The impact of comorbid disease history on all-cause and cancer-specific mortality in myeloid leukemia and myeloma – a Swedish population-based study

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Abstract

Background

Comorbidity increases overall mortality in patients diagnosed with hematological malignancies. The impact of comorbidity on cancer-specific mortality, taking competing risks into account, has not been evaluated.

Methods

Using the Swedish Cancer Register, we identified patients aged >18 years with a first diagnosis of acute myeloid leukemia (AML, N = 2,550), chronic myeloid leukemia (CML, N = 1,000) or myeloma ( N = 4,584) 2002–2009. Comorbid disease history was assessed through in- and out-patient care as defined in the Charlson comorbidity index. Mortality rate ratios (MRR) were estimated through 2012 using Poisson regression. Probabilities of cancer-specific death were computed using flexible parametric survival models.

Results

Comorbidity was associated with increased all-cause as well as cancer-specific mortality (cancer-specific MRR: AML = 1.27, 95 % CI: 1.15–1.40; CML = 1.28, 0.96–1.70; myeloma = 1.17, 1.08–1.28) compared with patients without comorbidity. Disorders associated with higher cancer-specific mortality were renal disease (in patients with AML, CML and myeloma), cerebrovascular conditions, dementia, psychiatric disease (AML, myeloma), liver and rheumatic disease (AML), cardiovascular and pulmonary disease (myeloma). The difference in the probability of cancer-specific death, comparing patients with and without comorbidity, was largest among AML patients <70 years, whereas in myeloma the difference did not vary by age among the elderly. The probability of cancer-specific death was generally higher than other-cause death even in older age groups, irrespective of comorbidity.

Conclusion

Comorbidities associated with organ failure or cognitive function are associated with poorer prognosis in several hematological malignancies, likely due to lower treatment tolerability. The results highlight the need for a better balance between treatment toxicity and efficacy in comorbid and elderly AML, CML and myeloma patients.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-015-1857-x) contains supplementary material, which is available to authorized users.

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Most cited references 40

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Socioeconomic status and health: how education, income, and occupation contribute to risk factors for cardiovascular disease.

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Socioeconomic status (SES) is usually measured by determining education, income, occupation, or a composite of these dimensions. Although education is the most commonly used measure of SES in epidemiological studies, no investigators in the United States have conducted an empirical analysis quantifying the relative impact of each separate dimension of SES on risk factors for disease. Using data on 2380 participants from the Stanford Five-City Project (85% White, non-Hispanic), we examined the independent contribution of education, income, and occupation to a set of cardiovascular disease risk factors (cigarette smoking, systolic and diastolic blood pressure, and total and high-density lipoprotein cholesterol). The relationship between these SES measures and risk factors was strongest and most consistent for education, showing higher risk associated with lower levels of education. Using a forward selection model that allowed for inclusion of all three SES measures after adjustment for age and time of survey, education was the only measure that was significantly associated with the risk factors (P less than .05). If economics or time dictate that a single parameter of SES be chosen and if the research hypothesis does not dictate otherwise, higher education may be the best SES predictor of good health.

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Accurate estimates of cancer survival are important for assessing optimal patient care and prognosis. Evaluation of these estimates via relative survival (a ratio of observed and expected survival rates) requires a population life table that is matched to the cancer population by age, sex, race and/or ethnicity, socioeconomic status, and ideally risk factors for the cancer under examination. Because life tables for all subgroups in a study may be unavailable, we investigated whether cause-specific survival could be used as an alternative for relative survival. We used data from the Surveillance, Epidemiology, and End Results Program for 2,330,905 cancer patients from January 1, 1992, through December 31, 2004. We defined cancer-specific deaths according to the following variables: cause of death, only one tumor or the first of multiple tumors, site of the original cancer diagnosis, and comorbidities. Estimates of relative survival and cause-specific survival that were derived by use of an actuarial method were compared. Among breast cancer patients who were white, black, or of Asian or Pacific Islander descent and who were older than 65 years, estimates of 5-year relative survival (107.5%, 106.6%, and 103.0%, respectively) were higher than estimates of 5-year cause-specific survival (98.6%, 95% confidence interval [CI] = 98.4% to 98.8%; 97.4%, 95% CI = 96.2% to 98.2%; and 99.2%, 95% CI = 98.4%, 99.6%, respectively). Relative survival methods likely underestimated rates for cancers of the oral cavity and pharynx (eg, for white cancer patients aged ≥65 years, relative survival = 54.2%, 95% CI = 53.1% to 55.3%, and cause-specific survival = 60.1%, 95% CI = 59.1% to 60.9%) and the lung and bronchus (eg, for black cancer patients aged ≥65 years, relative survival = 10.5%, 95% CI = 9.9% to 11.2%, and cause-specific survival = 11.9%, 95% CI = 11.2 % to 12.6%), largely because of mismatches between the population with these diseases and the population used to derive the life table. Socioeconomic differences between groups with low and high status in relative survival estimates appeared to be inflated (eg, corpus and uterus socioeconomic status gradient was 13.3% by relative survival methods and 8.8% by cause-specific survival methods). Although accuracy of the cause of death on a death certificate can be problematic for cause-specific survival estimates, cause-specific survival methods may be an alternative to relative survival methods when suitable life tables are not available.

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Survival for older patients with acute myeloid leukemia: a population-based study.

D J Weisdorf, Betul Oran (2012)

Acute myeloid leukemia is the second most common leukemia among United States adults with a median age of 69 years. We investigated recent clinical practices related to treatments and disease outcomes in older patients with acute myeloid leukemia in the United States. In this retrospective cohort study, we used Surveillance, Epidemiology, and End Results program data from 2000 through 2007 linked to Medicare enrollment and utilization data in the United States. Among 5,480 patients with acute myeloid leukemia (median age 78 years, range 65-93), 38.6% received leukemia therapy within three months of diagnosis (treated group). Practice changed with 16.3% of treated patients receiving hypomethylating agents after 2004 when those agents became available. Median survival was two months in the untreated group versus six months in the treated group (P<0.01) with the biggest improvements seen in those aged 65-69 years (10 months vs. 4 months; P<0.01) and 70-74 years (8 months vs. 3 months; P<0.01). In 46 patients receiving allogeneic hematopoietic cell transplantation (0.8%), the median survival from diagnosis was 22 months. Therapy for leukemia improves overall survival in older acute myeloid leukemia patients. Based on their comorbidities, most patients up to 80 years of age should be considered for treatment. New therapies including hypomethylating agents and allogeneic hematopoietic cell transplantation are promising and must be compared with other chemotherapy regimens.

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Author and article information

Contributors

Mohammad Mohammadi: mohammad.mohammadi@ki.se

Yang Cao: yang.cao@ki.se

Ingrid Glimelius: Ingrid.glimelius@onkologi.uu.se

Matteo Bottai: matteo.bottai@ki.se

Sandra Eloranta: Sandra.eloranta@ki.se

Karin E. Smedby: Karin.ekstrom.smedby@ki.se

Journal

Journal ID (nlm-ta): BMC Cancer

Journal ID (iso-abbrev): BMC Cancer

Title: BMC Cancer

Publisher: BioMed Central (London )

ISSN (Electronic): 1471-2407

Publication date (Electronic): 5 November 2015

Publication date PMC-release: 5 November 2015

Publication date Collection: 2015

Volume: 15

Electronic Location Identifier: 850

Affiliations

[ ]Division of Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden

[ ]Institute of Environmental Medicine, Unit of Biostatistics, Division of Epidemiology, Karolinska Institutet, Stockholm, Sweden

[ ]Department of Medicine, Clinical Epidemiology Unit, Solna, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden

[ ]Department of Immunology, Genetics and Pathology, Unit of Oncology, Uppsala University, Uppsala, Sweden

[ ]Hematology Center, Karolinska University Hospital, Stockholm, Sweden

Article

Publisher ID: 1857

DOI: 10.1186/s12885-015-1857-x

PMC ID: 4634819

PubMed ID: 26537111

SO-VID: 0a00b9de-264c-4722-8803-02888780bdb1

License:

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

History

Date received : 10 June 2015

Date accepted : 27 October 2015

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ScienceOpen disciplines: Oncology & Radiotherapy

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ScienceOpen disciplines: Oncology & Radiotherapy

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The impact of comorbid disease history on all-cause and cancer-specific mortality in myeloid leukemia and myeloma – a Swedish population-based study

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Abstract

Background

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Electronic supplementary material

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Karger: Oncology

Most cited references 40

Socioeconomic status and health: how education, income, and occupation contribute to risk factors for cardiovascular disease.

Improved estimates of cancer-specific survival rates from population-based data.

Survival for older patients with acute myeloid leukemia: a population-based study.

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Cited by 24

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