrolimus or cyclosporine was used, and therapeutic drug monitoring was conducted according to standard procedures.
- Aged 18 years or older at the time of admission.
- Length of hospital stay \> 48 hours.
- At least 2 serum creatinine tests were conducted during hospitalization.
Who cannot join
- Chronic kidney disease stage 5 was achieved before admission.
- Incomplete clinical data.
- Serum creatinine levels were 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, 250117
About this study
In this study, the investigators will focus on hospitalized patients using cyclosporine and develop an acute kidney injury risk prediction model through in-depth analysis of electronic medical record data, employing interpretable deep learning methods. This model aims to provide timely decision-making support for clinicians regarding prevention and treatment. Compared to traditional machine learning models, deep neural network models can extract deeper features from complex medical data and perform more precise pattern recognition, thereby improving the accuracy and reliability of predictions. By developing a prediction tool based on interpretable deep learning models, the investigators will be able to better assess the association between the use of CNI-class immunosuppressants and acute kidney injury, explore targeted prevention strategies, and offer more accurate prediction and intervention guidance for clinicians. Additionally, this study has significant socioeconomic benefits and promising prospects for application and promotion.
- Conditions
- AKI, Therapeutic Drug Monitoring (TDM)
- Keywords
- deep learning, AKI, ADM
What is being measured
The primary outcome is the main question the study is designed to answer.
AKI
Acute kidney injury occurred in hospitalized patients treated with cyclosporine
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.
- 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 Li, Principal investigator, Qianfoshan Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06596811
- Sponsor's protocol ID
- LCYX-LX-20240103
- 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 Cyclosporine Based on Explainable Deep Neural Networks and Therapeutic Drug Monitoring
- ClinicalTrials.gov ID
- NCT06596811
- Acronym / sponsor ID
- LCYX-LX-20240103
- Conditions
- AKI, Therapeutic Drug Monitoring (TDM)
- Keywords
- deep learning, AKI, ADM
- 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 will focus on hospitalized patients using cyclosporine and develop an acute kidney injury risk prediction model through in-depth analysis of electronic medical record data, employing interpretable deep learning methods. This model aims to provide timely decision-making support for clinicians regarding prevention and treatment. Compared to traditional machine learning models, deep neural network models can extract deeper features from complex medical data and perform more precise pattern recognition, thereby improving the accuracy and reliability of predictions. By developing a prediction tool based on interpretable deep learning models, the investigators will be able to better assess the association between the use of CNI-class immunosuppressants and acute kidney injury, explore targeted prevention strategies, and offer more accurate prediction and intervention guidance for clinicians. Additionally, this study has significant socioeconomic benefits and promising prospects for application and promotion.
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
- Inpatients treated with tacrolimus or cyclosporine and monitored for therapeutic drug concentrations at three medical centers from January 2020 to December 2023, including Shandong First Medical University Affiliated Hospital, Binzhou Medical University Affiliated Hospital, and Jinan First People's Hospital.
Key inclusion criteria (4 listed)
- During hospitalization, tacrolimus or cyclosporine was used, and therapeutic drug monitoring was conducted according to standard procedures.
- Aged 18 years or older at the time of admission.
- Length of hospital stay \> 48 hours.
- At least 2 serum creatinine tests were conducted during hospitalization.
Key exclusion criteria (3 listed)
- Chronic kidney disease stage 5 was achieved before admission.
- Incomplete clinical data.
- Serum creatinine levels were 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 in hospitalized patients treated with cyclosporine
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 Li 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 and Therapeutic Drug Monitoring (TDM), 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; AzTrials last refreshed its copy on 26 Oct 2026.