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Predictive model for bacterial co-infection in patients hospitalized for COVID-19: a multicenter observational cohort study.
Giannella, Maddalena; Rinaldi, Matteo; Tesini, Giulia; Gallo, Mena; Cipriani, Veronica; Vatamanu, Oana; Campoli, Caterina; Toschi, Alice; Ferraro, Giuseppe; Horna, Clara Solera; Bartoletti, Michele; Ambretti, Simone; Violante, Francesco; Viale, Pierluigi; Curti, Stefania.
  • Giannella M; Infectious Diseases Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Department of Medical and Surgical Sciences, University of Bologna, Via Massarenti 11, 40137, SantBologna, Italy. maddalena.giannella@unibo.it.
  • Rinaldi M; Infectious Diseases Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Department of Medical and Surgical Sciences, University of Bologna, Via Massarenti 11, 40137, SantBologna, Italy.
  • Tesini G; Infectious Diseases Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Department of Medical and Surgical Sciences, University of Bologna, Via Massarenti 11, 40137, SantBologna, Italy.
  • Gallo M; Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy.
  • Cipriani V; Infectious Diseases Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Department of Medical and Surgical Sciences, University of Bologna, Via Massarenti 11, 40137, SantBologna, Italy.
  • Vatamanu O; Infectious Diseases Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Department of Medical and Surgical Sciences, University of Bologna, Via Massarenti 11, 40137, SantBologna, Italy.
  • Campoli C; Infectious Diseases Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Department of Medical and Surgical Sciences, University of Bologna, Via Massarenti 11, 40137, SantBologna, Italy.
  • Toschi A; Infectious Diseases Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Department of Medical and Surgical Sciences, University of Bologna, Via Massarenti 11, 40137, SantBologna, Italy.
  • Ferraro G; Infectious Diseases Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Department of Medical and Surgical Sciences, University of Bologna, Via Massarenti 11, 40137, SantBologna, Italy.
  • Horna CS; Infectious Diseases Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Department of Medical and Surgical Sciences, University of Bologna, Via Massarenti 11, 40137, SantBologna, Italy.
  • Bartoletti M; Infectious Diseases Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Department of Medical and Surgical Sciences, University of Bologna, Via Massarenti 11, 40137, SantBologna, Italy.
  • Ambretti S; Microbiology Unit, IRCCS Policlinico Sant'Orsola, Bologna, Italy.
  • Violante F; Occupational Medicine Unit, Department of Medical and Surgical Sciences, IRCCS Policlinico Sant'Orsola, University of Bologna, Bologna, Italy.
  • Viale P; Infectious Diseases Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Department of Medical and Surgical Sciences, University of Bologna, Via Massarenti 11, 40137, SantBologna, Italy.
  • Curti S; Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy.
Infection ; 50(5): 1243-1253, 2022 Oct.
Article in English | MEDLINE | ID: covidwho-1821023
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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Bacterial Infections / Coinfection / COVID-19 Type of study: Cohort study / Diagnostic study / Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Limits: Adult / Aged / Female / Humans / Male Language: English Journal: Infection Year: 2022 Document Type: Article Affiliation country: S15010-022-01801-2

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Bacterial Infections / Coinfection / COVID-19 Type of study: Cohort study / Diagnostic study / Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Limits: Adult / Aged / Female / Humans / Male Language: English Journal: Infection Year: 2022 Document Type: Article Affiliation country: S15010-022-01801-2