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

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 β-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.

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

“…Two-hundred-forty-five studies were excluded for not reporting data on antibiotic use in COVID-19 patients. From the remaining 118 studies, 36 studies were included in this systematic review ( Figure 1 ) [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 ].…”

“…Of the 36 studies included in the systematic review, 32 studies were on the use of antibiotics in COVID-19 inpatients [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ]; 4 reported on antibiotic use in COVID-19 outpatients [ 37 , 38 , 39 , 40 ].…”

“…Table 3 summarizes the findings of studies on bacterial infection and antibiotics use in hospitalized COVID-19 patients [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 31 , 32 , 33 , 34 , 35 , 36 ]. The majority of these studies report a low prevalence of bacterial coinfection in hospitalized COVID-19 patients.…”

“…The reported rate of bacterial infections was 6.3%. The most common types of bacterial coinfection were community-acquired pneumonia, urinary tract infection and bloodstream infection [ 33 ].…”

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

“…In our study, the rate of non-respiratory bacterial infections was 11% (61/553), but since the patients enrolled had a relatively long stay in the hospital, some of these were hospital-acquired. In order to prevent overprescription, Giannella et al proposed a predictive model using leucocytes, procalcitonin, and the Charlson index to quantify the risk of bacterial co-infection in hospitalised COVID-19 patients [ 24 ].…”