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This analysis, of 2483 patients with acute myeloid leukaemia (AML) aged 60+ years entered into two UK trials, was performed to determine the baseline parameters related to survival and to develop a risk index. The Medical Research Council (MRC) AML11 trial (n = 1071) was used to develop the index; this was validated using data from the Leukaemia Research fund (LRF) AML14 trial on 1137 intensively (AML14I) and 275 non-intensively (AML14NI) treated patients. In AML11, cytogenetic group, age, white blood count, performance status and type of AML (de novo, secondary) were all highly significantly related to prognosis in multivariate analysis. The regression coefficients were used to define good, standard and poor risk groups, with 1-year survival of 53%, 43% and 16% respectively (P < 0·0001). The risk index showed very good discrimination in both AML14I and AML14NI (both P < 0·0001), thereby providing validation, although survival in all groups was very poor in AML14NI. The risk factors for survival in older AML patients were similar to those in younger ones and discrimination of patient groups with relatively good to very poor prognosis was possible. These risk groups apply to both intensively and non-intensively treated patients. Randomized trials of intensive versus non-intensive therapy are needed to determine which types of patient should be given which type of treatment.
Acute myeloid leukaemia (AML) in older patients has a much poorer prognosis than in younger patients, which is partly related to increased frequencies of secondary AML, adverse cytogenetic features (Grimwade et al, 2001) and overexpression of multidrug resistance (MDR) phenotypes (Leith et al, 1997). Age is an important independent prognostic factor in AML, with 5-year survival rates ranging from over 50% in children (Gibson et al, 2005) down to <10% in patients aged over 70 years in whom intensive therapy with curative intent is attempted (Goldstone et al, 2001). Older patients are also much less likely to be treated intensively than younger ones, and it is estimated that <10% of patients aged 60–69 years, and <5% of those aged over 70 years, were entered into the United Kingdom Medical Research Council (MRC) and Leukaemia Research Fund (LRF) trials, compared to well over 50% of younger adults. The primary reasons for this are probably that AML in older patients is seen as a disease that is less likely to respond to attempts at cure and that older patients are also more susceptible to the toxic effects of intensive treatment. AML is a heterogeneous disease and a number of important prognostic factors have been described (Leith et al, 1997; Wheatley et al, 1999; Grimwade et al, 2001; Kottaridis et al, 2001). This report presents the results of a prognostic factor analysis of the patients aged 60 years or over who were entered into the MRC AML11 trial, with validation of the model using data from the subsequent LRF AML14 trial in which there were both intensive and non-intensive treatment options.
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The large number of patients (n = 2483) available make this the biggest and, therefore, one of the most reliable analyses of prognostic factors for survival in older AML patients ever performed. It confirms the importance, on both univariate and multivariate analysis, of age, WBC, performance status, type of disease and, particularly, cytogenetics. These factors are similar to those that are important in younger patients (Grimwade et al, 1998; Wheatley et al, 1999). This indicates that, while AML is a heterogeneous disease, there are no clear cut distinctions between younger (age <60 years) and older (age 60+ years) patients, though clearly the pattern of the disease changes with increasing age, e.g. secondary AML and adverse cytogenetic abnormalities become more frequent. The only other parameter that has been reliably shown, based on large series, to be a major prognostic factor in elderly AML is multidrug resistance protein expression (Leith et al, 1999). The AML11 trial commenced in 1990 before the importance of MDR was recognised, so no data on this were collected. Some data are available from AML14 but on a limited subset of patients. It will be important for new proposed prognostic factors to be evaluated as they emerge in the context of known factors and in large series.
Indices based on regression equations produce a continuum of risk, from the poorest to the best outlook. For practical purposes, this continuum needs to be divided into a number of discrete groups, the cut-off points for which are inevitably to some extent arbitrary. Although it only takes a few seconds to enter a patient’s parameters into a computer and obtain a risk classification based on the regression equation, for ease of use in the clinical setting, some clinicians may prefer to use a simple scoring system and we have shown that such a system provides good discrimination consistent with that from the multivariate index.
A problem with multivariate models is that they can suffer from ‘regression-dilution bias’ (Hughes, 1993). It is, therefore, important that such models be validated on independent data sets. In this case, we have used both an intensively treated cohort of patients from AML14I, and a non-intensively treated cohort from AML14NI (as well as the overall AML14 population). In both cases, the AML11 risk groups provided strong prognostic discrimination. In AML14I, it was as good as in AML11, so it is well validated in the intensive treatment setting. It also provided good discrimination in AML14NI between standard and poor risk groups, although the small number of good risk patients did not do better than standard risk patients. The latter finding may be due to the play of chance with small numbers, although insufficient chemotherapy in a group of patients whose good prognosis is related to the chemosensitivity of their disease could be a factor.
It may be that a different model will provide optimum discrimination in the somewhat different clinical setting of therapy without curative intent. We have performed a preliminary investigation of this using the AML14NI data set, but the model obtained will require validation (using data from the current AML16 trial) before it can be reported. In a smaller cohort of 416 intensively treated patients, Malfuson et al (2008) recently proposed a model based largely on cytogenetics, with age performance status and WBC also being taken into account, thereby providing further confirmation of the relevance of these parameters to the prognosis of AML in the elderly.
We do not, however, recommend that decisions on how to treat a patient should be made on the basis of entering the individual’s value for each prognostic factor into a regression equation. A number of other factors need to be taken into account when determining how to treat a patient, including the patient’s wishes and the general clinical impression (which may not relate to quantifiable parameters). It is not easy to identify general classes of patients who are likely to do well with intensive therapy. Patients with a combination of younger age, de novo disease, good performance status, low WBC and favourable cytogenetics would be expected to have the best outcome. Even then, it may not be clear whether this is related to the intensive therapy received or to the more favourable prognosis of the disease.
This is perhaps the most pressing current issue in therapy for AML in older patients. The mean age at diagnosis of AML is about 65 years (Cartwright et al, 1990), yet the majority of patients entered into clinical trials are younger than this. Many older patients are not offered, or choose not to accept, intensive induction therapy. There is very little reliable evidence available as to whether this is the correct decision. In this report, we have shown that patient groups defined by their outcome in AML11 experienced large differences in outcome between the intensive and non-intensive parts of AML14: with survival on intensive treatment at one year being much better in each risk group: 35%, 15% and 20% better for good, standard and poor risk groups respectively. Just considering the AML14NIA patients who received more effective therapy with low-dose cytarabine, the differences were still mainly large, at 24%, 6% and 16% respectively (the apparently more similar outcome at 1 year in the standard risk AML14NIA group compared with AML14I standard risk patients – 42% vs. 48%– was not maintained out to 3 years – 5% vs. 17% (Fig 2A and C) – by which time almost all AML14NIA patients had died). The important question is whether this is because intensive therapy actually is much better for all, or the great majority of, patients, or whether the unknown and/or unquantifiable selection factors, such as clinical impression of ‘fitness’, are large enough to explain the differences, or perhaps a combination of these considerations applies. We cannot quantify any such selection factors but, were they to be insufficient to explain the observed differences in outcome, one possible interpretation of the data would be that intensive therapy may be better for the majority of patients.
In relation to the practical application of this index, it should be noted that cytogenetic results are not available at diagnosis and may take several days to obtain. This would be a potential problem if the index were to be used to determine therapy and if therapy needed to be initiated rapidly following diagnosis. With regard to the latter consideration, Malfuson et al (2008) recently recommended waiting until the cytogenetic result is available before starting therapy, but many clinicians considering the use of intensive induction therapy may not be happy to delay treatment. Alternatively, could an index be developed that did not include cytogenetics which would enable treatment decisions to be made at diagnosis? We investigated this by analysis of the data excluding cytogenetics but, since cytogenetics is by far the most powerful predictor of outcome, the resulting model provided much poorer discrimination.
However, as discussed above, the indices developed in this report simply tell us that elderly AML patients with differing prognoses can be identified, both amongst those treated intensively and those treated non-intensively; it does not tell us which patients should be treated intensively or not.
It is not possible to be certain that non-randomized methods of comparison (e.g. multivariate modelling, matched pair analyses, propensity scores) are not subject to selection biases, so the only way to reliably resolve the issue of which patients should be treated intensively, and which not, is through randomized trials. Very few randomized trials have addressed the issue of whether elderly patients should be treated intensively or not. One trial showed a survival benefit for the intensive arm (Lowenberg et al, 1989), while another did not (Tilly et al, 1990). Both were small studies, possibly with selected populations, and the general relevance of these results more widely is unclear. In order to reliably determine which patients do benefit from intensive therapy, and which do not, one needs evidence from further, large randomized trials, in which appropriately heterogeneous populations are randomized between intensive versus non-intensive therapy and with stratification by risk group. Whether such trials can actually be performed is uncertain. The AML14 trial did attempt to address this question with a randomization available between entry to AML14I or AML14NI, but the vast majority of patients either chose or were electively assigned by their clinicians to the intensive arm or the non-intensive arm, and very few patients were randomized between the two treatment pathways (n = 8). In the absence of such trials, many important decisions on whether to treat older AML patients intensively or not will remain ad hoc and not be evidence-based.