Kidney Injury, AcuteNCT07030166
A Machine Learning Prediction Model for Postoperative Acute Kidney Injury in Non-Cardiac Surgery Patients
An observational study of Kidney Injury, Acute, run by Lanyue Zhu at 1 site in China.
Trial summary
Primary objectives of this study is to develop and validate a predictive model for acute kidney injury after non-cardiac surgery based on machine learning. Secondary objectives of this study is to incorporate frailty assessment as a new predictor into the model and measure its incremental value was measured. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Kidney Injury, Acute
- How many people
- 10,000 participants
- Where
- 1 siteChina
- Study dates
- 1 Jul 2025to 31 Dec 2026
- How long will I take part?
- 1 Year
- 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
- No healthy volunteers
- Adult, Older adult
Study population: Patients who are scheduled to undergo surgery at Zhongda Hospital Southeast University.
Who may be able to join
- 18 years old or above
- Undergo non-cardiac surgery
Who cannot join
- At least one measurement of serum creatinine (SCr) was not conducted before and after the operation
- End-stage renal disease (ESRD) that has received dialysis within the past year
- Baseline SCr ≥ 4.5 mg/dl (because the clinical criteria for AKI based on elevated SCr may not be applicable to these patients)
- Acute kidney injury occurred within 7 days before the operation
- The surgical procedure is renal surgery
- The operation time is less than 2 hours
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
Zhongda Hospital Southeast University
Nanjing
About this study
Official title: A Machine Learning Prediction Model for Postoperative Acute Kidney Injury in Non-Cardiac Surgery Patients: Development, Validation, and the Incremental Value of Frailty Assessment
Primary objectives of this study is to develop and validate a predictive model for acute kidney injury after non-cardiac surgery based on machine learning. Secondary objectives of this study is to incorporate frailty assessment as a new predictor into the model and measure its incremental value was measured.
Read the detailed description
The data in this study are divided into two parts: retrospective and prospective. The retrospective data served as the development set, sourced from the electronic medical records of adult patients who underwent non-cardiac surgery during hospitalization between July 2015 and June 2025. The prospective data constituted an external (temporal) validation set, with data collection commencing in July 2025 and expected to conclude in February 2026.
- Conditions
- Kidney Injury, Acute
- Being tested
- No intervention measures were used.
- Medical subject headings
- Acute Kidney Injury
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
No intervention measures were used.Other
The exposure factors were the perioperative related operations experienced by the patients and their individual conditions
| Study group | Role | What they receive |
|---|---|---|
| Development group | The development group was used for model development, five-fold cross-validation, and model optimization. The investigators collected a comprehensive set of variables for feature selection, including preoperative demographic characteristics (sex, age, body mass index, marital status, occupation, etc.), laboratory indicators (routine blood and urine tests, liver and kidney function, coagulation function, etc.), preoperative comorbidities, and surgical information (surgical department, surgical classification, American Society of Anesthesiologists physical status classification, intraoperative position, fluid intake and output, vital signs, intraoperative medication use, etc.). Subsequently, multiple machine learning methods, including logistic regression, extreme gradient boosting, decision tree, random forest, and Bayesian approaches, were employed for model building and optimization. Receives: Other: No intervention measures were used. | |
| External (time) validation group | The external (time) validation group is used for future generalization ability assessment. The investigators prospectively collected patient-related data. In addition to the same variables as those in the development group and the testing group, the investigators also evaluated and collected the frailty status of patients before the operation, and recorded prognostic indicators such as the incidence of in-hospital complications, in-hospital mortality, length of hospital stay and hospitalization cost of patients. The investigators used the data from the external (time) validation group to validate the model performance, incorporated the frailty assessment as a new predictor into the model, calculated the incremental values and evaluated the performance of the updated model. Receives: Other: No intervention measures were used. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Acute kidney injury
Measured: Within 7 days after the operation
Secondary outcomes (4)
Postoperative complications
Measured: Perioperative period
Postoperative mortality
Measured: Perioperative period
Hospitalization costs
Measured: Perioperative period
Hospital stays
Measured: Perioperative period
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 Jun 2025
- Study start1 Jul 2025
- Primary completion31 Dec 2026 (estimated)
- Study completion31 Dec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Kidney Injury, AcuteStudies 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
- Cohort
- Time perspective
- Other
- Enrolment
- 10,000 participants (estimated)
- Target duration
- 1 Year
Sponsor and investigators
- Lead sponsor
- Lanyue Zhu
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Lanyue Zhu, Zhongda Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07030166
- Sponsor's protocol ID
- 2025ZDSYLL200-P01
- Status last verified
- Mar 2026
- First submitted
- 7 Jun 2025
- Last updated
- 2 Apr 2026
- 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 2 Apr 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Machine Learning Prediction Model for Postoperative Acute Kidney Injury in Non-Cardiac Surgery Patients: Development, Validation, and the Incremental Value of Frailty Assessment
- ClinicalTrials.gov ID
- NCT07030166
- Acronym / sponsor ID
- 2025ZDSYLL200-P01
- Conditions
- Kidney Injury, Acute
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 10,000 participants (estimated)
- Lead sponsor
- Lanyue ZhuUniversity, hospital or other
- Study start
- 1 Jul 2025 (actual)
- Primary completion
- 31 Dec 2026 (estimated)
- Study completion
- 31 Dec 2026 (estimated)
- First posted
- 22 Jun 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.
Primary objectives of this study is to develop and validate a predictive model for acute kidney injury after non-cardiac surgery based on machine learning. Secondary objectives of this study is to incorporate frailty assessment as a new predictor into the model and measure its incremental value was measured.
Detailed description
The data in this study are divided into two parts: retrospective and prospective. The retrospective data served as the development set, sourced from the electronic medical records of adult patients who underwent non-cardiac surgery during hospitalization between July 2015 and June 2025. The prospective data constituted an external (temporal) validation set, with data collection commencing in July 2025 and expected to conclude in February 2026.
Study design
- Observational model
- Cohort
- Time perspective
- Other
- Target follow-up
- 1 Year
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Development group | – | Other: No intervention measures were used.The development group was used for model development, five-fold cross-validation, and model optimization. The investigators collected a comprehensive set of variables for feature selection, including preoperative demographic characteristics (sex, age, body mass index, marital status, occupation, etc.), laboratory indicators (routine blood and urine tests, liver and kidney function, coagulation function, etc.), preoperative comorbidities, and surgical information (surgical department, surgical classification, American Society of Anesthesiologists physical status classification, intraoperative position, fluid intake and output, vital signs, intraoperative medication use, etc.). Subsequently, multiple machine learning methods, including logistic regression, extreme gradient boosting, decision tree, random forest, and Bayesian approaches, were employed for model building and optimization. |
| External (time) validation group | – | Other: No intervention measures were used.The external (time) validation group is used for future generalization ability assessment. The investigators prospectively collected patient-related data. In addition to the same variables as those in the development group and the testing group, the investigators also evaluated and collected the frailty status of patients before the operation, and recorded prognostic indicators such as the incidence of in-hospital complications, in-hospital mortality, length of hospital stay and hospitalization cost of patients. The investigators used the data from the external (time) validation group to validate the model performance, incorporated the frailty assessment as a new predictor into the model, calculated the incremental values and evaluated the performance of the updated model. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients who are scheduled to undergo surgery at Zhongda Hospital Southeast University.
Key inclusion criteria (2 listed)
- 18 years old or above
- Undergo non-cardiac surgery
Key exclusion criteria (6 listed)
- At least one measurement of serum creatinine (SCr) was not conducted before and after the operation
- End-stage renal disease (ESRD) that has received dialysis within the past year
- Baseline SCr ≥ 4.5 mg/dl (because the clinical criteria for AKI based on elevated SCr may not be applicable to these patients)
- Acute kidney injury occurred within 7 days before the operation
- The surgical procedure is renal surgery
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 |
|---|---|---|
| No intervention measures were used.The exposure factors were the perioperative related operations experienced by the patients and their individual conditions | Other | Development group, External (time) validation group |
Primary outcome
Acute kidney injury
Time frame: Within 7 days after the operation
4 secondary outcomes
Postoperative complications
Time frame: Perioperative period
Postoperative mortality
Time frame: Perioperative period
Hospitalization costs
Time frame: Perioperative period
Hospital stays
Time frame: Perioperative period
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 2 Apr 2026.
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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.
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 Kidney Injury, Acute, run by Lanyue Zhu 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 6 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: looking for participants now, as of the sponsor's last update on 2 Apr 2026. 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?
Contact Yue Lan Zhu (+8618795969178, zhulanyue1993@126.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Acute kidney injury (time frame: Within 7 days after the operation). 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 Apr 2026.