OBJECTIVE: The objective of this study was to determine the quality of life (QOL) scores among breast cancer patients at a Malaysian public hospital.
MATERIALS AND METHODS: This cross-sectional study of breast cancer patients was conducted between March to June 2013. QOL scores were determined using the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30) and its breast cancer supplementary measure (QLQ-BR23). Both the QLQ-C30 and QLQ-BR23 assess items from functional and symptom scales. The QLQ-C30 in addition also measures the Global Health Status (GHS). Systematic random sampling was used to recruit patients.
RESULTS: 223 breast cancer patients were recruited with a response rate of 92.1%. The mean age of the patients was 52.4 years (95% CI = 51.0, 53.7, SD=10.3). Majority of respondents are Malays (60.5%), followed by Chinese (19.3%), Indians (18.4%), and others (1.8%). More than 50% of respondents are at stage III and stage IV of malignancy. The mean Global Health Status was 65.7 (SD = 21.4). From the QLQ-C30, the mean score in the functioning scale was highest for 'cognitive functioning' (84.1, SD=18.0), while the mean score in the symptom scale was highest for 'financial difficulties' (40.1, SD=31.6). From the QLQ-BR23, the mean score for functioning scale was highest for 'body image' (80.0, SD=24.6) while the mean score in the symptom scale was highest for 'upset by hair loss' (36.2, SD=29.4). Two significant predictors for Global Health Status were age and employment. The predictors explained 10.6% of the variation of global health status (R2=0.106).
CONCLUSIONS: Age and employment were found to be significant predictors for Global Health Status (GHS). The Quality of Life among breast cancer patients reflected by the GHS improves as age and employment increases.
PATIENTS AND METHODS: CGA data was collected from 249 Asian patients aged 70 years or older. Nutritional risk was assessed based on the Nutrition Screening Initiative (NSI) checklist. Univariate and multivariate logistic regression analyses were applied to assess the association between patient clinical factors together with domains within the CGA and moderate to high nutritional risk. Goodness of fit was assessed using Hosmer-Lemeshow test. Discrimination ability was assessed based on the area under the receiver operating characteristics curve (AUC). Internal validation was performed using simulated datasets via bootstrapping.
RESULTS: Among the 249 patients, 184 (74%) had moderate to high nutritional risk. Multivariate logistic regression analysis identified stage 3-4 disease (Odds Ratio [OR] 2.54; 95% CI, 1.14-5.69), ECOG performance status of 2-4 (OR 3.04; 95% CI, 1.57-5.88), presence of depression (OR 5.99; 95% CI, 1.99-18.02) and haemoglobin levels <12 g/dL (OR 3.00; 95% CI 1.54-5.84) as significant independent factors associated with moderate to high nutritional risk. The model achieved good calibration (Hosmer-Lemeshow test's p = 0.17) and discrimination (AUC = 0.80). It retained good calibration and discrimination (bias-corrected AUC = 0.79) under internal validation.
CONCLUSION: Having advanced stage of cancer, poor performance status, depression and anaemia were found to be predictors of moderate to high nutritional risk. Early identification of patients with these risk factors will allow for nutritional interventions that may improve treatment tolerance, quality of life and survival outcomes.
METHODS: We retrieved the records of 25,323 women diagnosed with primary stage IV breast cancer in the surveillance, epidemiology, and end results 18 registries database from 1990 to 2012. For each case, we extracted information on age at diagnosis, tumour size, nodal status, oestrogen receptor status, progesterone receptor status, ethnicity, cause of death and date of death. The Cox proportional hazards model was used to estimate the unadjusted and adjusted hazard ratio (HR) of death due to stage IV breast cancer, according to age group.
RESULTS: Among 25,323 women with stage IV breast cancer, 2542 (10.0 %) were diagnosed at age 40 or below, 5562 (22.0 %) were diagnosed between ages 41 and 50 and 17,219 (68.0 %) were diagnosed between ages 51 and 70. After a mean follow-up of 2.2 years, 16,387 (64.7 %) women died of breast cancer (median survival 2.3 years). The ten-year actuarial breast cancer-specific survival rate was 15.7 % for women ages 40 and below, 14.9 % for women ages 41-50 and 11.7 % for women ages 51 to 70 (p
METHODS: Using Singapore Malaysia Hospital-Based Breast Cancer Registry, clinical information was retrieved from 7064 stage I to III breast cancer patients who were diagnosed between 1990 and 2011 and underwent surgery. Predicted and observed probabilities of positive nodes and survival were compared for each subgroup. Calibration was assessed by plotting observed value against predicted value for each decile of the predicted value. Discrimination was evaluated by area under a receiver operating characteristic curve (AUC) with 95 % confidence interval (CI).
RESULTS: The median predicted probability of positive lymph nodes is 40.6 % which was lower than the observed 43.6 % (95 % CI, 42.5 %-44.8 %). The calibration plot showed underestimation for most of the groups. The AUC was 0.71 (95 % CI, 0.70-0.72). Cancermath predicted and observed overall survival probabilities were 87.3 % vs 83.4 % at 5 years after diagnosis and 75.3 % vs 70.4 % at 10 years after diagnosis. The difference was smaller for patients from Singapore, patients diagnosed more recently and patients with favorable tumor characteristics. Calibration plot also illustrated overprediction of survival for patients with poor prognosis. The AUC for 5-year and 10-year overall survival was 0.77 (95 % CI: 0.75-0.79) and 0.74 (95 % CI: 0.71-0.76).
CONCLUSIONS: The discrimination and calibration of CancerMath were modest. The results suggest that clinical application of CancerMath should be limited to patients with better prognostic profile.
METHODS: Over half a million participants from 10 European countries were followed up for over 11 years, after which 865 newly diagnosed exocrine pancreatic cancer cases were identified. Adherence to the MD was estimated through an adapted score without the alcohol component (arMED) to discount alcohol-related harmful effects. Cox proportional hazards regression models, stratified by age, sex and centre, and adjusted for energy intake, body mass index, smoking status, alcohol intake and diabetes status at recruitment, were used to estimate hazard ratios (HRs) associated with pancreatic cancer and their corresponding 95% confidence intervals (CIs).
RESULTS: Adherence to the arMED score was not associated with risk of pancreatic cancer (HR high vs low adherence=0.99; 95% CI: 0.77-1.26, and HR per increments of two units in adherence to arMED=1.00; 95% CI: 0.94-1.06). There was no convincing evidence for heterogeneity by smoking status, body mass index, diabetes or European region. There was also no evidence of significant associations in analyses involving microscopically confirmed cases, plausible reporters of energy intake or other definitions of the MD pattern.
CONCLUSIONS: A high adherence to the MD is not associated with pancreatic cancer risk in the EPIC study.