Deep Learning Model and Risk Factors for Tacrolimus-related Acute Kidney Injury

NCT ID: NCT06596798

Last Updated: 2024-09-19

Basic Information

• Recruitment Status: ACTIVE_NOT_RECRUITING
• Total Enrollment: 1200 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2024-09-01
• Study Completion Date: 2026-12-30

Brief Summary

In this study, the investigators aim to develop a risk prediction model for acute kidney injury (AKI) in hospitalized patients using the calcineurin inhibitor tacrolimus. This will be achieved by mining electronic medical record data and employing explainable deep learning methods. The model will provide clinical decision support for timely intervention and treatment. Compared to traditional machine learning models, deep neural networks can extract more nuanced features from complex medical data and perform more precise pattern recognition, thereby enhancing prediction accuracy and reliability. By constructing a predictive tool based on explainable deep learning models, the investigators will better assess the association between the use of calcineurin inhibitors and AKI, explore targeted prevention strategies, and offer more precise predictions and intervention guidance to clinicians. Additionally, this research has significant socio-economic benefits and application potential. By reducing the incidence of AKI, the investigators can lower patient hospitalization duration and re-treatment costs, conserve medical resources, and improve patient quality of life. Preventive healthcare not only alleviates the physical and psychological burden on patients but also reduces the strain on the healthcare system, enhances healthcare efficiency, and promotes the rational allocation of medical resources.

Conditions

AKI

Study Design

• Observational Model Type: OTHER
• Study Time Perspective: RETROSPECTIVE

Eligibility Criteria

Inclusion Criteria

• Use of tacrolimus during hospitalization, with standardized therapeutic drug monitoring
• Age of 18 years or older at the time of admission
• Length of hospital stay ≥ hours
• At least two serum creatinine level tests conducted during the hospital stay

Exclusion Criteria

• Stage 5 chronic kidney disease prior to admission
• Incomplete clinical data
• Serum creatinine levels consistently below 40 mmol/L during hospitalization

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Qianfoshan Hospital

OTHER

Sponsor Role: lead

Responsible Party

Xiao Li，MD

Associate professor of pharmacy

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Xiao Lii

Role: PRINCIPAL_INVESTIGATOR

Qianfoshan Hospital

Locations

The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital

Jinan, Shandong, China

Countries

China

Other Identifiers

LCYX-LX-20240102

Identifier Type: -

Identifier Source: org_study_id