Department of Statistics, College of Natural and Computational Science, Oda Bultum University, Chiro, Ethiopia.
Department of Statistics, College of Natural and Computational Science, University of Gondar, Gondar, Ethiopia.
BMC Womens Health. 2024 Feb 15;24(1):120. doi: 10.1186/s12905-024-02954-y.
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.
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.
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.
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.
尽管存在很大的困难,但埃塞俄比亚的生存率和死亡预测因素尚未确定。本研究的主要目的是确定影响门诊乳腺癌患者生存时间的因素。
对门诊乳腺癌患者进行回顾性研究。为了实现这一目标,我们从 2016 年 5 月 15 日至 2020 年 5 月 15 日在埃塞俄比亚贡德尔大学转诊医院登记的患者的病历中获取信息,纳入了 382 名门诊乳腺癌患者。为了比较生存功能,使用 Kaplan-Meier 图和对数秩检验。然后使用 Cox-PH 模型和贝叶斯参数生存模型来检查门诊乳腺癌患者的生存时间。使用了集成分层拉普拉斯逼近技术。
本研究共纳入 382 名门诊乳腺癌患者,其中 148 名(38.7%)患者死亡。估计患者的中位生存时间为 42 个月。通过模型选择标准,确定贝叶斯 Weibull 加速失效时间模型是合适的。根据该模型的结果,分期、2 级、3 级和 4 级、合并症、组织学类型、FIGO 分期、化疗、转移数 1、2 和>=3、肿瘤大小对门诊乳腺癌患者的生存时间有显著影响。贝叶斯 Weibull 加速失效时间模型能够正确预测门诊乳腺癌患者的生存时间。
与高收入和中等收入国家相比,整体生存率较低。影响乳腺癌诊断后生存时间的显著变量包括体重减轻、浸润性髓样组织学、合并症、肿瘤较大、转移增加、国际妇产科联合会分期增加、分级增加、淋巴管血管空间侵犯、阳性区域淋巴结和癌症晚期。作者建议,最好增加乳腺癌的早期筛查计划和治疗中心,并与大众媒体合作,提高对疾病预防、筛查和治疗选择的认识。