Colorectal Cancer Screening in the Elderly: Is Age Just a Number?

Alswealmeen, Waed; Sadri, Lili; Perrotti, Gabrielle; Heilman, Jaclyn; Bakshi, Kirran; Kim, Soo Y.; Zebley, D. Mark; Fassler, Steven A.

doi:10.1016/j.clcc.2021.11.011

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…Subsequently, a questionnaire was developed to evaluate early frailty risk factors post-enterostomy in the elderly population. These factors include gender ( 4 ), age ( 13 ) education ( 4 ), marital status ( 14 ), acute and chronic diseases ( 15 ), self-perceived health status ( 14 ) monthly income ( 4 ), stoma complications (such as fecal skin dermatitis, stoma bleeding, injury, allergic dermatitis, necrosis, and mucosal separation) ( 16 ), receipt of chemoradiotherapy ( 17 ), stoma acceptance ( 16 ), sleep quality ( 18 ), social support ( 4 ), anxiety ( 14 ), preoperative frailty ( 19 ), and cancer stage ( 16 ). The recovery process post-enterostomy was divided into three stages: early surgery, recovery period, and post-discharge recovery.…”

Section: Methodsmentioning

confidence: 99%

Comparative study of the risk prediction model of early postoperative frailty in elderly enterostomy patients based on machine learning methods

Ya-juan,

Fang-hui,

Yi-wei

et al. 2024

Front. Med.

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ObjectiveBased on machine learning method, four types of early postoperative frailty risk prediction model of enterostomy patients were constructed to compare the performance of each model and provide the basis for preventing early postoperative frailty of elderly patients with enterostomy.MethodsThe prospective convenience sampling method was conducted and 362 early postoperative enterostomy patients were selected in three hospitals from July 2020 to November 2023 in Shanghai, four different prediction models of Support Vector Machine (SVM), Bayes, XG Boost, and Logistic regression were used and compared the test effects of the four models (MCC, F1, AUC, and Brier index) to judge the classification performance of the four models in the data of this study.ResultsA total of 21 variables were included in this study, and the predictors mainly covered demographic information, stoma-related information, quality of life, anxiety and depression, and frailty. The validated models on the test set are XGBoost, Logistic regression, SVM prediction model, and Bayes on the MCC and F1 scores; on the AUC, XGBoost, Logistic regression, Bayes, and SVM prediction model; on the Brier scores, Bayes, Logistic regression, and XGBoost.ConclusionXGBoost based on machine learning method is better than SVM prediction model, Logistic regression model and Bayes in sensitivity and accuracy. Quality of life in the early postoperative period can help guide clinical patients to identify patients at high risk of frailty and reduce the incidence of early postoperative frailty in elderly patients with enterostomy.

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Section: Methodsmentioning

confidence: 99%

Comparative study of the risk prediction model of early postoperative frailty in elderly enterostomy patients based on machine learning methods

Ya-juan,

Fang-hui,

Yi-wei

et al. 2024

Front. Med.

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“…Colorectal cancer (CRC) remains the third most prevalent cancer globally, with the most recent data estimating its incidence to be the second highest mortality rate [ 1 ]. Smoking, processed meat, alcohol intake, red meat, low intake of vegetables and fruits, body fat, and obesity were all identified as risk factors for the pathogenesis of CRC [ 2 , 3 ]. Despite advances in combination treatment regimens and individualized therapeutic planning over the past decade, the average survival time for advanced CRC has improved significantly [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

IL1RN and PRRX1 as a Prognostic Biomarker Correlated with Immune Infiltrates in Colorectal Cancer: Evidence from Bioinformatic Analysis

Wang

Huang

Zhou

et al. 2022

International Journal of Genomics

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The extensive morbidity of colorectal cancer (CRC) and the inferior prognosis of terminal CRC urgently call for reliable prognostic biomarkers. For this, we identified 704 differentially expressed genes (DEGs) by intersecting three datasets, GSE41328, GSE37364, and GSE15960 from Gene Expression Omnibus database, to maximize the accuracy of the results. Preliminary analysis of the DEGs was then performed using online gene analysis datasets, such as DAVID, UCSC Cancer Genome Browser, CBioPortal, STRING, and UCSC Cancer Genome Browser. Cytoscape was utilized to visualize the protein perception interaction network of DEGs, and the bubble map of GO and KEGG enrichment function was demonstrated using the R package. The Molecular Complex Detection (MCODE), Biological Network Gene Oncology (BiNGO) plug-in in Cytoscape, was applied to further screen the DEGs to obtain 15 seed genes, which were IL1RN, GALNT12, ADH6, SCN7A, CXCL1, FGF18, SOX9, ACACB, PRRX1, MZB1, SLC22A3, CNNM4, LY6E, IFITM2, and GDPD3. Among them, IL1RN, ADH6, SCN7A, ACACB, MZB1, and GDPD3 exhibited statistically significant survival differences, whereas limited studies were conducted in CRC. Based on the enrichment results of the “Gene Ontology“(GO) and “Kyoto Encyclopedia of Genes and genomes “(KEGG) as well as documented findings of key genes, we further emphasized the potential of IL1RN and PRRX1 as markers of immune infiltrates in CRC and confirmed our hypothesis by compiling data from the UALCAN, Tumor Immune Estimation Resource, and TISIDB databases for these two genes. The above-mentioned genes might offer a valuable insight into the diagnosis, immunotherapeutic targets, and prognosis of CRC.

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Analysis on Prognostic Predictors of Elderly Colorectal Cancer Patients Combined with Type 2 Diabetes Mellitus

张¹

2022

ACM

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Colorectal Cancer Screening in the Elderly: Is Age Just a Number?

Cited by 4 publications

References 12 publications

Comparative study of the risk prediction model of early postoperative frailty in elderly enterostomy patients based on machine learning methods

Comparative study of the risk prediction model of early postoperative frailty in elderly enterostomy patients based on machine learning methods

IL1RN and PRRX1 as a Prognostic Biomarker Correlated with Immune Infiltrates in Colorectal Cancer: Evidence from Bioinformatic Analysis

Analysis on Prognostic Predictors of Elderly Colorectal Cancer Patients Combined with Type 2 Diabetes Mellitus

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