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Assessing the survival time of women with breast cancer in Northwestern Ethiopia: using the Bayesian approach.
Gashu, Chalachew; Aguade, Aragaw Eshetie.
Afiliação
  • Gashu C; Department of Statistics, College of Natural and Computational Science, Oda Bultum University, Chiro, Ethiopia. chalachew154@gmail.com.
  • Aguade AE; Department of Statistics, College of Natural and Computational Science, University of Gondar, Gondar, Ethiopia.
BMC Womens Health ; 24(1): 120, 2024 Feb 15.
Article em En | MEDLINE | ID: mdl-38360619
ABSTRACT

BACKGROUND:

Despite the significant weight of difficulty, Ethiopia's survival rate and mortality predictors have not yet been identified. Finding out what influences outpatient breast cancer patients' survival time was the major goal of this study.

METHODS:

A retrospective study was conducted on outpatients with breast cancer. In order to accomplish the goal, 382 outpatients with breast cancer were included in the study using information obtained from the medical records of patients registered at the University of Gondar referral hospital in Gondar, Ethiopia, between May 15, 2016, and May 15, 2020. In order to compare survival functions, Kaplan-Meier plots and the log-rank test were used. The Cox-PH model and Bayesian parametric survival models were then used to examine the survival time of breast cancer outpatients. The use of integrated layered Laplace approximation techniques has been made.

RESULTS:

The study included 382 outpatients with breast cancer in total, and 148 (38.7%) patients died. 42 months was the estimated median patient survival time. The Bayesian Weibull accelerated failure time model was determined to be suitable using model selection criteria. Stage, grade 2, 3, and 4, co-morbid, histological type, FIGO stage, chemotherapy, metastatic number 1, 2, and >=3, and tumour size all have a sizable impact on the survival time of outpatients with breast cancer, according to the results of this model. The breast cancer outpatient survival time was correctly predicted by the Bayesian Weibull accelerated failure time model.

CONCLUSIONS:

Compared to high- and middle-income countries, the overall survival rate was lower. Notable variables influencing the length of survival following a breast cancer diagnosis were weight loss, invasive medullar histology, comorbid disease, a large tumour size, an increase in metastases, an increase in the International Federation of Gynaecologists and Obstetricians stage, an increase in grade, lymphatic vascular space invasion, positive regional nodes, and late stages of cancer. The authors advise that it is preferable to increase the number of early screening programmes and treatment centres for breast cancer and to work with the public media to raise knowledge of the disease's prevention, screening, and treatment choices.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias da Mama Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Female / Humans País/Região como assunto: Africa Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias da Mama Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Female / Humans País/Região como assunto: Africa Idioma: En Ano de publicação: 2024 Tipo de documento: Article