Clinical and Neuroimaging Characteristics of Ischemic Stroke in Rhino-Orbito-Cerebral Mucormycosis Associated with COVID-19.

PURPOSE: The aim of this study was to compare clinical, neuroimaging, and laboratory features of rhino-orbito-cerebral mucormycosis (ROCM) in COVID-19 patients with and without ischemic stroke complications. METHODS: This observational study was conducted between August and December 2021 and 48 patients who had confirmed ROCM due to COVID-19, according to neuroimaging and histopathology/mycology evidence were included. Brain, orbit and paranasal sinus imaging was performed in all included patients. Data pertaining to clinical, neuroimaging, and laboratory characteristics and risk factors were collected and compared between patients with and without ischemic stroke complications. RESULTS: Of the patients 17 were diagnosed with ischemic stroke. Watershed infarction was the most common pattern (N = 13, 76.4%). Prevalence of conventional risk factors of stroke showed no significant differences between groups (patients with stroke vs. without stroke). Cavernous sinus (p = 0.001, odds ratio, OR = 12.8, 95% confidence interval, CI: 2.3-72) and ICA (p < 0.001, OR = 16.31, 95%CI: 2.91-91.14) involvement was more common in patients with stroke. Internal carotid artery (ICA) size (on the affected side) in patients with ischemic stroke was significantly smaller than in patients without stroke (median = 2.4 mm, interquartile range, IQR: 1.3-4 vs. 3.8 mm, IQR: 3.2-4.3, p = 0.004). Superior ophthalmic vein (SOV) size (on the affected side) in patients with stroke was significantly larger than patients without stroke (2.2 mm, IQR: 1.5-2.5 vs. 1.45 mm IQR: 1.1-1.8, p = 0.019). Involvement of the ethmoid and frontal sinuses were higher in patients with stroke (p = 0.007, OR = 1.85, 95% CI: 1.37-2.49 and p = 0.011, OR = 5, 95% CI: 1.4-18.2, respectively). Patients with stroke had higher D­dimer levels, WBC counts, neutrophil/lymphocyte ratios, and BUN/Cr ratio (all p < 0.05). CONCLUSION: Stroke-related ROCM was not associated with conventional ischemic stroke risk factors. Neuroimaging investigations including qualitative and quantitative parameters of cavernous sinus, ICA and SOV are useful to better understand the mechanism of stroke-related ROCM in COVID-19 patients.

Clinical characteristics of COVID-19-infected cancer patients, Isfahan, Iran.

Background: Cancer patients, as a highly vulnerable population, are receiving a great deal of attention in the current crisis of coronavirus 2019 (COVID-19). To date, shreds of evidence are not sufficient to the description of COVID-19 outcomes in patients with cancer. This study was performed to evaluate the demographic and clinical characteristics and subsequent outcomes of COVID-19 in cancer patients. Materials and Methods: A hospital-based study was conducted involving 66 cancer patients with a confirmed diagnosis of COVID-19 from January 15, 2020, to December 21, 2020, in Isfahan, Iran. The clinical information was collected by interview and medical records. The statistical analyses were performed to describe categorical variables as well as mean, standard deviation, median, and the interquartile range for quantitative variables. Results: In our study, 66 cancer patients with confirmed COVID-19 (age: 17-97 years; 50% female) were included. Leukemia and bone marrow cancer with a frequency of 25.7% were the most common types of cancer among them. Cancer patients mostly complained of fever, cough and fatigue, and shortness of breath. Among 76.9% of patients discharged from the hospital with relative recovery, 23% died; the most common cause of death was acute respiratory distress syndrome. Age, gender, and type of cancer did not affect cancer mortality. COVID-19 had no potential effect to increase the risk of side effects of anticancer therapies. Conclusion: The results of our studies revealed that cancer is an important risk factor for the higher rate of mortality in patients with COVID-19. These findings could help physicians for the management, treatment, and supportive care of COVID-19 cancer patients.

A novel reliability-based regression model to analyze and forecast the severity of COVID-19 patients.

BACKGROUND: Coronavirus outbreak (SARS-CoV-2) has become a serious threat to human society all around the world. Due to the rapid rate of disease outbreaks and the severe shortages of medical resources, predicting COVID-19 disease severity continues to be a challenge for healthcare systems. Accurate prediction of severe patients plays a vital role in determining treatment priorities, effective management of medical facilities, and reducing the number of deaths. Various methods have been used in the literature to predict the severity prognosis of COVID-19 patients. Despite the different appearance of the methods, they all aim to achieve generalizable results by increasing the accuracy and reducing the errors of predictions. In other words, accuracy is considered the only effective factor in the generalizability of models. In addition to accuracy, reliability and consistency of results are other critical factors that must be considered to yield generalizable medical predictions. Since the role of reliability in medical decisions is significant, upgrading reliable medical data-driven models requires more attention. METHODS: This paper presents a new modeling technique to specify and maximize the reliability of results in predicting the severity prognosis of COVID-19 patients. We use the well-known classic regression as the basic model to implement our proposed procedure on it. To assess the performance of the proposed model, it has been applied to predict the severity prognosis of COVID-19 by using a dataset including clinical information of 46 COVID-19 patients. The dataset consists of two types of patients' outcomes including mild (discharge) and severe (ICU or death). To measure the efficiency of the proposed model, we compare the accuracy of the proposed model to the classic regression model. RESULTS: The proposed reliability-based regression model, by achieving 98.6% sensitivity, 88.2% specificity, and 93.10% accuracy, has better performance than classic accuracy-based regression model with 95.7% sensitivity, 85.5% specificity, and 90.3% accuracy. Also, graphical analysis of ROC curve showed AUC 0.93 (95% CI 0.88-0.98) and AUC 0.90 (95% CI 0.85-0.96) for classic regression models, respectively. CONCLUSIONS: Maximizing reliability in the medical forecasting models can lead to more generalizable and accurate results. The competitive results indicate that the proposed reliability-based regression model has higher performance in predicting the deterioration of COVID-19 patients compared to the classic accuracy-based regression model. The proposed framework can be used as a suitable alternative for the traditional regression method to improve the decision-making and triage processes of COVID-19 patients.

Chest Computed Tomography Severity Score to Predict Adverse Outcomes of Patients with COVID-19.

BACKGROUND: The novel coronavirus disease 2019 (COVID-19) continues to wreak havoc worldwide. This study assessed the ability of chest computed tomography (CT) severity score (CSS) to predict intensive care unit (ICU) admission and mortality in patients with COVID-19 pneumonia. MATERIALS AND METHODS: A total of 192 consecutive patients with COVID-19 pneumonia aged more than 20 years and typical CT findings and reverse-transcription polymerase chain reaction positive admitted in a tertiary hospital were included. Clinical symptoms at admission and short-term outcome were obtained. A semi-quantitative scoring system was used to evaluate the parenchymal involvement. The association between CSS, disease severity, and outcomes were evaluated. Prediction of CSS was assessed with the area under the receiver-operating characteristic (ROC) curves. RESULTS: The incidence of admission to ICU was 22.8% in men and 14.1% in women. CSS was related to ICU admission and mortality. Areas under the ROC curves were 0.764 for total CSS. Using a stepwise binary logistic regression model, gender, age, oxygen saturation, and CSS had a significant independent relationship with ICU admission and death. Patients with CSS ≥12.5 had about four-time risk of ICU admission and death (odds ratio 1.66, 95% confidence interval 1.66 - 9.25). The multivariate regression analysis showed the superiority of CSS over other clinical information and co-morbidities. CONCLUSION: CSS was a strong predictor of progression to ICU admission and death and there was a substantial role of non-contrast chest CT imaging in the presence of typical features for COVID-19 pneumonia as a reliable predictor of clinical severity and patient's outcome.

Fatal disseminated aspergillosis in an immunocompetent patient with COVID-19 due to Aspergillus ochraceus.

Aspergillus infection is a well-known complication of severe influenza and severe acute respiratory syndrome coronavirus (SARS-CoV), and these infections have been related with significant morbidity and mortality even when appropriately diagnosed and treated. Recent studies have indicated that SARS-CoV-2 might increase the risk of invasive pulmonary aspergillosis (IPA). Here, we report the first case of Aspergillus ochraceus in a SARS-CoV-2 positive immunocompetent patient, which is complicated by pulmonary and brain infections. Proven IPA is supported by the positive Galactomannan test, culture-positive, and histopathological evidence. The patient did not respond to voriconazole, and liposomal amphotericin B was added to his anti-fungal regimen. Further studies are needed to evaluate the prevalence of IPA in immunocompetent patients infected with SARS-CoV-2. Consequently, testing for the incidence of Aspergillus species in lower respiratory secretions and Galactomannan test of COVID-19 patients with appropriate therapy and targeted anti-fungal therapy based on the primary clinical suspicion of IPA are highly recommended.

Fatal Invasive Pulmonary Aspergillosis in COVID-19 Patient with Acute Myeloid Leukemia in Iran.

Although patients with severe immunodeficiency and hematological malignancies has been considered at highest risk for invasive fungal infection, patients with severe pneumonia due to influenza, and severe acute respiratory syndrome coronavirus (SARS-CoV) are also at a higher risk of developing invasive pulmonary aspergillosis (IPA). Recently, reports of IPA have also emerged among SARS-CoV-2 infected patients admitted to intensive care units (ICUs). Here, we report a fatal case of probable IPA in an acute myeloid leukemia patient co-infected with SARS-CoV-2 and complicated by acute respiratory distress syndrome (ARDS). Probable IPA is supported by multiple pulmonary nodules with ground glass opacities which indicate halo sign and positive serum galactomannan results. Screening studies are needed to evaluate the prevalence of IPA in immunocompromised patients infected with SARS-CoV-2. Consequently, testing for the presence of Aspergillus in lower respiratory secretions and galactomannan in consecutive serum samples of COVID-19 patients with timely and targeted antifungal therapy based on early clinical suspicion of IPA are highly recommended.

Risk factors associated with intensive care unit (ICU) admission and in-hospital death among adults hospitalized with COVID-19: a two-center retrospective observational study in tertiary care hospitals.

BACKGROUND: The COVID-19 pandemic is straining the health care systems worldwide. Therefore, health systems should make strategic shifts to ensure that limited resources provide the highest benefit for COVID-19 patients. OBJECTIVE: This study aimed to describe the risk factors associated with poor in-hospital outcomes to help clinicians make better patient care decisions. MATERIAL AND METHODS: This retrospective observational study enrolled 176 laboratory-confirmed COVID-19 patients. Demographic characteristics, clinical data, lymphocyte count, CT imaging findings on admission, and clinical outcomes were collected and compared. Two radiologists evaluated the distribution and CT features of the lesions and also scored the extent of lung involvement. The receiver operating characteristic (ROC) curve was used to determine the optimum cutoff point for possible effective variables on patients' outcomes. Multivariable logistic regression models were used to determine the risk factors associated with ICU admission and in-hospital death. RESULT: Thirty-eight (21.5%) patients were either died or admitted to ICU from a total of 176 enrolled ones. The mean age of the patients was 57.5 ± 16.1 years (males: 61%). The best cutoff point for predicting poor outcomes based on age, CT score, and O2 saturation was 60 years (sensitivity: 71%, specificity: 62%), 10.5 (sensitivity: 73%, specificity: 58%), and 90.5% (sensitivity: 73%, specificity: 59%), respectively. CT score cutoff point was rounded to 11 since this score contains only integer numbers. Multivariable-adjusted regression models revealed that ages of ≥ 60 years, CT score of ≥ 11, and O2 saturation of ≤ 90.5% were associated with higher worse outcomes among study population (odds ratio (OR): 3.62, 95%CI: 1.35-9.67, P = 0.019; OR: 4.38, 95%CI: 1.69-11.35, P = 0.002; and OR: 2.78, 95%CI: 1.03-7.47, P = 0.042, respectively). CONCLUSION: The findings indicate that older age, higher CT score, and lower O2 saturation could be categorized as predictors of poor outcome among COVID-19-infected patients. Other studies are required to prove these associations.

Fulminant encephalitis as a sole manifestation of COVID-19.

Novel coronavirus (SARS-CoV-2) occurred in December 2019 in Wuhan, China, and has become a global health emergency. Coronavirus primarily is a respiratory virus, but it has been detected in the brain and cerebrospinal fluid of infected individuals. The present report describes a case of fulminant encephalitis in a patient affected by COVID-19.