ABSTRACT
Introduction: Breast cancer is the most common cancer in women and the leading cause of cancerrelated death in women worldwide. The high prevalence of physical and psychosocial suffering among breast cancer patients and their families justifies the need for an early interdisciplinary approach by a palliative care team. The effectiveness of early palliative care for patients with advanced cancer has been demonstrated in many studies. Early referral to outpatient palliative care services improves symptom control, reduces suffering and improves quality of end-of-life care. Aim(s): Evaluation of referral patterns of metastatic breast cancer patients to the outpatient embedded palliative care team. Method(s): We retrospectively retrieved data from electronic medical records of patients who were treated at a private community oncology practice in Brazil who died from metastatic breast cancer during the years of 2018 until 2021.We evaluated the patient's follow-up time by the palliative care team (follow-up > 12 weeks or not) and the year of referral to the service (pre-2020 vs 2020 and later) associated to the service referral type: Late referral (more than 8 weeks of metastatic diagnosis) or early referral. Each group was followed-up by cancer physicians and after referral was also followed-up by a palliative care multidisciplinary team who regularly evaluated cancer patients during their treatments at outpatient setting. During COVID-19 pandemic, some patients were evaluated by telemedicine appointments. We performed univariate comparisons analysis by Fisher's Exact Test. p < 0.1 was deemed as statistically significant. Result(s): Of the 211 patients whose data were assessed, 99 patients were referred to Palliative Care team before 2020 and 112 patients after 2020. 13.1% of patients pre-2020 received early palliative care versus 33.9% of patients in the post-2020 referral group, resulting in a 3.37-fold odds of an early palliative care integration after 2020 (OR 3.37, CI95: 1.61 - 7.45;p< 0.001). Overall, 30.4% of longer follow-up patients were an early referral versus 19.3% of the shorter follow-up, resulting in an 82% greater chance (OR 1.82, CI: 0.92-3.63;p< 0.1) of prolonged assistance with early referral. Conclusion(s): In this analysis, early palliative care integration for patients with metastatic breast cancer has increased after 2019 despite the COVID-19 pandemic, leading to prolonged time of accompaniment by the multidisciplinary palliative care team. This suggests that even in the face of this challenging moment, a mature and consolidated service is offered by the palliative care team. Also, according to previous data in literature, prematurely integration show signs of correlation with better quality of life and death, supporting early palliative care for this group of patients. However, further work is needed to examine the effect of this care model in our cohort.
ABSTRACT
Purpose: The SARS-CoV-2 pandemic was declared a global public health emergency. Determinants of mortality in the general population are now clear, but specific data on patients with breast cancer (BC) remain limited, particularly in developing nations. Material(s) and Method(s): We conducted a longitudinal, multicenter cohort study in patients with BC and confirmed SARS-CoV-2 infection. The primary end point was the proportion of patients on treatment for severe SARS-CoV-2 infection (defined as need for hospitalization) or early death (within 30 days of diagnosis). Data were evaluated sequentially in the following way: i) univariate Fisher's exact test;ii) multivariable logistic regression analysis;and iii) multivariable logistic regression. In items i and ii only those with P< 0.1 are considered significant and in stage iii only those with p< 0.05 were the final significant variables. We divided patients' data into three major variable domains: a) signs and symptoms;b) comorbidities;and c) tumor and treatment characteristics;in item ii each variable domain was tested separately, finally, in item iii the significant variables of all domains were tested together and we called it the integrative step. Result(s): From April 2020 to June 2021, 413 patients with BC and COVID-19 were retrospectively registered, of which 288 (70%) had an identified molecular subtype and 273 (66%) had stage information. Most patients were on active systemic therapy or radiotherapy (73.2%), most of them in the curative setting (69.5%). The overall rate of severe SARS-CoV-2 was 19.7% (95% CI, 15.3-25.1). In the integrative multivariate analysis, factors associated with severe infection were metastatic setting, chronic pain, acute dyspnea, and cardiovascular comorbidities. Recursive partitioning modeling used acute dyspnea, metastatic setting, and cardiovascular comorbidities to predict nonprogression to severe infection, yielding a negative predictive value of 84.9% (95% CI, 78.9%-88.3%). Conclusion(s): The rate of severe COVID-19 in patients with BC is influenced by prognostic factors that partially overlap with those reported in the general population. High-risk patients should be considered candidates to active preventive measures to reduce the risk of infection, close monitoring in the case of exposure or SARS-CoV-2 -related symptoms and prophylactic treatment once infected.
ABSTRACT
Background: As a reaction to the COVID-19 pandemic, a nation-wide lockdown was enforced in Brazil in March 2020, cancer care was impacted, and cancer screening reduced. Therefore, an increase in cancer diagnoses at more advanced stages was expected. In this study, we extracted data from our nationwide real-world database to evaluate the impact of the COVID-19 pandemic on the stage at diagnosis of breast cancer (BC) cases. Methods: We explored curated electronic medical record data of female patients, over 18 years of age, diagnosed with BC and with established disease stage based on the AJCC 8th edition, who started treatment or follow-up in the Oncoclínicas (OC) between Jan 1, 2018, and Dec 31, 2021. The primary objective was to compare stage distribution at first visit during COVID- 19 pandemic (2020-2021) with a historical control cohort from a period prior to the pandemic (2018- 2019). We investigated stage distribution according to age at diagnosis and tumor ER/HER2 subtype in univariate models. Associations were considered significant if they had a minimum significance (P < 0.1 in Chi-square test). The historical numbers of patients with BC at OC make it possible to identify differences in the prevalence of stages in the order of 5% comparing pre and post pandemic periods with a statistical power greater than 80%. Results: We collected data for 11,752 patients with initial diagnosis of BC, with 6,492 patients belonging to the pandemic (2020-2021) and 5,260 patients to the pre-pandemic period (2018-2019). For both ER+/ HER2- and HER2+ tumors, there was a lower percentage of patients with early-stage (defined as stage I-II) in the years 2020-2021 vs 2018-2019 and a considerable increase in advanced-stage disease (defined as stage IV). For triple negative BC (TNBC), there was a significant higher percentage of patients with advanced-stage disease in the pandemic vs pre-pandemic period (table 1). Age over 50 years was associated with a greater risk of advanced stage at diagnosis after the onset of the pandemic, with an absolute increase of 7% (P twosided <0.01). Conclusions: We observed a substantial increase in cases of advanced-stage BC in OC institutions as a result of delays in BC diagnoses due to the COVID-19 pandemic. The impact appeared greater in older adults, potentially because of stricter confinement in this group.
ABSTRACT
Background: COVID-19 is a challenge for clinical decision-making in cancer patients and the allocation of healthcare resources. An accurate prognosis prediction to effectively triage patients is needed, especially in the community oncology practice. Methods:Nationwide cohort from Oncoclínicas Brazil was used to validate previously developed multivariable logistic regression (mLR) model (Ferrari et al, JCO GO 2021) and to construct a machine learning Random Forest (RF) algorithm as predictor of 30-day mortality after SARS-CoV-2 detection by RT-PCR in cancer patients diagnosed in an outpatient setting. To find the most important baseline clinical determinants of early COVID19-related death via Gini index, a RF with 100,000 trees was trained in 75% of the dataset, and the performance was assessed in the remaining 25%. We then compared the accuracy of different models in terms of sensitivity, specificity and area under the receiver operating characteristics curves (AUC). Results:From March to December 2020, 533 patients with COVID-19 were prospectively registered in the database. Median age was 60 years (19-93) and 67% were female. Most frequent cancers were breast in 34%, hematological in 16%, and gastrointestinal in 15%. Comorbidities were common (52%), as was current/former smoking history (17%). Most patients were on active systemic therapy or radiotherapy (84%) in the advanced or metastatic disease setting (55%). The overall mortality rate was 15% (CI95% 12%-18%). We validated the original mLR model trained in the first 198 patients: management in a noncurative setting (odds ratio [OR] 3.7), age ≥ 60 years (OR 2.3), and current/former smoking (OR 1.9) were significant predictors of death in the expanded cohort. Presence of comorbidities (OR 1.9) also defined poor outcome in the updated mLR model, which yielded low sensitivity (74%), specificity (68%) and AUC (0.78). With RF modeling, the most significant predictors of 30-day death after COVID-19 (in decreasing order) were older age, treatment of advanced or metastatic disease, tumor type (respiratory tract, brain and unknown primary cancers had higher mortality), COVID-related symptom burden at baseline evaluation and treatment regimen (immunotherapy combinations had higher mortality). The RF model demonstrated high sensitivity (89%), specificity (88%) and AUC (0.96). Conclusions:The results highlight the possibility that machine learning algorithms are able to predict early mortality after COVID-19 in cancer patients with high accuracy. The proposed prediction model may be helpful in the prompt identification of high-risk patients based on clinical features alone, without having to wait for the results of additional tests such as laboratory or radiologic studies. It can also help prioritize medical resources and redefine vaccination strategies. A web-based mortality risk calculator will be created for clinical decision support.