Dataset Information

The Prognostic Value of Eight Comorbidity Indices in Older Patients with Cancer: The ELCAPA Cohort Study.

ABSTRACT:

Background

A prognostic assessment is crucial for making cancer treatment decisions in older patients. We assessed the prognostic performance (relative to one-year mortality) of eight comorbidity indices in a cohort of older patients with cancer.

Methods

We studied patients with cancer aged ≥70 included in the Elderly Cancer Patient (ELCAPA) cohort between 2007 and 2010. We assessed seven nonspecific indices (Charlson Comorbidity Index (CCI), three modified versions of the CCI, the Elixhauser Comorbidity Index, the Gagne index, and the Cumulative Illness Rating Scale for Geriatrics (CIRS-G)) and the National Cancer Institute Comorbidity Index.

Results

Overall, 510 patients were included. Among patients with nonmetastatic cancer, all the comorbidity indices were independently associated with 1-year mortality (adjusted hazard ratios (aHRs) of 1.44 to 2.51 for one standard deviation increment; p < 0.05 for all) and had very good discriminant ability (Harrell's C > 0.8 for the eight indices), but were poorly calibrated. Among patients with metastatic cancer, only the CIRS-G was independently associated with 1-year mortality (aHR (95% confidence interval): 1.26 [1.06-1.50]). Discriminant ability was moderate (0.61 to 0.70) for the subsets of patients with metastatic cancer and colorectal cancer.

Conclusion

Comorbidity indices had strong prognostic value and discriminative ability for one-year mortality in older patients with nonmetastatic cancer, although calibration was poor. In older patients with metastatic cancer, only the CIRS-G was predictive of one-year mortality.

SUBMITTER: Canoui-Poitrine F

PROVIDER: S-EPMC9105640 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Publications

The Prognostic Value of Eight Comorbidity Indices in Older Patients with Cancer: The ELCAPA Cohort Study.

Canoui-Poitrine Florence F Segaux Lauriane L Benderra Marc-Antoine MA About Frédégonde F Tournigand Christophe C Laurent Marie M Caillet Philippe P Audureau Etienne E Ferrat Emilie E Lagrange Jean-Leon JL Paillaud Elena E Bastuji-Garin Sylvie S On Behalf Of The Elcapa Study Group

Cancers 20220429 9

Background: A prognostic assessment is crucial for making cancer treatment decisions in older patients. We assessed the prognostic performance (relative to one-year mortality) of eight comorbidity indices in a cohort of older patients with cancer. Methods: We studied patients with cancer aged ≥70 included in the Elderly Cancer Patient (ELCAPA) cohort between 2007 and 2010. We assessed seven nonspecific indices (Charlson Comorbidity Index (CCI), three modified versions of the CCI, the Elixhauser ...[more]

PMID: 35565364

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Project description:BackgroundPancreatic cancer is one of the most common malignant tumors of the digestive tract, and it has a poor prognosis. Traditional methods are not effective to accurately assess the prognosis of patients with pancreatic cancer. Immunotherapy is a new promising approach for the treatment of pancreatic cancer; however, some patients do not respond well to immunotherapy, which may be related to tumor microenvironment regulation. In this study, we use gene expression database to mine important immune genes and establish a prognostic prediction model for pancreatic cancer patients. We hope to provide a feasible method to evaluate the prognosis of pancreatic cancer and provide valuable targets for pancreatic cancer immunotherapy.ResultsWe used univariate COX proportional hazard regression analysis, the least absolute shrinkage and selection operator, and multivariate COX regression analysis to screen 8 genes related to prognosis from the 314 immune-related genes, and used them to construct a new clinical prediction model in the TCGA pancreatic cancer cohort. Subsequently, we evaluated the prognostic value of the model. The Kaplan-Meier cumulative curve showed that patients with low risk scores survived significantly longer than patients with high risk scores. The area under the ROC curve (AUC value) of the risk score was 0.755. The univariate COX analysis showed that the risk score was significantly related to overall survival (HR 1.406, 95% CI 1.237-1.598, P < 0.001), and multivariate analysis showed that the risk score was an independent prognostic factor (HR 1.400, 95% CI 1.287-1.522, P < 0.001). Correlation analysis found that immune genes are closely related to tumor immune microenvironment.ConclusionsBased on the TCGA-PAAD cohort, we identified immune-related markers with independent prognostic significance, validated, and analyzed their biological functions, to provide a feasible method for the prognosis of pancreatic cancer and provide potentially valuable targets for pancreatic cancer immunotherapy.

Project description:BackgroundThe expression of depressive symptoms in older people with cancer is heterogeneous because of specific features of age or cancer comorbidity. We aimed to identify depressive symptom profiles in this population and describe the associated features including survival.Materials and methodsPatients ≥70 years who were referred to geriatric oncology clinics were prospectively included in the ELCAPA study. In this subanalysis, depressive symptoms were used as indicators in a latent class analysis. Multinomial multivariable logistic regression and Cox models examined the association of each class with baseline characteristics and mortality.ResultsFor the 847 complete-case patients included (median age, 79 years; interquartile range, 76-84; women, 47.9%), we identified five depressive symptom classes: "no depression/somatic only" (38.8%), "no depression/pauci-symptomatic" (26.4%), "severe depression" (20%), "mild depression" (11.8%), and "demoralization" (3%). Compared with the no depression/pauci-symptomatic class, the no depression/somatic only and severe depression classes were characterized by more frequent comorbidities with poorer functional status and higher levels of inflammation. "Severe" and "mild" depression classes also featured poorer nutritional status, more medications, and more frequent falls. Severe depression was associated with poor social support, inpatient status, and increased risk of mortality at 1 year (adjusted hazard ratio, 1.62, 95% confidence interval, 1.06-2.48) and 3 years (adjusted hazard ratio, 1.49; 95% confidence interval, 1.06-2.10).ConclusionA data-driven approach based on depressive symptoms identified five different depressive symptom profiles, including demoralization, in older patients with cancer. Severe depression was independently and substantially associated with poor survival.Implications for practiceOlder patients with cancer present with distinct profiles of depressive symptomatology, including different severity levels of depression and the demoralization syndrome. Clinicians should use a systematic assessment of depressive symptoms to adequately highlight these distinct profiles. Geriatric and oncological features are differently associated with these profiles. For instance, severe depression was associated with more frequent comorbidities with poorer functional, poor nutritional status, polypharmacy, frequent falls, inpatient status and poor social support. Also, severe depression was independently and substantially associated with poor survival so that the identification and management of depression should be considered a high priority in this population.

Dataset Information

The Prognostic Value of Eight Comorbidity Indices in Older Patients with Cancer: The ELCAPA Cohort Study.

Background

Methods

Results

Conclusion

Publications

The Prognostic Value of Eight Comorbidity Indices in Older Patients with Cancer: The ELCAPA Cohort Study.

Similar Datasets

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