About this trial
Postoperative acute kidney injury (AKI) is a serious complication often linked to low blood pressure during surgery. This study aims to better protect patients' kidneys by personalizing how blood pressure is managed during an operation.
The project has two main goals:
First, researchers will analyze data from over 44,000 past surgeries to identify which specific blood pressure measurements are the most critical warning signs for kidney damage.
Second, using this knowledge, they will build a smart tool (a machine learning model) to predict a unique, safe blood pressure target for each individual patient before their surgery begins.
This personalized approach is intended to give doctors a specific target to maintain during surgery, helping to prevent kidney injury and improve patient safety.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
Patients undergoing non-cardiac surgery
Access to intraoperatively available invasive/noninvasive blood pressure monitoring data
Disqualifiers
Patients for Age < 18 yr
Patients without preoperative or postoperative creatinine records
Patients undergoing urinary surgeries
Patients with chronic kidney injury before surgery
Trial population
All non-cardiac surgery data from INSPIRE database and all non-cardiac surgery data from 2013-2024 in Zhongda Hospital Southeast University, 2016-2024 in Nanjing First Hospital and 2013-2023 in Anhui Medical University Affiliated First Hospital were selected.
Trial design
Cohort
Retrospective
Treatments tested in this trial
Not listed
Trial groups
Trial outcomes
Primary outcomes
Postoperative AKI
KDIGO criteria for diagnosis (≥0.3 mg/dL or ≥1.5-fold increase in creatinine value)
Individualized Optimal Intraoperative Hypotension Threshold.
A patient-specific blood pressure threshold predicted by a dynamic machine learning model. This threshold is identified as the blood pressure value at which the model-predicted risk of postoperative Acute Kidney Injury (AKI) falls below 10%.
Secondary outcomes
Postoperative AKD
KDIGO criteria for diagnosis (≥0.3 mg/dL or ≥1.5-fold increase in creatinine value)
length of hospital stay
the interval from the end of surgery to discharge
Predicted Risk of Postoperative AKI.
The probability of developing postoperative AKI, as calculated by a machine learning model using preoperative baseline data. Measured by AUROC, AUPRC and Calibration Curve
Predicted Lowest Intraoperative Blood Pressure.
The predicted cumulative 5-minute lowest intraoperative blood pressure value, generated by a machine learning model based on preoperative data. Measured by R2 and MAE
Sponsors and contacts
Click on the lead sponsor to view all of their trials.
Lanyue Zhu
Lead sponsor
Zhongda Hospital
Sponsor institution
This trial is not recruiting at the moment. You can still explore other options: