Predictive model for bacterial co-infection in patients hospitalized for COVID-19: a multicenter observational cohort study
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/$ mm^{3} $, 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..
| Medienart: |
Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:50 |
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| Enthalten in: |
Infection - 50(2022), 5 vom: 29. Apr., Seite 1243-1253 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Giannella, Maddalena [VerfasserIn] |
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| Links: |
Volltext [lizenzpflichtig] |
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| Themen: |
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| Anmerkungen: |
© The Author(s) 2022. corrected publication 2022 |
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| doi: |
10.1007/s15010-022-01801-2 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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OLC2079647814 |
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| 520 | |a 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/$ mm^{3} $, 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. | ||
| 650 | 4 | |a SARS-CoV-2 | |
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| 700 | 1 | |a Curti, Stefania |4 aut | |
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