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

NCT06596811 | Active Not Recruiting

• 1 other identifier: LCYX-LX-20240103
• Study Type: observational
• Target: 1,200
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

AKI; Therapeutic Drug Monitoring (TDM)

Keywords

deep learning; AKI; ADM

Outcome Measures

Primary Outcomes (1)

• AKI

  Acute kidney injury occurred in hospitalized patients treated with cyclosporine

  From January 2020 to December 2023

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Inpatients treated with tacrolimus or cyclosporine and monitored for therapeutic drug concentrations at three medical centers from January 2020 to December 2023, including Shandong First Medical University Affiliated Hospital, Binzhou Medical University Affiliated Hospital, and Jinan First People's Hospital.

You may qualify if:

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

You may not qualify if:

• Chronic kidney disease stage 5 was achieved before admission.
• Incomplete clinical data.
• Serum creatinine levels were consistently below 40 mmol/L during hospitalization.

Sponsors & Collaborators

• Qianfoshan Hospital: lead

Study Sites (1)

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

Jinan, 250117, China

Location

Study Officials

• Xiao Li

  Qianfoshan Hospital

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Associate professor of pharmacy

Study Record Dates

• First Submitted: September 8, 2024
• First Posted: September 19, 2024
• Study Start: September 1, 2024
• Primary Completion (Estimated): September 1, 2026
• Study Completion (Estimated): December 30, 2026
• Last Updated: September 19, 2024

Record last verified: 2024-09

Locations

CN (1)