Impact of Intraoperative Blood Pressure Components on Postoperative AKI: A Phenotype-Stratified Analysis

Not yet recruiting

• Conditions: Postoperative Acute Kidney Injury; Intraoperative Hypotension

• Registration Number: NCT07156474
• Lead Sponsor: Lanyue Zhu

Brief Summary

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.

Detailed Description

1. Study Rationale Postoperative Acute Kidney Injury (AKI) is a common and severe complication following non-cardiac surgery, leading to prolonged hospital stays, increased medical costs, and a higher risk of persistent kidney failure or death. Intraoperative hypotension (abnormally low blood pressure during surgery) is recognized as a significant contributor to AKI, as it can reduce blood flow and oxygen supply to the kidneys.

Currently, there is no clinical consensus on which component of blood pressure-such as systolic (SBP), diastolic (DBP), or mean arterial pressure (MAP)-is the most critical to monitor for preventing organ injury. Furthermore, current guidelines often recommend a universal "one-size-fits-all" threshold for hypotension (e.g., MAP \< 65 mmHg). This approach fails to account for individual patient differences, such as baseline blood pressure and co-existing health conditions, which may mean that the optimal blood pressure target varies significantly from person to person.

This study aims to address these gaps by using a large, multi-center dataset to first identify the most critical blood pressure components linked to AKI and then to develop a tool that predicts a personalized, optimal blood pressure threshold for individual patients.

2. Study Objectives

This study is divided into two parts:

Part 1: Risk Assessment and Blood Pressure Component Analysis

1. Primary Objective: To evaluate the association between different intraoperative blood pressure components (SBP, DBP, MAP) and the incidence of postoperative AKI in distinct patient subgroups, which will be identified by clustering patients based on their preoperative baseline characteristics.

2. Secondary Objective: To assess the association between these blood pressure components and the incidence of postoperative Acute Kidney Disease (AKD), a condition of longer-duration kidney damage.

Part 2: Development of a Personalized Prediction Model

1. Primary Objective: To develop and validate a dynamic machine learning model that provides an individualized prediction of the optimal intraoperative hypotension threshold for patients undergoing non-cardiac surgery.

2. Secondary Objectives:

* To build a machine learning model that predicts a patient's risk of developing postoperative AKI based on their preoperative data.

②To explore the relationship between the cumulative 5-minute lowest intraoperative blood pressure and postoperative AKI, using this relationship to refine the predictive model.

3\. Study Design and Methods This is a retrospective, multi-center cohort study.

* Data Sources: The study will utilize de-identified electronic health record data from several large sources: the public INSPIRE database, Zhongda Hospital affiliated with Southeast University (2013-2024), Nanjing First Hospital (2016-2024), and the First Affiliated Hospital of Anhui Medical University (2013-2023).

* Study Population: The study will include adult patients (age ≥ 18) who underwent non-cardiac surgery and had available intraoperative blood pressure monitoring data. An initial dataset of approximately 850,000 surgical records will be screened, with an estimated final cohort of 44,504 eligible patients after applying inclusion and exclusion criteria. Key exclusions include patients with pre-existing severe chronic kidney disease, those undergoing urological or kidney-related surgeries, and procedures with a short duration of anesthesia (\<60 min).

* Analysis Plan:

In Part 1, the research team will use unsupervised clustering methods (e.g., K-means) to group patients based on preoperative characteristics. Subsequently, multivariable logistic regression models will be used to analyze the association between intraoperative hypotension metrics and the risk of postoperative AKI/AKD within the overall cohort and across the different patient clusters.

In Part 2, machine learning algorithms, including XGBoost and Random Forest, will be employed to develop a series of predictive models. The final model will be designed to dynamically adjust a patient's hypotension threshold by iteratively calculating the AKI risk until it falls below a predefined safety level (10%). The model's performance will be rigorously evaluated using metrics such as the Area Under the Curve (AUC), and its interpretability will be assessed using SHAP (Shapley Additive Explanations) analysis.

Study Timeline

• Study First Submitted: 2025-08-27
• Status Verified: 2025-09
• Study First Submitted QC: 2025-09-04
• Last Update Submitted: 2025-09-04
• Study First Posted: 2025-09-05
• Last Update Posted: 2025-09-05
• Study Start: 2025-09-09
• Primary Completion: 2026-12-31
• Study Completion: 2026-12-31

Recruitment & Eligibility

• Status: NOT_YET_RECRUITING
• Sex: All
• Target Recruitment: 44504

Inclusion Criteria

1. Patients undergoing non-cardiac surgery
2. Access to intraoperatively available invasive/noninvasive blood pressure monitoring data

Exclusion Criteria

1. Patients for Age < 18 yr
2. Patients without preoperative or postoperative creatinine records
3. Patients undergoing urinary surgeries
4. Patients with chronic kidney injury before surgery
5. ASA grade > 5 class
6. Anesthesia duration < 60 min
7. Patients for non-available lowest blood pressure data during surgery
8. Hemodynamic records loss more than 25% of the total anesthesia duration
9. Patients for categorical variables records missing

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Postoperative AKI	Within 7 days post-op	KDIGO criteria for diagnosis (≥0.3 mg/dL or ≥1.5-fold increase in creatinine value)
Individualized Optimal Intraoperative Hypotension Threshold.	preoperative period	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 Outcome Measures

Name	Time	Method
Postoperative AKD	Within 8-90 days post-op	KDIGO criteria for diagnosis (≥0.3 mg/dL or ≥1.5-fold increase in creatinine value)
length of hospital stay	Through hospital discharge, an average of 1 weak	the interval from the end of surgery to discharge
Predicted Risk of Postoperative AKI.	preoperative period	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.	preoperative period	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

Trial Locations

Locations (1)

Zhongda Hospital Southeast University; 🇨🇳 Nanjing, Jiangsu Provincce, China