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

Abstract

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.