Survival of colorectal cancer patients in Brunei Darussalam: comparison between 2002–09 and 2010–17

Lee, Shirley; Rahman, Hanif Abdul; Abidin, Nadiah 'Aqilah; Ong, Sok King; Leong, Elvynna; Naing, Lin

doi:10.1186/s12885-021-08224-6

Cited by 4 publications

(4 citation statements)

References 75 publications

(93 reference statements)

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“…mCRC in more than 65% of patients recur after surgical treatment ( 19 ). The long-term survival of patients with mCRC has attracted wide public attention; however, studies focusing on early death in patients with mCRC are rare and should be conducted ( 20 , 21 ).…”

Section: Discussionmentioning

confidence: 99%

Construction and validation of nomograms combined with novel machine learning algorithms to predict early death of patients with metastatic colorectal cancer

Zhang

Wei

et al. 2022

Front. Public Health

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PurposeThe purpose of this study was to investigate the clinical and non-clinical characteristics that may affect the early death rate of patients with metastatic colorectal carcinoma (mCRC) and develop accurate prognostic predictive models for mCRC.MethodMedical records of 35,639 patients with mCRC diagnosed from 2010 to 2019 were obtained from the SEER database. All the patients were randomly divided into a training cohort and a validation cohort in a ratio of 7:3. X-tile software was utilized to identify the optimal cutoff point for age and tumor size. Univariate and multivariate logistic regression models were used to determine the independent predictors associated with overall early death and cancer-specific early death caused by mCRC. Simultaneously, predictive and dynamic nomograms were constructed. Moreover, logistic regression, random forest, CatBoost, LightGBM, and XGBoost were used to establish machine learning (ML) models. In addition, receiver operating characteristic curves (ROCs) and calibration plots were obtained to estimate the accuracy of the models. Decision curve analysis (DCA) was employed to determine the clinical benefits of ML models.ResultsThe optimal cutoff points for age were 58 and 77 years and those for tumor size of 45 and 76. A total of 15 independent risk factors, namely, age, marital status, race, tumor localization, histologic type, grade, N-stage, tumor size, surgery, radiation, chemotherapy, bone metastasis, brain metastasis, liver metastasis, and lung metastasis, were significantly associated with the overall early death rate of patients with mCRC and the cancer-specific early death rate of patients with mCRC, following which nomograms were constructed. The ML models revealed that the random forest model accurately predicted outcomes, followed by logistic regression, CatBoost, XGBoost, and LightGBM models. Compared with other algorithms, the random forest model provided more clinical benefits than other models and can be used to make clinical decisions in overall early death and specific early death caused by mCRC.ConclusionML algorithms combined with nomograms may play an important role in distinguishing early deaths owing to mCRC and potentially help clinicians make clinical decisions and follow-up strategies.

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Section: Discussionmentioning

confidence: 99%

Construction and validation of nomograms combined with novel machine learning algorithms to predict early death of patients with metastatic colorectal cancer

Zhang

Wei

et al. 2022

Front. Public Health

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“…The 3-year survival rate for CRC patients in Ethiopia was found to be 48.8%. This rate is much lower compared to the survival rates observed in other countries such as Iran 64–65% [ 52 , 53 ], Eastern Mediterranean Region Countries 70.67% [ 54 ], China 72–74% [ 55 , 57 ], and Brunei Darussalam 62.5% [ 58 ]. For the 5-year survival rate, Ethiopia had a rate of 26.6%, which was again lower compared to colorectal cancer survival rates in sub-Saharan Africa 43.5% [ 59 ], Iran 52–54% [ 52 , 53 ], Eastern Mediterranean region 57.26% [ 54 ], China 62–68% [ 55 , 57 ], Jordan 58.2% [ 60 ], German 63.0% [ 61 ], Brazil 63.5% [ 62 ], Spain 55.5% [ 63 ] and Australia 63.3% [ 11 ].…”

Section: Discussionmentioning

confidence: 86%

Mortality rate and predictors of colorectal cancer patients in Ethiopia: a systematic review and meta-analysis

Aynalem,

Adal,

Ayele

et al. 2024

BMC Cancer

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Introduction The incidence of colorectal cancer (CRC) has been increasing in Sub-Saharan countries, including Ethiopia. However, the real mortality rate for CRC patients in Ethiopia has not been established. Therefore, this systematic review and meta-analysis aimed to determine the overall mortality rate and identify predictors among CRC patients in Ethiopia. Methods PubMed, EMBASE, Web of Science, Scopus, Science Direct, and Google Scholar were searched to identify relevant articles. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) were followed. The quality of the included studies was assessed using the Newcastle-Ottawa Scale Critical Appraisal checklist. A random effect model was used to estimate the pooled mortality rate and adjusted hazard ratio (AHR). Publication bias was assessed using funnel plots and Egger’s regression test, while heterogeneity was evaluated through the Cochran Q test and I2 statistics. Results After reviewing 74 articles, only 7 studies met the criteria and were included in the analysis. The analysis revealed that the overall mortality rate among CRC patients in Ethiopia was 40.5% (95% confidence interval [CI]: 32.05, 48.87) while the survival rates at 1 year, 3 years, and 5 years were 82.3% (95% CI: 73.33, 91.31), 48.8% (95% CI: 43.35, 54.32), and 26.6% (95% CI: 21.26, 31.91) respectively. Subgroup analysis indicated that studies conducted after 2017 had higher mortality rates compared to those studied earlier (43.0% vs. 38.2%). Older age (AHR: 1.89, 95% CI: 1.27, 2.82); being married (AHR: 2.53, 95% CI: 1.79, 3.57); having comorbidities (AHR: 1.84, 95% CI: 1.45, 2.35); having high CEA levels (AHR: 2.06, CI: 1.35, 3.13); being in stage II (AHR: 4.13, 95% CI: 1.85, 9.22), III (AHR: 8.62, 95% CI: 3.88, 19.15), and IV (AHR: 8.06, CI: 2.89, 22.49) were the most important predictors. Conclusion In Ethiopia, the mortality rate among individuals diagnosed with CRC is high, with two out of five patients dying from this disease. Age, marital status, CEA level, comorbidities, and cancer stage were identified as predictors of mortality in CRC patients. Therefore, early detection and screening should be prioritized, particularly for older patients, those who are married, have comorbidities, elevated CEA levels, and advanced cancer stages.

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“…Since the establishment of electronic medical records, Brunei Healthcare Information Management System (BruHIMS) in 2013, data accuracy was enhanced with finer verification of information in the medical records (Leong et al, 2019). Multiple studies analysing BDCR's national database on different types of malignancy earned publications including survival prognosis of colorectal cancer (Lee et al, 2021), breast cancer (Leong et al, 2019), and childhood cancer , to name a few. Based on the above criteria, there were a total of 797 patients and all eligible patients were included without sampling.…”

Section: Study Design Population and Samplementioning

confidence: 99%

Survival Analysis of Lung Cancer patients in Brunei Darussalam

Kong

Ong

Leong

et al. 2023

Asian Pac J Cancer Prev

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Background: Cancer has been the most common cause of deaths in Brunei Darussalam including lung cancer as one of the top killers. This study presents the survival analysis of primary lung cancer patients in Brunei Darussalam. Methods: This is a retrospective cohort study of a 15-year period (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016) involving 797 primary lung cancer cases. De-identified data extracted from Brunei Darussalam Cancer Registry was analyzed using Kaplan-Meier method and log-rank test. Bonferroni corrections were performed. Results: 6 months, 1-year, 3-year, 5-year survival rates of lung cancer patients were 62.2%, 51.0%, 38.0% and 34.7% respectively. The median survival time was 12.4 months (95% Cl: 9.2, 15.7). Patients diagnosed 2002-2006 had significant longer survival than patients diagnosed 2007-2011 (p=0.039) and those diagnosed 2011-2016 (p<0.001). Survival time of patients diagnosed 2007-2011 was significantly longer than patients diagnosed 2012-2016 (p=0.049). Patients with localised disease had significantly longer survival time than patients with regional involvement (p<0.001) and distal site involvement (p<0.001). Patients with distal disease had significantly poorer survival rate than patients with regional involvement (p<0.001). Patients aged 50 or above had significantly shorter survival time than patients diagnosed at age 40-49 (p=0.003). There was no significant difference between survival time of other demographic characteristics. Conclusions: The median survival time for lung cancer patients in this study was 12.4 months. Early diagnosis, early stage of cancer, and younger age were associated with longer survival time among lung cancer patients in Brunei Darussalam.

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Survival of colorectal cancer patients in Brunei Darussalam: comparison between 2002–09 and 2010–17

Cited by 4 publications

References 75 publications

Construction and validation of nomograms combined with novel machine learning algorithms to predict early death of patients with metastatic colorectal cancer

Construction and validation of nomograms combined with novel machine learning algorithms to predict early death of patients with metastatic colorectal cancer

Mortality rate and predictors of colorectal cancer patients in Ethiopia: a systematic review and meta-analysis

Survival Analysis of Lung Cancer patients in Brunei Darussalam

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