SARS-CoV-2 infection in cancer patients undergoing active treatment: analysis of clinical features and predictive factors for severe respiratory failure and death

On March, 11 2020 WHO declared novel SARS-CoV-2 outbreak as pandemic (Coronavirus disease, COVID-19), which took away almost 4 million lives of our planet population. Management of severe COVID-19 represents the most challenging problem being associated with high level of mortality. Aim of the study: to assess the clinical features of severe COVID-19, demographic factors, laboratory markers and lung pathology findings associated with severe course and lethal outcome. Material and methods. In retrospective cohort survey we recruited 171 adult patients (age > 18 years) with severe COVID-19, admitted to 2nd infection disease department of municipal hospital �Volyn regional clinical hospital� (CE �Voklen�). 101 patients were discharged after completion of treatment. 70 patients died. In two groups (discharged or deceased) we analyzed demographic data, clinical diagnosis, comorbidity and complications, duration of disease and hospital stay, body temperature at admission, blood oxygen saturation at admission and during the course of treatment, major laboratory parameters (WBC, neutrophils, lymphocytes, thrombocytes, RBC, neutrophil/lymphocyte ration (NLR), C-RP, AST, ALT, creatinine, total protein, blood glucose and procalcitonin). Almost all patients were tested for D-dimer, lupus anticoagulant (LA) and blood gases. In part of deceased patients (n=10) an autopsy was performed with subsequent lung tissue histological examination. Results and discussion. Acute respiratory distress syndrome (ARDS) and severe respiratory failure were the major cause of death from COVID19. Concomitant conditions, which worsened the clinical course and prognosis: renal failure, thrombotic events, in part associated with elevation of D-dimer and LA, neoplasm, cardiovascular conditions and diabetes mellitus. Female sex and younger age were the demographic factors of favorable outcome. Leukocytosis, high NLR, increased creatinine (as an indicator of renal failure), hypoproteinemia and high serum glucose level were the laboratory markers of unfavorable prognosis. LA, associated with severe respiratory failure, stroke and vascular thrombosis, were found positive in 40 % of patients with severe COVID-19. Key words: COVID-19, severe course, mortality, prognosis factors.

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Identification and Characterization of Predictive Factors of Onset of Bone Metastases in Cancer Patients

Case Medical Research ◽

10.31525/ct1-nct03895216 ◽

2019 ◽

Author(s):

Keyword(s):

Bone Metastases ◽

Cancer Patients ◽

Predictive Factors ◽

Identification And Characterization

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Faculty Opinions recommendation of Use of N-terminal pro-brain natriuretic peptide to detect cardiac origin in critically ill cancer patients with acute respiratory failure.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1142840.599925 ◽

2009 ◽

Author(s):

Patrick Ray

Keyword(s):

Respiratory Failure ◽

Acute Respiratory Failure ◽

Brain Natriuretic Peptide ◽

Cancer Patients ◽

Critically Ill ◽

Natriuretic Peptide

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Faculty Opinions recommendation of Predictive factors for the outcome of noninvasive ventilation in pediatric acute respiratory failure.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.6456956.6573054 ◽

2010 ◽

Author(s):

Francis Leclerc

Keyword(s):

Respiratory Failure ◽

Acute Respiratory Failure ◽

Predictive Factors ◽

Noninvasive Ventilation

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Favorable Anakinra Responses in COVID-19 Patients with Severe Respiratory Failure

SSRN Electronic Journal ◽

10.2139/ssrn.3578042 ◽

2020 ◽

Author(s):

George Dimopoulos ◽

Quirijn de Mast ◽

Nikolaos Markou ◽

Maria Theodorakopoulou ◽

Apostolos Komnos ◽

...

Keyword(s):

Respiratory Failure ◽

Severe Respiratory Failure

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Predictive Factors for the Development of Dyspnea Within 7 Days After Admission Among Terminally Ill Cancer Patients

American Journal of Hospice and Palliative Medicine® ◽

10.1177/10499091211028817 ◽

2021 ◽

pp. 104990912110288

Author(s):

Ryo Matsunuma ◽

Takashi Yamaguchi ◽

Masanori Mori ◽

Tomoo Ikari ◽

Kozue Suzuki ◽

...

Keyword(s):

Lung Cancer ◽

Cancer Patients ◽

Predictive Factors ◽

Performance Status ◽

Univariate Analysis ◽

Terminally Ill ◽

Primary Lung Cancer ◽

Poor Performance Status ◽

Terminally Ill Cancer Patients ◽

Development Group

Background: Predictive factors for the development of dyspnea have not been reported among terminally ill cancer patients. Objective: This current study aimed to identify the predictive factors attributed to the development of dyspnea within 7 days after admission among patients with cancer. Methods: This was a secondary analysis of a multicenter prospective observational study on the dying process among patients admitted in inpatient hospices/palliative care units. Patients were divided into 2 groups: those who developed dyspnea (development group) and those who did not (non-development group). To determine independent predictive factors, univariate and multivariate analyses using the logistic regression model were performed. Results: From January 2017 to December 2017, 1159 patients were included in this analysis. Univariate analysis showed that male participants, those with primary lung cancer, ascites, and Karnofsky Performance Status score (KPS) of ≤40, smokers, and benzodiazepine users were significantly higher in the development group. Multivariate analysis revealed that primary lung cancer (odds ratio [OR]: 2.80, 95% confidence interval [95% CI]: 1.47-5.31; p = 0.002), KPS score (≤40) (OR: 1.84, 95% CI: 1.02-3.31; p = 0.044), and presence of ascites (OR: 2.34, 95% CI: 1.36-4.02; p = 0.002) were independent predictive factors for the development of dyspnea. Conclusions: Lung cancer, poor performance status, and ascites may be predictive factors for the development of dyspnea among terminally ill cancer patients. However, further studies should be performed to validate these findings.

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630. A 5-mRNA host response whole-blood classifier trained using patients with non-COVID-19 viral infections accurately predicts severity of COVID-19

Open Forum Infectious Diseases ◽

10.1093/ofid/ofaa439.824 ◽

2020 ◽

Vol 7 (Supplement_1) ◽

pp. S375-S376

Author(s):

ljubomir Buturovic ◽

Purvesh Khatri ◽

Benjamin Tang ◽

Kevin Lai ◽

Win Sen Kuan ◽

...

Keyword(s):

Logistic Regression ◽

Respiratory Failure ◽

Viral Infections ◽

Lamp Assay ◽

Training Data ◽

Diagnostic Tools ◽

Severe Respiratory Failure ◽

Proof Of Concept ◽

Risk Of Death ◽

Qpcr Platform

Abstract Background While major progress has been made to establish diagnostic tools for the diagnosis of SARS-CoV-2 infection, determining the severity of COVID-19 remains an unmet medical need. With limited hospital resources, gauging severity would allow for some patients to safely recover in home quarantine while ensuring sicker patients get needed care. We discovered a 5 host mRNA-based classifier for the severity of influenza and other acute viral infections and validated the classifier in COVID-19 patients from Greece. Methods We used training data (N=705) from 21 retrospective clinical studies of influenza and other viral illnesses. Five host mRNAs from a preselected panel were applied to train a logistic regression classifier for predicting 30-day mortality in influenza and other viral illnesses. We then applied this classifier, with fixed weights, to an independent cohort of subjects with confirmed COVID-19 from Athens, Greece (N=71) using NanoString nCounter. Finally, we developed a proof-of-concept rapid, isothermal qRT-LAMP assay for the 5-mRNA host signature using the QuantStudio 6 qPCR platform. Results In 71 patients with COVID-19, the 5 mRNA classifier had an AUROC of 0.88 (95% CI 0.80-0.97) for identifying patients with severe respiratory failure and/or 30-day mortality (Figure 1). Applying a preset cutoff based on training data, the 5-mRNA classifier had 100% sensitivity and 46% specificity for identifying mortality, and 88% sensitivity and 68% specificity for identifying severe respiratory failure. Finally, our proof-of-concept qRT-LAMP assay showed high correlation with the reference NanoString 5-mRNA classifier (r=0.95). Figure 1. Validation of the 5-mRNA classifier in the COVID-19 cohort. (A) Expression of the 5 genes used in the logistic regression model in patients with (red) and without (blue) mortality. (B) The 5-mRNA classifier accurately distinguishes non-severe and severe patients with COVID-19 as well as those at risk of death. Conclusion Our 5-mRNA classifier demonstrated very high accuracy for the prediction of COVID-19 severity and could assist in the rapid, point-of-impact assessment of patients with confirmed COVID-19 to determine level of care thereby improving patient management and healthcare burden. Disclosures ljubomir Buturovic, PhD, Inflammatix Inc. (Employee, Shareholder) Purvesh Khatri, PhD, Inflammatix Inc. (Shareholder) Oliver Liesenfeld, MD, Inflammatix Inc. (Employee, Shareholder) James Wacker, n/a, Inflammatix Inc. (Employee, Shareholder) Uros Midic, PhD, Inflammatix Inc. (Employee, Shareholder) Roland Luethy, PhD, Inflammatix Inc. (Employee, Shareholder) David C. Rawling, PhD, Inflammatix Inc. (Employee, Shareholder) Timothy Sweeney, MD, Inflammatix, Inc. (Employee)

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