Clinical trial · Observational
Prospective Validation of Machine Learning Model to Predict Platinum Induced Nephrotoxicity in Cancer Patients
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (4)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Acute Kidney Disease | — | UNRESOLVED | — |
| Acute Kidney Injury | — | UNRESOLVED | — |
| Chemotherapy Side Effects | — | UNRESOLVED | — |
| Machine Learning | — | UNRESOLVED | — |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Machine learning models predictions of acute kidney injury and acute kidney disease | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- label
- Patients received cisplatin or carboplatin
- description
- Standardized chemotherapy
- interventionNames
- Other: Machine learning models predictions of acute kidney injury and acute kidney disease
Primary outcomes (1)
- measure
- AUROC comparison
- timeFrame
- 89 days
- description
- 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
Secondary outcomes (1)
- measure
- Incidence and odd ratios in each risk level group
- timeFrame
- 89 days
- description
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 20 Years
- Maximum age
- 89 Years
Show eligibility criteria text
Inclusion Criteria: * 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. Exclusion Criteria: * 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.
References
Publications (0)
Data not yet available