validation of the hematopoietic cell transplantation - blood

Microsoft Word - 6730822-file00Validation of the Hematopoietic Cell Transplantation-Specific Comorbidity Index: a prospective, multicenter GITMO study Roberto Raimondi1, Alberto Tosetto1, Rosi Oneto2 , Riccardo Cavazzina1, Francesco Rodeghiero1, Andrea Bacigalupo2 , Renato Fanin3, Alessandro Rambaldi4 and Alberto Bosi5 1 Department of Hematology, S. Bortolo Hospital, Vicenza, Italy; 2 Division of Hematology, Ospedale San Martino, Genova, Italy ; 3 Department of Hematology, Azienda Ospedaliera, Università di Udine, Italy; 4 Hematology and Bone Marrow Unit, Ospedali Riuniti, Bergamo, Italy; 5 Department of Hematology, Azienda Ospedaliera di Careggi e Università di Firenze, Italy Correspondence to: Dr. Roberto Raimondi, Hematology Department, S. Bortolo Hospital, Via Rodolfi 37, 36100 Vicenza, Italy (phone: 0039-0444-753626; fax: 0039-0444-920708; e-mail: [email protected])
Blood First Edition Paper, prepublished online June 27, 2012; DOI 10.1182/blood-2012-03-414573
Copyright © 2012 American Society of Hematology
For personal use only.on December 24, 2018. by guest www.bloodjournal.orgFrom
decision. Recently, the Hematopoietic Cell Transplantation-Specific Comorbidity Index (HCT-CI)
has been associated with increased NRM risk in several retrospective studies, but its clinical utility
has never been demonstrated prospectively in an adequately sized cohort. To this aim, we
prospectively evaluated a consecutive cohort of 1937 patients receiving HSCT in Italy over two
years. HCT-CI was strongly correlated with both two-year NRM (14.7%, 21.3% and 27.3% in
patients having an HCT-CI score of 0, 1-2, and ≥ 3 respectively) and OS (56.4%, 54.5%, and 41.3%
respectively). There was an excellent calibration between the predicted and observed two-year
NRM in patients having an HCT-CI score of 0 and 1-2, whereas in the ≥ 3 group the predicted
NRM overestimated the observed NRM (41% vs. 27.3%). HCT-CI alone was the strongest
predictor of NRM in patients with lymphoma, myelodisplastic syndrome, and acute myeloid
leukemia in first remission (c-statistics 0.66, 064 and 0.59 respectively). We confirm the clinical
utility of the HCT-CI score that could also identify patients at low NRM risk possibly benefiting
from a HSCT-based treatment strategy.
and non-malignant hematological disorders. In the recent years, novel approaches such as the
reduced-intensity conditioning regimens have expanded the use of HSCT also to elderly patients or
to patients otherwise ineligible for conventional transplants. HSCT still remains associated with a
significant mortality and morbidity although the Seattle team has recently observed a substantial
reduction of nonrelapse mortality (NRM) and overall mortality in the last years 1. Careful
assessment of risks and benefits prior to transplantation remains however an essential issue. Three
major factors influence nonrelapse mortality and overall survival (OS) after HSCT: the patient’s
disease, the type of transplant procedure and donor, and the patient’s risk profile, which includes
age, performance status and presence of comorbidities. In an attempt to improve quantification of
the patient’s risk profile, Sorror et al. recently proposed the Hematopoietic Cell Transplantation-
Specific Comorbidity Index (HCT-CI) developed from a single-center retrospective analysis and
internal validation 2. The HCT-CI demonstrated to capture more pretransplant comorbidities than
the previously used Charlson Comorbidity Index and to provide better assessment of NRM,
defining three risk groups, respectively with HCT-CI score of 0 (low-risk), 1-2 (intermediate risk)
and ≥ 3 (high-risk) showing linear predictions of NRM and OS.
On this premise, the HCT-CI score has been included as an eligibility criterion in some clinical
trials, but it has never been externally validated by a large multicenter longitudinal study.
Furthermore, it is not known how the clinical usefulness of the HCT-CI applies to all the different
malignant hematologic diseases or if its use should be preferably restricted to selected disorders. In
this study, we prospectively collected comorbidity data to compute the HCT-CI score in a
consecutive series of patients undergoing allogeneic bone marrow transplantation in Italy. The
primary aim of the study was to externally validate the HCT-CI in terms of calibration and
discrimination in a multicenter, prospective study setting. As a secondary aim, we evaluated the
usefulness of the HCT-CI in different patient subgroups.
and belonging to the national bone marrow transplantation network (Gruppo Italiano Trapianto di
Midollo Osseo, GITMO) were asked to participate in this prospective, multicenter observational
study. As it is mandatory for any GITMO center to enter information for all their consecutive
transplants into the European Bone Marrow Transplantation (EBMT) database (Promise, Project
Manager Internet Service, http://www.ebmt.org), we first updated the Minimum Essential Data
section (MED-A) of the Promise database to include questions specifically assessing the
presence/absence of all the comorbidities required to calculate the HCT-CI score. Permission to
perform this study was obtained from the GITMO Clinical Studies Board. All patients provided
informed consent in accordance with the Declaration of Helsinki for the analysis of their clinical
data.
Eligibility criteria. Per protocol, we considered eligible for analysis only those first transplants
performed from January 1st 2008 to February 1st 2011 on patients > 18 years for malignant or non-
malignant hematological disease, and using peripheral blood stem cells (PBSC) or bone marrow
(BM) as cell source (thus excluding cord blood cells). This time-frame was chosen because from
preliminary analysis of previous enrollment data it was expected to yield at least about 200 cases for
each of four pre-specified diagnoses (acute leukemias; non-Hodgkin lymphomas; multiple
myeloma; myelodisplastic syndromes).
Definitions. According to the EBMT criteria 3, we considered as myeloablative (MAC) any regimen
with a total busulfan dose > 8 mg/kg, or cyclophosphamide dose > 120 mg/kg (or > 60 mg/kg if in
combination with other drugs) , or melphalan dose > 140 mg/m2 or TBI dose > 6 Gy; reduced
intensity/non myeloablative conditioning regimens (RIC) were all others regimens with dosages
below the above mentioned limits. According to the original paper of Sorror et al., we defined acute
leukemia in first complete remission, chronic myeloid leukemia in chronic phase and
myelodisplastic syndrome-refractory anemia as low risk diseases; high risk diseases were all other
diagnoses 2.
Follow-up procedures. Nonrelapse mortality (NRM) was defined as death from nonrelapse causes;
overall survival (OS) was defined as the time from transplantation to death for any cause. Data was
censored at time of death or last available follow-up, as available from the mandatory EBMT update
from each GITMO Center.
Statistical Methods. Multiple imputation was used to account for sporadic missing values in
covariates other than those affecting the HCT-CI, to allow multivariate analyses be carried in the
whole dataset, using the Stata mi impute procedure 4. Competing risks analysis was used to
calculate the 12 and 24 months NRM cumulative incidence, using the Gray’s test to test differences
between HCT-CI score groups 5,6. Overall Survival (OS) was estimated using the Kaplan-Meier
method; hazard ratios were computed between subgroups using Cox regression for OS and
competing risk regression for NRM, both stratified for centre. Competing risk regression was used
to compute NRM cumulative incidence rates, considering non-transplant mortality as the competing
event. The predictive ability of the HCT-CI score was assessed using time-dependent receiver-operator
curves (ROC) analysis 7. All computations were performed using Stata and the procedures cmprsk
and survivalROC of the R statistical package 8,9.
Results Patients. 44/46 (95.6%) of all GITMO centres performing HSCTs in adult patients agreed to
participate in the study. From 3318 HSCTs performed during the considered time-frame by the
enrolling Centres, 1937 were available for analysis. The reasons for exclusion from the study were:
comorbidities not reported (n=1167), second or more transplants (n=111), incomplete follow-up
data (n=55), non-hematologic diseases (n=31), lost to follow-up (n=17). There were no material
differences in terms of OS and NRM between the 1937 evaluated patients and the 1167 who were
excluded because of failure to report comorbidities (24 months OS, 53.9% [95% CI 50.8-56.8] vs.
56.6% [95% CI 52.5-60.5], p=0.23, respectively in the evaluated/excluded groups; 24 months NRM
23.8% [95% CI 21.3-26.5] vs. 26.1% [95% CI 23.3-30.6], p=0.15 in the evaluated/excluded
groups).
Table 1 reports the main characteristics and the prevalence of comorbidities in the cohort, 1119
patients (58%) being classified as low risk (HCT-CI score=0), 441 (23%) as intermediate risk
(HCT-CI score=1-2) and 377 (19%) as high risk group (HCT-CI score≥3).
Follow-up. Patient’s follow-up totalled 1681 patient-years with a median time of 10.1 months from
transplant (range 0.03-38.77). During follow-up, 666 deaths were observed in the cohort (332
NRM, 334 disease-related deaths). At multivariate analysis, HCT-CI score, age above 50 years,
high-risk disease and unrelated donor were all associated with increased NRM and decreased OS
(Table 2). HCT-CI score and high-risk disease were the strongest predictors of both NRM and OS,
as apparent from multivariate analysis. Table 3 and Figure 1 report the overall survival and NRM
for the low, intermediate and high risk HCT-CI score groups.
NRM prediction using the HCT-CI score. To validate the predictive ability of the HCT-CI score, we
compared its performance in our dataset in terms of calibration and discrimination 10,11. Two-year
NRM cumulative incidences, accounting for competing risks, and OS were used to compare the
calibration of the HCT-CI model, and are presented in Figure 1 and Figure 2 and stratified
according to different clinical conditions in Table 4. Figure 3 presents NRM and OS probabilities
stratified for reduced intensity or myeloablative conditioning regimens and HCT-CI, after Cox (OS)
or competing-risk regression (NRM) adjustment for all the variables presented in Table 2.
Increasing HCT-CI score confirmed to be associated with higher 1- and 2-year probability of NRM
and with lower OS, with a double NRM and overall mortality risk in those patients having a score ≥
3. However, while the Sorror’s two-year predicted NRM nearly overlapped those observed in our
cohort for low and intermediate-risk HCT-CI scores, the HCT-CI predicted two-year NRM did
overestimate the observed two-year NRM for the high-risk category (41% vs. 27.3% respectively,
Figure 4).
In terms of discriminatory capability, the HCT-CI score showed a c-value of 0.60 and 0.54 for
NRM and OS, respectively. Pre-specified subgroup analysis disclosed significant differences in
NRM prediction by the HCT-CI score, being higher in patients undergoing transplantation for
lymphoma, myelodisplastic syndrome, and acute myeloid leukemia in first remission (Table 4). In
contrast, in patients with multiple myeloma the predictive ability was lower, with no clear NRM
gradient between the three HCT-CI score groups and the lowest observed c-value.
Finally, we assessed the accuracy of data collected for HCT-CI scoring on a random set of 244
patients. These audits were made at the participating sites by researchers independent from the
original abstractors, who recalculated from the patient charts the HCT-CI score completely blinded
from the previous results. Discrepant results were observed in 26/244 patients, resulting in a shift of
the HCT-CI score group in 9.8% of the audited sample: 13/26 had an increased HCT-CI score,
11/26 a decreased score and in two cases the score remained unchanged (though with different
individual comorbidities). However, there were no differences between the original and recalculated
mean HCT-CI score (1.58 vs. 1.60, p=0.66), and there were no differences in terms of NRM
predictive ability between the original and recalculated HCT-CI, (c statistic 0.699 vs. 0.700,
p=0.911; both adjusted for age, sex, high-risk disease, donor type, stem cell source).
Discussion
In this study we primarily aimed at externally validate the Hematopoietic Cell Transplantation-
Specific Comorbidity Index (HCT-CI), a widely used prognostic index originally proposed in 2005 2 whose usefulness has been subsequently reported only in studies based on limited, retrospective
and mostly single-center patient series 12-46. To this aim, we prospectively enrolled a wide cohort of
unselected patients consecutively undergoing allogeneic bone marrow transplantation in Italy, and
evaluated the NRM and OS predictive ability of the HCT-CI applying the same selection criteria
originally used by Sorror et al. 2. At our knowledge, this is the first study aimed to validate the
HCT-CI in a prospective large cohort analysis using a multicentre national registry.
As a first result, we were able to confirm that the HCT-CI is an independent predictor of both NRM
and OS. However, while we observed a very good linear correlation between the predicted and the
observed NRM in patients having a HCT-CI score of 0 and 1-2, the observed NRM were
considerably lower in patients having a HCT-CI score greater than 2. This finding therefore reduces
the overall predictive value of the HCT-CI score, as indicated by a lower discriminatory c-value in
our study (0.60 vs. 0.65 reported by Sorror et al 2) and could be partly explained by an overfitting of
the original regression model, a well-known statistical artifact that justifies the need for validation
in external cohorts 11. The reliability of the HCT-CI scoring is another important issue, particularly
in multicenter studies, in which exposure misclassification could be a consequence both of intra and
between-Centers variability, and reduces the degree of association between HCT-CI and NRM. To
assess the degree of intra-Centers data accuracy, we performed an audit on about 12% of patient
charts, finding only minimal changes in HCT-CI score that did not affect the final results of our
study. Between-Centers variability could not be formally evaluated in our study, but we tackle this
issue by stratifying our analyses for centers. Finally, another possible explanation of the different
predictive capability of the HCT-CI score could well be the higher percentage of patients
transplanted for myelodisplastic syndrome and chronic myeloid leukemia in the Sorror’s cohort. In
these two disease subgroups, our analysis disclosed that the NRM is remarkably high in those
patients having a HCT-CI score ≥ 3. Therefore, an additional explanation of the reduced observed
NRM in patients with an HCT-CI score ≥ 3 could therefore lie in the different sample composition
of our cohort.
Additional major differences exist between our study population and the population of the original
study of Sorror et al. First, we analysed only adult patients. Second, our cohort had a different
composition in terms of a higher percentage of high risk diseases (69% vs. 41%) and of unrelated
donors (50% vs. 42%), more infections (11% vs. 4%) but less psychiatric (3% vs. 9%), mild hepatic
(5% vs. 16%) and mild pulmonary (10% vs. 24%) comorbidities. Our cohort showed a higher
proportion of patients having an HCT-CI score of 0. It is however worthwhile to note that in our
cohort the baseline two-year NRM risk in patients with a HCT-CI score of 0 was 14.7%, a figure
that nearly overlaps the Sorror’s predicted NRM (14%). Therefore, despite the unavoidable
differences in patient composition and the different (prospective) design of our study, the
and the generalizability of the HCT-CI.
Pre-specified subgroup analyses showed that in patients having a HCT-CI score equal to 0 the NRM
was similar for myeloablative and reduced-intensity/non-myeloablative conditioning regimens,
while in patients having a HCT-CI score ≥ 1 the use of reduced-intensity/non-myeloablative
conditioning regimens was correlated with lower NRM rates during the first year. After the first
year, however, the myeloablative group reaches a plateau whereas in the reduced-intensity/non-
myeloablative conditioning regimen group, NRM continues to increase. The advantage of related
donor compared with unrelated donor is evident across all the HCT-CI risk groups, but in the
setting of transplant from related donor the absence (HCT-CI score = 0) or presence (HCT-CI score
≥ 1) of comorbidities show a significant impact on NRM.
As a secondary finding, we demonstrated that the predictive ability of HCT-CI was higher in
patients having lymphoma, as already reported, 21,26,27 or myelodisplastic syndrome (MDS), as
already reported, 14,15,18,28,29 and in those receiving PBSCs as stem cell source. On the other hand,
the predictive value was much lower in patients having acute leukemias. However, since the
decision to offer elective HSCT in patients with acute myeloid leukemia (AML) in first complete
remission without high-risk characteristics is controversial, we analysed separately the 413 AML
patients who received HSCT in first remission. In these patients, the 2-year NRM was 9.1%, 11.4%,
and 19.4% for the HCT-CI score groups 0, 1-2, ≥3 respectively. Although these findings should be
taken very cautiously given the lack of randomization to HSCT in our cohort, they nonetheless
suggest that the NRM risk in selected patients with no or few comorbidities could be very limited,
and below the reported rate of death attributable to disease relapse even in patients with AML at
low risk of relapse (estimated to be around 20% 47). The same reasoning may be applied to patients
with lymphoma or myelodisplastic syndrome, since the NRM risk is increased more than two-folds
in patients having an HCT-CI ≥3 as compared with those having a score equal to 0. Therefore, our
study further support the need for appropriately designed studies investigating HSCT transplant in
those patients having a low HCT-CI score predicting a low NRM risk.
In an additional subgroup analysis, we evaluated the predictive role of HCT-CI in patients
undergoing reduced-intensity or myeloablative conditioning regimens. Given the observational
nature of our study, we used a multivariate approach to weight the reciprocal contributions of HCT-
CI and conditioning regimens to overall survival and transplant related mortality, adjusted for high-
risk disease, age and donor type. In this analysis, HCT-CI was a determinant of both OS and NRM,
while type of conditioning was related to OS but not with NRM, a finding that seems in keeping
with a previously published report in a smaller series 19. Our results support the hypothesis that
HCT-CI score groups and myeloablative conditioning have an apparently additive effect in
predicting survival: for instance, two-years survival is similarly reduced by an HCT-CI score 1- 2 in
patients receiving RIC or by an HCT-CI score 0 in patients receiving MAC.
A possible limitation of the present study is the unavailability of the HCT-CI in a relevant fraction
of all potentially eligible patients, since 1167/3318 (35%) were excluded from analysis because,
despite that the study formally started on January 1st 2008, some GITMO Centres started to
prospectively enter comorbidity data into the MEDAB database with some delay. This explains why
several patients (potentially eligible for study, since they received BMT in the per-protocol time-
frame) were considered as missing. However, we did not observed any difference both in terms of
OS and NRM between those patients in whom comorbidities were reported and those without,
supporting the validity of our study and the absence of a relevant selection bias.
To summarize, in the largest recent cohort of unselected patients undergoing HSCT so far
described, we were able to confirm the clinical utility of the HCT-CI score, although with a slightly
reduced discriminant capability. Furthermore, our findings suggest that the HCT-CI could have the
potential to identify patients at low NRM risk that could benefit from a more intensive, transplant-
based treatment strategy in selected disease subgroups. This latter finding needs to be further
explored by appropriately designed randomized clinical trials.
Contributions: R.R., A. Bosi, and R.F. designed the research; R.R. and R.O. collected data; R.R., A.T., F.R., R.F., A.R.,
A. Bosi, A. Bacigalupo analyzed and interpreted data; R.C. performed statistical analysis; R.R and A.T. wrote the
paper.
Acknowledgments
This work was supported in part by grants from Fondazione Progetto Ematologia (Hematology Project Foundation,
Vicenza, Italy) and AViLL/AIL (Associazione Vicentina per le Leucemie, i Linfomi e il Mieloma/Associazione Italiana
Leucemie, Vicenza, Italy).
R.R. thanks Michela Trentin for her support in data collection.
The Authors thank Caterina Micò, Irene Donnini, Alessandra Sperotto and Carlo Borghero for the audit re-evaluation of
patients’ charts.
Appendix
The authors wish to thank the colleagues of the following institutions (GITMO centers) in Italy that contributed to the
study: Department of Hematology; Nuovo Ospedale Torrette, Ancona (P. Leoni); Division of Hematology, Ospedale
S.G. Moscati, Ascoli Piceno (P. Galieni); Division of Hematology, Ospedale “S. S. Antonio e Biagio” Alessandria (A.
Levis); Division of Hematology, University of Bari, Bari (G. Specchia); Division of Hematology, Ospedali Riuniti,
Bergamo (A. Rambaldi); Institute of Hematology and Clinical Oncology “L. A. Seragnoli,” Ospedale “S. Orsola-
Malpighi,” University of Bologna, Bologna (G. Bandini, M. Baccarani); Department of Hematology, Ospedale
Regionale, Bolzano (M. Casini, S. Cortelazzo); Bone Marrow Transplant Center, Spedali Civili, Brescia (D. Russo);
Division of Hematology and Bone Marrow Transplant Center, Ospedale Oncologico “A. Businco,” Cagliari (E.
Angelucci, D. Baronciani); Bone Marrow Transplantation Unit, Ospedale “R. Binaghi,” University of Cagliari, Cagliari
(G. La Nasa); Division of Hematology and Bone Marrow Transplantation, Ospedale “Ferrarotto,” Catania (G. Milone);
Division of Hematology, Ospedale “S. Croce e Carlo,” Cuneo (N. Mordini); Department of Hematology, Ospedale
“Careggi,” University of Florence, Firenze (A. Bosi, S. Guidi); Division of Hematology, Ospedale “S. Martino,”
Genova (A. Bacigalupo, M. T. Van Lint); Hematology–Bone Marrow Transplantation Unit, Istituto Nazionale dei
Tumori, University of Milano, Milano (P. Corradini); Istituto Europeo di Oncologia, Milano (G. Martinelli);
Division of Hematology Ospedale “Cà Granda” Niguarda, Milano (E. Morra, P. Marenco); Department of Hematology,
Fondazione IRCCS Ospedale Maggiore Policlinico, Mangiagalli e Regina Elena, Milano (G. Lambretenghi Deliliers, F.
Onida); Hematology and Bone Marrow Transplantation Unit, S. Raffaele Scientific Institute, Milano (F. Ciceri, J.
Peccatori); Transplantation Unit Department of Oncology-Hematology, IRCCS Clinica Humanitas, Rozzano (L.
Castagna); Department of Oncology and Hematology University of Modena and Reggio Emilia, Modena (F. Narni);
Division of Hematology and Transplant Unit, Ospedale “S. Gerardo,” University of Milano-Bicocca, Monza (P.
Pioltelli); Division of Hematology, University of Napoli “Federico II” Medical School, Napoli (C. Selleri); Division of
Hematology and Transplant Unit, Ospedale “V. Cervello,” Palermo (R. Scimè); Department of Oncology, Hematology
Unit, Ospedale “La Maddalena,” Palermo (M. Musso); Division of Hematology, University of Pavia, Fondazione
Salvatore,” Pesaro (G. Visani); Department of Hematology, Ospedale Civile, Pescara (P. Di Bartolomeo); Oncology
and Hematology Department, Ospedale “Guglielmo da Saliceto,” Piacenza (D. Vallisa, L. Cavanna); Division of
Hematology, Univeristy of Pisa, Pisa (M. Petrini, F. Papineschi); Transplant Unit “A. Neri,” Ospedale “Bianchi-
Melacrino-Morelli,” Reggio Calabria (P. Iacopino, G. Messina); Hematology Unit, Arcispedale “S. Maria Nuova,”
Reggio Emilia (F. Merli, L. Gugliotta); Division of Hematology, Department of Cellular Biotechnologies and
Hematology, University “La Sapienza” (A. P. Iori, R. Foà); Hematology and Stem Cell Transplantation Unit Ospedale
“S. Camillo,” Roma (A. Locasciulli, I. Majolino); Division of Hematology, University “Cattolica S. Cuore”, Roma (G.
Leone, S. Sica); Hemato-Oncology Transplant Unit, University “Tor Vergata,” Transplant Network, Roma (W.
Arcese); Unit of Hematology and Bone Marrow Transplantation, IRCCS, “Casa Sollievo della Sofferenza,” S. Giovanni
Rotondo (A. M. Carella, N. Cascavilla); Division of Hematology and Bone Marrow Unit, Azienda Ospedaliera
Universitaria Senese “S. Maria alle Scotte”, Siena (G. Marotta); Institute of Hematology, Ospedale “San Giusepppe
Moscati”, Taranto (P. Mazza); Division of Hematology, Ospedale “S. Giovanni Battista,” Torino (M. Falda, B. Bruno);
Division of Hematology, Ospedale “C. Panico”, Tricase (V. Pavone); Division of Hematology and Bone Marrow
Transplantation, University of Udine, Udine (R. Fanin, F. Patriarca); Division of Hematology and Bone Marrow Unit,
Policlinico “G.B. Rossi”, Verona (G. Pizzolo, F. Benedetti); Department of Hematology, Ospedale “S. Bortolo,”
Vicenza (F. Rodeghiero, R. Raimondi).
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Predictor (n) NRM OS
HR p HR p
1-2 (n=441) 1.54 <0.001 1.29 0.009
≥ 3 (n=377) 1.90 < 0.000 1.93 < 0.000
High risk disease (n=1355) vs. low-risk 1.62 < 0.000 1.75 < 0.000
Age >50 years (n=824) 1.33 0.008 1.25 0.004
Myeloablative regimen (n=1083) vs. RIC 1.04 0.675 1.33 0.002
PBSC (n=1466) vs. bone marrow 1.03 0.813 0.98 0.910
Male gender (n=1108) vs. female 0.87 0.234 1.00 0.925
Unrelated donor (n=979) vs. related 2.01 < 0.000 1.38 < 0.000
Female donor/Male recipient (n=390) vs. other 1.07 0.571 1.00 0.952
CMV serostatus Donor -/Recipient - (n=192) vs. other 0.92 0.592 0.86 0.171
RIC: reduced-intensity/non-myeloablative conditioning regimens, PBSC: peripheral blood stem cells.
OS NRM OS NRM
Predictor HCT-CI Score
Related Donor 8.9 (6.2-12.3) 20.8* (13.7-28.9) 22.0+ (14.2-31.0) < 0.001 0.63
Unrelated Donor 22.4 (18.3-26.6) 24.9 (18.2-32.2) 27.3 (19.6-34.9) 0.23 0.53
Bone marrow (BM) 20.2 (15.2-25.8) 17.7 (9.8-27.3) 26.29 (16.58-37.02) 0.23 0.53
PBSC 15.5 (12.4-18.3) 26.9*(21.4-32.9) 32.3+ (25.70-39.10) < 0.001 0.61
RIC 17.9 (13.4-22.9) 29.2*(21.2-37.6) 33.1+(25.2-41.0) <0.001 0.59
Myeloablative Conditioning 16.0 (12.9-19.2) 20.9 (15.1-27.0) 29.6+ (21.6-37.3) < 0.001 0.58
Disease:
Acute leukemia 15.7 (12.4-19.4) 18.3 (13.0-24.4) 22.0 (15.4-29.1) 0.12 0.54
Acute myeloid leukemia 13.4 (10.2-16.9) 13.7 (9.0-19.4) 20.7 (14.4-27.8) 0.08 0.54
Acute myeloid leukemia, 1st remission 9.1 (5.9-13-1) 11.4 (6.2-18.3) 19.4 (10.9-29.7) 0.03 0.59
Multiple myeloma 21.5 (13.6-30.7) 34.3 (18.2-51.2) 26.1 (9.0-45.9) 0.31 0.57
Lymphoma (Hodgkin and non-Hodgkin) 12.3 (8.2-17.3) 23.4*(14.5-33.4) 30.1+ (20.0-40.9) <0.001 0.66
Myelodisplastic syndrome 18.9 (12.7-26.1) 30.6*(19.6-42.4) 47.4+(34.1-59.7) <0.001 0.64
Chronic myeloid leukemia 16.9 (8.2-28.3) 15.3 (2.1-39.9) 42.8 (7.1-76.1) 0.22 0.60
All diseases 14.7 (12.7-16.8) 21.3* (17.6-25.2) 27.3 (22.9-31.8) <0.001 0.60
* comparison group 0 vs. group 1-2: P-value < 0.05; + comparison group 0 vs. group ≥ 3: P-value < 0.05
RIC: reduced-intensity/non-myeloablative conditioning regimens, PBSC: peripheral blood stem cells.
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Bacigalupo, Renato Fanin, Alessandro Rambaldi and Alberto Bosi Roberto Raimondi, Alberto Tosetto, Rosi Oneto, Riccardo Cavazzina, Francesco Rodeghiero, Andrea Index: a prospective, multicenter GITMO study Validation of the Hematopoietic Cell Transplantation-Specific Comorbidity
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