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D. Fuerst et al.
Introduction
Hematopoietic stem cell transplantation has been estab- lished as a curative treatment for various high-risk hemato- logic disorders.1 The success of hematopoietic stem cell transplantation is determined by multiple factors including disease-specific predictors, patient and donor characteris- tics, as well as treatment choices.2,3 Improvements in clini- cal care and identification of compatible donors have enhanced safety and efficacy leading to increasing num- bers of patients being transplanted.4 Outcome is tradition- ally measured in terms of overall survival and disease-free survival. However, these endpoints represent a summary of events with different etiologies, but mainly events relat- ed to treatment complications and disease relapse. In order to characterize effects in clinical studies from the perspec- tive of transplantation biology, subanalyses for event types are necessary.5 Competing-risk analysis is the standard approach to time-to-event analyses for endpoints which represent competing components of a composite outcome.6 Some clinical predictors are strongly associated with outcome and some of them are particularly involved in increased early mortality, leading to violation of the pro- portional hazards assumption in a standard Cox regression model.7,8 Such variables include poor Karnofsky perform- ance score (KPS) and advanced disease stage at the time of transplantation as well as the pre-transplant toxicity of myeloablative conditioning. We have previously shown that these variables have a strong time-dependent effect on survival endpoints (overall and disease-free survival).9 In this analysis we aimed at investigating potential time- dependent effects of different variables in an event-specific fashion. This procedure provides a deeper insight into the relation between covariables and outcome, extending the
scope of the previous analysis. The primary hypothesis was that poor KPS, conditioning toxicity, bone marrow as the graft source, and advanced disease stage are associated with significantly higher early mortality rates in analyses of mortality endpoints, i.e. transplant-related mortality and non-relapse mortality. In addition, we aimed to explore the time dependency in the effect of these covariables on relapse incidence.
Methods
Patients
We analyzed data from 14951 patients registered in the German Registry for Stem Cell Transplantation (DRST). Adult patients having received a first hematopoietic stem cell transplant for acute myeloid leukemia, acute lymphoblastic leukemia, myelodysplastic syndrome, and aggressive or indolent non- Hodgkin lymphoma between 1976 and 2013 were included. Only transplants for which the graft source was bone marrow or peripheral blood were included in this study (Table 1).
Definitions
The KPS at transplant was dichotomized into good (80-100%) and poor (<80%). Early disease stage was defined as transplan- tation in first complete remission for acute leukemia and as untreated or in first complete remission for myelodysplastic syn- drome and non-Hodgkin lymphoma. Intermediate disease stage grouped together patients with acute leukemia transplanted in second complete remission, those with myelodysplastic syn- drome transplanted in second complete or partial remission, and patients with lymphoma transplanted in second complete remis- sion, partial remission or stable disease. Stages other than early or intermediate were classified as advanced disease stage.10 Conditioning regimen intensity was categorized into myeloab- lative and reduced intensity according to guidelines of the European Group for Blood and Marrow Transplantation (EBMT) Med-AB manual. Two competing risk models were considered: one with the endpoints transplant-related mortality, death from other causes and death from unknown causes, and a second with the endpoints relapse and non-relapse mortality (Table 2, Figure 1). Death from other causes comprises death due to sec- ondary malignancies, relapse or progression of disease, and other causes (not transplant-related). Thus, death from other causes is one of three competing events, the other two being transplant-related mortality and death from unknown causes.
Table 1. Patients’ characteristics.
Variable
Diagnosis
Patients’ age
Graft source
Conditioning
Disease stage
Year
of transplantation
Karnofsky performance score
Donor type
Characteristics
Acute myeloid leukemia Acute lymphoblastic leukemia Myelodysplastic syndromes NHL-indolent NHL-aggressive
mean (SD)
median (range)
Bone marrow
Peripheral blood stem cells
Myeloablative
Reduced intensity
Early Intermediate Advanced
1976-2000 2001-2005 2006-2013
Good (80-100%) Poor (<80%) Missing
Mismatched related Mismatched unrelated Matched related Matched unrelated
N (%)
7133 (47.7%) 2696 (18.0%) 2380 (15.9%) 1545 (10.3%) 1197 (8.0%)
46.79 (14.1)
48 (18-78)
2303 (15.4%) 12648 (84.6%)
9684 (64.8%)
5267 (35.2%)
6238 (41.7%) 4511 (30.2%) 4202 (28.1%)
2727 (18.2%) 3713 (24.8%) 8511 (56.9%)
12244 (81.9%) 1018 (6.8%) 1689 (11.3%)
676 (4.5%) 2708 (18.1%) 5795 (38.8%) 5772 (38.6%)
Table 2. Competing-risk characteristics.
Variable
CR setting: OS
CR setting: DFS
Characteristics
Event indicator:
Time:
Event indicator:
Time:
N
Censored 7073
TRM 3845
DOC 3673
Unknown death 360
Mean (SD): 919 days (1396) Median (range): 289 days (1-12059)
Censored 6334
Relapse 4002
Death without prior relapse 4344 Missing 271
Mean (SD): 844 days (1374) Median (range): 218 days (1-12059)
NHL: non-Hodgkin lymphoma; SD: standard deviation.
CR: competing risks; OS: overall survival; DFS: disease-free survival; TRM: transplant-related mor- tality, DOC: death of other cause; SD: standard deviation; censored: patients lost to follow up with- out having had an event or still event-free at data request.
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haematologica | 2018; 103(9)