Research on the Risk Warning Model and Prevention Strategies for Acute Kidney Injury Associated With Cyclosporine Based on Explainable Deep Neural Networks and Therapeutic Drug Monitoring

NCT ID: NCT06596811

Last Updated: 2024-09-19

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

ACTIVE_NOT_RECRUITING

Total Enrollment

1200 participants

Study Classification

OBSERVATIONAL

Study Start Date

2024-09-01

Study Completion Date

2026-12-30

Brief Summary

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In this study, the investigators will focus on hospitalized patients using cyclosporine and develop an acute kidney injury risk prediction model through in-depth analysis of electronic medical record data, employing interpretable deep learning methods. This model aims to provide timely decision-making support for clinicians regarding prevention and treatment. Compared to traditional machine learning models, deep neural network models can extract deeper features from complex medical data and perform more precise pattern recognition, thereby improving the accuracy and reliability of predictions. By developing a prediction tool based on interpretable deep learning models, the investigators will be able to better assess the association between the use of CNI-class immunosuppressants and acute kidney injury, explore targeted prevention strategies, and offer more accurate prediction and intervention guidance for clinicians. Additionally, this study has significant socioeconomic benefits and promising prospects for application and promotion.

Detailed Description

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Conditions

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AKI Therapeutic Drug Monitoring (TDM)

Study Design

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Observational Model Type

OTHER

Study Time Perspective

RETROSPECTIVE

Eligibility Criteria

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Inclusion Criteria

1. During hospitalization, tacrolimus or cyclosporine was used, and therapeutic drug monitoring was conducted according to standard procedures.
2. Aged 18 years or older at the time of admission.
3. Length of hospital stay \> 48 hours.
4. At least 2 serum creatinine tests were conducted during hospitalization.

Exclusion Criteria

1. Chronic kidney disease stage 5 was achieved before admission.
2. Incomplete clinical data.
3. Serum creatinine levels were consistently below 40 mmol/L during hospitalization.
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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Qianfoshan Hospital

OTHER

Sponsor Role lead

Responsible Party

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Xiao Li,MD

Associate professor of pharmacy

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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Xiao Li

Role: PRINCIPAL_INVESTIGATOR

Qianfoshan Hospital

Locations

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

Jinan, , China

Site Status

Countries

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China

Other Identifiers

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LCYX-LX-20240103

Identifier Type: -

Identifier Source: org_study_id

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