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    2022-05-30

    r> Fig. 5. Overall survival by TRS risk group in patients with rectal cancer.
    Fig. 6. Comorbidity heat map for unplanned hospitalization in all patients.
    weights) and retains the highest scoring disease for that category. A more granular analysis taking and grading each disease independently might identify specific diseases or clusters of diseases. This would be a logical extension of a heat map approach and would need integrating clustering and false discovery SR 11302 algorithms to compensate for the number of diseases analyzed. Our trial was also relatively small and may have lacked the power of detecting mild to moderate associations. Another possible explanation is that diseases come with associated med-ications (e.g. metformin, aspirin, immune treatments) which may con-found further their overall impact. Finally, rather than specific diseases, the somatic response to them might be the key driver, and assessment of biologic vulnerability factors, such as inflammatory cytokines, might have a better correlation with toxicity and hospitalization. Further miti-gation factors such as social support likely play a role in influencing un-planned hospitalization rates.
    The most common comorbidities in our study were vascular, eye/ear/ nose/throat, respiratory and endocrine/metabolic/breast disease. Similar findings, according to the CCI, were found in the Danish elderly population-based study. In the latter, vascular, cardiopulmonary, ulcer dis-ease and diabetes had the higher prevalence in patients with colorectal cancer than in controls [7]. Several other studies had results similar to ours.
    In conclusion, the number of CIRS-G score 4 diseases and TRS of pa-tients with rectal cancer were associated with worse OS but no specific CIRS-G category was individually associated with OS in older patients with metastatic colorectal cancer treated with chemotherapy. Our ap-proach identified no association of comorbidity with toxicity from che-motherapy or unplanned hospitalizations in ray-finned patients. Future research projects may have to account for diseases more individually and include assessment of medications and biologic markers.
    Authors Contribution and Conflicts of Interest
    Drs KH Kim, Lee, J Kim, Sehovic, and Extermann participated in the de-sign of the study. Drs.
    KH Kim, Lee, Gomes, Sehovic and Extermann took part in the data col-lection and abstraction.
    All authors contributed to the data analysis and article writing.
    There are no conflicts of interest disclosures from any authors.
    Acknowledgements
    This work was supported by a grant from Research year of Inje University in 2016 (20160042) and National Cancer Institute grant P30-CA076292 (NCI Comprehensive Cancer Core grant: Moffitt Cancer Center Biostatistics Core).
    References
    [7] Jørgensen T, et al. Comorbidity in elderly cancer patients in relation to overall and cancer-specific mortality. Br J Cancer 2012;106(7):1353–60. [8] Erichsen R, et al. Does comorbidity interact with colorectal cancer to increase mortality? a nationwide population-based cohort study. Br J Cancer 2013; 109(7):2005–13.
    [13] Sorror ML, et al. Hematopoietic cell transplantation (HCT)-specific comorbidity index: a new tool for risk assessment before allogeneic HCT. Blood 2005;106(8): 2912–9.
    [14] Extermann M. Measuring comorbidity in older cancer patients. Eur J Cancer 2000;36
    [15] Kirkhus L, et al. Comparing comorbidity scales: attending physician score versus the cumulative illness rating scale for geriatrics. J Geriatr Oncol 2016;7(2):90–8. [16] Extermann M, et al. Comorbidity and functional status are independent in older can-cer patients. J Clin Oncol 1998;16(4):1582–7.