Carbapenem-resistant gram-negative bacterial infection in intensive care unit patients: Antibiotic resistance analysis and predictive model development

Abstract

In this study, we analyzed the antibiotic resistance of carbapenem-resistant gram-negative bacteria (CR-GNB) in intensive care unit (ICU) patients and developed a predictive model. We retrospectively collected the data of patients with GNB infection admitted to the ICU of the First Affiliated Hospital of Fujian Medical University, who were then divided into a CR and a carbapenem-susceptible (CS) group for CR-GNB infection analysis. Patients admitted between December 1, 2017, and July 31, 2019, were assigned to the experimental cohort (n = 205), and their data were subjected to multivariate logistic regression analysis to identify independent risk factors for constructing the nomogram-based predictive model. Patients admitted between August 1, 2019, and September 1, 2020, were assigned to the validation cohort for validating the predictive model (n = 104). The Hosmer−Lemeshow test and receiver operating characteristic (ROC) curve analysis were used to validate the model’s performance. Overall, 309 patients with GNB infection were recruited. Of them, 97 and 212 were infected with CS-GNB and CR-GNB, respectively. Carbapenem-resistant Klebsiella pneumoniae (CRKP), carbapenem-resistant Acinetobacter baumannii (CRAB) and carbapenem-resistant Pseudomonas aeruginosa (CRPA) were the most prevalent CR-GNB. The multivariate logistic regression analysis results of the experimental cohort revealed that a history of combination antibiotic treatments (OR: 3.197, 95% CI: 1.561–6.549), hospital-acquired infection (OR: 3.563, 95% CI: 1.062–11.959) and mechanical ventilation ≥ 7 days (OR: 5.096, 95% CI: 1.865–13.923) were independent risk factors for CR-GNB infection, which were then used for nomogram construction. The model demonstrated a good fit of observed data (p = 0.999), with an area under the ROC curve (AUC) of 0.753 (95% CI: 0.685–0.820) and 0.718 (95% CI: 0.619–0.816) for the experimental and validation cohort, respectively. The decision curve analysis results suggested that the model has a high practical value for clinical practice. The Hosmer−Lemeshow test indicated a good fit of the model in the validation cohort (p-value, 0.278). Overall, our proposed predictive model exhibited a good predictive value in identifying patients at high risk of developing CR-GNB infection in the ICU and could be used to guide preventive and treatment measures.

Economic impact of metagenomic next-generation sequencing versus traditional bacterial culture for postoperative central nervous system infections using a decision analysis mode: study protocol for a randomized controlled trial

“…CNSIs are characterized by substantial morbidity, fatality rates, and significant costs imposed on the healthcare system ( 14 ). In recent years, there has been an increasing prevalence of Acinetobacter baumannii and Klebsiella pneumoniae in hospital-acquired infections ( 15 ). Bacterial infections affecting the central nervous system represent a medical emergency, necessitating prompt diagnosis and immediate treatment.…”

Development and application of a risk nomogram for the prediction of risk of carbapenem-resistant Acinetobacter baumannii infections in neuro-intensive care unit: a mixed method study

“…A previous study [ 35 ] suggested that three or more antibiotic classes were a risk factor for multidrug-resistant A. baumannii infections. This might be attributed to the fact that combination antibiotic treatment causes dysbiosis in the microbiota, whereby the majority of susceptible bacteria are eradicated, while the antibiotic-resistant strains survive [ 36 ]. Additionally, inappropriate combination antibiotic treatment can create selective pressure, thus increasing the likelihood of antibiotic-resistant infection and promoting the selection of drug-resistant bacteria [ 37 ].…”