RESUMO
Introduction Chemotherapy-induced nausea and vomiting (CINV) is a common and debilitating adverse effect of breast cancer chemotherapy. The incidence of CINV in the first cycle of chemotherapy is essential, as it sets the tone for anticipatory CINV and the overall patients' treatment experience. We aimed to investigate the risk factors of first cycle CINV in breast cancer patients and to develop a classification and regression tree (CART) model to predict its occurrence. Methods This is a cross-sectional study that nested in a prospective cohort. One hundred and thirty-seven female breast cancer patients receiving highly emetogenic chemotherapy were included. We used the Common Toxicity Criteria for Adverse Events (CTCAE) version 4.0 to assess patient-reported nausea and vomiting in the first chemotherapy cycle. The proportional difference of CINV between sociodemographic and clinicopathologic variables was analyzed using chi-square, and the strength and direction of the relationship with CINV were analyzed using bivariable logistic regression analysis. Multivariable logistic regression and CART analysis included variables with a p-value <0.250. Results The incidence of first-cycle CINV was 43.1%. The chi-square test revealed a significant association between insurance status and CINV (p<0.001) and between the stage at diagnosis and CINV (p<0.001). Underweight to normal body mass index (BMI) patients are significantly associated with an increased risk of first-cycle CINV (OR =2.17, 95% CI 1.03-4.56, p =0.041). In hierarchical order, three variables (stage at diagnosis, BMI, and age) were included in the CART model, which significantly influenced the probability of first cycle CINV. With an accuracy of 61.3%, the CART model had a sensitivity of 28.8%, a specificity of 85.9%, a positive predictive value of 60.7%, a negative predictive value of 61.5%, and an area under curve (AUC) of 0.602. Conclusion Breast cancer patients with an underweight to normal BMI have a higher risk of developing first-cycle CINV. Our CART model was better at identifying patients who would not develop CINV than those who would. The CART model may provide a simple and effective way to individualize patient care for first-cycle CINV.
RESUMO
BACKGROUND: Breast cancer is a significant public health concern worldwide, including in Indonesia. Little is known about the spatial and temporal patterns of breast cancer incidence in Indonesia. This study aimed to analyze temporal and spatial variations of breast cancer incidence in Yogyakarta Province, Indonesia. METHODS: The study used breast cancer case data from the Yogyakarta Population-Based Cancer Registry (PBCR) from 2008 to 2019. The catchment areas of the PBCR included the 48 subdistricts of 3 districts (Sleman, Yogyakarta City, and Bantul). Age-standardized incidence rates (ASR) were calculated for each subdistrict. Joinpoint regression was used to detect any significant changes in trends over time. Global Moran's and Local Indicators of Spatial Association (LISA) analyses were performed to identify any spatial clusters or outliers. RESULTS: The subdistricts had a median ASR of 41.9, with a range of 15.3-70.4. The majority of cases were diagnosed at a late stage, with Yogyakarta City having the highest proportion of diagnoses at stage 4. The study observed a significant increasing trend in breast cancer incidence over the study period the fastest of which is in Yogyakarta City with an average annual percentage change of 18.77%, with Sleman having an 18.21% and Bantul having 8.94% average changes each year (p <0.05). We also found a significant positive spatial autocorrelation of breast cancer incidence rates in the province (I = 0.581, p <0.001). LISA analysis identified 11 subdistricts which were high-high clusters in the central area of Yogyakarta City and six low-low clusters in the southeast region of the catchment area in the Bantul and Sleman Districts. No spatial outliers were identified. CONCLUSIONS: We found significant spatial clustering of BC ASR in the Yogyakarta Province, and there was a trend of increasing ASR across the region. These findings can inform resource allocation for public health efforts to high-risk areas and develop targeted prevention and early detection strategies. Further res is needed to understand the factors driving the observed temporal and spatial patterns of breast cancer incidence in Yogyakarta Province, Indonesia.
