[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100654768":3},{"organization":4,"outcomesModule":7,"designInfo":35,"detailedDescription":37,"studyPopulation":38,"armGroups":39,"interventions":34,"overallOfficials":46,"centralContacts":51,"locations":61,"responsibleParty":85,"collaborators":87,"id":91,"slug":92,"hasResults":93,"nctId":94,"briefTitle":95,"officialTitle":96,"acronym":97,"eligibilityCriteria":98,"healthyVolunteers":93,"sex":99,"minAge":100,"maxAge":34,"enrollmentInfo":101,"targetDuration":34,"studyType":104,"phases":34,"briefSummary":105,"conditions":106,"keywords":108,"overallStatus":110,"whyStopped":34,"lastUpdateSubmitDate":111,"lastUpdatePostDateStruct":112,"startDateStruct":115,"completionDateStruct":117,"leadSponsor":119,"locationsCount":120},{"fullName":5,"class":6},"Latin American Cooperative Oncology Group","OTHER",{"primaryOutcomes":8,"secondaryOutcomes":13,"otherOutcomes":34},[9],{"measure":10,"description":11,"timeFrame":12},"Predictive Accuracy of the PROTEGER Machine Learning Model","Discrimination performance of the machine learning predictive model in recommending oncogeriatric treatment decisions (standard treatment, dose-adjusted treatment, or supportive care\u002Fno 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\u002Fchance) to 1.0 (perfect discrimination).","Up to 6 months post-enrollment.",[14,18,22,25,28,31],{"measure":15,"description":16,"timeFrame":17},"Incidence of High-Grade Chemotherapy-Related Adverse Events","Percentage of participants experiencing Grade 3 or higher toxicities\u002Fadverse reactions evaluated using the Common Terminology Criteria for Adverse Events (CTCAE) version 5.0.","At 3 and 6 months post-enrollment.",{"measure":19,"description":20,"timeFrame":21},"General Quality of Life Score (EORTC QLQ-C30)","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.",{"measure":23,"description":24,"timeFrame":21},"Elderly-Specific Quality of Life Score (EORTC QLQ-ELD14)","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\u002Fproblems); for functional scales, higher scores represent better outcomes.",{"measure":26,"description":27,"timeFrame":17},"Incidence of Hospitalizations","Number of patients requiring unplanned hospital admissions during the treatment course",{"measure":29,"description":30,"timeFrame":17},"Treatment Discontinuation and Dose Reduction Rates","Percentage of patients undergoing chemotherapy dose reductions (planned vs. received dose) or early treatment discontinuation.",{"measure":32,"description":33,"timeFrame":17},"Patient Survival and Disease Progression","Overall survival status and disease progression rate assessed according to RECIST 1.0 criteria.",null,{"allocation":34,"interventionModel":34,"interventionModelDescription":34,"primaryPurpose":34,"observationalModel":36,"timePerspective":6,"maskingInfo":34},"COHORT","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.\n\nTo 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:\n\nPhase 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.\n\nPhase 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.\n\nStudy Objectives:\n\nThe 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.\n\nAll 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.","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.",[40,43],{"label":41,"type":34,"description":42,"interventionNames":34},"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.",{"label":44,"type":34,"description":45,"interventionNames":34},"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.",[47],{"name":48,"affiliation":49,"role":50},"Luciola Pontes Leite de Barros","Hospital do Coração (HCOR)","PRINCIPAL_INVESTIGATOR",[52,57],{"name":53,"role":54,"phone":55,"phoneExt":34,"email":56},"Project Manager","CONTACT","+55 51 3384 5334","lacog0325@lacog.group",{"name":58,"role":54,"phone":59,"phoneExt":34,"email":60},"Head of Clinical Operations","+55 (51) 3384.5334","laura.voelcker@lacog.group",[62,76],{"facility":63,"status":34,"city":64,"state":64,"zip":65,"country":66,"countryCode":67,"cosmosGeoPoint":68,"geoPoint":73,"contacts":74},"BR192 HCOR - Hospital do Coração","São Paulo","04004-060","Brazil","BR",{"type":69,"coordinates":70},"Point",[71,72],-46.63611,-23.5475,{"lat":72,"lon":71},[75],{"name":48,"role":50,"phone":34,"phoneExt":34,"email":34},{"facility":77,"status":34,"city":64,"state":64,"zip":78,"country":66,"countryCode":67,"cosmosGeoPoint":79,"geoPoint":81,"contacts":82},"HIAE - Hospital Israelita Albert Einstein","05653-000",{"type":69,"coordinates":80},[71,72],{"lat":72,"lon":71},[83],{"name":84,"role":50,"phone":34,"phoneExt":34,"email":34},"Ludmila de Oliveira Muniz Koch",{"type":86,"investigatorFullName":34,"investigatorTitle":34,"investigatorAffiliation":34,"oldNameTitle":34,"oldOrganization":34},"SPONSOR",[88],{"name":89,"class":90},"ONCOGER SpA","INDUSTRY","100654768","oncogeriatric-screening-and-evaluation-program-100654768",false,"NCT07805642","Oncogeriatric Screening and Evaluation Program","PROgrama de Tamizaje y Evaluación oncoGERiátrica","PROTEGER","Inclusion Criteria:\n\n* Age 65 years or older.\n* Diagnosis of solid tumor cancer.\n* Must have been evaluated and followed up by a local oncology team.\n* Signed Informed Consent Form (ICF) applied in accordance with the local ethics committee.\n* Must have undergone a Comprehensive Geriatric Assessment (CGA).\n\nExclusion Criteria:\n\n\\- Patients who are unable or unwilling to consent to providing information will be excluded.","ALL","65 Years",{"count":102,"type":103},412,"ESTIMATED","OBSERVATIONAL","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.",[107],"Solid Tumor Cancer",[109],"Oncogeriatrics","NOT_YET_RECRUITING","2026-09-01",{"date":113,"type":114},"2026-09-04","ACTUAL",{"date":116,"type":103},"2026-09",{"date":118,"type":103},"2027-07",{"name":5,"class":6},2]