MedicalResearch.com Interview with:
Yu Uneno, M.D.
Department of Therapeutic Oncology,
Graduate School of Medicine, Kyoto University
Kyoto city, Kyoto Japan
MedicalResearch.com: What is the background for this study? What are the main findings?
Response: Prognosis prediction is one of the most important issues to make an optimal treatment decision for both cancer patients and health care professionals. Previous prognosis prediction models were developed using data from single time point (at the baseline, for example), limiting the use of the models at the similar situation.
Recently, we have developed the Six Adaptable Prognostic (SAP) models which can be repeatedly used at any time point after the initiation of treatment for patients with cancer receiving chemotherapy. Those models use only three laboratory items (albumin, neutrophil, lactate dehydrogenase) which are routinely monitored in daily clinical practice.
MedicalResearch.com: What should readers take away from your report?
Response: The aim of this study is to validate the SAP model for patients under palliative care. We found that the SAP models showed a good performance for predicting the death occurrence within one to three months. The prediction was accurate in 75–80% of cases.
MedicalResearch.com: What recommendations do you have for future research as a result of this study?
Response: Currently many prognosis prediction models are available including the SAP model. Patients are encouraged to discuss about end-of-life issues including prognosis with their physicians to receive better medical care.
MedicalResearch.com: Is there anything else you would like to add?
Response: Benefits of using those prognostic models have been beyond the scope of the main issue in this research field for a long time. The clinical utility of those prognostic models need to be validated scientifically.
MedicalResearch.com: Thank you for your contribution to the MedicalResearch.com community.
Citation: ESMO Asia 2016 abstract
Validation of the set of six adaptable prognosis prediction (SAP) models for cancer patients in palliative care settings: A sub analysis of the Japan-prognostic assessment tools validation (J-ProVal) study
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