|Year : 2016 | Volume
| Issue : 2 | Page : 168-171
Association of comorbidities with breast cancer: An observational study
Neeti Sharma1, Satya Narayan1, Rajani Sharma2, Akhil Kapoor1, Narender Kumar3, Rajkumar Nirban1
1 Department of Radiation Oncology, Acharya Tulsi Regional Cancer Treatment and Research Institute, Bikaner, Rajasthan, India
2 Department of Pharmacy and Oncology, Health Professional, PBM Hospital, Bikaner, Rajasthan, India
3 Department of Oncology, Sarder Patel Medical College, Bikaner, Rajasthan, India
|Date of Web Publication||5-Jul-2016|
Apartment No. 26, PG Hostel, PBM Hospital Campus, Bikaner - 334 003, Rajasthan
Background: The aim of this study was to describe the prevalence of comorbidity in newly diagnosed female breast cancer patients in north-west India. The second end point of the study was compliance for multimodality treatment. Comorbidity assessed by counting the number of coexisting diseases diagnosed in a cancer patient or by using a comorbidity index that combines the number and severity of the diseases. The most widely used index is the Charlson Comorbidity Index (CCI). Materials and Methods: The data of female patients with breast cancer were recorded, having comorbidities during the cancer registration or comorbidities diagnosed during the treatment at the host institute between January and December 2012. The patients were distributed on the basis of physical parameters such as age, stage, tumor grade, hormone receptor status, ECOG status at diagnosis and CCI. Scores of CCI are summed to provide a total score to predict mortality. Results: During the period of January to December 2012, 156 biopsy-proven breast cancer patients were included in the study. During this period, female breast cancer patients enrolled were 13.94% out of total patient enrollment. The most prevalent comorbidities associated with breast cancer are hypertension (21.8%), chronic obstructive pulmonary disease (COPD) (19.9%), rheumatologic disease (18.6%), and diabetes mellitus (16.7%), all four conditions have been reported in around 75% of the cases. The planning of multimodality management in comorbidity arm was significantly lower (P > 0.01) as compared to patients without comorbidity. Conclusions: The planning of multimodality management in comorbidity arm was significantly lower as compared to patients without comorbidity. Because of the comorbid condition, the definitive treatment of breast cancer was not given so this will also affect the treatment of breast cancer. When the CCI score increases with an increase in the number of comorbidities will decrease survival.
Keywords: Breast cancer, Charlson Comorbidity Index, comorbidity, north-west India
|How to cite this article:|
Sharma N, Narayan S, Sharma R, Kapoor A, Kumar N, Nirban R. Association of comorbidities with breast cancer: An observational study. Trop J Med Res 2016;19:168-71
|How to cite this URL:|
Sharma N, Narayan S, Sharma R, Kapoor A, Kumar N, Nirban R. Association of comorbidities with breast cancer: An observational study. Trop J Med Res [serial online] 2016 [cited 2019 Oct 17];19:168-71. Available from: http://www.tjmrjournal.org/text.asp?2016/19/2/168/185449
| Introduction|| |
Comorbidity is defined as "any additional clinical condition that has existed simultaneously or that may occur during the clinical course of a patient with an index disease under study." , Comorbidity must also be distinguished from complications that arise as a consequence of cancer or its treatment. A number of studies have examined the prognostic impact of patients' "performance status" at the time of cancer diagnosis. Performance status is a measure of a cancer patient's well-being, defined as the amount of normal daily activity the patient can maintain. ,, However, the performance status is affected by cancer, complications of cancer, and comorbid conditions.  The aim of this study was to describe the prevalence of comorbidity, deviation from standard treatment because of comorbidity, and in newly diagnosed breast cancer patients in north-west India. The second end point of the study was comparison of progression free survival with noncomorbid breast cancer patients.
| Materials and Methods|| |
The patients with histologically proven malignancy attending the Department of Oncology, at Regional Cancer Treatment, during the period January 2012 to December 2012. During this period, female breast cancer patients enrolled were 13.94% (976/7001) out of total patient enrollment. Out of these 976 (13.94%) patients, 156 (15.98%) patients were associated with comorbidities. The treatment planning done for 156 randomly selected patients without any co morbidity was compared with that done for patients with co morbidity. All patients were examined clinically and requisite investigations (e.g., hematological, biochemical, and imaging) to have an assessment of the extent of malignancy were done. The attendants of the patients were subjected to a counseling session regarding the nature of disease, associated co-morbidities, treatment options, and prognosis. All the patients were assessed by taking a detailed history with complete general physical examination. The performance was determined as per the Eastern Cooperative Oncology Group (ECOG) scale and the co-morbidity score was calculated by Charlson Comorbidity Index (CCI). Hematological, biochemical, and radiological investigations were performed before the actual treatment started, during treatment, and at the follow-up. Clinical staging and stage grouping were done according to the tumor node metastasis (TNM) [Union for International Cancer Control-American Joint Committee on Cancer (UICC-AJCC) staging] classification. The comorbidities were searched by detailed history; complete general physical examination; and required biochemical, hematological, and radiological investigations [viz. blood pressure test, electrocardiogram (ECG), echocardiography, color Doppler, joint X-rays, ultrasound (abdomen + pelvis)]. The detected comorbidities were treated accordingly by respective specialized specialists. Based on the ECOG performance status, the site of the disease, stage, and affordability, the patients were treated with either combined chemotherapy and radiotherapy ± surgery, or chemotherapy ± surgery, or radiotherapy ± surgery, or surgery alone. The drugs used were cyclophosphamide, methotrexate, 5-fluorouracil, doxorubicin, platinum agents, paclitaxel, etc., as per the chemotherapy schedule chosen. Nursing care was taken for proper drug route selection, preparation of the patient, pretreatment evaluation, and assessment of the hematological and clinical parameters. The patients were monitored after the treatment to detect any toxicity. Radiotherapy was delivered using appropriate portals, the radiotherapy planning was done on the simulator, and the treatment planning system was Co 60 teletherapy unit (Theratron 780E/C, Best Theratronics Canada). Toxicity monitoring was done. The definitions of complete and partial response, stable disease, and progressive disease were based on the standardized response definitions of the World Health Organization (WHO).
| Results|| |
During the period of January 2012 and December 2012, 156 biopsy-proven breast cancer patients with associated comorbidity were included in the study. In this study the comorbidities prevalent in female breast cancer patients was studied. The demographic profile is shown in [Table 1], and the patients were arranged according to the associated comorbid condition. Most prevalent associated comorbidities related to breast cancer are hypertension [34 (21.8%)], chronic obstructive pulmonary disease (COPD) [31 (19.9%)], rheumatologic disease [29 (18.6%)] and diabetes mellitus [26 (16.7%)], all four conditions were reported in more than 75% of the cases [Table 2]. Another finding was that patients with comorbidity do not receive standard cancer treatments such as surgery, chemotherapy, and radiation therapy as often as patients without comorbidity. The planning of multimodality management in comorbidity arm was significantly lower (Complete Response (CR) = 68.3% vs Partial Response (PR)/Stable disease (SD)/Progressive disease (PD) 37.4% P > 0.01) as compare to patients without co-morbidity [Table 3]. This study shows that older women (>60 years), at the time of breast cancer diagnosis, have more prevalence of comorbid conditions than the younger population. In addition, the stage at diagnosis is associated with the variability in the prevalence of many conditions. The CCI score is depends upon number of comorbidities and age. According to CCI score mortality at 12 months and survival at 10 years can be calculated. When the CCI score increases due to increase in number of comorbidities, It affects survival at 12 months year as well as at 10 year survival outcome. Similarly when CCI score increases because of age group (i.e. <40, 41-50 years) than the 12 months survival is remains same for all age group but only 10-year survival outcome affected [Table 4]. ,, These results are calculated by using the CCI.
|Table 2: Prevalence proportions for comorbid conditions in breast cancer patients |
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|Table 3: Treatment profiles: Treatment compliance, type of treatment received, and treatment response |
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|Table 4: Distribution of the patients according to the CCI score, with 12-month mortality and 10-year survival analysis |
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| Discussion|| |
Due to the presence of comorbidities, the complexities have influenced the cancer patient's preferences for the treatment and treatment outcomes. Comorbidity generally increases with increasing age, and it may be the cause behind age-related differences in cancer diagnosis, treatment, and outcome. The significant findings of the study that patients with comorbidities were aged >60 years mostly. Seventy-five percent of the comorbid conditions are chronic diseases such as hypertension (21.8%), COPD (19.9%), rheumatoid arthritis (18.6%), diabetes mellitus (16.7%). All of the above-mentioned diseases are partly associated with the lifestyle behavior. Because the incidence of most cancers increases with advancing age, it is not surprising that comorbidity has been frequently found among patients with cancer.  Comorbidity has often been associated with less aggressive treatment and poor cancer outcomes even after the treatment. ,,,,,, The 1-year survival across the demographic age group in the study is not significantly altered, although it has a significant effect on the 10-year survival and overall quality of life. The comorbidity could also serve as a competing demand for primary care physicians, decreasing the likelihood of cancer screening recommendations.  The CCI (Co-morbidity-Adjusted Life Expectancy) predicts the 10-year mortality for a patient who may have a range of comorbid conditions. Each condition is assigned a score of 1, 2, 3, or 6, depending on the risk of dying associated with each one. In contrast, the incidence rates of co-morbid conditions; stratified by stage at diagnosis, show that most advanced cancer stage is associated with a greater likelihood of new comorbid conditions. Because of the comorbid condition, the definitive treatment of breast cancer was not given so this will also affect the cure of breast cancer. The strength of this study lies in its detailed information of the patient complaints, obtained through work-up, proper questionnaire, nursing care, psychological counseling, regular follow-up, work-up at every phase of treatment for adverse events if any at any point of the study was taken. The limitation of the study is the analysis of the study using CCI. The limitations of CCI, potentially resulting in bias and confounding are as follows: First, it incorporates 48 available information about comorbid conditions into an aggregate index, which precludes estimation of effects of individual comorbid diseases. Second, it does not include all medical conditions and psychiatric diseases that can confer substantial morbidity even in patients with diagnosis of index diseases. Third, duration is not accounted for, and severity is only considered to a very limited extent. As an example, consider the effect of diabetes, which increases risk of death with duration, whereas the effect of cancer diseases often decreases with survival beyond 5 years. In the CCI, only diabetes and liver disease are divided into only two severity groups, both disease types can be more finely parsed, and other CCI diseases have important severity grades, for example COPD. Fourth, the CCI diseases can be measured using several methods, 133 patients with varying weaknesses and no gold standard. This study highlights the effect of comorbidities on the treatment of the patient. In rural centers where the patients are not able for regular follow-ups, the comorbidities can further deter the compliance of the patients. As enumerated in this study, there are considerable modifications in the treatment of the patients and this further delay the definitive treatment. This study further raises a question on the protocols as per the comorbidities that are encountered in the practice. Studies in larger groups need to be undertaken to establish best possible guidelines to improve the quality of care, the quality of life, and the quality of treatment in such patients.
CCI = Charlson Comorbidity Index.
ECOG = Eastern Cooperative Oncology Group.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Last JM. A Dictionary of Epidemiology. 4 th
ed. New York, NY: Oxford University Press; 2001.
Feinstein AR. The pre-therapeutic classification of co-morbidity in chronic diseases. J Chronic Dis 1970;23:455-68.
de Rijke JM, Schouten LJ, ten Velde GP, Wanders SL, Bollen EC, Lalisang RI, et al
. Influence of age, comorbidity and performance status on the choice of treatment for patients with non-small cell lung cancer; results of a population-based study. Lung Cancer 2004;46:233-45.
Langer CJ. Neglected and underrepresented subpopulations: Elderly and performance status 2 patients with advanced-stage non-small-cell lung cancer. Clin Lung Cancer 2006;7(Suppl 4):S126-37.
Quoix E. Optimal pharmacotherapeutic strategies for elderly patients with advanced non-small cell lung cancer. Drugs Aging 2011;28:885-94.
Grose D, Devereux G, Brown L, Jones R, Sharma D, Selby C, et al
.; Scottish Lung Cancer Forum. Variation in comorbidity and clinical management in patients newly diagnosed with lung cancer in four Scottish centers. J Thorac Oncol 2011;6:500-50.
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;40:373-83.
D'Hoore W, Bouckaert A, Tilquin C. Practical considerations on the use of the Charlson comorbidity index with administrative data bases. J Clin Epidemiol 1996;49:1429-33.
Charlson M, Szatrowski TP, Peterson J, Gold J. Validation of a combined comorbidity index. J Clin Epidemiol 1994;47:1245-51.
Yancik R, Havlik RJ, Wesley MN, Ries L, Long S, Rossi WK, et al
. Cancer and comorbidity in older patients: A descriptive profile. Ann Epidemiol 1996;6:399-412.
Desch CE, Penberthy L, Newschaffer CJ, Hillner BE, Whittemore M, McClish D, et al
. Factors that determine the treatment for local and regional prostate cancer. Med Care 1996;34:152-62.
Newschaffer CJ, Penberthy L, Desch CE, Retchin SM, Whittemore M. The effect of age and comorbidity in the treatment of elderly women with nonmetastatic breast cancer. Arch Intern Med 1996;156:85-90.
Newschaffer CJ, Bush TL, Penberthy LT. Comorbidity measurement in elderly female breast cancer patients with administrative and medical records data. J Clin Epidemiol 1997;50:725-33.
Payne JE, Meyer HJ. The influence of other diseases upon the outcome of colorectal cancer patients. Aust N Z J Surg 1995;65:398-402.
Satariano WA. Aging, comorbidity, and breast cancer survival: An epidemiologic view. Adv Exp Med Biol 1993;330:1-11.
Satariano WA, Ragland DR. The effect of comorbidity on 3-year survival of women with primary breast cancer. Ann Intern Med 1994;120:104-10.
West DW, Satariano WA, Ragland DR, Hiatt RA. Comorbidity and breast cancer survival: A comparison between black and white women. Ann Epidemiol 1996;6:413-9.
Jaen CR, Stange KC, Nutting PA. Competing demands of primary care: A model for the delivery of clinical preventive services. J Fam Pract 1994;38:166-71.
[Table 1], [Table 2], [Table 3], [Table 4]