A Prognostic Tool for Early Stage CLL

Oncology Institute of Southern Switzerland (IOSI)

Status: Completed

Conditions: Chronic Lymphocytic Leukemia

Study type: Observational

Funder types: Other

Identifiers

NCT03436524

IOSI-EMA-005

Details and patient eligibility

About

The study aims at developing a model for the prediction of time to first treatment in chronic lymphocytic leukemia patients presenting with asymptomatic early stage disease

Full description

Already existing and coded health-related personal data will be retrospectively collected from the CLL databases of the Institute of Oncology Research and of the Division of Hematology of the University of Eastern Piedmont.

The adjusted association between exposure variables and time to first treatment will be estimated by Cox regression. This approach will provide the covariates independently associated with progression free survival that will be utilized in the development of a model to predict time to first treatment.

Model performance (c-index and net reclassification improvement) in discriminating patients who will eventually be treated vs patients who will not be eventually treated will be compared with that of already existing prognostic model that have been validated to predict overall survival but not time to first treatment in CLL (i.e. CLL-IPI, MDACC score, Barcelona-Brno score).

Enrollment: 4,933 patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Male or female adults 18 years or older
• Diagnosis of chronic lymphocytic leukemia
• Binet A stage at presentation
• No treatment need at presentation
• Availability of the baseline and follow-up annotations

Exclusion criteria

• None.

Trial design

4,933 participants in 2 patient groups

Training cohort

Description:

Cohort of chronic lymphocytic leukemia patients at Binet A stage for the development of the risk stratification model

Validation cohorts

Description:

Cohorts of chronic lymphocytic leukemia patients at Binet A stage for the validation of the risk stratification model