Prediction ModelNCT06597838
Development and Validation of a Real-time Prediction Model for Acute Kidney Injury in Hospitalized Patients
An observational study of Prediction Model, run by Peking University First Hospital at 1 site in China.
Trial summary
Early prediction of acute kidney injury (AKI) may provide a crucial opportunity for AKI prevention. To date, no prediction model targeting AKI among general hospitalized patients in developing countries has been published. Read more about this study
- Who is it for?
- Ages 18 to 100 years
- Conditions the study is for
- Prediction Model
- How many people
- 161,876 participants
- Where
- 1 siteChina
- Study dates
- 28 Feb 2023to 30 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 to 100 years
- All sexes
- Adult, Older adult
Study population: Adult patients admitted to five hospitals during the study period.
Who may be able to join
- Adult patients (18 years and older) admitted to five hospitals during the study period
Who cannot join
- Have less than 2 documented serum creatinine (Scr) measurements during hospitalization
- Being diagnosed with end-stage renal disease (ESRD)
- Maintained on dialysis or had an initial Scr greater than or equal to 4.0 mg/dL at admission
- Developed AKI prior to admission or within 24 hours after admission
- Length of stay shorter than 24 hours
- Underwent kidney transplantation or nephrectomy during hospitalization
- With all Scr measurements lower than or equal to 0.6 mg/dL from 90 days prior to admission until discharge.
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Peking University First Hospital
Beijing, Beijing Municipality, 100034
Yuhui Zhang, +86 18813037026, 451076336@qq.com
About this study
Early prediction of acute kidney injury (AKI) may provide a crucial opportunity for AKI prevention. To date, no prediction model targeting AKI among general hospitalized patients in developing countries has been published. We developed a simple, real-time, interpretable AKI prediction model for general hospitalized patients from a large tertiary hospital in China, and validated it across five independent, geographically distinct, different tiered hospitals.
Read the detailed description
Early prediction of acute kidney injury (AKI) may provide a crucial opportunity for AKI prevention. To date, no prediction model targeting AKI among general hospitalized patients in developing countries has been published. We developed a simple, real-time, interpretable AKI prediction model for general hospitalized patients from a large tertiary hospital in China using the machine learning technique, and then validated the performance of the prediction model across five independent, geographically distinct, different tiered hospitals in China.
- Conditions
- Prediction Model
What is being measured
The primary outcome is the main question the study is designed to answer.
Predictive performance of the prediction model for Acute Kidney Injury
Evaluated using AUC
Measured: through study completion, varied from one to three years in different validation cohorts.
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 start28 Feb 2023
- Primary completion30 Oct 2024 (estimated)
- Study completion30 Dec 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Prediction ModelStudies of the same condition, open and under way.
- More trials run by Peking University First 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
- 161,876 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Peking University First Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Li Yang, Peking University First Hospital
- Investigators
- Li Yang, Study director, Peking University First Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06597838
- Sponsor's protocol ID
- 2023[069-001]
- Status last verified
- Sep 2024
- First submitted
- 9 Sep 2024
- Last updated
- 19 Sep 2024
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- 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
- Development and Validation of a Real-time Prediction Model for Acute Kidney Injury in Hospitalized Patients
- ClinicalTrials.gov ID
- NCT06597838
- Acronym / sponsor ID
- 2023[069-001]
- Conditions
- Prediction Model
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 161,876 participants (estimated)
- Lead sponsor
- Peking University First HospitalUniversity, hospital or other
- Study start
- 28 Feb 2023 (actual)
- Primary completion
- 30 Oct 2024 (estimated)
- Study completion
- 30 Dec 2024 (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.
Early prediction of acute kidney injury (AKI) may provide a crucial opportunity for AKI prevention. To date, no prediction model targeting AKI among general hospitalized patients in developing countries has been published. We developed a simple, real-time, interpretable AKI prediction model for general hospitalized patients from a large tertiary hospital in China, and validated it across five independent, geographically distinct, different tiered hospitals.
Detailed description
Early prediction of acute kidney injury (AKI) may provide a crucial opportunity for AKI prevention. To date, no prediction model targeting AKI among general hospitalized patients in developing countries has been published. We developed a simple, real-time, interpretable AKI prediction model for general hospitalized patients from a large tertiary hospital in China using the machine learning technique, and then validated the performance of the prediction model across five independent, geographically distinct, different tiered hospitals in China.
Study design
- Observational model
- Other
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 to 100 years
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- Adult patients admitted to five hospitals during the study period.
Key inclusion criteria (1 listed)
- Adult patients (18 years and older) admitted to five hospitals during the study period
Key exclusion criteria (7 listed)
- Have less than 2 documented serum creatinine (Scr) measurements during hospitalization
- Being diagnosed with end-stage renal disease (ESRD)
- Maintained on dialysis or had an initial Scr greater than or equal to 4.0 mg/dL at admission
- Developed AKI prior to admission or within 24 hours after admission
- Length of stay shorter than 24 hours
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
Predictive performance of the prediction model for Acute Kidney Injury
Time frame: through study completion, varied from one to three years in different validation cohorts.
Evaluated using AUC
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 19 Sep 2024.
- Individual participant dataSharing planned: No
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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.
Li Yang study director
Peking University First 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 Prediction Model, run by Peking University First 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 1 inclusion and 7 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 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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Yuhui Zhang (+86 18813037026, 451076336@qq.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Predictive performance of the prediction model for Acute Kidney Injury (time frame: through study completion, varied from one to three years in different validation cohorts.). 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.