How to select older patients with acute myeloid leukemia fit for intensive treatment?
| Authors | |
|---|---|
| Year of publication | 2021 |
| Type | Article in Periodical |
| Magazine / Source | Hematological Oncology |
| MU Faculty or unit | |
| Citation | |
| Web | https://onlinelibrary.wiley.com/doi/full/10.1002/hon.2798 |
| Doi | http://dx.doi.org/10.1002/hon.2798 |
| Keywords | AML; elderly; geriatric assessment; negative prognostic factors; older patients |
| Description | Outcomes of the treatment of older patients with acute myeloid leukemia (AML) are unsatisfactory due to a higher incidence of negative patient- and disease-related risk factors connected with aging. Prediction of poor tolerance to aggressive treatment and low response to standard intensive chemotherapy are the main root causes why the treatment decision is challenging. For a long time, negative prognostic factors for treatment outcomes, overall survival, and early death such as the age itself, low-performance status, high-comorbidity burden, adverse cytogenetics, and secondary AML have been known, and they are routinely taken into account during therapeutic balance. In consideration of the risk factors and specific laboratory results, prognostic models have been created. Despite the abovementioned facts, the survival of older patients with AML remains very poor, that holds true even for the intensive therapy. For that reason, there is an increased effort to find a better approach how to select patients who would benefit from intensive treatment without decreasing their quality of life through severe complications with risk of high treatment-related mortality. Based on the results of clinical studies, the geriatric assessment could be the missing step which would help select older patients who are really fit for intensive treatment and who will benefit from it the most. This review focuses on the risk factors that should be taken under advisement when the decision about the treatment is made. With reference to the published information, we propose an algorithm how to identify fit, vulnerable, and frail patients. |