Assessing survival time of outpatients with cervical cancer: at the university of Gondar referral hospital using the Bayesian approach

Common predictors of cervical cancer related mortality in Ethiopia. A systematic review and meta-analysis

BACKGROUND

Cervical cancer accounts for 7.5% of all female cancer related deaths worldwide; peaking between the ages of 35 and 65, and not only kills young women but also destroys families with young children.

OBJECTIVE

This review was intended to measure national level magnitude and the most common predictors of cervical cancer related mortality in Ethiopia.

METHODS

Common Public databases like Science Direct, Embase, the Cochrane Library, and PubMed were thoroughly searched. The STATA 14 and Rev-Manager 5.3 statistical software packages were used for analysis, as well as a standardized data abstraction tool created in Microsoft Excel. The Cochrane Q-test statistics and the I2 test were used to assess non-uniformity. The pooled magnitude and predictors of cervical cancer related mortality were estimated using fixed-effect and random-effect models, respectively.

RESULT

The pooled mortality among cervical cancer patients was estimated that 16.39% at 95% confidence level fall in 13.89-18.88% in Ethiopia. The most common predictors of cervical cancer related mortality were late diagnosed, radiation therapy alone, and Being anemic were identified by this review. Among cervical cancer treatment modalities effectiveness of surgery with adjuvant therapy was also approved in this meta-analysis.

CONCLUSION AND RECOMMENDATION

In this study high cervical cancer-related mortality was reported as compared to national strategies to alleviate cervical cancer related mortality. Advanced implementation of cervical cancer screening at the national level for early diagnosis, anaemia detection, and combination anticancer therapy during initiation, as well as combination therapy, is critical to improve cervical cancer patient survival and decreasing mortality rates.

Improving cervical cancer continuum of care towards elimination in Ethiopia: a scoping review

BACKGROUND

Cervical cancer is the second-leading cause of death among all cancers in Ethiopia. Ethiopia plans to eliminate cervical cancer as a public health problem by 2030, following the World Health Organization's call for action. A scoping review was conducted on the status of the cervical cancer continuum towards elimination in Ethiopia.

METHODS

We searched articles in PubMed, Scopus, and Google Scholar. All studies conducted on cervical cancer in Ethiopia, from first date of publication to March 15, 2023, type of article, or language of publication, were included. However, conference abstracts, commentaries, and letters to the editors were excluded. We used EndNote X9 software to merge articles from different databases and automatically remove duplicates. Screening of titles, abstracts, and full texts was performed independently by two co-authors. The cancer care continuum was employed as a framework to guide data synthesis and present the findings.

RESULTS

Of the 569 retrieved articles, 159 were included in the review. They found that most of the articles focused on knowledge, attitude, and practice. However, there were few studies on health-seeking behavior, perception and acceptability of cervical cancer services, as well as the availability and readiness of a screening program. The review identified inadequate knowledge, attitude, and perception about cervical cancer, and highlighted that screening for cervical cancer is not widely utilized in Ethiopia. Knowledge, attitude, education status, and income were repeatedly reported as precursors influencing cervical cancer screening. Most studies concluded that there is a high prevalence of precancerous lesions and cervical cancer, as well as high mortality rates or short survival times. The review also identified significant heterogeneity in findings across time and geographic settings within each component of the cancer care continuum.

CONCLUSIONS

Overall, there is inadequate knowledge, perception, health-seeking behavior, screening, and treatment services, indicating that the country is falling behind its targets in eliminating cervical cancer, despite the availability of effective interventions and tools. We argue that implementation research is necessary to identify implementation issues, challenges, and strategies to scale up both primary and secondary prevention services. By doing so, Ethiopia can address cervical cancer as a public health problem and work towards its elimination.

Assessing the survival time of women with breast cancer in Northwestern Ethiopia: using the Bayesian approach

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.

An Accelerated Failure Time Model to Predict Cause-Specific Survival and Prognostic Factors of Lung and Bronchus Cancer Patients with at Least Bone or Brain Metastases: Development and Internal Validation Using a SEER-Based Study

BACKGROUND

This study addresses the significant challenge of low survival rates in patients with cause-specific lung cancer accompanied by bone or brain metastases. Recognizing the critical need for an effective predictive model, the research aims to establish survival prediction models using both parametric and non-parametric approaches.

METHODS

Clinical data from lung cancer patients with at least one bone or brain metastasis between 2000 and 2020 from the SEER database were utilized. Four models were constructed: Cox proportional hazard, Weibull accelerated failure time (AFT), log-normal AFT, and Zografos-Balakrishnan log-normal (ZBLN). Independent prognostic factors for cause-specific survival were identified, and model fit was evaluated using Akaike's and Bayesian information criteria. Internal validation assessed predictive accuracy and discriminability through the Harriel Concordance Index (C-index) and calibration plots.

RESULTS

A total of 20,412 patients were included, with 14,290 (70%) as the training cohort and 6122 (30%) validation. Independent prognostic factors selected for the study were age, race, sex, primary tumor site, disease grade, total malignant tumor in situ, metastases, treatment modality, and histology. Among the accelerated failure time (AFT) models considered, the ZBLN distribution exhibited the most robust model fit for the 3- and 5-year survival, as evidenced by the lowest values of Akaike's information criterion of 6322 and 79,396, and the Bayesian information criterion of 63,495 and 79,396, respectively. This outperformed other AFT and Cox models (AIC = [156,891, 211,125]; BIC = [158,848, 211,287]). Regarding predictive accuracy, the ZBLN AFT model achieved the highest concordance C-index (0.682, 0.667), a better performance than the Cox model (0.669, 0.643). The calibration curves of the ZBLN AFT model demonstrated a high degree of concordance between actual and predicted values. All variables considered in this study demonstrated significance at the 0.05 level for the ZBLN AFT model. However, differences emerged in the significant variations in survival times between subgroups. The study revealed that patients with only bone metastases have a higher chance of survival compared to only brain and those with bone and brain metastases.

CONCLUSIONS

The study highlights the underutilized but accurate nature of the accelerated failure time model in predicting lung cancer survival and identifying prognostic factors. These findings have implications for individualized clinical decisions, indicating the potential for screening and professional care of lung cancer patients with at least one bone or brain metastasis in the future.

Analysis of the survival time of patients with heart failure with reduced ejection fraction: a Bayesian approach via a competing risk parametric model

PURPOSE

Heart failure (HF) is a widespread ailment and is a primary contributor to hospital admissions. The focus of this study was to identify factors affecting the extended-term survival of patients with HF, anticipate patient outcomes through cause-of-death analysis, and identify risk elements for preventive measures.

METHODS

A total of 435 HF patients were enrolled from the medical records of the Rajaie Cardiovascular Medical and Research Center, covering data collected between March and August 2018. After a five-year follow-up (July 2023), patient outcomes were assessed based on the cause of death. The survival analysis was performed with the AFT method with the Bayesian approach in the presence of competing risks.

RESULTS

Based on the results of the best model for HF-related mortality, age [time ratio = 0.98, confidence interval 95%: 0.96-0.99] and ADHF [TR = 0.11, 95% (CI): 0.01-0.44] were associated with a lower survival time. Chest pain in HF-related mortality [TR = 0.41, 95% (CI): 0.10-0.96] and in non-HF-related mortality [TR = 0.38, 95% (CI): 0.12-0.86] was associated with a lower survival time. The next significant variable in HF-related mortality was hyperlipidemia (yes): [TR = 0.34, 95% (CI): 0.13-0.64], and in non-HF-related mortality hyperlipidemia (yes): [TR = 0.60, 95% (CI): 0.37-0.90]. CAD [TR = 0.65, 95% (CI): 0.38-0.98], CKD [TR = 0.52, 95% (CI): 0.28-0.87], and AF [TR = 0.53, 95% (CI): 0.32-0.81] were other variables that were directly related to the reduction in survival time of patients with non-HF-related mortality.

CONCLUSION

The study identified distinct predictive factors for overall survival among patients with HF-related mortality or non-HF-related mortality. This differentiated approach based on the cause of death contributes to the estimation of patient survival time and provides valuable insights for clinical decision-making.

Predictors of cervical tumour size for outpatients with cervical cancer at the University of Gondar referral hospital: a retrospective study design

BACKGROUND

Cervical cancer is one of the most serious threats to women's lives. Modelling the change in tumour size over time for outpatients with cervical cancer was the study's main goal.

METHODS

A hospital conducted a retrospective cohort study with outpatients who had cervical cancer. The information about the tumour size was taken from the patient's chart and all patient data records between May 20, 2017, and May 20, 2021. The data cover 322 cervical cancer outpatients' basic demographic and medical information. When analysing longitudinal data, the linear mixed effect model and the connection between tumour sizes in outpatients were taken into consideration. A linear mixed model, a random intercept model, and a slope model were used to fit the data.

RESULT

A sample of 322 cervical cancer outpatients was examined, and 148 (or 46% of the outpatients) tested positive for HIV. The linear mixed model with a first-order autoregressive covariance structure revealed that a change in time of one month led to a 0.009 cm2 reduction in tumour size. For every kilogramme more in weight, the tumour size change in cervical cancer patients decreased considerably by 0.0098 cm2. The tumour size change in the cervical cancer patient who was HIV-positive was 0.4360 cm squared greater than that in the HIV-negative outpatients.

CONCLUSION

As a consequence, there was a significant association between the longitudinal change in tumour size and the predictor variables visit time, therapy, patient weight, cancer stage, HIV, oral contraceptive use, history of abortion, and smoking status.