Predictive model for bacterial co-infection in patients hospitalized for COVID-19: a multicenter observational cohort study

The Evolving Challenge of Appropriate Antibiotics Use in Hospitalized COVID-19 Patients: A Systematic Literature Review

The issue of bacterial infections in COVID-19 patients has received increasing attention. Scant data are available on the impact of bacterial superinfection and antibiotic administration on the outcome of hospitalized COVID-19 patients. We conducted a literature review from 1 January 2022 to 31 March 2024 to assess the current burden of bacterial infection and the evidence for antibiotic use in hospitalized COVID-19 patients. Published articles providing data on antibiotic use in COVID-19 patients were identified through computerized literature searches with the search terms [(antibiotic) AND (COVID-19)] or [(antibiotic treatment) AND (COVID-19)]. PubMed and SCOPUS databases were searched from 1 January 2022 to 31 March 2024. No attempt was made to obtain information about unpublished studies. English language restriction was applied. The quality of the included studies was evaluated by the tool recommended by the Joanna Briggs Institute. Both quantitative and qualitative information were summarized by means of textual descriptions. Five hundred fifty-one studies were identified, and twenty-nine studies were included in this systematic review. Of the 29 included studies, 18 studies were on the prevalence of bacterial infection and antibiotic use in hospitalized COVID-19 patients; 4 studies reported on the efficacy of early antibiotic use in COVID-19; 4 studies were on the use of sepsis biomarkers to improve antibiotic use; 3 studies were on the efficacy of antimicrobial stewardship programs and predictive models among COVID-19-hospitalized patients. The quality of included studies was high in 35% and medium in 62%. High rates of hospital-acquired infections were reported among COVID-19 patients, ranging between 7.5 and 37.7%. A high antibiotic resistance rate was reported among COVID-19 patients developing hospital-acquired infections, with a high in-hospital mortality rate. The studies evaluating multi-faceted antimicrobial stewardship interventions reported efficacy in decreasing antibiotic consumption and lower in-hospital mortality.

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.

Host transcriptomics and machine learning for secondary bacterial infections in patients with COVID-19: a prospective, observational cohort study

BACKGROUND

Viral respiratory tract infections are frequently complicated by secondary bacterial infections. This study aimed to use machine learning to predict the risk of bacterial superinfection in SARS-CoV-2-positive individuals.

METHODS

In this prospective, multicentre, observational cohort study done in nine centres in six countries (Australia, Indonesia, Singapore, Italy, Czechia, and France) blood samples and RNA sequencing were used to develop a robust model of predicting secondary bacterial infections in the respiratory tract of patients with COVID-19. Eligible participants were older than 18 years, had known or suspected COVID-19, and symptoms of a recent respiratory infection. A control cohort of participants without COVID-19 who were older than 18 years and with no infection symptoms was also recruited from one Australian centre. In the pre-analysis phase, data were filtered to include only individuals with complete blood transcriptomics and patient data (ie, age, sex, location, and WHO severity score at the time of sample collection). The dataset was then divided randomly (4:1) into a training set (80%) and a test set (20%). Gene expression data in the training set and control cohort were used for differential expression analysis. Differentially expressed genes, along with WHO severity score, location, age, and sex, were used for feature selection with least absolute shrinkage and selection operator (LASSO) in the training set. For LASSO analysis, samples were excluded if gene expression data were not obtained at study admission, no longitudinal clinical information was available, a bacterial infection at the time of study admission was present, or a fungal infection in the absence of a bacterial infection was detected. LASSO regression was performed using three subsets of predictor variables: patient data alone, gene expression data alone, or a combination of patient data and gene expression data. The accuracy of the resultant models was tested on data from the test set.

FINDINGS

Between March, 2020, and October, 2021, we recruited 536 SARS-CoV-2-positive individuals and between June, 2013, and January, 2020, we recruited 74 participants into the control cohort. After prefiltering analysis and other exclusions, samples from 158 individuals were analysed in the training set and 47 in the test set. The expression of seven host genes (DAPP1, CST3, FGL2, GCH1, CIITA, UPP1, and RN7SL1) in the blood at the time of study admission was identified by LASSO as predictive of the risk of developing a secondary bacterial infection of the respiratory tract more than 24 h after study admission. Specifically, the expression of these genes in combination with a patient's WHO severity score at the time of study enrolment resulted in an area under the curve of 0·98 (95% CI 0·89-1·00), a true positive rate (sensitivity) of 1·00 (95% CI 1·00-1·00), and a true negative rate (specificity) of 0·94 (95% CI 0·89-1·00) in the test cohort. The combination of patient data and host transcriptomics at hospital admission identified all seven individuals in the training and test sets who developed a bacterial infection of the respiratory tract 5-9 days after hospital admission.

INTERPRETATION

These data raise the possibility that host transcriptomics at the time of clinical presentation, together with machine learning, can forward predict the risk of secondary bacterial infections and allow for the more targeted use of antibiotics in viral infection.

FUNDING

Snow Medical Research Foundation, the National Health and Medical Research Council, the Jack Ma Foundation, the Helmholtz-Association, the A2 Milk Company, National Institute of Allergy and Infectious Disease, and the Fondazione AIRC Associazione Italiana per la Ricerca contro il Cancro.

Coinfection and superinfection in ICU critically ill patients with severe COVID-19 pneumonia and influenza pneumonia: are the pictures different?

Background

Similar to influenza, coinfections and superinfections are common and might result in poor prognosis. Our study aimed to compare the characteristics and risks of coinfections and superinfections in severe COVID-19 and influenza virus pneumonia.

Methods

The data of patients with COVID-19 and influenza admitted to the intensive care unit (ICU) were retrospectively analyzed. The primary outcome was to describe the prevalence and pathogenic distribution of coinfections/ICU-acquired superinfections in the study population. The secondary outcome was to evaluate the independent risk factors for coinfections/ICU-acquired superinfections at ICU admission. Multivariate analysis of survivors and non-survivors was performed to investigate whether coinfections/ICU-acquired superinfections was an independent prognostic factor.

Results

In the COVID-19 (n = 123) and influenza (n = 145) cohorts, the incidence of coinfections/ICU-acquired superinfections was 33.3%/43.9 and 35.2%/52.4%, respectively. The most common bacteria identified in coinfection cases were Enterococcus faecium, Pseudomonas aeruginosa, and Acinetobacter baumannii (COVID-19 cohort) and A. baumannii, P. aeruginosa, and Klebsiella pneumoniae (influenza cohort). A significant higher proportion of coinfection events was sustained by Aspergillus spp. [(22/123, 17.9% in COVID-19) and (18/145, 12.4% in influenza)]. The COVID-19 group had more cases of ICU-acquired A. baumannii, Corynebacterium striatum and K. pneumoniae. A. baumannii, P. aeruginosa, and K. pneumoniae were the three most prevalent pathogens in the influenza cases with ICU-acquired superinfections. Patients with APACHE II ≥18, CD8+ T cells ≤90/μL, and 50 < age ≤ 70 years were more susceptible to coinfections; while those with CD8+ T cells ≤90/μL, CRP ≥120 mg/L, IL-8 ≥ 20 pg./mL, blood glucose ≥10 mmol/L, hypertension, and smoking might had a higher risk of ICU-acquired superinfections in the COVID-19 group. ICU-acquired superinfection, corticosteroid administration for COVID-19 treatment before ICU admission, and SOFA score ≥ 7 were independent prognostic factors in patients with COVID-19.

Conclusion

Patients with COVID-19 or influenza had a high incidence of coinfections and ICU-acquired superinfections. The represent agents of coinfection in ICU patients were different from those in the general ward. These high-risk patients should be closely monitored and empirically treated with effective antibiotics according to the pathogen.

An Observational Study to Develop a Predictive Model for Bacterial Pneumonia Diagnosis in Severe COVID-19 Patients-C19-PNEUMOSCORE

In COVID-19 patients, antibiotics overuse is still an issue. A predictive scoring model for the diagnosis of bacterial pneumonia at intensive care unit (ICU) admission would be a useful stewardship tool. We performed a multicenter observational study including 331 COVID-19 patients requiring invasive mechanical ventilation at ICU admission; 179 patients with bacterial pneumonia; and 152 displaying negative lower-respiratory samplings. A multivariable logistic regression model was built to identify predictors of pulmonary co-infections, and a composite risk score was developed using β-coefficients. We identified seven variables as predictors of bacterial pneumonia: vaccination status (OR 7.01; 95% CI, 1.73-28.39); chronic kidney disease (OR 3.16; 95% CI, 1.15-8.71); pre-ICU hospital length of stay ≥ 5 days (OR 1.94; 95% CI, 1.11-3.4); neutrophils ≥ 9.41 × 10⁹/L (OR 1.96; 95% CI, 1.16-3.30); procalcitonin ≥ 0.2 ng/mL (OR 5.09; 95% CI, 2.93-8.84); C-reactive protein ≥ 107.6 mg/L (OR 1.99; 95% CI, 1.15-3.46); and Brixia chest X-ray score ≥ 9 (OR 2.03; 95% CI, 1.19-3.45). A predictive score (C19-PNEUMOSCORE), ranging from 0 to 9, was obtained by assigning one point to each variable, except from procalcitonin and vaccine status, which gained two points each. At a cut-off of ≥3, the model exhibited a sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 84.9%, 55.9%, 69.4%, 75.9%, and 71.6%, respectively. C19-PNEUMOSCORE may be an easy-to-use bedside composite tool for the early identification of severe COVID-19 patients with pulmonary bacterial co-infection at ICU admission. Its implementation may help clinicians to optimize antibiotics administration in this setting.

Epidemiological Impact on Use of Antibiotics in Patients Hospitalized for COVID-19: A Retrospective Cohort Study in Italy

The increased incidence of antimicrobial resistance during coronavirus disease 2019 (COVID-19) is a very important collateral damage of global concern. The cause is multifactorial and is particularly related to the high rates of antibiotic use in COVID-19 patients with a relatively low rate of secondary co-infection. To this end, we conducted a retrospective observational study of 1269 COVID-19 patients admitted during the years 2020, 2021 and 2022 in two Italian hospitals, with a focus on bacterial co-infections and antimicrobial therapy. Multivariate logistic regression was used to analyze the association between bacterial co-infection, antibiotic use and hospital death after adjustment for age and comorbidity. Bacterial co-infection was detected in 185 patients. The overall mortality rate was 25% (n = 317). Concomitant bacterial infections were associated with increased hospital mortality (β = 1.002, p < 0.001). A total of 83.7% (n = 1062) of patients received antibiotic therapy, but only 14.6% of these patients had an obvious source of bacterial infection. There was a significantly higher rate of hospital mortality in patients who received antibiotics than in those who did not (χ² = 6.22, p = 0.012). Appropriate prescribing and the rational use of antimicrobials according to the principles of antimicrobial stewardship can help prevent the emergence of antibiotic resistance.

An Overview of the Impact of Bacterial Infections and the Associated Mortality Predictors in Patients with COVID-19 Admitted to a Tertiary Center from Eastern Europe

1.

BACKGROUND

Literature data on bacterial infections and their impact on the mortality rates of COVID-19 patients from Romania are scarce, while worldwide reports are contrasting. 2.

MATERIALS AND METHODS

We conducted a unicentric retrospective observational study that included 280 patients with SARS-CoV-2 infection, on whom we performed various microbiological determinations. Based on the administration or not of the antibiotic treatment, we divided the patients into two groups. First, we sought to investigate the rates and predictors of bacterial infections, the causative microbial strains, and the prescribed antibiotic treatment. Secondly, the study aimed to identify the risk factors associated with in-hospital death and evaluate the biomarkers' performance for predicting short-term mortality. 3.

RESULTS

Bacterial co-infections or secondary infections were confirmed in 23 (8.2%) patients. Acinetobacter baumannii was the pathogen responsible for most of the confirmed bacterial infections. Almost three quarters of the patients (72.8%) received empiric antibiotic therapy. Multivariate logistic regression has shown leukocytosis and intensive care unit admission as risk factors for bacterial infections and C-reactive protein, together with the length of hospital stay, as mortality predictors. The ROC curves revealed an acceptable performance for the erythrocyte sedimentation rate (AUC: 0.781), and C-reactive protein (AUC: 0.797), but a poor performance for fibrinogen (AUC: 0.664) in predicting fatal events. 4.

CONCLUSIONS

This study highlighted the somewhat paradoxical association of a low rate of confirmed infections with a high rate of empiric antibiotic therapy. A thorough assessment of the risk factors for bacterial infections, in addition to the acknowledgment of various mortality predictors, is crucial for identifying high-risk patients, thus allowing a timely therapeutic intervention, with a direct impact on improving patients' prognosis.

Antibiotics Use in COVID-19 Patients: A Systematic Literature Review

The issue of bacterial infections in COVID-19 patients has received increasing attention among scientists. Antibiotics were widely prescribed during the early phase of the pandemic. We performed a literature review to assess the reasons, evidence and practices on the use of antibiotics in COVID-19 in- and outpatients. Published articles providing data on antibiotics use in COVID-19 patients were identified through computerized literature searches on the MEDLINE and SCOPUS databases. Searching the MEDLINE database, the following search terms were adopted: ((antibiotic) AND (COVID-19)). Searching the SCOPUS database, the following search terms were used: ((antibiotic treatment) AND (COVID-19)). The risk of bias in the included studies was not assessed. Both quantitative and qualitative information were summarized by means of textual descriptions. Five-hundred-ninety-three studies were identified, published from January 2020 to 30 October 2022. Thirty-six studies were included in this systematic review. Of the 36 included studies, 32 studies were on the use of antibiotics in COVID-19 inpatients and 4 on antibiotic use in COVID-19 outpatients. Apart from the studies identified and included in the review, the main recommendations on antibiotic treatment from 5 guidelines for the clinical management of COVID-19 were also summarized in a separate paragraph. Antibiotics should not be prescribed during COVID-19 unless there is a strong clinical suspicion of bacterial coinfection or superinfection.

Antibiotic Prescription and in-Hospital Mortality in COVID-19: A Prospective Multicentre Cohort Study

Background: Since the beginning of the COVID-19 pandemic, empiric antibiotics (ATBs) have been prescribed on a large scale in both in- and outpatients. We aimed to assess the impact of antibiotic treatment on the outcomes of hospitalised patients with moderate and severe coronavirus disease 2019 (COVID-19). Methods: We conducted a prospective multicentre cohort study in six clinical hospitals, between January 2021 and May 2021. Results: We included 553 hospitalised COVID-19 patients, of whom 58% (311/553) were prescribed antibiotics, while bacteriological tests were performed in 57% (178/311) of them. Death was the outcome in 48 patients—39 from the ATBs group and 9 from the non-ATBs group. The patients who received antibiotics during hospitalisation had a higher mortality (RR = 3.37, CI 95%: 1.7–6.8), and this association was stronger in the subgroup of patients without reasons for antimicrobial treatment (RR = 6.1, CI 95%: 1.9–19.1), while in the subgroup with reasons for antimicrobial therapy the association was not statistically significant (OR = 2.33, CI 95%: 0.76–7.17). After adjusting for the confounders, receiving antibiotics remained associated with a higher mortality only in the subgroup of patients without criteria for antibiotic prescription (OR = 10.3, CI 95%: 2–52). Conclusions: In our study, antibiotic treatment did not decrease the risk of death in the patients with mild and severe COVID-19, but was associated with a higher risk of death in the subgroup of patients without reasons for it.