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Oncogeriatric Screening and Evaluation Program (PROTEGER)

1. September 2026 aktualisiert von: Latin American Cooperative Oncology Group

PROgrama de Tamizaje y Evaluación oncoGERiátrica

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.

Studienübersicht

Status

Noch keine Rekrutierung

Bedingungen

Detaillierte Beschreibung

Cancer incidence increases significantly with age, and up to 70% of cancer mortality occurs in patients aged 65 years or older. Despite this, older patients are frequently undertreated due to the high risk of secondary toxicity, which is associated with quality of life deterioration, increased hospitalizations, and higher mortality. Comprehensive Geriatric Assessment (CGA) has proven to be an effective tool to identify vulnerability, reduce chemotherapy-related toxicity, and tailor interventions. However, the lack of geriatricians, especially in Latin American public health systems, creates significant barriers to accessing CGA-guided oncological care.

To overcome these barriers, the PROTEGER program proposes an innovative digital health solution by developing and validating a machine learning-based clinical decision support system (CDSS) for oncogeriatric care. The study is an observational, multicenter, bidirectional cohort study conducted in two phases:

Phase 1: Retrospective Training Phase This phase uses anonymized clinical data (2021-2023) from the Oncogeriatric Tele-Committee of the Chilean Ministry of Health's Digital Hospital. Data from older patients with solid tumors who underwent a CGA will be used to train and test predictive models using machine learning techniques (e.g., Gradient Boosting Trees and Random Forest) following the CRISP-DM methodology. The predictive model aims to learn the Committee's treatment recommendation patterns based on patient functionality, comorbidities, and geriatric syndromes.

Phase 2: Prospective Validation Phase A prospective, multicenter cohort will be enrolled across healthcare centers in Chile, Peru, and Brazil. Eligible patients (aged 65+ with a solid tumor diagnosis) who undergo routine CGA and oncological care will be followed for 6 months. Data regarding baseline characteristics, treatment decisions (made by local oncology teams blinded to the AI model's recommendation), dose reductions, treatment discontinuation, disease progression, quality of life (EORTC QLQ-C30 and ELD14), and survival will be collected.

Study Objectives:

The primary objective is to develop, train, and clinically validate the PROTEGER machine learning predictive model to provide an accurate treatment recommendation (e.g., standard treatment, dose-adjusted treatment, or supportive care only) capable of assisting clinical decision-making by oncology teams. A secondary objective involves the design and development of an intuitive graphical user interface capable of being used by healthcare providers and patients for data management and result interpretation.

All predictive models will be evaluated using standard metrics, such as the Area Under the ROC Curve (AUC) and the C-statistic, to determine their discriminatory capacity in a real-world clinical setting.

Studientyp

Beobachtungs

Einschreibung (Geschätzt)

412

Kontakte und Standorte

Dieser Abschnitt enthält die Kontaktdaten derjenigen, die die Studie durchführen, und Informationen darüber, wo diese Studie durchgeführt wird.

Studienkontakt

Studieren Sie die Kontaktsicherung

Studienorte

    • São Paulo
      • São Paulo, São Paulo, Brasilien, 05653-000
        • HIAE - Hospital Israelita Albert Einstein
        • Hauptermittler:
          • Ludmila de Oliveira Muniz Koch
      • São Paulo, São Paulo, Brasilien, 04004-060
        • BR192 HCOR - Hospital do Coração
        • Hauptermittler:
          • Luciola Pontes Leite de Barros

Teilnahmekriterien

Forscher suchen nach Personen, die einer bestimmten Beschreibung entsprechen, die als Auswahlkriterien bezeichnet werden. Einige Beispiele für diese Kriterien sind der allgemeine Gesundheitszustand einer Person oder frühere Behandlungen.

Zulassungskriterien

Studienberechtigtes Alter

  • Älterer Erwachsener

Akzeptiert gesunde Freiwillige

Nein

Probenahmeverfahren

Nicht-Wahrscheinlichkeitsprobe

Studienpopulation

The study population consists of older adults (aged 65 years and older) diagnosed with solid tumor cancers who receive care in public and private health institutions in South America (Chile, Brazil, and Peru). The retrospective cohort includes anonymized historical data from patients evaluated by the Oncogeriatric Tele-Committee of the Chilean Ministry of Health. The prospective cohort comprises consecutive patients seen in routine oncological care who undergo a Comprehensive Geriatric Assessment (CGA) and are managed by local oncogeriatric teams.

Beschreibung

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.

Studienplan

Dieser Abschnitt enthält Einzelheiten zum Studienplan, einschließlich des Studiendesigns und der Messung der Studieninhalte.

Wie ist die Studie aufgebaut?

Designdetails

Kohorten und Interventionen

Gruppe / Kohorte
Retrospective Training Cohort (Chile)
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.
Prospective Validation Cohort (Chile, Brasil, Peru)
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.

Was misst die Studie?

Primäre Ergebnismessungen

Ergebnis Maßnahme
Maßnahmenbeschreibung
Zeitfenster
Predictive Accuracy of the PROTEGER Machine Learning Model
Zeitfenster: Up to 6 months post-enrollment.
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).
Up to 6 months post-enrollment.

Sekundäre Ergebnismessungen

Ergebnis Maßnahme
Maßnahmenbeschreibung
Zeitfenster
Incidence of High-Grade Chemotherapy-Related Adverse Events
Zeitfenster: At 3 and 6 months post-enrollment.
Percentage of participants experiencing Grade 3 or higher toxicities/adverse reactions evaluated using the Common Terminology Criteria for Adverse Events (CTCAE) version 5.0.
At 3 and 6 months post-enrollment.
General Quality of Life Score (EORTC QLQ-C30)
Zeitfenster: Baseline, 3 months, and 6 months post-enrollment.
Global health status and quality of life assessed using the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30). Scores range from 0 to 100, where higher scores represent a better overall quality of life and higher functioning.
Baseline, 3 months, and 6 months post-enrollment.
Elderly-Specific Quality of Life Score (EORTC QLQ-ELD14)
Zeitfenster: Baseline, 3 months, and 6 months post-enrollment.
Elderly-specific quality of life issues assessed using the EORTC QLQ-ELD14 module. Scores range from 0 to 100. For symptom scales, higher scores represent worse outcomes (higher level of symptoms/problems); for functional scales, higher scores represent better outcomes.
Baseline, 3 months, and 6 months post-enrollment.
Incidence of Hospitalizations
Zeitfenster: At 3 and 6 months post-enrollment.
Number of patients requiring unplanned hospital admissions during the treatment course
At 3 and 6 months post-enrollment.
Treatment Discontinuation and Dose Reduction Rates
Zeitfenster: At 3 and 6 months post-enrollment.
Percentage of patients undergoing chemotherapy dose reductions (planned vs. received dose) or early treatment discontinuation.
At 3 and 6 months post-enrollment.
Patient Survival and Disease Progression
Zeitfenster: At 3 and 6 months post-enrollment.
Overall survival status and disease progression rate assessed according to RECIST 1.0 criteria.
At 3 and 6 months post-enrollment.

Mitarbeiter und Ermittler

Hier finden Sie Personen und Organisationen, die an dieser Studie beteiligt sind.

Sponsor

Mitarbeiter

Ermittler

  • Hauptermittler: Luciola Pontes Leite de Barros, Hospital do Coração (HCOR)

Studienaufzeichnungsdaten

Diese Daten verfolgen den Fortschritt der Übermittlung von Studienaufzeichnungen und zusammenfassenden Ergebnissen an ClinicalTrials.gov. Studienaufzeichnungen und gemeldete Ergebnisse werden von der National Library of Medicine (NLM) überprüft, um sicherzustellen, dass sie bestimmten Qualitätskontrollstandards entsprechen, bevor sie auf der öffentlichen Website veröffentlicht werden.

Haupttermine studieren

Studienbeginn (Geschätzt)

1. September 2026

Primärer Abschluss (Geschätzt)

1. Januar 2027

Studienabschluss (Geschätzt)

1. Juli 2027

Studienanmeldedaten

Zuerst eingereicht

24. August 2026

Zuerst eingereicht, das die QC-Kriterien erfüllt hat

1. September 2026

Zuerst gepostet (Tatsächlich)

4. September 2026

Studienaufzeichnungsaktualisierungen

Letztes Update gepostet (Tatsächlich)

4. September 2026

Letztes eingereichtes Update, das die QC-Kriterien erfüllt

1. September 2026

Zuletzt verifiziert

1. August 2026

Mehr Informationen

Begriffe im Zusammenhang mit dieser Studie

Schlüsselwörter

Andere Studien-ID-Nummern

  • LACOG 0325

Arzneimittel- und Geräteinformationen, Studienunterlagen

Studiert ein von der US-amerikanischen FDA reguliertes Arzneimittelprodukt

Nein

Studiert ein von der US-amerikanischen FDA reguliertes Geräteprodukt

Nein

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