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

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT06596811

Enrollment

1200

Registered

2024-09-19

Start date

2024-09-01

Completion date

2026-12-30

Last updated

2024-09-19

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

AKI, Therapeutic Drug Monitoring (TDM)

Keywords

deep learning, AKI, ADM

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.

Interventions

None listed

Study design

Observational model

OTHER

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to No maximum

Healthy volunteers

Yes

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.

Design outcomes

Primary

Measure	Time frame	Description
AKI	From January 2020 to December 2023	Acute kidney injury occurred in hospitalized patients treated with cyclosporine

Countries

China

Outcome results