Cardiovascular Surgery Early Prediction System for AKI :CARDS-AKI Registry

NCT ID: NCT07330453

Last Updated: 2026-01-09

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

NOT_YET_RECRUITING

Total Enrollment

5000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2025-12-25

Study Completion Date

2029-07-31

Brief Summary

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This multicenter, prospective study prospectively enrolled patients undergoing cardiovascular surgery. Detailed perioperative clinical data and biospecimens were collected at multiple time points. The primary aim is to develop an early warning model for postoperative acute kidney injury (AKI) by integrating clinical data and biomarkers. Additionally, through long-term follow-up, the study seeks to characterize outcome trajectories and establish a prognostic model for AKI patients.

This study addresses four key questions: 1) Integration of clinical information and biomarkers to develop an early predictive model for cardiac surgery-associated acute kidney injury (CSA-AKI); 2) Identification of risk factors for CSA-AKI occurrence; 3) Determinants of prognosis in patients with CSA-AKI; and 4) Enhanced prediction of near- and long-term clinical event risks in this patient population.

Participants will receive standard perioperative management. The study protocol includes the following procedures:(1) Clinical Data Collection: Comprehensive perioperative clinical data will be systematically recorded. (2) Biospecimen Sampling: Serial blood and urine samples will be obtained at predefined time points throughout the perioperative period. (3) In-Hospital Monitoring: Clinical outcomes will be continuously monitored during the hospital stay. (3) Post-discharge Follow-up: Participants will be assessed at regular intervals after discharge to track the occurrence of major adverse events.

These findings provide a foundational basis for the development of a data-driven early-warning system. Such a system is designed to facilitate the prompt identification of high-risk patients and enable the initiation of personalized treatment strategies, thereby potentially improving clinical outcomes and optimizing resource allocation

Detailed Description

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Conditions

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Cardiovascular Surgery

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Study Groups

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Patients who did not develop acute kidney injury(AKI) after cardiovascular surgery

Observation

Intervention Type OTHER

As an observational study, patients' therapeutic approach will not be modified during the course of this research.

Patients who developed AKI after cardiovascular surgery

Observation

Intervention Type OTHER

As an observational study, patients' therapeutic approach will not be modified during the course of this research.

Interventions

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Observation

As an observational study, patients' therapeutic approach will not be modified during the course of this research.

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

* Age ≥ 18 years;
* Scheduled to undergo elective cardiac surgery;
* Able to complete baseline data collection and provide written informed consent.

Exclusion Criteria

* Preoperative serum creatinine \> 353 μmol/L, history of or currently receiving dialysis therapy.
* History of kidney transplantation.
* Presence of malignant tumors.
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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Chinese Academy of Medical Sciences, Fuwai Hospital

OTHER

Sponsor Role lead

Responsible Party

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Sheng Liu

Principal Investigator

Responsibility Role PRINCIPAL_INVESTIGATOR

Locations

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Fuwai Hospital, Chinese Academy of Medical Science

Beijing, Beijing Municipality, China

Site Status

Countries

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China

Central Contacts

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Sheng Liu

Role: CONTACT

8613501331366

Facility Contacts

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Sheng Liu

Role: primary

8613501331366

Other Identifiers

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2025ZD0547500

Identifier Type: -

Identifier Source: org_study_id

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