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

NCT06596798 · Status: ACTIVE_NOT_RECRUITING · Type: OBSERVATIONAL · Enrollment: 1200

Last updated 2024-09-19

No results posted yet for this study

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

Sponsors & Collaborators

  • Qianfoshan Hospital

    lead OTHER

Principal Investigators

  • Xiao Lii · Qianfoshan Hospital

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2024-09-01
Primary Completion
2026-09-01
Completion
2026-12-30

Countries

  • China

Study Locations

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06596798 on ClinicalTrials.gov