A new hematopoietic cell transplantation–specific comorbidity index (HCT-CI) was effective in predicting outcomes among patients with hematologic malignancies who underwent HCT at Fred Hutchinson Cancer Research Center (FHCRC). Here, we compared the performance of the HCT-CI to 2 other indices and then tested its capacity to predict outcomes among 2 cohorts of patients diagnosed with a single disease entity, acute myeloid leukemia in first complete remission, who underwent transplantation at either FHCRC or M. D. Anderson Cancer Center (MDACC). FHCRC patients less frequently had unfavorable cytogenetics (15% versus 36%) and HCT-CI scores of 3 or more (21% versus 58%) compared with MDACC patients. We found that the HCT-CI had higher sensitivity and outcome predictability compared with the other indices among both cohorts. HCT-CI scores of 0, 1 to 2, and 3 or more predicted comparable nonrelapse mortality (NRM) among FHCRC and MDACC patients. In multivariate models, HCT-CI scores were associated with the highest hazard ratios (HRS) for NRM and survival among each cohort. The 2-year survival rates among FHCRC and MDACC patients were 71% versus 56%, respectively. After adjustment for risk factors, including HCT-CI scores, no difference in survival was detected (HR: 0.98, P = .94). The HCT-CI is a sensitive and informative tool for comparing trial results at different institutions. Inclusion of comorbidity data in HCT trials provides valuable, independent information.

Introduction

Allogeneic hematopoietic cell transplantation (HCT) is a potentially curative therapy for many patients diagnosed with acute myeloid leukemia (AML).1  Cytogenetics and, to a lesser extent, age have been the most important factors predicting survival among patients with AML in first complete remission (CR).1,,,5  However, the literature has been devoid of a systematic assessment of the impact of comorbidities on the ability of AML patients to tolerate HCT. Therefore, it has been suggested to incorporate comorbidities into the risk-adapted decision-making for AML patients to improve outcome prediction, comparison of trial results from different institutions, and design of new clinical trials.6,8  Comorbidity indices have been extensively studied in the field of solid malignancies,9,11  while less work has been done in hematologic malignancies. Recently, a new HCT-specific comorbidity index (HCT-CI) has been modeled to effectively capture comorbidities and predict HCT outcomes in a cohort of patients with various hematologic malignancies, treated at FHCRC.12  It remained to be determined whether this index could yield reproducible information at other institutions and if it could be added to other established prognostic variables to refine estimates of outcomes of newly reported investigational treatments.13  Here, we took additional steps to validate the effectiveness of the HCT-CI. In a group of 224 patients with a single disease entity, AML in first CR, who underwent transplantation at 2 different institutions, Fred Hutchinson Cancer Research Center (FHCRC) and M. D. Anderson Cancer Center (MDACC), we investigated (1) the sensitivity and discriminative capacity of the HCT-CI compared with 2 other comorbidity indices, the Charlson Comorbidity Index (CCI)14  and the Adult Comorbidity Evaluation-27 (ACE-27),15  (2) the ability of the HCT-CI scores to predict outcomes among FHCRC and MDACC patients, and (3) the correlations between HCT-CI scores and patient age.

Patients and methods

Informed consent was obtained from all patients in accordance with the Declaration of Helsinki. This retrospective analysis has been approved by the institutional review boards of the FHCRC and the MDACC.

Patients

Consecutive patients from both institutions, who had comorbidity data available, were included in this study after exclusion of FHCRC patients who had contributed to the initial development of the HCT-CI.12  Nine patients were excluded because of lack of sufficient data for scoring comorbidities. All 244 patients had a diagnosis of AML in first CR. Among those, 177 patients underwent transplantation at FHCRC, between 1990 and 2004, and 67 patients underwent transplantation at MDACC, between 1990 and 2001. Conditioning regimens were either myeloablative, including busulphan (BU) + cyclophosphamide (CY) ± other agents (77 FHCRC and 15 MDACC patients), cyclophosphamide + 12 Gy or higher total body irradiation (TBI) ± other agents (72 FHCRC and 29 MDACC patients), and BU + 12 Gy TBI (8 FHCRC patients); reduced-intensity, including BU + fludarabine (3 FHCRC and 7 MDACC patients), and fludarabine + melphalan (9 MDACC patients); or nonmyeloablative, including fludarabine + 2 Gy TBI (17 FHCRC patients) or fludarabine + cytarabine + idarubicin (7 MDACC patients). Patients and donors were matched for HLA-A, -B, and -C antigens by either intermediate resolution DNA typing or high-resolution techniques. HLA matching for -DRB1 and -DQB1 was done on the basis of allele-level typing.16 

Based on cytogenetics, patients were divided into 3 risk groups: favorable, intermediate, and unfavorable, according to the criteria of the Southwest Oncology Group (SWOG)/Eastern Cooperative Oncology Group (ECOG).3 

Assessment of comorbidities

Information concerning comorbidities was obtained from medical notes, pathology reports, and laboratory data. Comorbidity data were extracted by M. Sorror for FHCRC patients and by M. Shahjahan for MDACC patients. M. Sorror was assessed for the time required to score a sample of patients for comorbidities. All patients were assigned scores according to 3 comorbidity indices. The CCI was used as a general comorbidity measure, and its scores were deployed according to their original version.14  The ACE-27 has been developed by modifying an older index, the Kaplan-Feinstein index,17  which was originally constructed for patients with diabetes. ACE-27 specifically targeted patients diagnosed with solid malignancies. It represents a comprehensive measure incorporating 27 comorbidities graded as mild, moderate, or severe. The overall comorbidity scores were assigned based on the highest ranked single comorbidity (none [score 0], mild [score 1], moderate [score 2], or severe [score 3]), where score 3 is considered the highest. Per the ACE-27, score 1 was assigned to 1 or more mild comorbidity, score 2 was assigned to any single moderate comorbidity, and score 3 was given to either more than 1 moderate comorbidity or 1 or more severe comorbidities. The definitions of comorbidities per the ACE-27 were applied according to the comorbidity data collection form displayed on the Washington University School of Medicine website.18  Finally, the HCT-CI was used as the comorbidity measure under study. The HCT-CI assigned weights of 1, 2, or 3 to comorbidities according to their impact on nonrelapse mortality (NRM). The final evaluation of the number and seriousness of all comorbidities was represented by a summation of scores with no upper limit.12  The specific criteria of the 17 comorbidities included in the HCT-CI are described in Table 1.

Table 1

Definitions and weighted scores of comorbidities as summarized by the hematopoietic cell transplantation–specific comorbidity index (HCT-CI)

ComorbiditiesDefinitionsHCT-CI weighted scores
Arrhythmia Atrial fibrillation or flutter, sick sinus syndrome, or ventricular arrhythmias 
Cardiac Coronary artery disease,* congestive heart failure, myocardial infarction, or EF of ≤50% 
Inflammatory bowel disease Crohn's disease or ulcerative colitis 
Diabetes Requiring treatment with insulin or oral hypoglycemic, but not controlled with diet alone 
Cerebrovascular disease Transient ischemic attacks or cerebrovascular accident 
Psychiatric disturbance Depression/anxiety requiring psychiatric consult and/or treatment at the time of HCT 
Hepatic, mild Chronic hepatitis, bilirubin >ULN to 1.5× ULN, or AST/ALT >ULN to 2.5× ULN 
Obesity Patients with a BMI of >35 for adults or with BMI-for-age percentile of ≥ 95th percentile for children 
Infection Documented infection or fever of unknown etiology requiring antimicrobial treatment before, during, and after the start of conditioning regimen 
Rheumatologic SLE, RA, polymyositis, mixed CTD, and polymyalgia rheumatica 
Peptic ulcer Requiring treatment 
Moderate/severe renal Serum creatinine >2 mg/dL, on dialysis, or prior to renal transplantation 
Moderate pulmonary DLco and/or FEV1 66%-80% or dyspnea on slight activity 
Prior solid malignancy Treated at any time point in the patient's history, excluding nonmelanoma skin cancer 
Heart valve disease Except asymptomatic mitral valve prolapse 
Severe pulmonary DLco and/or FEV1 ≤65% or dyspnea at rest or requiring oxygen 
Moderate/severe hepatic Liver cirrhosis, bilirubin >1.5× ULN, or AST/ALT >2.5× ULN 
ComorbiditiesDefinitionsHCT-CI weighted scores
Arrhythmia Atrial fibrillation or flutter, sick sinus syndrome, or ventricular arrhythmias 
Cardiac Coronary artery disease,* congestive heart failure, myocardial infarction, or EF of ≤50% 
Inflammatory bowel disease Crohn's disease or ulcerative colitis 
Diabetes Requiring treatment with insulin or oral hypoglycemic, but not controlled with diet alone 
Cerebrovascular disease Transient ischemic attacks or cerebrovascular accident 
Psychiatric disturbance Depression/anxiety requiring psychiatric consult and/or treatment at the time of HCT 
Hepatic, mild Chronic hepatitis, bilirubin >ULN to 1.5× ULN, or AST/ALT >ULN to 2.5× ULN 
Obesity Patients with a BMI of >35 for adults or with BMI-for-age percentile of ≥ 95th percentile for children 
Infection Documented infection or fever of unknown etiology requiring antimicrobial treatment before, during, and after the start of conditioning regimen 
Rheumatologic SLE, RA, polymyositis, mixed CTD, and polymyalgia rheumatica 
Peptic ulcer Requiring treatment 
Moderate/severe renal Serum creatinine >2 mg/dL, on dialysis, or prior to renal transplantation 
Moderate pulmonary DLco and/or FEV1 66%-80% or dyspnea on slight activity 
Prior solid malignancy Treated at any time point in the patient's history, excluding nonmelanoma skin cancer 
Heart valve disease Except asymptomatic mitral valve prolapse 
Severe pulmonary DLco and/or FEV1 ≤65% or dyspnea at rest or requiring oxygen 
Moderate/severe hepatic Liver cirrhosis, bilirubin >1.5× ULN, or AST/ALT >2.5× ULN 

Data are from Sorror et al.12 

EF indicates ejection fraction; ULN, upper limit of normal; AST, aspartate aminotransferase; ALT, alanine aminotransferase; BMI, body mass index; SLE, systemic lupus erythematosus; RA, rheumatoid arthritis; CTD, connective tissue disease; DLco, diffusion capacity of carbon monoxide; and FEV1, forced expiratory volume in one second.

*

One or more vessel-coronary artery stenoses, requiring medical treatment, stent, or bypass graft.

To convert creatinine from milligrams per deciliter to micromoles per liter, multiply milligrams per deciliter by 88.4.

Statistical methods

Survival curves and probabilities were estimated using the Kaplan-Meier method, while cumulative incidence curves and probabilities for NRM were estimated using methods described elsewhere.19  Multivariate hazard ratios (HRs) for NRM and survival outcomes were estimated from Cox regression models, treating NRM and disease relapse/progression as competing risks where appropriate. The c-statistic or concordance index was used, as previously described,12  to compare the discriminative capacity of each of the 3 indices (predictive validity). For a binary end point (eg, NRM within 2 years), the c-statistic is identical to the area under a receiver operating characteristic curve. The c-statistic ranges from 0.5 to 1.0; a value of 0.5 indicates random predictions and a value of 1.0 indicates perfect separation of patients who die from patients who survive. A model with a c-statistic value of more than approximately 0.65 has some use in predicting the response of individual patients. Concurrent validity was evaluated using Pearson correlation coefficient. Although it is very difficult to determine cutoff points for correlation coefficients, because many factors influenced their values,20  correlation coefficients exceeding 0.40 were considered to be moderate, and those exceeding 0.75 were considered to be high.21 

Results

Patient characteristics

Patients and disease characteristics are described in Table 2. Patients who underwent transplantation at FHCRC and MDACC had similar median ages (41 vs 39 years) and median intervals (4.8 vs 5.1 months) from diagnosis to HCT. Compared with MDACC patients, FHCRC patients were more frequently given non–TBI-based myeloablative (44% vs 24%), less frequently given reduced-intensity (2% vs 22%), and with similar frequency given TBI-based myeloablative (45% vs 43%) and nonmyeloablative (10% vs 10%) conditioning regimens. Cyclosporine/methotrexate and tacrolimus/methotrexate were the most frequent postgrafting immunosuppressive regimens for FHCRC and MDACC patients, respectively. FHCRC patients more frequently received marrow (65% vs 58%) and unrelated grafts (28% vs 15%), while they less frequently had recipient cytomegalovirus (CMV) seropositivity (55% vs 68%) and unfavorable cytogenetics (15% vs 36%) compared with MDACC patients.

Table 2

Patient and disease characteristics

CharacteristicFHCRC patients, n = 177MDACC patients, n = 67
Median age at transplantation, y (range) 41 (19-75) 39 (19-67) 
Median interval from diagnosis to HCT, mo 4.8 5.1 
Conditioning regimens, % 
    Myeloablative, TBI-based 45 43 
    Myeloablative, non–TBI-based 44 24 
    Reduced-intensity* 22 
    Nonmyeloablative 10 10 
Postgrafting immunosuppression, % 
    CSP + MTX 78 22 
    Tacrolimus + MTX 61 
    CSP + MMF 
    Other 11 17 
Hematopoietic cell source, % 
    G-PBMCs 35 42 
    Marrow 65 58 
Donor type, % 
    HLA-matched sibling 68 78 
    HLA-mismatched related 
    HLA-matched unrelated 22 15 
    HLA-allele–mismatched unrelated 
    HLA-antigen–mismatched unrelated 
Patient CMV serostatus, % 
    Negative 45 32 
    Positive 55 68 
Cytogenetic risks,% 
    Favorable 
    Intermediate 82 61 
    Unfavorable 15 36 
CharacteristicFHCRC patients, n = 177MDACC patients, n = 67
Median age at transplantation, y (range) 41 (19-75) 39 (19-67) 
Median interval from diagnosis to HCT, mo 4.8 5.1 
Conditioning regimens, % 
    Myeloablative, TBI-based 45 43 
    Myeloablative, non–TBI-based 44 24 
    Reduced-intensity* 22 
    Nonmyeloablative 10 10 
Postgrafting immunosuppression, % 
    CSP + MTX 78 22 
    Tacrolimus + MTX 61 
    CSP + MMF 
    Other 11 17 
Hematopoietic cell source, % 
    G-PBMCs 35 42 
    Marrow 65 58 
Donor type, % 
    HLA-matched sibling 68 78 
    HLA-mismatched related 
    HLA-matched unrelated 22 15 
    HLA-allele–mismatched unrelated 
    HLA-antigen–mismatched unrelated 
Patient CMV serostatus, % 
    Negative 45 32 
    Positive 55 68 
Cytogenetic risks,% 
    Favorable 
    Intermediate 82 61 
    Unfavorable 15 36 

HCT indicates hematopoietic cell transplantation; TBI, total body irradiation; CSP, cyclosporine; MTX, methotrexate; MMF, mycophenolate mofetil; G-PBMC, granulocyte colony-stimulating factor–peripheral blood mononuclear cells; HLA, human leukocyte antigen; and CMV, cytomegalovirus.

*

Reduced-intensity conditioning regimens included fludarabine plus busulfan or melphalan.

Nonmyeloablative regimens included 2 Gy TBI plus or minus fludarabine or fludarabine/Ara-C/idarubicin.

Cytogenetic risks were assessed and broken down into 3 risk groups: favorable, intermediate, and unfavorable, according to the criteria of the Southwest Oncology Group (SWOG)/Eastern Cooperative Oncology Group.3 

The sensitivity of the HCT-CI compared with CCI and ACE-27

Higher percentages of pulmonary, hepatic, infection, and obesity comorbidities were detected by the HCT-CI compared with ACE-27 and CCI among both FHCRC and MDACC patients (Figure 1). HCT-CI and ACE-27 were comparable in capturing cardiac, prior malignancy, and depression comorbidities but with higher frequencies than the CCI. Almost no differences were seen between the 3 indices in defining other comorbidities.

Figure 1

The distribution of individual comorbidities. Individual comorbidities among (A) FHCRC and (B) MDACC patients as captured by each of the 3 indices, the HCT-CI, the ACE-27, and the CCI.

Figure 1

The distribution of individual comorbidities. Individual comorbidities among (A) FHCRC and (B) MDACC patients as captured by each of the 3 indices, the HCT-CI, the ACE-27, and the CCI.

Comorbidity scores were collapsed into 3 groups to facilitate comparison between indices and later assessment of the predictive capacity (Table 3). These groups incorporated HCT-CI scores of 0, 1 to 2, and 3 or more, while the cutoffs for the ACE-27 and CCI were scores 0, 1, and 2 or more due to scarcity of patients with scores higher than 2. Nevertheless, the HCT-CI showed higher sensitivity with smaller proportions of patients with scores of 0 and higher proportions of patients with scores 1 to 2 and 3 or more compared with the other 2 indices among both FHCRC and MDACC patients.

Table 3

Comorbidity groups as stratified by scores of the HCT-CI, the ACE-27, and the CCI among patients who underwent transplantation at FHCRC and MDACC

Index/scoresComorbidity groupFHCRC patients, %, n = 177MDACC patients, %, n = 67
HCT-CI 
    0 Low 51 21 
    1 to 2 Intermediate 28 21 
    3 or more High 21 58 
ACE-27 
    0 Low 65 48 
    1 Intermediate 26 30 
    2 or more High 22 
CCI 
    0 Low 83 70 
    1 Intermediate 11 10 
    2 or more High 20 
Index/scoresComorbidity groupFHCRC patients, %, n = 177MDACC patients, %, n = 67
HCT-CI 
    0 Low 51 21 
    1 to 2 Intermediate 28 21 
    3 or more High 21 58 
ACE-27 
    0 Low 65 48 
    1 Intermediate 26 30 
    2 or more High 22 
CCI 
    0 Low 83 70 
    1 Intermediate 11 10 
    2 or more High 20 

HCT-CI indicates hematopoietic cell transplantation–specific comorbidity index; ACE-27, adult comorbidity evaluation-27; and CCI, Charlson comorbidity index.

Using the Pearson correlation coefficient, fairly moderate correlations were found between the scores of all indices. HCT-CI and ACE-27 had the highest correlation (0.65), while the correlations between CCI and ACE-27 or HCT-CI were 0.62 and 0.53, respectively.

A sample of 10 patients was assessed for the median time required to evaluate and score comorbidities by the same investigator using the 3 different indices. The CCI consumed the least median time (5 minutes) compared with HCT-CI (7 minutes) and ACE-27 (15 minutes).

Prediction of HCT outcomes among all patients by the HCT-CI versus other indices

Cox regression models were constructed to assess the predictive capacity of each index for relapse, NRM, and survival among all patients (Table 4). These models were adjusted for age, stem-cell source, cytogenetic risk, donor type, conditioning type, and institution. The HCT-CI comorbidity groups were associated with more consistent linear increases in the adjusted HR for relapse, NRM, and survival compared with those of the ACE-27 and the CCI. The predictive capacity of the HCT-CI scores was more significant than that of ACE-27 or the CCI. The CCI scores were more significant in predicting outcomes compared with ACE-27 scores. Overall, the HCT-CI had higher c-statistics for discriminating NRM (0.69) and survival (0.71) compared with ACE-27 (0.59 and 0.58) and CCI (0.59 and 0.60), respectively.

Table 4

The predictive capacity of the HCT-CI compared with ACE-27 and CCI for NRM, relapse, and survival among all patients as assessed by Cox regression models taking into account other risk factors

Index/comorbidity groupRelapse
NRM
Survival
HR*PHR*PHR*P
HCT-CI  .01  <.00  <.00 
    Low 1.0  1.0  1.0  
    Intermediate 2.74  3.95  3.68  
    High 2.67  7.16  5.60  
ACE-27  .13  .27  .22 
    Low 1.0  1.0  1.0  
    Intermediate 1.54  1.53  1.59  
    High 2.10  0.69  1.23  
CCI  .03  .08  .01 
    Low 1.0  1.0  1.0  
    Intermediate 0.92  2.49  2.09  
    High 2.46  2.06  2.25  
Index/comorbidity groupRelapse
NRM
Survival
HR*PHR*PHR*P
HCT-CI  .01  <.00  <.00 
    Low 1.0  1.0  1.0  
    Intermediate 2.74  3.95  3.68  
    High 2.67  7.16  5.60  
ACE-27  .13  .27  .22 
    Low 1.0  1.0  1.0  
    Intermediate 1.54  1.53  1.59  
    High 2.10  0.69  1.23  
CCI  .03  .08  .01 
    Low 1.0  1.0  1.0  
    Intermediate 0.92  2.49  2.09  
    High 2.46  2.06  2.25  

NRM indicates nonrelapse mortality; and HR, hazard ratio.

*

HR was adjusted for age, stem-cell source, cytogenetic risk, donor type, conditioning type, and institution type.

Impact of HCT-CI comorbidity groups on outcomes of FHCRC compared with MDACC patients

Using the HCT-CI (Figure 1), pulmonary (26% and 46%), hepatic (14% and 30%), cardiac (7% and 16%), prior malignancy (6% and 19%), and obesity (3% and 16%) were the most prevalent comorbidities with lower frequencies among FHCRC compared with MDACC patients, respectively. Hence, more frequent HCT-CI scores of 0 (51% vs 21%) and less frequent HCT-CI scores of more than 3 (21% vs 58%) were found among FHCRC versus MDACC patients, respectively (Table 2).

Overall, FHCRC and MDACC patients had 2-year cumulative incidences of NRM of 17% and 22%, respectively, and 2-year survival rates of 71% and 56%, respectively. FHCRC and MDACC patients with low (7% vs 7%, Figure 2A), intermediate (19% vs 21%, Figure 2B), or high (37% vs 27%, Figure 2C) HCT-CI risk groups had comparable cumulative incidences of NRM. Incremental increases of the cumulative incidences of NRM were seen among both cohorts with increasing HCT-CI risk groups. This impact on NRM was reflected in overall survival (OS; Figure 3), where decreasing survival rates were observed with increasing HCT-CI scores among both FHCRC and MDACC patients.

Figure 2

Cumulative incidences of NRM. Comparison of NRM among FHCRC and MDACC patients as stratified by HCT-CI scores of (A) 0, (B) 1 to 2, and (C) 3 or more. Solid lines represent FHCRC patients; dashed lines represent MDACC patients.

Figure 2

Cumulative incidences of NRM. Comparison of NRM among FHCRC and MDACC patients as stratified by HCT-CI scores of (A) 0, (B) 1 to 2, and (C) 3 or more. Solid lines represent FHCRC patients; dashed lines represent MDACC patients.

Figure 3

Kaplan-Meier curves for overall survival. OS as stratified by HCT-CI scores of 0, 1 to 2, and more than or equal to 3 among patients transplanted in (A) FHCRC and (B) MDACC.

Figure 3

Kaplan-Meier curves for overall survival. OS as stratified by HCT-CI scores of 0, 1 to 2, and more than or equal to 3 among patients transplanted in (A) FHCRC and (B) MDACC.

Causes of NRM were noninfectious pulmonary complications alone (n = 8), associated with graft-versus-host disease (GVHD; n = 3), or associated with multiorgan failure (n = 1); pneumonia alone (n = 5) or associated with GVHD (n = 5); infection alone (n = 3), associated with renal insufficiency (n = 1), associated with graft failure (n = 2), associated with GVHD (n = 5), or associated with GVHD and renal insufficiency (n = 2); hepatic failure (n = 4); cardiac complications (n = 3); multiorgan failure (n = 2); cerebrovascular accident associated with meningoencephalitis (n = 1); and unknown (n = 1).

In multivariate analyses, HCT-CI scores consistently had the highest HR for risks of NRM and poor survival compared with other risk factors among both FHCRC and MDACC patients (Table 5). When all patients were included in the multivariate models, HCT-CI intermediate- and high-risk groups were independently significant for predicting increased HR of NRM (HR: 3.95 and 7.16, respectively, P < .001) and poor survival (HR: 3.68 and 5.60, respectively, P < .001). Other factors were not statistically significant with the exception of marrow grafts, which compared with peripheral blood grafts had higher risk for NRM (HR: 2.13, P = .05), and unrelated grafts, which compared with related grafts had worse survival (HR: 1.83, P = .01). No statistically significant differences were found between FHCRC and MDACC patients for NRM (HR: 0.69, P = .34) and survival (HR: 0.98, P = .94), after adjustment for other risk factors.

Table 5

Multivariate analyses of risk factors for NRM and survival among FHCRC and MDACC patients

Risk factorNRM
Survival
FHCRC
MDACC
FHCRC
MDACC
HR95% CIHR95% CIHR95% CIHR95% CI
HCT-CI risk 
    Low 1.0 — 1.0 — 1.0 — 1.0 — 
    Intermediate 3.43 1.3-9.2 4.93 0.4-55 3.31 1.6-6.9 2.60 0.7-9.6 
    High 8.12 3.0-22 5.05 0.6-43 7.45 3.6-15 2.32 0.8-7.1 
Cytogenetics 
    Favorable/intermediate 1.0 — 1.0 — 1.0 — 1.0 — 
    Unfavorable 1.61 0.6-4.2 1.19 0.4-3.9 1.44 0.7-3.1 1.07 0.5-2.3 
Donor 
    Related 1.0 — 1.0 — 1.0 — 1.0 — 
    Unrelated 2.16 0.9-5.0 0.44 0.1-3.7 1.91 1.0-3.6 1.61 0.6-4.4 
Stem-cell source 
    G-PBMC 1.0 — 1.0 — 1.0 — 1.0 — 
    Marrow 3.39 1.2-10 1.47 0.4-5.5 2.62 1.2-5.7 0.82 0.3-2.0 
Conditioning 
    Ablative 1.0 — 1.0 — 1.0 — 1.0 — 
    Reduced-intensity 0.00 0-Undefined 2.51 0.6-11 3.85 0.7-20 1.62 0.6-4.3 
    Nonablative 0.81 0.1-4.5 0.87 0.1-11 0.87 0.3-3.0 0.57 0.1-3.3 
Age 
    Younger than 50 y 1.0 — 1.0 — 1.0 — 1.0 — 
    50 y or older 1.64 0.7-3.8 0.83 0.2-3.6 1.74 0.9-3.3 0.93 0.3-2.7 
Risk factorNRM
Survival
FHCRC
MDACC
FHCRC
MDACC
HR95% CIHR95% CIHR95% CIHR95% CI
HCT-CI risk 
    Low 1.0 — 1.0 — 1.0 — 1.0 — 
    Intermediate 3.43 1.3-9.2 4.93 0.4-55 3.31 1.6-6.9 2.60 0.7-9.6 
    High 8.12 3.0-22 5.05 0.6-43 7.45 3.6-15 2.32 0.8-7.1 
Cytogenetics 
    Favorable/intermediate 1.0 — 1.0 — 1.0 — 1.0 — 
    Unfavorable 1.61 0.6-4.2 1.19 0.4-3.9 1.44 0.7-3.1 1.07 0.5-2.3 
Donor 
    Related 1.0 — 1.0 — 1.0 — 1.0 — 
    Unrelated 2.16 0.9-5.0 0.44 0.1-3.7 1.91 1.0-3.6 1.61 0.6-4.4 
Stem-cell source 
    G-PBMC 1.0 — 1.0 — 1.0 — 1.0 — 
    Marrow 3.39 1.2-10 1.47 0.4-5.5 2.62 1.2-5.7 0.82 0.3-2.0 
Conditioning 
    Ablative 1.0 — 1.0 — 1.0 — 1.0 — 
    Reduced-intensity 0.00 0-Undefined 2.51 0.6-11 3.85 0.7-20 1.62 0.6-4.3 
    Nonablative 0.81 0.1-4.5 0.87 0.1-11 0.87 0.3-3.0 0.57 0.1-3.3 
Age 
    Younger than 50 y 1.0 — 1.0 — 1.0 — 1.0 — 
    50 y or older 1.64 0.7-3.8 0.83 0.2-3.6 1.74 0.9-3.3 0.93 0.3-2.7 

— indicates not applicable.

Correlation of HCT-CI scores and age

Patients were separated into 4 age categories: younger than 40, 40 to 49, 50 to 59, and older than 59 years and 3 HCT-CI score groups; 0, 1 to 2, and 3 or more (Figure 4). Increasing age by decade was associated with poorer comorbidity scores. Only 2 patients (11%) older than 59 years had HCT-CI scores of 0 compared with 68 patients (55%) younger than 40 years. HCT-CI scores of 3 or more were found among 42% and 26% of those age groups, respectively.

Figure 4

Correlation between HCT-CI scores and age. Distribution of HCT-CI scores of 0, 1 to 2, and 3 or more among 4 age categories; less than 40, 40 to 49, 50 to 59, and more than 59 years of all patients (n = 224) with AML in first CR transplanted at FHCRC and MDACC. HCT-CI score 0, white area; HCT-CI scores 1 to 2, gray area; HCT-CI scores more than or equal to 3, black area.

Figure 4

Correlation between HCT-CI scores and age. Distribution of HCT-CI scores of 0, 1 to 2, and 3 or more among 4 age categories; less than 40, 40 to 49, 50 to 59, and more than 59 years of all patients (n = 224) with AML in first CR transplanted at FHCRC and MDACC. HCT-CI score 0, white area; HCT-CI scores 1 to 2, gray area; HCT-CI scores more than or equal to 3, black area.

Discussion

Reliable comorbidity information, along with patient demographics, disease features, and treatment characteristics, has been found to be essential for comprehensive risk adjustment in patients with cancer.22,,,26  Accurate risk adjustment27  is the basis of observational research including comparison of outcomes of different treatment modalities. We are beginning to learn about the influence of multiple comorbidities on treatment outcomes of patients enrolled in HCT trials. Here, AML was chosen as a suitable disease entity for studying the impacts of comorbidities since it has been the disease for which HCT is carried out most frequently.28  Further, AML is a disease of the elderly,27  a population where comorbidities interfere substantially with curative treatment options.29  In addition, uncertainty still exists on how to identify suitable AML patients for different treatment modalities (reviewed in Deschler et al7 ), in part due to lack of incorporation of accurate assessment of medical problems in decision-making.30  First CR was preferred over advanced stages of AML primarily because comorbidity information may have greater impact in cancer patients with long compared with short life expectancy.15 

The HCT-CI was a valid tool in comparing HCT trials for 2 patient cohorts diagnosed with the same disease and treated at 2 different institutions. First, the HCT-CI sensitively mapped the differences in comorbidity burden between FHCRC and MDACC patients (21% and 58% of patients had scores of 3 or more, respectively). Next, the HCT-CI risk groups predicted comparable incidences of NRM among both patient cohorts despite differences in conditioning regimens and other pretransplantation variables. Indeed, the impact of increasing HCT-CI scores on HCT outcomes at the 2 institutions was consistently the most influential risk factor compared with other variables in the multivariate models. Finally, the difference in the unadjusted OS rate in favor of FHCRC compared with MDACC patients (71% vs 56%) turned out to be negligible (HR: 0.98, P = .94) after adjustment for all pretransplantation variables. Lack of adjustment for the higher percentage of HCT-CI scores of 3 or more among MDACC versus FHCRC patients (58% vs 21%) would have resulted in biased conclusions. These results demonstrated how the universal use of comorbidity scoring would enhance the validity of result comparisons of clinical trials conducted at different academic institutions.

In the current analysis, HCT-CI scores correlated directly with age. This finding added to the construct validity of this index since it agreed with findings in the typical geriatric series.29  Improvement in supportive care and the advent of minimally toxic conditioning regimens have allowed to expand the age range for enrollment of AML patients in allogeneic HCT protocols (reviewed in de Lima and Giralt8  and Baron and Storb31 ). Therefore, comorbidity assessment appears important for treatment decisions in the HCT field. Moreover, SWOG trials of induction chemotherapy have shown that older AML patients presented with poorer performance status, and the combination of advanced age and poor performance status resulted in the worst outcome.5  Comorbidity scores seem to be independent of performance status in predicting treatment outcomes of solid32  as well as blood33,34  malignancies. It will be important in the future to evaluate the correlations and impacts of these 3 prognostic parameters on outcomes of AML patients.

Since we did not have a “gold standard” comorbidity index, we assessed the concurrent validity of the HCT-CI by comparing it with 2 already established indices, the CCI and ACE-27. CCI was considered a general comorbidity index known for its simplicity and wide-scale use.35,,,,,,42  The ACE-27 had the advantages of being recently developed and incorporated some laboratory data to the definition of comorbidities. Even so, the HCT-CI was more sensitive in assigning scores to current patients (n = 140) compared with the CCI (n = 49) and ACE-27 (n = 97). This was mainly due to the incorporation of laboratory data to refine comorbidity definitions within the HCT-CI resulting in higher detection of pulmonary and hepatic comorbidities. Impaired pretransplantation pulmonary function tests were previously shown to predict both post-HCT noninfectious and infectious pulmonary complications,43,45  which actually constituted the majority of the current nonrelapse deaths.

The higher sensitivity of the HCT-CI resulted in fairly accurate assessment of comorbidity burdens as evidenced by higher predictive capacity for NRM and survival compared with the ACE-27 and the CCI. These findings supported the hypothesis that comorbidity indices that target specific patient populations would be superior to the more general indices. High comorbidity scores were also associated with increased relapse. This might be explained by the reported linkage between comorbidities such as prior malignancy,46  diabetes,47,48  obesity,49,50  autoimmune diseases,51,53  or smoking-associated lung disease54,55  and aggressive leukemia (reviewed in Extermann56 ). Cytogenetic risks did not independently predict overall mortality in the current analysis, which agreed with results from the SWOG study among patients who underwent allogeneic HCT.3 

The HCT-CI was relatively simple to use in comparison with a comprehensive measure such as ACE-27. Comprehensive measures have the limitations of time consumption and possible lack of information needed for accurate assignment. In addition, the applicability of ACE-27 has been limited by the scoring methodology, where only 4 grades were allowed regardless of the numbers of comorbidities. In this report, for example, relatively poor OS (19%) was observed among patients with HCT-CI scores of 5 or more. Information generated from such a sensitive scoring system could be of importance in evaluating which patients would benefit from lower intensity conditioning and which patients would not benefit from HCT at all.

We were not able to address tolerability to different conditioning regimens due to the relatively small numbers of patients receiving nonconventional conditioning regimens. Collection of comorbidity data from large patient cohorts that underwent transplantation at multiple institutions is warranted to investigate such comparisons. Another potential limitation: the 2 cohorts of patients were not exactly concurrent. However, an adjustment for the impact of year of HCT did not alter any of the results (data not shown). The current comorbidity data in patients with AML were derived only from those patients who had already been offered HCT. Two recent reports have used the HCT-CI to define comorbidities and accurately predicted outcomes of elderly untreated AML patients given conventional or novel induction agents.57,58  Future prospective trials should address the roles of comorbidities in treatment decision-making for AML in first CR comparing allogeneic HCT versus autologous HCT versus conventional chemotherapy. The availability of such data could aid in determining the benefit of either one of the 3 treatment modalities for AML patients with low versus high comorbidity burden.

The present report provides an initial step for the use of comorbidity assessment in the understanding of differences between trial results obtained at different institutions. The data suggest that the HCT-CI should be the preferable comorbidity index among patients with hematologic malignancies. The incorporation of the HCT-CI in the future design of HCT clinical trials might improve treatment decision-making and provide the rationale for testing novel treatment modalities.

The publication costs of this article were defrayed in part by page charge payment. Therefore, and solely to indicate this fact, this article is hereby marked “advertisement” in accordance with 18 USC section 1734.

Acknowledgments

This work was supported by grants CA78902, CA18029, and CA15704 from the National Cancer Institute and grant HL088021 from the National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD. Additional support was provided from the Paros Family Fund.

The authors thank the data coordinators, Chris Davis, Gary Schoch, Heather Hildebrant, and Jennifer Freese, and the study nurses, Mary Hinds, John Sedgwick, Michelle Bouvier, and Joanne Greene, for their invaluable help in making the study possible. Bonnie Larson, Helen Crawford, Sue Carbonneau, and Karen Carbonneau provided assistance with paper preparation. We also thank all the Transplant Teams at both institutions.

Authorship

Contribution: M.L.S. designed the overall study, collected data, analyzed and interpreted the data, and wrote the paper; S.G. and B.M.S. contributed study patients and contributed to study design and to data interpretation; M.D.L. and D.G.M. contributed study patients; M.S. contributed to data collection; H.J.D. and F.R.A. contributed to data interpretation and revised the paper; B.E.S. performed the statistical analysis; R.S. contributed to study design, contributed to data analysis and interpretation, and revised the paper.

Conflict-of-interest disclosure: The authors declare no competing financial interests.

Correspondence: Mohamed Sorror, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Mailstop D1-100, PO Box 19024, Seattle, WA 98109-1024; e-mail:msorror@fhcrc.org.

References

References
1
Suciu
S
Mandelli
F
de Witte
T
et al
Allogeneic compared with autologous stem cell transplantation in the treatment of patients younger than 46 years with acute myeloid leukemia (AML) in first complete remission (CR1): an intention-to-treat analysis of the EORTC/GIMEMAAML-10 trial.
Blood
2003
102
1232
1240
2
Grimwade
D
Walker
H
Oliver
F
et al
The importance of diagnostic cytogenetics on outcome in AML: analysis of 1,612 patients entered into the MRC AML 10 trial: The Medical Research Council Adult and Children's Leukaemia Working Parties.
Blood
1998
92
2322
2333
3
Slovak
ML
Kopecky
KJ
Cassileth
PA
et al
Karyotypic analysis predicts outcome of pre-remission and postremission therapy in adult acute myeloid leukemia: a Southwest Oncology Group/Eastern Cooperative Oncology Group study.
Blood
2000
96
4075
4083
4
Ogawa
H
Ikegame
K
Kawakami
M
et al
Impact of cytogenetics on outcome of stem cell transplantation for acute myeloid leukemia in first remission: a large-scale retrospective analysis of data from the Japan Society for Hematopoietic Cell Transplantation.
Int J Hematol
2004
79
495
500
5
Appelbaum
FR
Gundacker
H
Head
DR
et al
Age and acute myeloid leukemia.
Blood
2006
107
3481
3485
6
Appelbaum
FR
Pearce
SF
Hematopoietic cell transplantation in first complete remission versus early relapse.
Best Pract Res Clin Haematol
2006
19
333
339
7
Deschler
B
de Witte
T
Mertelsmann
R
Lubbert
M
Treatment decision-making for older patients with high-risk myelodysplastic syndrome or acute myeloid leukemia: problems and approaches.
Haematologica
2006
91
1513
1522
8
de Lima
M
Giralt
S
Allogeneic transplantation for the elderly patient with acute myelogenous leukemia or myelodysplastic syndrome (Review).
Semin Hematol
2006
43
107
117
9
Extermann
M
Measurement and impact of comorbidity in older cancer patients.
Crit Rev Oncol Hematol
2000
35
181
200
10
Piccirillo
JF
Costas
I
The impact of comorbidity on outcomes.
J Oto-Rhino-Laryngol Related Specialties
2004
66
180
185
11
Piccirillo
JF
Importance of comorbidity in head and neck cancer.
Laryngoscope
2000
110
593
602
12
Sorror
ML
Maris
MB
Storb
R
et al
Hematopoietic cell transplantation (HCT)-specific comorbidity index: a new tool for risk assessment before allogeneic HCT.
Blood
2005
106
2912
2919
13
Jones
RB
HCT outcomes: a new tool?
Blood
2005
106
2602
2603
14
Charlson
ME
Pompei
P
Ales
KL
MacKenzie
CR
A new method of classifying prognostic comorbidity in longitudinal studies: development and validation.
J Chronic Dis
1987
40
373
383
15
Piccirillo
JF
Tierney
RM
Costas
I
Grove
L
Spitznagel
EL
Jr
Prognostic importance of comorbidity in a hospital-based cancer registry.
JAMA
2004
291
2441
2447
16
Petersdorf
EW
Gooley
TA
Anasetti
C
et al
Optimizing outcome after unrelated marrow transplantation by comprehensive matching of HLA class I and II alleles in the donor and recipient.
Blood
1998
92
3515
3520
17
Kaplan
MH
Feinstein
AR
The importance of classifying initial co-morbidity in evaluatin the outcome of diabetes mellitus.
J Chronic Dis
1974
27
387
404
18
Clinical Outcomes Research Office
Accessed September 13, 2007
St Louis
Washington University School of Medicine
Comorbidity data collection form. http://oto.wustl.edu/clinepi/Forms/com_form.doc
19
Kalbfleisch
JD
Prentice
RL
The Statistical Analysis of Failure Time Data
1980
New York, NY
John Wiley & Sons
20
Streiner
DL
Norman
GR
Health Measurement Scales: A Practical Guide to Their Development
2003
Oxford, United Kingdom
Oxford University Press
21
Fleiss
JL
The Design and Analysis of Clinical Experiments
1999
New York, NY
John Wiley & Sons
22
Kurtz
ME
Kurtz
JC
Stommel
M
Given
CW
Given
B
The influence of symptoms, age, comorbidity and cancer site on physical functioning and mental health of geriatric women patients.
Women Health
1999
29
1
12
23
Greimel
ER
Padilla
GV
Grant
MM
Physical and psychosocial outcomes in cancer patients: a comparison of different age groups.
Br J Cancer
1997
76
251
255
24
Given
CW
Given
B
Azzouz
F
Stommel
M
Kozachik
S
Comparison of changes in physical functioning of elderly patients with new diagnoses of cancer.
Med Care
2000
38
482
493
25
van Spronsen
DJ
Janssen-Heijnen
ML
Lemmens
VE
Peters
WG
Coebergh
JW
Independent prognostic effect of co-morbidity in lymphoma patients: results of the population-based Eindhoven Cancer Registry.
Eur J Cancer
2005
41
1051
1057
26
van Spronsen
DJ
Janssen-Heijnen
ML
Breed
WP
Coebergh
JW
Prevalence of co-morbidity and its relationship to treatment among un-selected patients with Hodgkin's disease and non-Hodgkin's lymphoma, 1993-1996.
Ann Hematol
1999
78
315
319
27
Risk Adjustment for Measuring Healthcare Outcomes.
2003
Ann Arbor, MI
Health Administration Press
28
Gratwohl
A
Baldomero
H
Frauendorfer
K
Urbano-Ispizua
A
Niederwieser
D
Results of the EBMT activity survey 2005 on haematopoietic stem cell transplantation: focus on increasing use of unrelated donors.
Bone Marrow Transplant
2007
39
71
87
29
Fried
LP
Bandeen-Roche
K
Kasper
JD
Guralnik
JM
Association of comorbidity with disability in older women: the Women's Health and Aging Study.
J Clin Epidemiol
1999
52
27
37
30
Sekeres
MA
Stone
RM
Zahrieh
D
et al
Decision-making and quality of life in older adults with acute myeloid leukemia or advanced myelodysplastic syndrome.
Leukemia
2004
18
809
816
31
Baron
F
Storb
R
Hematopoietic cell transplantation after reduced-intensity conditioning for older adults with acute myeloid leukemia in complete remission.
Curr Opin Hematol
2007
14
145
151
32
Extermann
M
Overcash
J
Lyman
GH
Parr
J
Balducci
L
Comorbidity and functional status are independent in older cancer patients.
J Clin Oncol
1998
16
1582
1587
33
Sorror
M
Storer
B
Sandmaier
BM
et al
The joint role of comorbidity and performance status (PS) in predicting morbidity after allogeneic nonmyeloablative hematopoietic cell transplantation (HCT) [abstract].
Blood
2006
108
pt 1
180a
Abstract no. 596
34
Artz
AS
Pollyea
DA
Kocherginsky
M
et al
Performance status and comorbidity predict transplant-related mortality after allogeneic hematopoietic cell transplantation.
Biol Blood Marrow Transplant
2006
12
954
964
35
Di Iorio
B
Cillo
N
Cirillo
M
De Santo
NG
Charlson Comorbidity Index is a predictor of outcomes in incident hemodialysis patients and correlates with phase angle and hospitalization.
Int J Artif Organs
2004
27
330
336
36
Firat
S
Byhardt
RW
Gore
E
Comorbidity and Karnofksy performance score are independent prognostic factors in stage III non-small-cell lung cancer: an institutional analysis of patients treated on four RTOG studies: Radiation Therapy Oncology Group.
Int J Radiat Oncol Biol Phys
2002
54
357
364
37
Goldstein
LB
Samsa
GP
Matchar
DB
Horner
RD
Charlson Index comorbidity adjustment for ischemic stroke outcome studies.
Stroke
2004
35
1941
1945
38
Hemmelgarn
BR
Manns
BJ
Quan
H
Ghali
WA
Adapting the Charlson Comorbidity Index for use in patients with ESRD.
Am J Kidney Dis
2003
42
125
132
39
Lubke
T
Monig
SP
Schneider
PM
Holscher
AH
Bollschweiler
E
Does Charlson-comorbidity index correlate with short-term outcome in patients with gastric cancer? [German].
Zentralblatt fur Chirurgie
2003
128
970
976
40
Sabin
SL
Rosenfeld
RM
Sundaram
K
Har-el
G
Lucente
FE
The impact of comorbidity and age on survival with laryngeal cancer.
Ear Nose Throat J
1999
78
578
581
584
41
Sachdev
M
Sun
JL
Tsiatis
AA
Nelson
CL
Mark
DB
Jollis
JG
The prognostic importance of comorbidity for mortality in patients with stable coronary artery disease.
J Am Coll Cardiol
2004
43
576
582
42
Singh
B
Bhaya
M
Stern
J
et al
Validation of the Charlson comorbidity index in patients with head and neck cancer: a multi-institutional study.
Laryngoscope
1997
107
1469
1475
43
Crawford
SW
Fisher
L
Predictive value of pulmonary function tests before marrow transplantation.
Chest
1992
101
1257
1264
44
Parimon
T
Madtes
DK
Au
DH
Clark
JG
Chien
JW
Pretransplant lung function, respiratory failure, and mortality after stem cell transplantation.
Am J Respir Crit Care Med
2005
172
384
390
45
Horak
DA
Schmidt
GM
Zaia
JA
Niland
JC
Ahn
C
Forman
SJ
Pretransplant pulmonary function predicts cytomegalovirus-associated interstitial pneumonia following bone marrow transplantation.
Chest
1992
102
1484
1490
46
Witherspoon
RP
Deeg
HJ
Storer
B
Anasetti
C
Storb
R
Appelbaum
FR
Hematopoietic stem-cell transplantation for treatment-related leukemia or myelodysplasia.
J Clin Oncol
2001
19
2134
2141
47
Coughlin
SS
Calle
EE
Teras
LR
Petrelli
J
Thun
MJ
Diabetes mellitus as a predictor of cancer mortality in a large cohort of US adults.
Am J Epidemiol
2004
159
1160
1167
48
Abe
S
Funato
T
Takahashi
S
et al
Increased expression of insulin-like growth factor i is associated with Ara-C resistance in leukemia.
Tohoku J Exp Med
2006
209
217
228
49
Calle
EE
Rodriguez
C
Walker-Thurmond
K
Thun
MJ
Overweight, obesity, and mortality from cancer in a prospectively studied cohort of U.S. adults.
N Engl J Med
2003
348
1625
1638
50
Chiu
BC
Gapstur
SM
Greenland
P
Wang
R
Dyer
A
Body mass index, abnormal glucose metabolism, and mortality from hematopoietic cancer.
Cancer Epidemiol Biomarkers Prev
2006
15
2348
2354
51
Patapanian
H
Graham
S
Sambrook
PN
et al
The oncogenicity of chlorambucil in rheumatoid arthritis.
Br J Rheumatol
1988
27
44
47
52
Kwong
YL
Au
WY
Liang
RH
Acute myeloid leukemia after azathioprine treatment for autoimmune diseases: association with −7/7q-.
Cancer Genetics Cytogenet
1998
104
94
97
53
Vineis
P
Crosignani
P
Sacerdote
C
et al
Haematopoietic cancer and medical history: a multicentre case control study.
J Epidemiol Community Health
2000
54
431
436
54
Chelghoum
Y
Danaila
C
Belhabri
A
et al
Influence of cigarette smoking on the presentation and course of acute myeloid leukemia.
Ann Oncol
2002
13
1621
1627
55
Thomas
X
Chelghoum
Y
Cigarette smoking and acute leukemia (Review).
Leuk Lymphoma
2004
45
1103
1109
56
Extermann
M
Interaction between comorbidity and cancer (Review).
Cancer Control
2007
14
13
22
57
Giles
F
Rizzieri
D
Karp
J
et al
Cloretazine (VNP40101M), a novel sulfonylhydrazine alkylating agent, in patients age 60 years or older with previously untreated acute myeloid leukemia.
J Clin Oncol
2007
25
25
31
58
Giles
FJ
Borthakur
G
Ravandi
F
et al
The haematopoietic cell transplantation comorbidity index score is predictive of early death and survival in patients over 60 years of age receiving induction therapy for acute myeloid leukaemia.
Br J Haematol
2007
136
624
627