ABSTRACT
OBJECTIVE: The aim of our study was to build a predictive model able to stratify the risk of bacterial co-infection at hospitalization in patients with COVID-19. METHODS: Multicenter observational study of adult patients hospitalized from February to December 2020 with confirmed COVID-19 diagnosis. Endpoint was microbiologically documented bacterial co-infection diagnosed within 72 h from hospitalization. The cohort was randomly split into derivation and validation cohort. To investigate risk factors for co-infection univariable and multivariable logistic regression analyses were performed. Predictive risk score was obtained assigning a point value corresponding to ß-coefficients to the variables in the multivariable model. ROC analysis in the validation cohort was used to estimate prediction accuracy. RESULTS: Overall, 1733 patients were analyzed: 61.4% males, median age 69 years (IQR 57-80), median Charlson 3 (IQR 2-6). Co-infection was diagnosed in 110 (6.3%) patients. Empirical antibiotics were started in 64.2 and 59.5% of patients with and without co-infection (p = 0.35). At multivariable analysis in the derivation cohort: WBC ≥ 7.7/mm3, PCT ≥ 0.2 ng/mL, and Charlson index ≥ 5 were risk factors for bacterial co-infection. A point was assigned to each variable obtaining a predictive score ranging from 0 to 5. In the validation cohort, ROC analysis showed AUC of 0.83 (95%CI 0.75-0.90). The optimal cut-point was ≥2 with sensitivity 70.0%, specificity 75.9%, positive predictive value 16.0% and negative predictive value 97.5%. According to individual risk score, patients were classified at low (point 0), intermediate (point 1), and high risk (point ≥ 2). CURB-65 ≥ 2 was further proposed to identify patients at intermediate risk who would benefit from early antibiotic coverage. CONCLUSIONS: Our score may be useful in stratifying bacterial co-infection risk in COVID-19 hospitalized patients, optimizing diagnostic testing and antibiotic use.
Subject(s)
Bacterial Infections , COVID-19 , Coinfection , Adult , Aged , Anti-Bacterial Agents/therapeutic use , Bacterial Infections/diagnosis , Bacterial Infections/epidemiology , COVID-19/diagnosis , COVID-19/epidemiology , COVID-19 Testing , Cohort Studies , Coinfection/diagnosis , Coinfection/epidemiology , Female , Hospitalization , Humans , Male , Retrospective StudiesABSTRACT
COVID-19 infection may predispose to secondary bacterial infection which is associated with poor clinical outcome especially among critically ill patients. We aimed to characterize the lower respiratory tract bacterial microbiome of COVID-19 critically ill patients in comparison to COVID-19-negative patients. We performed a 16S rRNA profiling on bronchoalveolar lavage (BAL) samples collected between April and May 2020 from 24 COVID-19 critically ill subjects and 24 patients with non-COVID-19 pneumonia. Lung microbiome of critically ill patients with COVID-19 was characterized by a different bacterial diversity (PERMANOVA on weighted and unweighted UniFrac Pr(> F) = 0.001) compared to COVID-19-negative patients with pneumonia. Pseudomonas alcaligenes, Clostridium hiranonis, Acinetobacter schindleri, Sphingobacterium spp., Acinetobacter spp. and Enterobacteriaceae, characterized lung microbiome of COVID-19 critically ill patients (LDA score > 2), while COVID-19-negative patients showed a higher abundance of lung commensal bacteria (Haemophilus influenzae, Veillonella dispar, Granulicatella spp., Porphyromonas spp., and Streptococcus spp.). The incidence rate (IR) of infections during COVID-19 pandemic showed a significant increase of carbapenem-resistant Acinetobacter baumannii (CR-Ab) infection. In conclusion, SARS-CoV-2 infection and antibiotic pressure may predispose critically ill patients to bacterial superinfection due to opportunistic multidrug resistant pathogens.