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1.
Cancer Research Conference ; 83(5 Supplement), 2022.
Article in English | EMBASE | ID: covidwho-2253926

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.

2.
Annals of Oncology ; 32:S1138-S1139, 2021.
Article in English | EMBASE | ID: covidwho-1432868

ABSTRACT

Background: The COVID-19 pandemic remains a public health emergency of global concern, with higher mortality rates in cancer patients as compared to the general population. However, early mortality of COVID19 in cancer patients has not been compared to historical real-world data from oncology population in pre-pandemic times. Methods: Longitudinal multicenter cohort study of patients with cancer and confirmed COVID-19 from Oncoclínicas Group in Brazil from March to December 2020. The primary endpoint was 30-day mortality after isolation of the SARS-CoV-2 by RT-PCR. As historical control, we selected patients from Oncoclínicas Data Lake treated before December 2019 and propensity score-matched to COVID-19 cases (3:1) based on the following clinical characteristics: age, gender, tumor type, disease setting (curative or palliative), time from diagnosis of cancer (or metastatic disease) to COVID-19 infection. Results: In total, 533 cancer patients with COVID-19 were prospectively registered in the database, with median age 60 years, 67% females, most frequent tumor types breast (34%), hematological (16%), gastrointestinal (15%), genitourinary (12%) and respiratory tract malignancies (10%). Most patients were on active systemic therapy or radiotherapy (84%), largely for advanced or metastatic disease (55%). In the overall population, early death rate was 15%, which was numerically higher than the Brazilian general population with COVID-19 diagnosis in 2020 (2.5%). We were able to match 442 cancer patients with COVID-19 to 1,187 controls with cancer from pre-pandemic times. The 30-day mortality rate was 12.4% in COVID-19 cases as compared to 5.4% in pre-pandemic controls with cancer (Odds Ratio 2.49, 95%CI 1.67 - 3.70;P value < 0.01, Power 97.5%). COVID-19 cancer patients had significantly higher death events than historical controls (Hazard Ratio 2.18, 95%CI 1.52 - 3.12;P value < 0.01, Power 99.7%), particularly from 20 to 30 days after diagnosis of the infection. Conclusions: Cancer patients with COVID-19 have an excess mortality 30 days after the infection when compared to matched cancer population from pre-pandemic times and the general population with COVID-19, reinforcing the need for priority vaccination in public health strategies. Legal entity responsible for the study: Oncoclínicas Group. Funding: Amgen. Disclosure: All authors have declared no conflicts of interest.

3.
Journal of Clinical Oncology ; 39(15 SUPPL), 2021.
Article in English | EMBASE | ID: covidwho-1339184

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.

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