Clinical trial · Observational
Oncogeriatric Screening and Evaluation Program
PROgrama de Tamizaje y Evaluación oncoGERiátrica
- 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 develop, train, and validate a machine learning-based prediction model (PROTEGER) to provide treatment decision recommendations for older adults diagnosed with solid tumor cancers. The study has a two-phase observational design: a retrospective cohort using anonymized data from an oncogeriatric telecommittee to train the predictive model, followed by a prospective multicenter cohort across Chile, Peru, and Brazil. Information from Comprehensive Geriatric Assessments (CGA), treatment decisions, and 3- and 6-month clinical outcomes will be collected to evaluate and validate the decision-support platform's performance in assisting oncology teams.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Solid Tumor Cancer | — | UNRESOLVED | — |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (2)
- label
- Retrospective Training Cohort (Chile)
- description
- Anonymized data from cases submitted to the Oncogeriatrics Telecommittee from 2021 to the end of 2023, obtained from the Chilean Ministry of Health's Digital Hospital database, along with patient survival data, will be used to train and validate the predictive model using machine learning techniques.
- label
- Prospective Validation Cohort (Chile, Brasil, Peru)
- description
- Older adults with cancer receive a comprehensive geriatric assessment at their respective centers and are introduced to an oncogeriatric team. They will share their baseline characteristics, the results of their CGA, clinical data related to cancer treatment, and 3- and 6-month follow-up for the development and validation of a predictive model using machine learning techniques.
Primary outcomes (1)
- measure
- Predictive Accuracy of the PROTEGER Machine Learning Model
- timeFrame
- Up to 6 months post-enrollment.
- description
- Discrimination performance of the machine learning predictive model in recommending oncogeriatric treatment decisions (standard treatment, dose-adjusted treatment, or supportive care/no treatment) based on Comprehensive Geriatric Assessment (CGA) data, measured by the Area Under the Receiver Operating Characteristic Curve (AUC-ROC), with scores ranging from 0.5 (no discrimination/chance) to 1.0 (perfect discrimination).
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 65 Years
Show eligibility criteria text
Inclusion Criteria: * Age 65 years or older. * Diagnosis of solid tumor cancer. * Must have been evaluated and followed up by a local oncology team. * Signed Informed Consent Form (ICF) applied in accordance with the local ethics committee. * Must have undergone a Comprehensive Geriatric Assessment (CGA). Exclusion Criteria: \- Patients who are unable or unwilling to consent to providing information will be excluded.
References
Publications (0)
Data not yet available