Background: The diagnosis of breast cancer requires a complicated series of diagnostic exams. The present study addressed the delay of patients who used publicly and privately financed diagnostic services. Non-governmental organizations (NGOs) donated diagnostic mammograms and biopsies.Design and Methods: Data from 304 patients were obtained from two Brazilian referral centres. In one referral centre (FAP), diagnostic mammography, clinic-histopathological exam and immunohistochemistry were outsourced, whereas in the other centre (HNL), these services were integrated. Cox regression, Kaplan-Meier analysis and non-parametric tests were used to compare variables and time intervals.Results: If diagnostic mammography was financed privately and covered by private health insurance, the likelihood of a delay of >90 days between the first medical visit and the initiation of treatment decreased 2.15-fold (95%CI: 1.06- 4.36; p=0.033) and 4.44-fold (95%CI: 1.58-12.46; p=0.004), respectively. If the clinic-histopathological exam was outsourced (FAP) and publicly or privately financed, the median time between diagnostic mammography and the diagnostic result was 53 and 65 days in the integrated (HNL) and outsourced public system, compared to 29 days in the outsourced private system (p<0.050). The median time between the first medical visit and the diagnostic results of patients who were supported by NGOs, who financed their diagnostic services privately, and who used exclusively public diagnostic services was, respectively, 28.0, 48.5 and 77.5 days (p<0.050).Conclusion: Patients who used privately financed health services had shorter delays. Compared to outsourcing, the integration of the publicly financed clinic-histopathological exam diminished the delay. The support of patients by NGOs accelerated patient flow.

Background: System delay (SD) is a leading cause of advanced stage of disease and poor prognosis among Brazilian breast cancer patients. Methods: Cox regression and Kaplan-Meier analysis were used to identify variables that contributed to SD among 128 breast cancer patients. Time intervals between first medical consultation and treatment initiation were compared among patients of two referral centres: Patients of a referral centre with outsourced (FAP), respectively, integrated (HNL) diagnostic services. Results: Women who used a specialized private clinic at the beginning of patient flow had an 2.32 fold increased chance (95% CI: 1.17-4.60; p = 0.016) of hospital admission within 90 days after first medical consultation, compared to women who used a public health care provider (HCP). Of 73 and 34 patients of the FAP hospital and the HNL, respectively, 10 (13.7%) and 11 (32.5%) used one HCP prior to hospital admission (p = 0.000). The median time between first medical consultation and treatment initiation was 150 days. The median time between first medical consultation and hospital admission was 136.0 and 52.0 days for patients of the FAP hospital, respectively the HNL (p < 0.050). The median time between first medical consultation and diagnostic mammography was 36.5 and 23.0 days for patients from the FAP hospital and the HNL (p < 0.050). Conclusions: Usage of public diagnostic services was associated with increased SD, whereas the usage of private diagnostic services diminished it. The usage of a lower number of HCPs accelerated patient flow.

Objective. To evaluate, using semiparametric methodologies of survival analysis, the relationship between covariates and time to death of patients with breast cancer, as well as the determination discriminatory power in the conditional inference tree of patients who had cancer. Methods. A retrospective cohort study was conducted using data collected from medical records of women who had breast cancer and underwent treatment between 2005 and 2015 at the Hospital da Fundação de Assistencial da Paraíba in Campina Grande, State of Paraiba, Brazil. Survival curves were estimated using the Kaplan–Meier method, Cox regression, and conditional decision tree. Results. Women with triple-negative molecular subtypes had a shorter survival time compared to women with positive hormone receptors. The addition of hormone therapy reduced the risk of a patient dying by 5.5%, and the risk of a HER2-positive patient dying was 34.5% lower compared to those who were negative for this gene. Patients undergoing hormone therapy had a median survival time of 4 753 days. Conclusions. This paper shows a favorable scenario for the use of immunotherapy for patients with HER2 overexpression. Further studies could assess the effectiveness of immunotherapy in patients with other conditions, to favor the prognosis and better quality of life for the patient.

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Machine learning algorithms are being increasingly used in healthcare settings but their generalizability between different regions is still unknown. This study aims to identify the strategy that maximizes the predictive performance of identifying the risk of death by COVID-19 in different regions of a large and unequal country. This is a multicenter cohort study with data collected from patients with a positive RT-PCR test for COVID-19 from March to August 2020 (n = 8477) in 18 hospitals, covering all five Brazilian regions. Of all patients with a positive RT-PCR test during the period, 2356 (28%) died. Eight different strategies were used for training and evaluating the performance of three popular machine learning algorithms (extreme gradient boosting, lightGBM, and catboost). The strategies ranged from only using training data from a single hospital, up to aggregating patients by their geographic regions. The predictive performance of the algorithms was evaluated by the area under the ROC curve (AUROC) on the test set of each hospital. We found that the best overall predictive performances were obtained when using training data from the same hospital, which was the winning strategy for 11 (61%) of the 18 participating hospitals. In this study, the use of more patient data from other regions slightly decreased predictive performance. However, models trained in other hospitals still had acceptable performances and could be a solution while data for a specific hospital is being collected.