Development and validation of a nomogram to predict secondary or concurrent bacterial infections in patients with COVID-19

Background Individuals experiencing viral respiratory infections have an increased susceptibility to subsequent or simultaneous bacterial infections. Accurately identifying secondary or concomitant bacterial infections in patients diagnosed with Coronavirus disease 2019 (COVID-19) is essential to guide optimal diagnostic and therapeutic interventions. Methods The enrolled patients were randomly allocated into modeling and validation groups at a ratio of 3:2. Each group was then stratified into two subgroups based on the occurrence or absence of secondary or simultaneous bacterial infections. Influential variables associated with bacterial infections in COVID-19 patients were determined using stepwise multivariate logistic regression analysis and least absolute shrinkage and selection operator (LASSO) analyses. A predictive nomogram was constructed to visually present the model. Receiver operating characteristic (ROC) curves were generated, and corresponding areas under the curves (AUC) were calculated. Furthermore, calibration curves and clinical decision curves were prepared to assess the model’s validity and practical applicability. Results A robust predictive model to identify COVID-19 patients with secondary or concurrent bacterial infections was successfully developed, achieving a concordance index (C-index) of 0.769. In the modeling group, the AUC was determined to be 0.769 (95% CI: 0.696–0.842), while the validation group had an AUC of 0.682 (95% CI: 0.583–0.782), signifying strong discriminatory performance. Calibration curves illustrated a high degree of agreement, and decision curve analysis confirmed favorable clinical utility. Conclusion The nomogram curves based on the predictive model was practical to be used as a reference for COVID-19 patients with secondary or concurrent bacterial infection.