The Use of Prognostic Models in Allogeneic Transplants: A Perspective Guide for Clinicians and Investigators

Abstract

Allogeneic hematopoietic cell transplantation (HCT) can cure many hematologic diseases, but it carries the potential risks of increased morbidity and mortality rates. Prognostic evaluation is a scientific entity at the core of care for potential recipients of HCT. It improves the decision-making process of transplant versus not and of which transplant strategy and allows for future trials targeting patients' intolerances to transplant; hence it ultimately improves transplant outcomes. Prognostic models are key for appropriate actuarial outcome estimates, which has been frequently shown to be better than physicians' subjective estimates. To make the most accurate prognostic evaluation for HCT, one should rely on more than one prognostic model. For relapse and relapse-related mortality risks, the refined disease risk index is currently the most informative model. It can be supplemented by disease-specific models that consider genetic mutations as predictors in addition to information on measurable residual disease. For non-relapse mortality and HCT-related morbidity risks, the HCT-comorbidity index and Karnofsky performance status have proven to be the most reliable and most accepted by physicians. These can be supplemented by gait speed as a measure of frailty. Some other global prognostic models might add additional prognostic information. Physicians' educated perceptions can then put all this information in context taking into consideration conditioning regimen and donor choices. The future of transplantation mandates 1) clinical investigators specifically trained in prognostication, 2) increased reliance on geriatric assessment, 3) the use of novel biomarkers such as genetic variants, and 4) successfully applying novel statistical methods such as machine-learning.