Novel Prediction Models of Nonrelapse Mortality in Patients Specific to Each Reduced-Intensity Conditioning before Allogeneic Hematopoietic Stem Cell Transplantation: A Multicenter Analysis from the Kanto Study Group for Cell Therapy (KSGCT)

Introduction

Despite recent developments on various transplantation procedures and supportive therapy, nonrelapse mortality (NRM) after allogeneic stem cell transplantation (allo-SCT) remains an essential issue. In choosing the appropriate regimen for allo-SCT, decision-making information that considers the complexity of different risk factors is vital. The Hematopoietic Cell Transplantation-Comorbidity Index (HCT-CI), which was initially derived and validated by investigators at the Fred Hutchinson Cancer Research Center to predict NRM, has become a widely validated tool for predicting outcomes in many transplant settings (Sorror et al. Blood. 2005). It can also stratify patients for the risk of other outcomes, including overall survival and graft versus host disease. Patients with a high HCT-CI score tend to prefer allo-SCT with reduced-intensity conditioning (RIC). Conversely, for those who has a low HCT-CI score but prefer allo-SCT with RIC, a prognostic indicator is unnecessary. Furthermore, the risk factors for NRM may differ among various conditioning regimens. Therefore, the current study aimed to establish a new prognostic model for patients specific to each RIC regimen before allo-SCT.

Methods

We performed a retrospective cohort study to develop prognostic models of NRM in patients conditioned with fludarabine/ busulfan (Flu/Bu) or fludarabine/melphalan (Flu/Mel). We selected patients who had leukemia and lymphoma in remission or had untreated or stable myelodysplastic syndrome and experienced initial allo-SCT relapse between 2007 and 2017 in the Kanto Study of Group for Cell Therapy. The primary outcome measure was 2-year NRM. Furthermore, we evaluated variables such as patient age, albumin value (Alb), liver function, renal function, respiratory function, ejection fraction (EF), C-reactive protein, stem cell source, donor type, usage of antithymocyte globulin, performance status (PS), recipient/donor sexes, time interval from diagnosis to transplant, usage of total body irradiation (TBI), and HCT-CI score. To identify a set of variables for Fine-Gray competing risk regression model, we used an Akaike Information Criterion (AIC)-based variable selection procedure. We assigned weights to individual parameters according to their prognostic significance in Fine-Gray models. The identified model’s discriminative ability was assessed by Harrell’s C-statistic.

Results

Among the 430 patients analyzed, 202 received Flu/Bu, and 228 received Flu/Mel. In Flu/Bu and Flu/Mel, the mean age was 60.6±6.2 and 57.8±8.5 years, the HCT-CI score ≤ 2 was observed in 77.2% and 78.1%, and 2-year NRM was found in 19.7% and 17.9% of the patients, respectively. Before transplantation, the most dominant parameters in Flu/Bu were Alb < 4.0 g/dL and EF < 55 %, whereas those in Flu/Mel were PS > 0, Alb < 4.0 g/dL, stem cell source, recipient/donor sexes and usage of TBI. Each of the abovementioned parameters, including PS > 0, was scored based on regression coefficients. To evaluate the 2-year NRM, we divided the total scores into several risk groups. In the Flu/Bu group, the NRM was 10.3 % in low (score 0, n = 69), 19.2 % in intermediate-1 (score 1, n =46), 25.4 % in intermediate-2 (score 2-3, n = 81) and 50.0 % in high (score 4-5, n = 6) scores. In the Flu/Mel group, the NRM was 8.6 % in low (score 0–2, n = 48), 15.2 % in intermediate (score 3-4, n = 139), and 37.4 % in high (score 5-8, n = 41) scores (Figure). Higher scores were strongly associated with worse NRM and survival. These models showed good discrimination, with C-statistic values of 0.64 in Flu/Bu and 0.67 in Flu/Mel.

Conclusions

Our prognostic models for NRM estimation can distinguish patients with a high NRM risk. To our knowledge, these models are the first prognostic models used to estimate NRM for standard-risk patients specific to each RIC regimen. This new simple index may help predict NRM and choose an appropriate conditioning regimen before allo-SCT.