CODAGLIO

The CODAGLIO project, that started in September 2016, aims to combine clinical and health-related quality of life (HRQoL) data of available randomized controlled trials in glioma (i.e., primary brain tumour) patients. The project is a collaboration between the Quality of Life Group, the Brain Tumour Group and the Quality of Life Department. We have included data of more than 6000 patients. With this large dataset we will be able to answer clinically relevant questions, such as: ‘What is the added prognostic value of HRQoL on overall survival and progression-free survival?’ and ‘Are specific symptom clusters associated with overall HRQoL?’. Ultimately, we will study the trade-off between quality and quantity of life of treatment strategies and address the question ‘Will a combined analysis of survival and HRQoL data facilitate interpretation on the net clinical benefit of a treatment strategy?’

Since the start of the project we have: received approval from the principal investigators of the RCTs to include the data, build the database, contacted collaborating researchers to gain expertise on certain statistical methods, published four manuscripts, of which we also presented the results on international neuro-oncology conferences.

Currently we are in the review process of the last manuscript. The project has come to an end.

We have applied for a grant to continue our work, answering more clinically relevant questions in brain tumor and other rare cancers (CODARARE project: submitted).

Patients with a glioma, the most prevalent primary malignant brain tumour, demonstrate a high symptom burden and experience many disease-specific symptoms such as cognitive dysfunction and seizures. Although patients receive treatment with surgery, radiotherapy and chemotherapy, current treatment options are not curative. Therefore, the quality of survival is for these patients at least as important as the duration of survival. Because primary brain tumours are relatively rare, and the number of trials that included assessments of health-related quality of life (HRQoL) are limited, it is essential to combine these datasets. By combining these datasets we will be able to answer clinically relevant questions that could not be answered until now. For example, we will study what specific concurrent symptoms cause a deficit in functioning, as management of these symptoms may result in an improved HRQoL. Also, we will use certain models that combine information on the impact of a treatment on both survival and HRQoL. Such information may help physicians to decide which treatment is best for a specific patient.