Impact of comorbidities at diagnosis on the 10-year colorectal cancer net survival: A population-based study

Michalopoulou, Eleftheria; Matthes, Katarina L.; Karavasiloglou, Nena; Wanner, Miriam; Limam, Manuela; Korol, Dimitri; Held, Leonhard; Rohrmann, Sabine

doi:10.1016/j.canep.2021.101962

Cited by 11 publications

(5 citation statements)

References 55 publications

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“…However, “metastasis presence” and “metastasis site” were the most potent prognosticating factors, showing the highest HR. The findings correlate with the results from other studies that some clinicopathological factors, such as tumour site [ 32 ], tumour metastasis and metastasis site [ 33 ], comorbidity presence and number [ 34 ], CEA level [ 35 ], and tumour histopathology [ 36 ], demonstrate better prognosis in CRC patients. CRC patients with “No metastases” are considered to have a better survival status than those with metastases, 64.5% vs. 32.1%, respectively.…”

Section: Discussionsupporting

confidence: 89%

The Prediction of Survival Outcome and Prognosis Factor in Association with Comorbidity Status in Patients with Colorectal Cancer: A Research-Based Study

et al. 2022

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Colorectal carcinoma (CRC) is rising exponentially in Asia, representing 11% of cancer worldwide. This study analysed the influence of CRC on patients’ life expectancy (survival and prognosis factors) via clinicopathology data and comorbidity status of CRC patients. Methodology: A retrospective study performed in HUSM using clinical data from the Surgery unit from 2015 to 2020. The demographic and pertinent clinical data were retrieved for preliminary analyses (data cleansing and exploration). Demographics and pathological characteristics were illustrated using descriptive analysis; 5-year survival rates were calculated using Kaplan–Meier methods; potential prognostic variables were analysed using simple and multivariate logistic regression analysis conducted via the Cox proportional hazards model, while the Charlson Comorbidity Scale was used to categorize patients’ disease status. Results: Of a total of 114 CRC patients, two-thirds (89.5%) were from Malay tribes, while Indian and Chinese had 5.3% each. The 50–69.9 years were the most affected group (45.6%). Overall, 40.4% were smokers (majorly male (95.7%)), 14.0% ex-smokers, and 45.6% non-smokers (p-value = 0.001). The Kaplan–Meier overall 5-year median survival time was 62.5%. From the outcomes, patients who were male and >70 years had metastasis present, who presented with per rectal bleeding and were classified as Duke C; and who has tumour in the rectum had the lowest survival rate. Regarding the prognosis factors investigated, “Gender” (adjusted hazard ratio (HR): 2.62; 95% CI: 1.56–7.81, p-value = 0.040), “Presence of metastases” (HR: 3.76; 95% CI: 1.89–7.32, p-value = 0.010), “Metastasis site: Liver” (HR: 5.04; 95% CI: 1.71–19.05, p-value = 0.039), “Lymphovascular permeation” (HR: 2.94; 95% CI: 1.99–5.92, p-value = 0.021), and “CEA-level” (HR: 2.43; 95% CI: 1.49–5.80, p-value = 0.001) remained significant in the final model for multiple Cox proportional hazard regression analyses. There was a significant mean association between tumour grades and the patient’s comorbidity status. Conclusions: Histopathological factors (gender, metastases presence, site of metastases, CEA level, and lymphovascular permeation) showed the best prognosis-predicting factors in CRC.

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Section: Discussionsupporting

confidence: 89%

The Prediction of Survival Outcome and Prognosis Factor in Association with Comorbidity Status in Patients with Colorectal Cancer: A Research-Based Study

et al. 2022

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“…Thus it may explain the higher risk of death and shorter CRC survival [ 34 ]. Diabetes appears to increase the risk for primary cancer recurrence [ 1 ]. The association between diabetes and shorter CRC survival we found among patients with stage IV is consistent with previous evidence [ 35 ].…”

Section: Discussionmentioning

confidence: 99%

“…The association between diabetes and shorter CRC survival we found among patients with stage IV is consistent with previous evidence [ 35 ]. It has been shown that diabetes can hide or modify cancer symptoms, thus delaying cancer diagnosis [ 1 ]. Interestingly age, smoking, dementia, renal and liver disease were associated with shorter survival among stages I-III lung cancer patients, and age, sex, and smoking among stage IV lung cancer patients.…”

Section: Discussionmentioning

confidence: 99%

“…Selecting the set of patient and tumour characteristics that better predict the survival probability of cancer patients is of primary interest in cancer epidemiology as this information can be used to inform policymakers, clinicians, and epidemiologists (see Michalopoulou et al [ 1 ] for a discussion) [ 2 ]. Moreover, quantifying the variable importance (in our context, for predicting survival) can be used to identify the most relevant patient’s characteristics that may affect their prognosis.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian variable selection and survival modeling: assessing the Most important comorbidities that impact lung and colorectal cancer survival in Spain

Rubio

Álvares

Redondo-Sánchez

et al. 2022

BMC Med Res Methodol

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Cancer survival represents one of the main indicators of interest in cancer epidemiology. However, the survival of cancer patients can be affected by several factors, such as comorbidities, that may interact with the cancer biology. Moreover, it is interesting to understand how different cancer sites and tumour stages are affected by different comorbidities. Identifying the comorbidities that affect cancer survival is thus of interest as it can be used to identify factors driving the survival of cancer patients. This information can also be used to identify vulnerable groups of patients with comorbidities that may lead to worst prognosis of cancer. We address these questions and propose a principled selection and evaluation of the effect of comorbidities on the overall survival of cancer patients. In the first step, we apply a Bayesian variable selection method that can be used to identify the comorbidities that predict overall survival. In the second step, we build a general Bayesian survival model that accounts for time-varying effects. In the third step, we derive several posterior predictive measures to quantify the effect of individual comorbidities on the population overall survival. We present applications to data on lung and colorectal cancers from two Spanish population-based cancer registries. The proposed methodology is implemented with a combination of the R-packages and . We provide the code for reproducibility at https://github.com/migariane/BayesVarImpComorbiCancer.

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“…Length of anesthesia duration during the operation was collected per 30 minutes. BMI and comorbidities were dichotomized based on clinical cut-offs and previous findings in literature [22,25,31].…”

Section: Definitions and Data Collectionmentioning

confidence: 99%

Timed Up and Go test and long term survival in older adults after oncologic surgery

Hendriks

Huisman

Ghignone

et al. 2022

European Journal of Surgical Oncology

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Impact of comorbidities at diagnosis on the 10-year colorectal cancer net survival: A population-based study

Cited by 11 publications

References 55 publications

The Prediction of Survival Outcome and Prognosis Factor in Association with Comorbidity Status in Patients with Colorectal Cancer: A Research-Based Study

The Prediction of Survival Outcome and Prognosis Factor in Association with Comorbidity Status in Patients with Colorectal Cancer: A Research-Based Study

Bayesian variable selection and survival modeling: assessing the Most important comorbidities that impact lung and colorectal cancer survival in Spain

Timed Up and Go test and long term survival in older adults after oncologic surgery

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