Candidemia in critically ill COVID-19 patients: Risk factors and impact on mortality.

Background: Risk factors of candidemia are well-described in intensive care units (ICUs) before the Coronavirus disease 2019 (COVID-19) pandemic. The increased rates of admission to ICUs have appeared during the pandemic. Methods: Patient characteristics and laboratory data of 80 candidemia with COVID-19, 101 candidemia without COVID-19, and 100 non-candidemia with COVID-19 patients were evaluated, in this study. Results: Systemic inflammatory response syndrome (SIRS) ≥ 2, solid malignancy, total parenteral nutrition (TPN), central venous catheterization (CVC), hypotension, fever, urea, alanine aminotransferase (ALT), D-dimer, procalcitonin, ferritin, and delta neutrophil index (DNI) was found to be associated with candidemia in COVID-19 patients. TPN, hypotension, and fever were identified as independent predictors of candidemia in COVID-19, and candidemia in COVID-19 is characterized by significantly high mortality rates. Urea, lactate, and procalcitonin were defined as independent predictors of hospital mortality in candidemia patients with COVID-19. Conclusion: The presence of candidemia increases mortality in COVID-19. TPN, fever, and hypotension werefound to be the most powerful predictors of candidemia in COVID-19. Overall, these data show that candidemia in COVID-19 is characterized by significantly high mortality rates. Determination of distinctive features can prevent candidemia and mortality.

Concordance and generalization of an AI algorithm with real-world clinical data in the pre-omicron and omicron era.

All viruses, including SARS-CoV-2, the virus responsible for COVID-19, continue to evolve, which can lead to new variants. The objective of this study is to assess the agreement between real-world clinical data and an algorithm that utilizes laboratory markers and age to predict the progression of disease severity in COVID-19 patients during the pre-Omicron and Omicron variant periods. The study evaluated the performance of a deep learning (DL) algorithm in predicting disease severity scores for COVID-19 patients using data from the USA, Spain, and Turkey (Ankara City Hospital (ACH) data set). The algorithm was developed and validated using pre-Omicron era data and was tested on both pre-Omicron and Omicron-era data. The predictions were compared to the actual clinical outcomes using a multidisciplinary approach. The concordance index values for all datasets ranged from 0.71 to 0.81. In the ACH cohort, a negative predictive value (NPV) of 0.78 or higher was observed for severe patients in both the pre-Omicron and Omicron eras, which is consistent with the algorithm's performance in the development cohort.

The usefulness of hematological parameters and cerebrospinal fluid indexes in the differential diagnosis of acute bacterial from viral meningitis.

Central nervous system (CNS) infection is a medical emergency with an important cause of mortality worldwide. The 79 patients with confirmed acute CNS infection (48 bacterial and 31 viral meningitis) were evaluated. Bacterial meningitis score, cerebrospinal fluid (CSF)/serum glucose ratio, and CSF/serum albumin ratio had the highest area under the curves (0.873, 0.843, 0.810, respectively) for discriminating bacterial meningitis. Neutrophil-to-lymphocyte (NLR), platelet-to-lymphocyte ratio (PLR) and CSF lactate dehydrogenase have a good ability for the differential diagnosis of bacterial meningitis. CSF/serum glucose ratio, NLR (with a cut-off value> 8.87), large unstained cell, total protein, albumin, and procalcitonin levels were found to be predictors for mortality. NLR can be used as a biomarker to differentiate bacterial meningitis from viral meningitis and to predict the prognosis of CNS infection. CSF/serum albumin ratio and CSF lactate dehydrogenase can be used to predict bacterial meningitis as well as CSF/serum glucose ratio.

An investigation of clinical characteristics and antimicrobial agent susceptibility patterns in clinical Comamonas testosteroni isolates: An increasingly prevalent nosocomial pathogen.

BACKGROUND: Comamonas testosteroni is a gram-negative bacillus, known before 1987 as Pseudomonas testosteroni. Although considered as a rare pathogen, its frequency has been increasing. Data regarding its antibiotic susceptibility are insufficient. To date, forty-four cases have been reported in the literature. In this study, we identified the C. testosteroni infections observed in our hospital and evaluated their antimicrobial agent susceptibility patterns compared with cases reported in the literature. METHODS: For the purposes of the present study, patients admitted to hospital between November 2019 and December 2020 were screened. Those with clinical and laboratory signs of infection with positive C. testosteroni growth in culture were enrolled. Clinical isolates obtained from the samples processed in accordance with standard microbiological examination procedures in our laboratory were defined by MALDI-TOF mass spectrometry with 99.9% probability as C. testosteroni. RESULTS: C testosteroni infection was detected between November 2019 and December 2020 in eight patients in our hospital. Six of them had a bloodstream infection (BSI), one had pneumonia, and one had urinary tract infection due to C. testosteroni. Coexistence of COVID-19 was detected in four patients. Six out of the eight cases with BSI had hospital-acquired infection and all of the infections were healthcare-associated. When antimicrobial agent susceptibility results reported in the literature were evaluated in combination with the current results, ceftazidime and meropenem were found to be the most susceptible agents (96.1% and 80%, respectively). CONCLUSIONS: The frequency of nosocomial C. testosteroni infections and resistance to antimicrobial agents are gradually increasing. While resistance to carbapenems is on the upswing, third-generation cephalosporins are still seen as suitable treatment options.

Neurological Presentations in Patients with COVID-19 in Cytokine Storm.

BACKGROUND: Coronavirus disease 2019 (COVID-19) infection causes a wide variety of neurological disorders by affecting both central and peripheral nervous systems. The cytokine storm (CS) has been blamed for the development of severe neurological disorders in COVID-19. However, the relationship between COVID-19 CS and neurological manifestations has not been adequately studied. Thus, we aimed to investigate the neurological presentations in patients with COVID-19 CS. METHODS: The study population consisted of hospitalized moderate-to-severe COVID-19 patients. It was divided into two groups CS (36 patients, 29.3%) and non-CS (87 patients, 70.7%) based on significant clinical symptoms, elevated inflammatory marker levels, radiological findings, and interleukin-6 levels (IL-6). RESULTS: The three most common neurological symptoms in the CS group were altered level of consciousness, headache, and unsteadiness. Altered level of consciousness was higher in the CS group (69.4%) than the non-CS group (25.3%) (p:0.001). The frequency of headache was comparable in both groups (p:0.186). The number of patients requiring intensive care unit and intubation was higher in the CS group (p:0.005 and p:0.001). The mortality rate in the CS group (38.9%) was higher than the non-CS group (8.0%) (p:0.001). IL-6, CRP, ferritin, neutrophil-lymphocyte ratio, procalcitonin, and D-dimer levels were higher in the CS group (for all p:0.001) while lymphocyte count was lower (p:0.003). CONCLUSION: The most common neurological presentation in patients with CS was altered level of consciousness. The presence of CS was an independent risk factor for high mortality.

Prognostic Value of Hemogram-Derived Ratios in Patients with Crimean-Congo Hemorrhagic Fever.

Background: Crimean-Congo hemorrhagic fever (CCHF) is an emerging infectious disease that has epidemic and pandemic potential and causes mortality. Predicting the outcome of the disease is important to guide the management of patients and prevent mortality. Materials and Methods: This study aimed to investigate hemogram parameters and hemogram-derived ratios for predicting mortality in 207 patients with CCHF (survivors = 177, nonsurvivors = 30). Results: Compared with the survivor group, the nonsurvivor group had higher neutrophils, neutrophil-to-lymphocyte ratio (NLR), derived NLR (d-NLR), and aspartate aminotransferase (AST), AST-to-lymphocyte ratio index (ALRI) on admission and third day of hospitalization. Higher white blood cells (WBCs), lower platelet-to-lymphocyte ratio on admission, and lower lymphocytes, and monocytes on the third day were found in the nonsurvivor group. Evaluating the change of admission and the third day of laboratory values, a downward trend in neutrophils, NLR, d-NLR, ALRI, and an upward trend in WBCs were found statistically significant in the survivor group. These dynamic changes were not found in the nonsurvivor group. AST (third day) and ALRI (third day) had the highest area under the curve (AUC) in the receiver operating characteristic analysis (0.939 and 0.934, respectively; p-value is <0.0001 for all). The NLR on the third day than on admission had a higher AUC, the optimal cutoff value was 1.44, which resulted in a sensitivity of 93.33 and a specificity of 40.34 (AUC: 0.790, p < 0.0001). The d-NLR on the third day had a higher AUC (with a sensitivity of 81.48 and a specificity of 67.43) than on admission (0.781 and 0.669, respectively). Conclusion: CCHF is a common vector-borne disease and mortality rates are high. This study revealed that NLR, d-NLR, and ALRI can be used as biomarkers to predict mortality. Patients who survived had better improvement in hemogram parameters and ratios. Therefore, patients who do not show this improvement should be followed more closely.

Evaluation of the models generated from clinical features and deep learning-based segmentations: Can thoracic CT on admission help us to predict hospitalized COVID-19 patients who will require intensive care?

BACKGROUND: The aim of the study was to predict the probability of intensive care unit (ICU) care for inpatient COVID-19 cases using clinical and artificial intelligence segmentation-based volumetric and CT-radiomics parameters on admission. METHODS: Twenty-eight clinical/laboratory features, 21 volumetric parameters, and 74 radiomics parameters obtained by deep learning (DL)-based segmentations from CT examinations of 191 severe COVID-19 inpatients admitted between March 2020 and March 2021 were collected. Patients were divided into Group 1 (117 patients discharged from the inpatient service) and Group 2 (74 patients transferred to the ICU), and the differences between the groups were evaluated with the T-test and Mann-Whitney test. The sensitivities and specificities of significantly different parameters were evaluated by ROC analysis. Subsequently, 152 (79.5%) patients were assigned to the training/cross-validation set, and 39 (20.5%) patients were assigned to the test set. Clinical, radiological, and combined logit-fit models were generated by using the Bayesian information criterion from the training set and optimized via tenfold cross-validation. To simultaneously use all of the clinical, volumetric, and radiomics parameters, a random forest model was produced, and this model was trained by using a balanced training set created by adding synthetic data to the existing training/cross-validation set. The results of the models in predicting ICU patients were evaluated with the test set. RESULTS: No parameter individually created a reliable classifier. When the test set was evaluated with the final models, the AUC values were 0.736, 0.708, and 0.794, the specificity values were 79.17%, 79.17%, and 87.50%, the sensitivity values were 66.67%, 60%, and 73.33%, and the F1 values were 0.67, 0.62, and 0.76 for the clinical, radiological, and combined logit-fit models, respectively. The random forest model that was trained with the balanced training/cross-validation set was the most successful model, achieving an AUC of 0.837, specificity of 87.50%, sensitivity of 80%, and F1 value of 0.80 in the test set. CONCLUSION: By using a machine learning algorithm that was composed of clinical and DL-segmentation-based radiological parameters and that was trained with a balanced data set, COVID-19 patients who may require intensive care could be successfully predicted.

Eggerthia catenaformis-related peritonitis in a patient with peritoneal dialysis.

Eggerthia catenaformis is a Gram-positive bacilli and an anaerobic and non-spore-forming bacterium, which rarely causes infections in humans. We present a case of peritonitis caused by E. catenaformis in a peritoneal dialysis patient. The isolate was identified as E. catenaformis with the MALDI-TOF MS method as in other cases in the literature. To the best of our knowledge, this is the first case of peritonitis caused by E. catenaformis in a human host.

Diagnostic value of ß-D-glucan alone or combined with Candida score, colonization index and C-reactive protein for candidemia.

INTRODUCTION: Candidemia causes high mortality and is occuring at increasing rate in intensive care units (ICUs). (1,3)- ß-D-glucan (BDG) testing is recommended in neutropenic patients. However the usefulness of BDG in ICUs is unclear. METHODOLOGY: This study was conducted to compare the diagnostic value of Candida score (CS), colonization index (CI), serum BDG detection, and routine laboratory parameters in ICU patients. Characteristics and laboratory data of 83 patients (15 patients with candidemia and 68 patients without candidemia) were evaluated. RESULTS: Median serum BDG was significantly higher in the candidemia group (129 pg/mL vs. 36 pg/mL, p < 0.001). BDG assay with standard cut-off value ≥ of 80 pg/mL had 93.33% sensitivity and 64.18% specificity (Areas under the ROC curve (AUC): 0.788). This study concluded that the optimal cut-off value for BDG assay was 112 pg/mL with sensitivity of 86.67% and specificity of 82.09% (AUC: 0.844). C-reactive protein (CRP) with optimal cut-off value ≥ 85 mg/L and BDG ≥ 80 pg/mL had the highest AUC (0.862, 95% CI: 0.768 - 0.928) with sensitivity 93.33% and specificity 79.1%. CONCLUSIONS: Predicting candidemia is essential in critically ill patients who are at high risk and have high mortality rates. The results of this study suggest that BDG testing is useful for predicting candidemia in ICU. However, BDG combined with CRP may be a stronger predictor for candidemia.

Are angiotensin converting enzyme (ACE1/ACE2) gene variants associated with the clinical severity of COVID-19 pneumonia? A single-center cohort study.

OBJECTIVE: The impact of the coronavirus disease 2019 (COVID-19) pandemic has been unceasingly ongoing worldwide. Recent bioinformatics analysis and epidemiologic studies have highlighted that the functional polymorphisms on the angiotensin converting enzyme (ACE) gene may have an impact on the clinical progress of COVID-19. In this study, we aimed to determine the impact of the ACE1 gene I/D polymorphism and ACE2 peptidase-2 domain variants on disease severity. METHODS: Hundred patients with confirmed COVID-19 related pneumonia [50 patients with severe disease in intensive care unit (ICU) and 50 patients not in ICU] were compared on the basis of genetic and clinical characteristics. Genomic DNA was purified from peripheral blood lymphocytes with an automated QIA symphony DSP DNA Mini-Kit. The Sanger sequencing analysis was performed. The frequencies of ACE1 gene polymorphism and ACE2 PD variants were compared in patients hospitalized in ICU and those not in ICU. The Statistical Package for Social Sciences version 22.0 was used for statistical analysis. RESULTS: The sequencing analysis of the ACE2 gene exon 1 and 2 revealed none of the polymorphisms investigated or any other variants in the present cohort. The frequencies of the ACE1 ID, DD, and II genotypes were 51%, 31%, and 18%, respectively. The frequency of the D allele was similar between the ICU and non-ICU groups (50.4% versus 49.6%). Older age and the presence of advanced stage radiologic abnormalities on admission were detected as independent predictors of ICU requirement. CONCLUSION: No effect of any ACE1 gene polymorphism on predicting ICU requirement was detected. To the best of our knowledge, this is the first study investigating the impact of ACE gene polymorphisms on clinical severity of COVID-19 in a Turkish cohort.

Tocilizumab treatment in severe COVID-19: a multicenter retrospective study with matched controls.

Coronavirus disease-2019 (COVID-19) associated pneumonia may progress into acute respiratory distress syndrome (ARDS). Some patients develop features of macrophage activation syndrome (MAS). Elevated levels of IL-6 were reported to be associated with severe disease, and anti-IL-6R tocilizumab has been shown to be effective in some patients. This retrospective multicenter case-control study aimed to evaluate the efficacy of tocilizumab in hospitalized COVID-19 patients, who received standard of care with or without tocilizumab. Primary outcome was the progression to intubation or death. PSMATCH (SAS) procedure was used to achieve exact propensity score (PS) matching. Data from 1289 patients were collected, and study population was reduced to 1073 based on inclusion-exclusion criteria. The composite outcome was observed more frequently in tocilizumab-users, but there was a significant imbalance between arms in all critical parameters. Primary analyses were carried out in 348 patients (174 in each arm) after exact PS matching according to gender, ferritin, and procalcitonin. Logistic regression models revealed that tocilizumab significantly reduced the intubation or death (OR 0.40, p = 0.0017). When intubation is considered alone, tocilizumab-users had > 60% reduction in odds of intubation. Multiple imputation approach, which increased the size of the matched patients up to 506, provided no significant difference between arms despite a similar trend for intubation alone group. Analysis of this retrospective cohort showed more frequent intubation or death in tocilizumab-users, but PS-matched analyses revealed significant results for supporting tocilizumab use overall in a subset of patients matched according to gender, ferritin and procalcitonin levels.

The changing dynamics of neutralizing antibody response within 10 months of SARS-CoV-2 infections.

There are limited data on how long neutralizing antibody (NAb) response elicited via primary SARS-CoV-2 infection will last. Eighty-four serum samples were obtained from a prospective cohort of 42 laboratory-confirmed COVID-19 inpatients at the time of discharge from the hospital and in the late convalescent phase. A virus neutralization assay was performed to determine the presence and titers of NAbs with authentic SARS-CoV-2. Long-term dynamics of NAbs and factors that may have an impact on humoral immunity were investigated. Mild and moderate/severe patients were compared. The mean sampling time was 11.12 ± 5.02 days (4-28) for the discharge test and 268.12 ± 11.65 days (247-296) for the follow-up test. NAb response was present in 83.3% of the patients about 10 months after infection. The detectable long-term NAb rate was significantly higher in mild patients when compared to moderate/severe patients (95.7% vs. 68.4%, p = 0.025). In the follow-up, NAb-positive and -negative patients were compared to determine the predictors of the presence of long-term humoral immunity. The only significant factor was disease severity. Patients with mild infections have more chance to have NAbs for a longer time. Age, gender, and comorbidity did not affect long-term NAb response. NAb titers decreased significantly over time, with an average rank of 24.0 versus 19.1 (p = 0.002). Multivariate generalized estimating equation analysis revealed that no parameter has an impact on the change of NAb titers over time. The majority of the late convalescent patients still had detectable low levels of neutralizing antibodies. The protective effect of these titers of NAbs from re-infections needs further studies.

Development and validation of nomogram to predict severe illness requiring intensive care follow up in hospitalized COVID-19 cases.

BACKGROUND: Early identification of severe COVID-19 patients who will need intensive care unit (ICU) follow-up and providing rapid, aggressive supportive care may reduce mortality and provide optimal use of medical resources. We aimed to develop and validate a nomogram to predict severe COVID-19 cases that would need ICU follow-up based on available and accessible patient values. METHODS: Patients hospitalized with laboratory-confirmed COVID-19 between March 15, 2020, and June 15, 2020, were enrolled in this retrospective study with 35 variables obtained upon admission considered. Univariate and multivariable logistic regression models were constructed to select potential predictive parameters using 1000 bootstrap samples. Afterward, a nomogram was developed with 5 variables selected from multivariable analysis. The nomogram model was evaluated by Area Under the Curve (AUC) and bias-corrected Harrell's C-index with 95% confidence interval, Hosmer-Lemeshow Goodness-of-fit test, and calibration curve analysis. RESULTS: Out of a total of 1022 patients, 686 cases without missing data were used to construct the nomogram. Of the 686, 104 needed ICU follow-up. The final model includes oxygen saturation, CRP, PCT, LDH, troponin as independent factors for the prediction of need for ICU admission. The model has good predictive power with an AUC of 0.93 (0.902-0.950) and a bias-corrected Harrell's C-index of 0.91 (0.899-0.947). Hosmer-Lemeshow test p-value was 0.826 and the model is well-calibrated (p = 0.1703). CONCLUSION: We developed a simple, accessible, easy-to-use nomogram with good distinctive power for severe illness requiring ICU follow-up. Clinicians can easily predict the course of COVID-19 and decide the procedure and facility of further follow-up by using clinical and laboratory values of patients available upon admission.

SARS-CoV-2 Detection with De Novo-Designed Synthetic Riboregulators.

SARS-CoV-2 is a human pathogen and the main cause of COVID-19 disease, announced as a global pandemic by the World Health Organization. COVID-19 is characterized by severe conditions, and early diagnosis can make dramatic changes for both personal and public health. Low-cost, easy-to-use diagnostic capabilities can have a very critical role in controlling the transmission of the disease. Here, we are reporting a state-of-the-art diagnostic tool developed with an in vitro synthetic biology approach by employing engineered de novo riboregulators. Our design coupled with a home-made point-of-care device can detect and report the presence of SARS-CoV-2-specific genes. The presence of SARS-CoV-2-related genes triggers the translation of sfGFP mRNAs, resulting in a green fluorescence output. The approach proposed here has the potential of being a game changer in SARS-CoV-2 diagnostics by providing an easy-to-run, low-cost diagnostic capability.

Assessment of Galectin-1, Galectin-3, and Prostaglandin E2 Levels in Patients with COVID-19.

It is important to determine the inflammatory biomarkers in the severity of coronavirus disease 2019 (COVID-19) with the emergence of the pandemic. Galectins and prostaglandins play important roles in the regulation of immune and inflammatory responses. Therefore, this study aimed to investigate Galectin-1 (Gal-1), Galectin-3 (Gal-3), and prostaglandin E2 (PGE2) levels in patients with COVID-19. Serum concentrations of Gal-1, Gal-3, and PGE2 were measured using enzyme-linked immunosorbent assay on 84 patients with COVID-19 (severe = 29 and nonsevere = 55) and 56 healthy controls. In this study, increased levels of Gal-1 (median, 9.86, 6.35, and 3.67 ng/mL), Gal-3 (median, 415.31, 326.33, and 243.13 pg/mL), and PGE2 (median, 193.17, 192.58, and 124.62 pg/mL) levels were found in patients with COVID-19 than in healthy controls (P < 0.001 for all). In the severe disease group, Gal-3 levels were higher, while no differences were noted in Gal-1 and PGE2 levels (P = 0.011, P = 0.263, and P = 0.921, respectively). Serum levels of Gal-1 were positively correlated with those of Gal-3 (P = 0.871 and P < 0.001). Gal-3, C-reactive protein, lymphocyte count, and age were found as independent predictors of disease severity (P = 0.002, P = 0.001, P = 0.007, and P = 0.003, respectively). With the emergence of effective drug needs in the COVID-19 pandemic, differentiation of severe disease is important. Therefore, Gal-3 could be a potential prognostic biomarker of COVID-19.

Efficacy and cost-effectivity analysis of outpatient parenteral antimicrobial therapy unit in infectious disease clinical practices: Turkey perspective.

BACKGROUND: Outpatient parenteral antimicrobial treatment (OPAT) has become a common treatment modality in developed countries. OPAT units are not widespread in Turkey, and their cost-effectivity analysis has not been studied, yet. AIMS: To analyze the clinical efficacy and cost-effectiveness of the OPAT unit, based on a 1000-bed teaching hospital. METHODS: The records of patients, who were treated between October 2013 and December 2017, in an OPAT unit of a tertiary hospital in Ankara, were obtained retrospectively. The cost that would arise if the patients were hospitalized for the same treatment period with the same diagnosis was calculated and compared with the actual treatment cost of the patients in the OPAT unit. RESULTS: A total of 594 patients who received antimicrobial treatment at the OPAT unit were enrolled. The mean age of the patients was 55.39 ± 16.37 years and 313 (52.7%) were males. Based on the end-of-treatment goals, 98.5% of the patients reached the treatment goal. An indirect cost analysis revealed that the OPAT unit was 487.625 94 TL/129.008 78 $ less costly than inpatient parenteral antibiotic treatment. In other words, OPAT cost was 75% of the equivalent inpatient costs. It was also determined that a total of 7078 bed days and 11.9 bed days per person were saved. CONCLUSIONS: OPAT units should be expanded increasingly in Turkey. The evaluation together with the health care system conditions in Turkey revealed that the OPAT program is safe, effective, and cost-efficient.

Multiple system inflammatory syndrome associated with SARS-CoV-2 infection in an adult and an adolescent.

Multisystem inflammatory syndrome in adults (MIS-A) is a new syndrome related with COVID-19. A case-based review was performed to present real-life experiences in terms of main findings and treatment options. We described two cases with the diagnosis of MIS and searched the literature to review all reported ≥ 18-year-old cases. The PubMed, Scopus, and Web of Science databases were searched. All relevant articles from January 2020 to February 2021 were reviewed. An adolescent and an adult patient (18 and 40 years-old, respectively) with the diagnosis of MIS were presented. Both had the consistent clinical findings with the case definition criteria. Although steroid, intravenous immunoglobulin (IVIG) and supportive care treatments have been suggested in the literature, there exists no treatment guideline for MIS-A. The clinical and laboratory findings of the patients progressively improved with the implementation of the IVIG and the pulse steroid treatments. A total of 51 cases (≥ 18 years-old) with MIS were analyzed. Mean age was 29.4 ± 10 years. Fever (80.4%), gastrointestinal (72.5%), and respiratory symptoms (54.9%) were the predominant symptoms. Cardiovascular abnormalities were the most frequent reported findings (82.4%, 42/51). The dermatological and conjunctival findings were reported in 39.2% and 35.3% of the patients, respectively. The increased level of inflammatory biomarkers was remarkable. Most of the patients were treated successfully with steroid and IVIG. Clinicians managing adult patients should keep in mind the development risk of MIS related with SARS-CoV-2 infection to perform necessary interventions properly without delay. IVIG and pulse steroid treatments are the effective options on clinical improvement.

A CT radiomics analysis of COVID-19-related ground-glass opacities and consolidation: Is it valuable in a differential diagnosis with other atypical pneumonias?

PURPOSE: To evaluate the discrimination of parenchymal lesions between COVID-19 and other atypical pneumonia (AP) by using only radiomics features. METHODS: In this retrospective study, 301 pneumonic lesions (150 ground-glass opacity [GGO], 52 crazy paving [CP], 99 consolidation) obtained from nonenhanced thorax CT scans of 74 AP (46 male and 28 female; 48.25±13.67 years) and 60 COVID-19 (39 male and 21 female; 48.01±20.38 years) patients were segmented manually by two independent radiologists, and Location, Size, Shape, and First- and Second-order radiomics features were calculated. RESULTS: Multiple parameters showed significant differences between AP and COVID-19-related GGOs and consolidations, although only the Range parameter was significantly different for CPs. Models developed by using the Bayesian information criterion (BIC) for the whole group of GGO and consolidation lesions predicted COVID-19 consolidation and AP GGO lesions with low accuracy (46.1% and 60.8%, respectively). Thus, instead of subjective classification, lesions were reclassified according to their skewness into positive skewness group (PSG, 78 AP and 71 COVID-19 lesions) and negative skewness group (NSG, 56 AP and 44 COVID-19 lesions), and group-specific models were created. The best AUC, accuracy, sensitivity, and specificity were respectively 0.774, 75.8%, 74.6%, and 76.9% among the PSG models and 0.907, 83%, 79.5%, and 85.7% for the NSG models. The best PSG model was also better at predicting NSG lesions smaller than 3 mL. Using an algorithm, 80% of COVID-19 and 81.1% of AP patients were correctly predicted. CONCLUSION: During periods of increasing AP, radiomics parameters may provide valuable data for the differential diagnosis of COVID-19.

Comparing ICU admission rates of mild/moderate COVID-19 patients treated with hydroxychloroquine, favipiravir, and hydroxychloroquine plus favipiravir.

BACKGROUND: In this study, we aimed to compare the intensive care unit (ICU) admission rate of hospitalized mild/moderate COVID-19 patients treated with hydroxychloroquine (HCQ), favipiravir, and HCQ plus favipiravir. METHODS: Single center retrospective designed observational study conducted in Ankara City Hospital. Patients who were hospitalized between March 15, 2020 and June 1, 2020 in COVID-19 inpatient clinics with laboratory confirmed diagnosis of COVID-19 were included in the study. An inverse probability of treatment weighting (IPTW) for multiple treatment groups approach was used to balance the differences in several variables on admission. RESULTS: Among 2441 patients hospitalized with diagnosis of COVID-19 during the study period, 824 were eligible for the analysis. Median age of patients was 42 (18-93 years). Among all, 347 (43.2%) of the patients had mild disease, 470 (56.8%) had pneumonia. Propensity scores ranged from 0.1841 to 0.9381 in the HCQ group, from 0.03643 to 0.29885 in the favipiravir group, and from 0.03542 to 0.56184 in the HCQ plus favipiravir group. After IPTW for multiple treatment groups was applied, all the covariates in the planned propensity score had weighted standardized effect sizes below 10% which were ranged from 0.005 to 0.092. Multivariate analysis of treatment effect (adjusted effect of treatment) was indicated that there is no statistically significant difference between HCQ, favipiravir, and HCQ plus favipiravir treatment. After using combination of SMOTE and Bootstrap resampling approach, we found no statistically significant difference between HCQ and HCQ plus favipiravir groups in terms of ICU admission. However, compared with the HCQ group, ICU admission rate was statistically significantly higher in the favipiravir group. We obtained the similar results after the sensitivity analysis. CONCLUSIONS: HCQ with or without favipiravir treatment is associated with reduced risk of ICU admission compared to favipiravir alone in mild to moderate COVID-19 adult patients.