Prospective Validation of Machine Learning Model to Predict Platinum Induced Nephrotoxicity in Cancer Patients
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 77
- 试验地点
- 1
- 主要终点
- AUROC comparison
研究概览
简要总结
This study aims to investigate the utility of predictive models for chemotherapy-induced nephrotoxicity in the Taiwanese cancer population.
The investigators will prospectively collect clinical data from enrolled participants, including demographic information, comorbidities, laboratory data, and chemotherapy treatment details. After chemotherapy administration, participants' renal function will be monitored over time to assess the development of nephrotoxicity, based on changes in serum creatinine (SCr) and other relevant clinical criteria.
The primary objective is to evaluate and compare the predictive performance of a machine learning model and clinical physicians, using the area under the receiver operating characteristic curve (AUROC) as the main metric for discrimination performance.
详细描述
This is a prospective cohort study conducted at Wan Fang Hospital, designed to evaluate the predictive performance and clinical utility of established machine learning models for detecting nephrotoxicity in cancer patients receiving platinum-based chemotherapy. The models were developed using a Long Short-Term Memory (LSTM) neural network architecture, trained on a retrospective dataset from January 1, 2009 to January 31, 2022. All model parameters were locked after training to ensure reproducibility, prevent data leakage, and maintain the integrity of prospective validation.
Cancer patients receiving platinum-containing agents (such as Cisplatin and Carboplatin) between October 2023 and August 2025 will be recruited, with follow-up until November 2025. After confirming eligibility based on the inclusion and exclusion criteria, written informed consent will be obtained from each participant prior to data collection.
Each administration of platinum chemotherapy is treated as a separate prediction case. For every administration, the following renal outcomes will be predicted:
- Acute Kidney Injury (AKI) : occurring within 14 days after chemotherapy, defined according to the Common Terminology Criteria for Adverse Events (CTCAE) version 5.0.
- Acute Kidney Disease (AKD) : occurring within 89 days after chemotherapy, defined according to the Acute Disease Quality Initiative (ADQI) 2016 criteria.
After obtaining informed consent, the investigators will collect general patient information (gender, age, height, weight), cancer-related information (stage), chemotherapy details (administration date, course, dosage, concomitant chemotherapy drugs, and the number of administrations), and laboratory data (SCr, glomerular filtration rate [GFR]). Relevant follow-up data will be used to evaluate treatment effects and disease prognosis. All data will be de-identified and recorded using a research identification number.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 20 Years 至 89 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Patients under clinical diagnosis of cancer with treatments including at least taking one course treatment of Cisplatin and Carboplatin from Dec,2022 to July,2026, at least having one serum creatinine data before and after the administration, willing to provide DNA sample and sign the informed consent will be recruited.
排除标准
- •Patients who are young than 20 years old or older than 89 years old, pregnant women, infected by Human Immunodeficiency Virus (HIV), administered by Ifosfamide, couldn't evaluate their kidney function, refuse to provide DNA sample and sign the informed consent will be excluded.
研究组 & 干预措施
Patients received cisplatin or carboplatin
Standardized chemotherapy
干预措施: Machine learning models predictions of acute kidney injury and acute kidney disease (Other)
结局指标
主要结局
AUROC comparison
时间窗: 89 days
Comparison of the area under the receiver-operator characteristic (ROC) curves between the predictions made by the machine learning models and by clinicians, to predict AKI within 14 days and AKD within 89 days
次要结局
- Incidence and odd ratios in each risk level group(89 days)