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.
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.
ABSTRACT
The acute form of histoplasmosis usually occurs after the exposition of more than one individual to a common environmental source harboring Histoplasma capsulatum. Here, we present two cases of acute pulmonary histoplasmosis seen within two weeks at a reference center for infectious diseases at Rio de Janeiro, Brazil. The patients did not present a common epidemiologic history for histoplasmosis, however both presented COVID-19 before the onset of histoplasmosis symptoms. Due to the difficulties in the diagnosis of acute histoplasmosis, novel laboratory methods such as Western Blot and PCR were included in the investigation of these cases. Both patients presented negative cultures for H. capsulatum and negative urinary galactomannan. However, they presented H and M bands in the Western blot as well as a positive H. capsulatum DNA detection in sputum. These results were available approximately 36 h after sample collection, fastening the beginning of treatment of one patient. Both patients progressed well with itraconazole treatment. These cases suggest that COVID-19 may facilitate the development of acute pulmonary histoplasmosis and, therefore, clinicians must be aware of this differential diagnosis in patients from endemic areas with fever and coughing after recovery from COVID-19.
ABSTRACT
Objective: To identify the implications, for Nursing, of pulmonary infections by coronavirus in people with chronic non-communicable diseases and to propose actions for care. Materials and method: A literature review, with a search for primary studies in the Biblioteca Regional Virtual de Saúde, Cumulative Index to Nursing and Allied Health Literature, National Library of Medicine and Scopus databases, from March 15th to March 30th, 2020, in Portuguese, English, and Spanish, with a quantitative and qualitative approach, in adults with chronic non-communicable diseases with respiratory infection by viruses of the coronavirus family, from 2010 to 2020. Results: A total of 11 articles were analyzed, which made it possible to identify guidelines for Nursing actions at the community and hospital levels and in critical care;among the care actions proposed for people with chronic diseases are education in health, encouragement to control the disease, immunization and lifestyle change, monitoring of suspected and confirmed cases, and use of masks in public environments. Conclusions: The study highlights the role of Nursing at all health care levels and the possibilities for learning and improving care actions through the use of evidence obtained from previous experiences. © 2021, Universidad de La Sabana. All rights reserved.
ABSTRACT
BACKGROUND: Treatment of mycoses is often ineffective, usually prolonged, and has some side effects. These facts highlight the importance of discovering new molecules to treat fungal infections. OBJECTIVES: To search the Medicines for Malaria Venture COVID Box for drugs with antifungal activity. METHODS: Fourteen human pathogenic fungi were tested against the 160 drugs of this collection at 1.0 µM concentration. We evaluated the ability of the drugs to impair fungal growth, their fungicidal nature, and morphological changes caused to cells. FINDINGS: Thirty-four molecules (21.25%) presented antifungal activity. Seven are antifungal drugs and one is the agricultural fungicide cycloheximide. The other drugs with antifungal activity included antibiotics (n=3), antimalarials (n=4), antivirals (n=2), antiparasitcs (n=3), antitumor agents (n=5), nervous system agents (n=3), immunosuppressants (n=3), antivomiting (n=1), antiasthmatic (n=1), and a genetic disorder agent (n=1). Several of these drugs inhibited Histoplasma capsulatum and Paracoccidioides brasiliensis growth (15 and 20, respectively), while Fusarium solani was not affected by the drugs tested. Most drugs were fungistatic, but niclosamide presented fungicidal activity against the three dimorphic fungi tested. Cyclosporine affected morphology of Cryptococcus neoformans. MAIN CONCLUSIONS: These drugs represent new alternatives to the development of more accessible and effective therapies to treat human fungal infections. © 2021, Fundacao Oswaldo Cruz. All rights reserved.