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  • br Background br Aging is associated with decreased physiolo

    2020-08-14


    1. Background
    Aging is associated with decreased physiologic reserve, comorbidity and polypharmacy, functional dependence, and inadequate social sup-port [1–4]. The rate and amount of change varies from individual to in-dividual, but some level of comorbidity is present in N90% of patients with cancer aged 70 and older, being severe in 40% of the cases [1]. Therefore, comorbidity is an important issue in geriatric oncology. Many studies showed that comorbidity is associated with poor survival
    Corresponding author at: Senior Adult Oncology Program, H. Lee Moffitt Cancer Center and Research Institute, 12902 USF Magnolia Dr, 33612 Tampa, FL, USA. E-mail address: [email protected] (M. Extermann).
    [5]. In most of the studies done on patients with colorectal cancer, comorbidity was measured using the Charlson Comorbidity Index (CCI), and the presence of comorbidities was shown to be negatively as-sociated with overall survival but not with cancer-specific mortality [6–9]. Although some studies found that there was a difference in the comorbidity burden between older adults with colorectal cancer and controls, no study was done on the association of comorbidity with tox-icity in older adults with colorectal cancer treated with chemotherapy [7,10]. Additionally, the CCI is a limited list of conditions that are weighted according to their relative risk of death. It was developed in a general hospital population, although it CORM-3 has been used in specific cancers including breast cancer, lung cancer, head and neck cancer and hematologic malignancy [11–14]. The Cumulative Illness Rating
    Scale-Geriatric (CIRS-G) is more sensitive and has more prognostic value than the CCI [14,15]. For example the measured prevalence of comorbidity in older patients with cancer with various tumor types was 94% by the CIRS-G and 36% by the CCI [16].
    Comorbidity is a complex multidimensional category of data. Traditional approaches have tried to summarize their severity using summary instruments, such as the CCI and the CIRS-G. Sever-ity is defined either as an impact on survival, as in the CCI, or a combination of survival, functional impairment, and need for treat-ment, as in the CIRS-G. These instruments have demonstrated their validity in geriatric oncology. They are associated with survival and have sometimes been associated with other outcomes such as risk of major toxicities and hospitalization [17,18]. It makes intuitive sense however, that different diseases may impact different out-comes: e.g. survival vs function vs chemotherapy toxicity. There-fore, a global rating of comorbidity might not identify the diseases most likely to impact each outcome. For example some data sug-gest that the impact of the CCI listed diseases on the survival of pa-tients with colorectal cancer is different from their CCI weight [19]. We saw a potential in using a heat-map style approach in identify-ing specific subgroups of comorbidities that would be linked to an outcome. In a first approach to the problem, we decided to build upon a grid of CIRS-G rated diseases, and assess separately both the frequency and impact of diseases in each organ categories. Heat maps and their associated algorithms have potential advan-tages when analyzing data. They allow visualizing both the fre-quency and the level of association with an outcome of the various variables; they may identify clustering patterns; they may also visualize the predominant level of severity of a given set of dis-eases [22]. Although technically tables could present similar data, such tables can rapidly become unwieldy in size and unclear: For example, a table reporting the simple heat map in our previous ar-ticle would have 5586 data points [20]. Our first publication did identify a subgroup of diseases associated with survival in a general cohort of patients with cancer, which we summarized as a “total risk score” (TRS) [20]. In this article and parallel projects [21], we are expanding further the analysis by a) focusing on specific can-cers, and b) analyzing other outcomes such as toxicity from chemo-therapy and unplanned hospitalizations. r> 2. Methods
    2.1. Patients and Methods
    We retrospectively reviewed patients with stage IV colorectal cancer who were over 65 years old and had received initial chemo-therapy for their metastatic disease at Moffitt Cancer Center from 2000 to 2015. Comorbidity was assessed by the CIRS-G. CIRS-G has 14 organ categories and grades each comorbidity according to se-verity (score 0–4) [14,23]. Five summary scores are: the total num-ber of categories endorsed, the total score, the ratio of total score/ number of endorsed categories (severity index), and the number of categories at score 3 and 4 for a given patient in CIRS-G. The 14 organ categories are: Heart, Vascular, Hematopoietic, Respiratory, Eye/Ear/Nose/Throat, Upper GI, Lower GI, Liver, Renal, Genitouri-nary, Musculoskeletal/integument, Neurological, Endocrine/Meta-bolic and Breast, and Psychiatric illness. The total score and severity index were calculated for each patient. The total score was defined as the sum of scores in all organ systems and severity index was defined as total score divided by the number of categories with a score N0. Severe comorbidity was defined as having one or more comorbidity grade 3 or 4 [15].