The trusted source for
healthcare information and
Cormorbid Disease May Refine MDS Outcomes
Abstract & Commentary
By Andrew S. Artz, MD, Hematology/Immunology Unit, National Institute on Aging, NIHDr. Artz reports no relationships relevant to this field of study.
Synopsis: Myelodysplastic syndromes (MDS) usually occur in older adults and, as such, comorbid conditions are common. The investigators found that among 418 MDS patients, at least one comorbid condition existed in 93% at diagnosis. Comorbidity scores were generated using three common scoring systems: the HCT-CI, MDS-CI, and CCI. Worse survival was linked to higher CCI (p = 0.01) and MDS-CI (p = 0.02) but not HCT-CI. Higher CCI scores were associated with non-leukemic death and progression of red blood cell dependency, whereas higher comorbidity by HCT-CI and MDS-CI did not. Higher comorbid burden by CCI in MDS predicts for worse survival and non-leukemic death. Comorbidity data may help refine prognosis for MDS patients.
Source: Breccia M, et al. Evaluation of comorbidities at diagnosis predicts outcome in myelodysplastic syndrome patients. Leukemia Research. 2011;:159-162.
Myelodysplastic syndromes (MDS) often present in older adults who have other health conditions. Prognostic scoring systems, such as the IPSS or WPSS, primarily account for disease-related features and age. Prognostic accuracy may be lacking when not accounting for non-MDS comorbid diseases. For example, the inferior outcomes of adults 60 years and older with low-risk MDS may reflect both adverse disease features in older adults and competing health problems.
Comorbid diseases include the medical illnesses in addition to the primary disease of interest (e.g., diabetes, congestive heart failure). Numerous instruments have been developed to uniformly count comorbid conditions with a goal of a simpler and more robust prognostic tool. A summary score can be generated for every patient rather than trying to determine the impact of each comorbid condition. The Charlson Comorbidity Index (CCI) represents one of the simplest and most widely utilized instruments.1 Other groups have developed comorbidity indexes specific for MDS (MDS-CI) or hematopoietic cell transplant (HCT-CI).2,3 In this study, the authors aim to assess the influence of comorbidity by several tools on outcome at the time of MDS diagnosis.
In this retrospective, single-institutional review, the authors evaluated 481 patients diagnosed with MDS between 1992 and 2005. The median age was 68.6 years. Ninety-three percent had at least one comorbid condition at the time of diagnosis. Cardiac conditions were most common at 38.4%, followed by diabetes at 18.7%. Categorizing comorbidity by each index revealed a CCI score of 0, 1, and 2 or more in 253 (60%), 111 (26.5%), and 54 (12.9%) patients, respectively. For HCT-CI, 50% scored 0 for having no conditions, 25% had one comorbidity, and 25% had two or more conditions by the HCT-CI. Scores of 0 and 1 or more by the MDS-CI were present in 68.9% and 30.8%, respectively.
Unadjusted analysis revealed a correlation between inferior survival and higher CCI (p = 0.01) and MDS-CI (p = 0.02), but not HCT-CI (p = 0.34). Higher CCI and MDS-CI scores were associated with developed red blood cell transfusion dependence and death not related to leukemic evolution, whereas HCT-CI did not. In multivariable analysis, both overall survival and event-free survival were strongly associated with higher CCI and MDS-CI (p from 0.01 to 0.004), but not HCT-CI.
Similar to many diseases and cancers, MDS is generally diagnosed in older adults. As a consequence, prognostication and treatment decisions must consider health conditions aside from the primary disease. The oncologist's toolbox has generally used performance status as a crude but reliable indicator of treatment tolerance, if not prognosis. Severe comorbid conditions (e.g., renal failure) may sometimes be used as well, but accurate data on the interaction between comorbid conditions and disease are often limited.
In this study, the authors shed light on the prognostic relevance of comorbidity by three different scoring systems: CCI, MDS-CI, and HCT-CI. In short, each index tabulates conditions slightly differently to come up with a score. The CCI has been in use for over two decades and is widely validated. The MDS-CI was developed in an MDS cohort and the HCT-CI validated in a transplant population but has been increasingly applied for hematologic malignancies. The findings that higher comorbidity by CCI and MDS-CI predicted for worse survival and more non-leukemic deaths are not surprising. One would expect long-standing MDS and the attendant consequences of anemia, iron overload, infections and/or bleeding could all interact with non-hematologic diseases. Whether treatment intolerance or toxicity contributed as well cannot be gleaned from these data. What is surprising is the lack of predictive value for the HCT-CI. The HCT-CI employs the CCI as a base, and incorporates and clarifies additional conditions found important for transplant outcome. Certainly, additional conditions in the HCT-CI, but not the CCI, such as obesity or psychiatric disturbance, may influence transplant outcomes more than long-term MDS outcomes. However, much of the value of the HCT-CI over the CCI derived from incorporating routine transplant testing in the score. For example, around 50% of the abnormal scores are driven by pulmonary abnormalities, most of which are derived from pulmonary function-test data that would not be available. The prevalence of conditions by CCI and HCT-CI was similar, as opposed to the marked increased sensitivity in transplant studies, further supporting the reduced sensitivity of the HCT-CI absent extensive pre-transplant testing. One shortcoming in many such studies is not recognizing the link between another cancer (e.g., history of breast cancer) and therapy-related MDS. Since therapy-related MDS has a worse prognosis, one also should analyze the data after excluding therapy-related disease.
The data also are consistent with a large epidemiologic investigation of MDS and comorbid disease combining the SEER and medicare database showing worse survival and higher non-leukemic death for higher CCI scores.4 Billing databases are more likely to under-report and misclassify conditions and, thus, this detailed study provides important confirmation.
How best to apply this information in patient decision-making is more challenging. One can tabulate comorbid burden to refine prognostic estimates to patients, although larger studies may be needed to provide estimates with reasonably narrow confidence intervals. As front-line therapy for MDS with hypomethylating agents has reasonable tolerance, and is primarily employed for higher-risk MDS, higher comorbid burden may not alter treatment decisions. However, when considering remission induction therapy, a high comorbid burden, and the accompanying poor non-leukemic survival, may pose an argument against such treatment, especially since higher comorbidity increases the risk of early death after AML induction.5
In summary, comorbidities are extremely common in MDS patients. Higher comorbidity, at least by the CCI, predicts for worse survival and non-leukemic death, and may help refine prognostication for MDS patients.
1. Charlson ME, et al. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40:373-383.
2. Sorror ML, et al. Hematopoietic cell transplantation (HCT)-specific comorbidity index: a new tool for risk assessment before allogeneic HCT. Blood. 2005;106:2912-2919.
3. Della Porta MG, et al. Risk stratification based on both disease status and extra-hematologic comorbidities in patients with myelodysplastic syndrome. Haematologica. 2010 Dec. 6 [Epub ahead of print]
4. Wang R, et al. Comorbidities and survival in a large cohort of patients with newly diagnosed myelodysplastic syndromes. Leuk Res. 2009;33:1594-1598.
5. Giles FJ, 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.