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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Cancer. Author manuscript; available in PMC 2010 June 1.
Published in final edited form as:
PMCID: PMC2688783
NIHMSID: NIHMS113305

Treating Octogenarian and Nonagenarian Acute Myeloid Leukemia Patients — Predictive Prognostic Models

Abstract

Background

Treating the octogenarian and nonagenarian acute myeloid leukemia (AML) patients with intensive chemotherapy is controversial. Several models to predict outcome were proposed including the use of a co-morbidity index. However, it is unclear whether Charlson Co-morbidity Index (CCI) or Hematopoietic Cell Transplant Co-morbidity Index (HCTCI) is more sensitive.

Methods

We analyzed our experience with 92 AML patients ≥80 years old. We recorded the patients’ pretreatment characteristics and their treatment outcome.

Results

All patients were offered intensive treatment; 59 (64%) were treated intensively with a variety of regimens while 33 (36%) elected to receive supportive care. CCI and HCTCI had similar predictive ability for outcome in both groups. Multivariate analyses of the prognostic factors identified near-normal albumin (48% of the patients; 1-year survival rate >27%) as a favorable factor for the whole cohort, age <83 (47% of the patients; 1-year survival rate >25%) and non-monocytic morphology (75% of the patients; 1-year survival rate >26%) for the intensively-treated cohort and bone marrow blasts <46% (50% of the patients; 1-year survival rate >19%) for those who received supportive care.

Conclusions

This retrospective analysis was developed to assist in treatment decisions for octogenarian and nonagenarian AML patients. These findings will need validation in a prospective study.

Introduction

The treatment of acute myeloid leukemia (AML) patients ≥60 years old is controversial and the majority of these patients probably do not undergo intensive treatment.1-4 A recent systematic review of the literature of large population-based investigations analyzed clinical trials with >40 patients.3 In a total of 36 AML trials, involving 12,370 patients with a median age of 70 years, the median overall survival approached 30 weeks for intensively treated patients while the median survival of those receiving best supportive care alone or best supportive care and non-intensive therapy was 7.5 and 12 weeks. The complete remission (CR) rate after induction therapy was 44% and in those patients who achieved CR, age no longer influenced prognosis.3 We were specifically interested in a subset of elderly AML, those ≥80 years old, because we have a significant number of these patients and to the best of our knowledge, very few articles concentrated specifically on this age group.

Four different groups addressed treatment outcomes of ≥80 AML patients.5-8 The CR rate after induction ranged between 20% to 37% but only a few patients survived beyond one year. Each of the groups attempted to define the precise risk factors that could be used as prognostic tools to identify the subgroup of patients who benefited from induction regimens and those for whom supportive care should be offered. While Mori and colleagues7 emphasized the importance of karyotype as a risk factor, Wahlin and colleagues6 assigned risk groups based on karyotype, presence of antecedent hematologic disorder and leukocytosis. Interestingly, DeLima and colleagues5 found that poor performance status and low albumin were significant risk factors in the very elderly AML patients. However, a more recent analysis from the same group,8 on a larger cohort of patients, revealed that karyotype, performance status, antecedent hematologic disorder and use of laminar air flow rooms were all important prognostic factors. These studies included between 24-82 AML patients ≥80 years old and none assessed the role of co-morbidities on the outcome of these elderly AML patients.

Co-morbid illness was defined in the 1960s by Feinstein9 as being a distinct additional clinical entity that has existed or may occur during the clinical course of a patient with a primary disease. Specifically in cancer, co-morbid illnesses have led to minimal enrollment of cancer patients onto clinical trials and often substandard treatment. The Charlson co-morbidity index (CCI) was developed in 198710 and was validated in many types of cancers. It takes into consideration 19 medical conditions with different weight for each one of them. The total score is between 0 and 4, the higher the score, the worse the outcome.11 CCI was recently shown to be an independent predictor of complete remission in AML patients ≥70 years old.12 The hematopoietic cell transplant co-morbidity index (HCTCI) was developed by Sorror and colleagues13 to predict the outcome of blood and marrow transplant patients; it is an adapted and more developed version of the CCI with a score from 0 to 11. The HCTCI was recently used to assess AML patients ≥60 years old.14, 15 While one study14 found it to be predictive of early death and event-free survival (EFS), the other15 did not. Of note, no comparison was done between the two scoring systems (CCI and HCTCI) in AML.

The ≥80 year old patients are a precarious group of patients as they are beyond the life expectancy in the United States (77.8 years).16 There is a clear need to help clinicians and other health care providers decide when to offer intensive approach and when to refer patients to supportive care and/or hospice.17 We therefore analyzed our institutional experience with these patients to define their prognosis based on pretreatment factors.

Methods

Patient Characteristics

Adults with diagnosis of AML ≥80 years of age evaluated at Roswell Park Cancer Institute between the years 1991 and 2007. Patients with acute promyelocytic leukemia were excluded from this analysis. We recorded patients’ age, gender, performance status, albumin, lactic dehydrogenase, white blood cell count, morphology [monocytic (including myelomonocytic, monoblastic and monocytic) vs. non-monocytic] including presence of extramedullary disease, bone marrow and peripheral blood blast percentages, CD34 and CD56 positivity of the leukemic blasts, karyotype [either favorable and intermediate18 were grouped together, or favorable and normal karyotype versus all others], de novo vs. secondary AML, CCI and HCTCI, and treatment outcome. Induction therapy varied based on protocol availability (supplementary Table 1). Supportive care consisted of blood product support with or without hydroxyurea. This analysis was approved by Roswell Park Cancer Institute’s Scientific Review Committee and the Institutional Review Board.

Response Criteria

Hematologic CR was defined by previously established criteria.19 Overall survival (OS) was defined as the interval between day of diagnosis for supportively-treated patients or first day of treatment for intensively-treated patients and death. Patients were censored at the date last seen alive. Relapse after CR was defined by the appearance of peripheral blood blasts, >5% leukemic cells in bone marrow aspirates or development of extramedullary leukemia.

Statistical Analyses

Descriptive statistics such as frequencies and relative frequencies were computed for all categorical variables. Numeric variables were summarized using simple descriptive statistics such as the mean, median, standard deviation, range, etc. The medians were used as cutoffs for analyses. A variety of graphical techniques were also used to display data. To compare CCI and HCTCI, the area under the Receiver Operating Characteristic (ROC) curve20 was computed for a variety of survival based outcomes (eg. 3-month survival). The ROC curve is constructed through calculation of the sensitivity and specificity associated with all possible cutoffs of the individual test scores. The area under the ROC curve (AUC) is a measure of the predictive power of a given variable, and thus a measure of the diagnostic accuracy of each test in terms of predictability of outcome. The two AUCs were derived from the same set of patient data and the measures themselves are therefore correlated so testing was based on the use of bootstrap methodology which incorporated this information. The bootstrapping algorithm involved re-sampling of the sample pairs. A nominal significance level of 0.05 was used. The estimated overall survival distributions were obtained using the Kaplan-Meier method for Binary variables. Using this distributional estimate, summary descriptive statistics such as the median survival were obtained. Statistical assessment of observed differences in the survival distributions of different groups of interest was done using the log-rank test. Cox proportional hazards models were used to assess the effect of study variables on overall survival for both univariate and multivariate analyses.

Results

A total of 92 patients were analyzed. All patients were offered intensive treatment without waiting for karyotype analysis and treatment decisions were made within 24-48 hours from diagnosis; 59 (64%) patients were treated intensively with a variety of regimens (supplementary Table 1) while 33 (36%) patients elected to receive supportive care. The patients’ characteristics are described in Table 1; except age and karyotype, those were similar between the two groups. Extramedullary disease was found in five of the patients (two central nervous system involvement and three at other sites); only two of these cases were in patients with monocytic morphology. Their response to treatment and overall survival are shown in Table 2. Because patients were evaluated since 1991, we compared the outcome by time (both dichotomous and continuous). There was no statistical difference in outcome by time of patients’ accrual among the whole cohort, those intensively- or supportively-treated (P=0.2140; P=0.2623; P=0.8369; respectively). There was also no significant difference between intensively- and supportively-treated patients in regards to overall survival (Figure 1A) though those who achieved CR had a longer survival (median survival 39.5 weeks) than those who did not (median survival 9.3 weeks); (P=0.0014) (Figure 1B).

Figure 1
Overall survival by treatment group (A) and CR (B).
Table 1
Patient characteristics
Table 2
Response data

ROC curves were constructed for both CCI and HCTCI to examine the sensitivity and specificity of the co-morbidity index scores in the prediction of 3-month survival (Figure 2). The AUCs (0.564 for CCI and 0.501 for HCTCI) were not statistically different (P=0.35). Both co-morbidity indexes had poor diagnostic accuracy. Results at one, six, nine, and 12 months were similar.

Figure 2
ROC Curve for 3 months survival (AUC=0.501 for HCTCI, AUC=0.564 for CCI).

A univariate analysis of pretreatment characteristics associated with OS is shown in supplementary Table 2.

Multivariate analysis, including all pretreatment characteristics (Table 3), identified near-normal albumin as an independent favorable prognostic factor for the whole cohort of patients (48% of the patients; 1-year survival rate >27%; Figure 3A). Similarly, age <83 (47% of the patients; 1-year survival rate >25%; Figure 3B) and non-monocytic morphology (75% of the patients; 1-year survival rate >26%; Figure 3C) were identified as independent favorable prognostic factors for the intensively-treated cohort and bone marrow blasts <46% (50% of the patients; 1-year survival rate >19%; Figure 3D) was identified as an independent favorable prognostic factor for those who received supportive care. Combining the prognostic factors did not result in better outcome prediction.

Figure 3
Overall survival by prognostic factors: albumin (A) for the intensively- and supportively-treated patients; age group (B) for the intensively-treated cohort; morphology group (C) for the intensively-treated cohort; and BM blasts (D) for the supportively-treated ...
Table 3
Stepwise Cox regression Multivariate analysis of prognostic factors associated with overall survival

Discussion

This retrospective analysis of octogenarian and nonagenarian AML patients represents our attempt to define the pretreatment characteristics that will predict who will benefit from treatment. Interestingly, our analyses detected four different prognostic variables, albumin for the whole cohort, age and non-monocytic morphology in the intensively-treated patients and bone marrow blast count in the supportively-treated patients.

The study could be criticized for the relatively small number of patients but to the best of our knowledge this is the largest single institute cohort of the ≥80 age group. The importance of a single institute approach should be underscored here because all patients were offered a uniform treatment approach by a relatively small group of physicians and were cared for by the same mid-level practitioners, nurses, social worker, case manager and psychologist.

The study may also be censured for the long period of accrual (16 years). However, since AML treatment has not changed significantly in the last two decades21 and we did not detect any difference in outcome by time-to-accrual to this retrospective analysis, we feel that the time period does not impact this analysis.

The age difference between the intensively-treated and the supportively-treated patients is intriguing. It may represent patient and/or physician choice. It is possible that the “younger” octogenarians preferred intensive treatment. Similarly, attitude disparities among us, the physicians and our support staff, may be unknowingly affecting decision-making when discussing treatment options with the “older” octogenarians and nonagenarians.22 Such quibbles were also raised at the other end of the age spectrum, treating young adults with acute lymphoblastic leukemia.23 Prospectively analyzing patient and physician attitudes may help answer the questions about the role of the patient versus the physician in decision-making about treatment assignment.

The karyotype difference between the intensively- and supportively-treated patients was unexpected, especially since we did not wait for cytogenetic results before offering treatment to our patients. We propose to evaluate this question prospectively.

Our results about similar outcome between intensively- and supportively-treated patients differ from other groups’ results. Specifically, the study by Löwenberg et al,24 randomizing patients between intensive induction therapy and supportive care demonstrated a significantly longer survival duration for the intensively treated patients. However, the median age in that study was 72 and included only a dearth of patients ≥80 years old. Similarly, three retrospective studies25-27 showed longer survival for intensively-treated compared to supportively-treated patients though all studies included only a few of patients ≥75 years old. On the contrary, our results are in line with three other retrospective studies28-30 demonstrating only a marginal, if any, advantage in overall survival for intensively-treated patients. Finally, our study included the largest number octogenarian and nonagenarian patients.

Performance status was a predictor of outcome in most AML studies of patients ≥60 years old1, 8, 31 but not all.24 Since most studies concentrated on the sexagenarians and above, the role of performance status in the ≥80 year old AML patients will need to be evaluated prospectively.

It was somewhat disappointing to realize that neither CCI nor HCTCI were of any predictive value in this patient population in spite of the fact that 24% of the patients had CCI of 0 (8% had HCTCI=0). While CCI was found to be predictive of CR by one group,12 HCTCI was described to be predictive of early death and EFS by one14 but not another group.15 None of these studies concentrated on the ≥80 year old patients. The scoring variability between CCI and HCTCI is inherent within the different systems. Both systems were developed for other purposes and thus, the lack of predictability suggests that a new system, analyzing the role of co-morbidities in this age group, needs to be devised.

Karyotype was shown to be prognostically significant in the ≥80 year old AML patients by several groups.6-8 One possibility for the discrepancy between our results and others may be the system used to define intermediate versus unfavorable karyotype cohorts. We used both the Cancer and Leukemia Group B system18 and separated the patients into two groups as well: favorable and normal karyotype versus all the others (data not shown). Neither method resulted in a significant value to predict outcome. Thus, the fact that karyotype lacked predictability power here may reflect the relatively smaller size of the intensively-treated cohort as compared to the other groups.

Three of the outcome predictors, albumin, age and bone marrow blast count, were previously shown to be of statistical significance in elderly AML. Albumin was found to be of prognostic value in elderly AML patients by at least one additional group.5 Its role in predicting outcome may be related to its relationship to the patients’ nutritional status and their hepatic function. Interestingly, albumin was shown to be an outcome predictor in several other malignancies.32, 33 Age is a well known prognostic factor in AML in toto and specifically in the elderly AML patients.8 Similarly, bone marrow blasts were found to be prognostically significant by at least one group in elderly AML patients.34 The percentage of bone marrow blasts may reflect disease activity; those with higher blast count may have more aggressive disease. Finally, our data demonstrate that those with non-monocytic morphology had a better outcome among the intensively-treated patients. Interestingly, and in contrast to previous published data,35, 36 extramedullary disease was less common in the monocytic morphology cases in this age group. If reproduced in wider patients’ cohorts in a prospective manner, these four variables could be helpful in assigning treatments to the ≥80 year old AML patients.

Recently, low-dose cytarabine was shown to be beneficial for the supportively-treated patients,37 None of our patients received this treatment modality. Further, the new term, “unfit for intensive treatment”, was not coined until recently.8 This term describes those patients with an eight-week mortality exceeding 50%. It includes not only induction death/treatment toxicity but also lack of efficacy of a particular approach. Our current model, if confirmed prospectively, could identify octogenarian and nonagenarian patients with normal albumin level and non-monocytic morphology as those who might be “fit for chemotherapy” in spite of their advanced age.

The question still remains what treatment to offer the ≥80 year old AML patients who are fit for chemotherapy. Several new drugs are currently in development, e.g., clofarabine, cloretazine, decitabine and arsenic trioxide. Their role will be determined in the next few years when the clinical trials will be completed. In the interim, we recommend that all octogenarian and nonagenarian patients will be enrolled onto clinical trials.

In summary, this retrospective study defined prognostic groups within the intensively-treated and the supportively-treated octogenarian and nonagenarian AML patients. If confirmed in prospective studies, these should allow physicians, allied care support staff and patients to consider treatments in a more informed manner.

Supplementary Material

01

Supplementary Table 1: Induction treatment regimens

Supplementary Table 2: Univariate analysis of prognostic factors associated with overall survival

Acknowledgments

Supported partially by grants from the National Cancer Institute Grant CA16056 (WT, GEW, SNJS, AWB MB, PKW, ESW, MW), the Szefel Foundation, Roswell Park Cancer Institute (ESW) and the Heidi Leukemia Research Fund, Buffalo, NY (MW).

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