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.

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.

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.

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.