Bacterial and Fungal Superinfections in COVID-19 Patients Hospitalized in an Intensive Care Unit from Timișoara, Romania

Development and validation of machine learning-based models for predicting healthcare-associated bacterial/fungal infections among COVID-19 inpatients: a retrospective cohort study

BACKGROUND

COVID-19 and bacterial/fungal coinfections have posed significant challenges to human health. However, there is a lack of good tools for predicting coinfection risk to aid clinical work.

OBJECTIVE

We aimed to investigate the risk factors for bacterial/fungal coinfection among COVID-19 patients and to develop machine learning models to estimate the risk of coinfection.

METHODS

In this retrospective cohort study, we enrolled adult inpatients confirmed with COVID-19 in a tertiary hospital between January 1 and July 31, 2023, in China and collected baseline information at admission. All the data were randomly divided into a training set and a testing set at a ratio of 7:3. We developed the generalized linear and random forest models for coinfections in the training set and assessed the performance of the models in the testing set. Decision curve analysis was performed to evaluate the clinical applicability.

RESULTS

A total of 1244 patients were included in the training cohort with 62 healthcare-associated bacterial/fungal infections, while 534 were included in the testing cohort with 22 infections. We found that patients with comorbidities (diabetes, neurological disease) were at greater risk for coinfections than were those without comorbidities (OR = 2.78, 95%CI = 1.61-4.86; OR = 1.93, 95%CI = 1.11-3.35). An indwelling central venous catheter or urinary catheter was also associated with an increased risk (OR = 2.53, 95%CI = 1.39-4.64; OR = 2.28, 95%CI = 1.24-4.27) of coinfections. Patients with PCT > 0.5 ng/ml were 2.03 times (95%CI = 1.41-3.82) more likely to be infected. Interestingly, the risk of coinfection was also greater in patients with an IL-6 concentration < 10 pg/ml (OR = 1.69, 95%CI = 0.97-2.94). Patients with low baseline creatinine levels had a decreased risk of bacterial/fungal coinfections(OR = 0.40, 95%CI = 0.22-0.71). The generalized linear and random forest models demonstrated favorable receiver operating characteristic curves (ROC = 0.87, 95%CI = 0.80-0.94; ROC = 0.88, 95%CI = 0.82-0.93) with high accuracy, sensitivity and specificity of 0.86vs0.75, 0.82vs0.86, 0.87vs0.74, respectively. The corresponding calibration evaluation P statistics were 0.883 and 0.769.

CONCLUSIONS

Our machine learning models achieved strong predictive ability and may be effective clinical decision-support tools for identifying COVID-19 patients at risk for bacterial/fungal coinfection and guiding antibiotic administration. The levels of cytokines, such as IL-6, may affect the status of bacterial/fungal coinfection.

A dynamic nomogram to predict invasive fungal super-infection during healthcare-associated bacterial infection in intensive care unit patients: an ambispective cohort study in China

Objectives

Invasive fungal super-infection (IFSI) is an added diagnostic and therapeutic dilemma. We aimed to develop and assess a nomogram of IFSI in patients with healthcare-associated bacterial infection (HABI).

Methods

An ambispective cohort study was conducted in ICU patients with HABI from a tertiary hospital of China. Predictors of IFSI were selected by both the least absolute shrinkage and selection operator (LASSO) method and the two-way stepwise method. The predictive performance of two models built by logistic regression was internal-validated and compared. Then external validity was assessed and a web-based nomogram was deployed.

Results

Between Jan 1, 2019 and June 30, 2023, 12,305 patients with HABI were screened in 14 ICUs, of whom 372 (3.0%) developed IFSI. Among the fungal strains causing IFSI, the most common was C.albicans (34.7%) with a decreasing proportion, followed by C.tropicalis (30.9%), A.fumigatus (13.9%) and C.glabrata (10.1%) with increasing proportions year by year. Compared with LASSO-model that included five predictors (combination of priority antimicrobials, immunosuppressant, MDRO, aCCI and S.aureus), the discriminability of stepwise-model was improved by 6.8% after adding two more predictors of COVID-19 and microbiological test before antibiotics use (P<0.01).And the stepwise-model showed similar discriminability in the derivation (the area under curve, AUC=0.87) and external validation cohorts (AUC=0.84, P=0.46). No significant gaps existed between the proportion of actual diagnosed IFSI and the frequency of IFSI predicted by both two models in derivation cohort and by stepwise-model in external validation cohort (P=0.16, 0.30 and 0.35, respectively).

Conclusion

The incidence of IFSI in ICU patients with HABI appeared to be a temporal rising, and our externally validated nomogram will facilitate the development of targeted and timely prevention and control measures based on specific risks of IFSI.

During the Omicron Pandemic Wave, the Severe Systemic Inflammatory Status of COVID-19 Indicated a Higher Risk of In-Hospital Mortality and Mediated the Clinical Efficacy of Corticosteroids

Background

For the distinct immune/inflammatory responses from Omicron variant infection, this study aimed to investigate the diagnostic efficacy of systemic inflammatory indicators and the clinical efficacy of corticosteroids on the in-hospital mortality among COVID-19 patients.

Methods

Under a retrospective cohort study, 1081 COVID-19 patients were recruited from Beijing Youan Hospital, Capital Medical University between November 16, 2022 and January 30, 2023. We chose neutrophil-to-lymphocyte ratio (NLR), CRP-to-lymphocyte ratio (CLR), and CRP-to-albumin ratio (CAR) as the systemic inflammatory indicators. Receiver operating curve (ROC) and multivariate logistic regression analysis were used to determine the diagnostic efficacy of systemic inflammatory indicators and the association between systemic inflammatory indicators and in-hospital mortality.

Results

Among 684 patients included in analysis, 96 died during hospitalization. NLR, CLR and CAR performed well (with an area under the curve (AUC) greater than 0.75) in discriminating in-hospital mortality among COVID-19 patients. The severe status of systemic inflammation, with optimal cut-off value derived from ROC analysis, significantly associated higher risk of in-hospital mortality (OR = 3.81 for NLR ≥ 6.131; OR = 3.76 for CLR ≥ 45.455; OR = 5.10 for CAR ≥ 1.436). Corticosteroids use within 72 hours of admission increased the in-hospital mortality 2.88-fold for COVID-19 patients. In the subgroup of patients with severe systemic inflammation, corticosteroids increased the risk of in-hospital mortality (OR = 2.11 for NLR, p = 0.055; OR = 2.94 for CLR, p = 0.005; OR = 2.31 for CAR, p = 0.036).

Conclusion

Systemic inflammatory indicators had good diagnostic performance for in-hospital mortality. Patients with severe systemic inflammatory status should not receive corticosteroid treatment and further studies are warranted for confirmation.

COVID-19 and Laboratory Markers from Romanian Patients-A Narrative Review

COVID-19 has significantly impacted the whole world, and Romania was no exception. Biomarkers play a crucial role in understanding and managing the disease. However, research regarding laboratory analyses for patients with COVID-19 is fairly limited. For detection, PCR testing is still considered the golden standard, while antibodies are still useful for monitoring both patients and their vaccination status. In our country, biomarkers such as CRP, LDH, transaminases, cardiac, and iron markers have been used to assess the status of patients and even predict illness outcome. CRP, IL-6, LDH, FER, fibrinogen, creatinine, and vitamin D levels have been associated with increased severity, risk of ICU admission, and death. Cardiac markers and D-dimers are also good predictors, but their role seems more important in patients with complications. HDL cholesterol and BUN levels were also suggested as potential biomarkers. Hematological issues in SARS-CoV-2 infections include neutrophilia, lymphopenia and their ratio, while PCT, which is a marker of bacterial infections, is better to be used in patients with co- or supra-infections. The current research is a narrative review that focuses on the laboratory results of Romanian COVID-19 patients. The goal of this article is to provide an update on the research on biomarkers and other laboratory tests conducted inside the borders of Romania and identify gaps in this regard. Secondly, options for further research are discussed and encouraged.

The emergence of multidrug-resistant bacteria causing healthcare-associated infections in COVID-19 patients - a retrospective multi-centre study

INTRODUCTION

We evaluated the prevalence, aetiologies and antibiotic resistance patterns of bacterial infections in hospitalized patients with laboratory-confirmed SARS-CoV-2. We also investigated comorbidities, risk factors, and the mortality rate in COVID-19 patients with bacterial infections.

METHODS

This retrospective observational study evaluated medical records of 7249 randomly selected patients with COVID-19 admitted to three clinical centres between January 1 2021 and February 16, 2022. A total of 6478 COVID-19 patients met the eligibility criteria for analysis.

RESULTS

The mean age of the patients with SARS-CoV-2 and bacterial infections was 68.6 ± 15.5 years (range: 24 to 94 years). The majority of patients (68.7%) were older than 65 years. The prevalence of bacterial infections among hospitalized COVID-19 patients was 12.9%, most of them being hospital-acquired (11.5%). Bloodstream (37.7%) and respiratory tract infections (25.6%) were the most common bacterial infections. Klebsiella pneumoniae and Acinetobacter baumannii caused 25.2% and 23.6% of all bacterial infections, respectively. Carbapenem-resistance in Enterobacterales, A. baumannii, and Pseudomonas aeruginosa were 72.6%, 93.7%, and 69.1%. Age >60 years and infections caused by ≥3 pathogens were significantly more prevalent among deceased patients compared to survivors (p<0.05). Furthermore, 95% of patients who were intubated developed ventilator-associated pneumonia. The overall in-hospital mortality rate of patients with SARS-CoV-2 and bacterial infections was 51.6%, while 91.7% of patients who required invasive mechanical ventilation died.

CONCLUSIONS

Our results reveal a striking association between healthcare-associated bacterial infections as an important complication of COVID-19 and fatal outcomes.

A Retrospective Assessment of Sputum Samples and Antimicrobial Resistance in COVID-19 Patients

Data on bacterial or fungal pathogens and their impact on the mortality rates of Western Romanian COVID-19 patients are scarce. As a result, the purpose of this research was to determine the prevalence of bacterial and fungal co- and superinfections in Western Romanian adults with COVID-19, hospitalized in in-ward settings during the second half of the pandemic, and its distribution according to sociodemographic and clinical conditions. The unicentric retrospective observational study was conducted on 407 eligible patients. Expectorate sputum was selected as the sampling technique followed by routine microbiological investigations. A total of 31.5% of samples tested positive for Pseudomonas aeruginosa, followed by 26.2% having co-infections with Klebsiella pneumoniae among patients admitted with COVID-19. The third most common Pathogenic bacteria identified in the sputum samples was Escherichia coli, followed by Acinetobacter baumannii in 9.3% of samples. Commensal human pathogens caused respiratory infections in 67 patients, the most prevalent being Streptococcus penumoniae, followed by methicillin-sensitive and methicillin-resistant Staphylococcus aureus. A total of 53.4% of sputum samples tested positive for Candida spp., followed by 41.1% of samples with Aspergillus spp. growth. The three groups with positive microbial growth on sputum cultures had an equally proportional distribution of patients admitted to the ICU, with an average of 30%, compared with only 17.3% among hospitalized COVID-19 patients with negative sputum cultures (p = 0.003). More than 80% of all positive samples showed multidrug resistance. The high prevalence of bacterial and fungal co-infections and superinfections in COVID-19 patients mandates for strict and effective antimicrobial stewardship and infection control policies.