Pathogen Distribution, Drug Resistance Risk Factors, and Construction of Risk Prediction Model for Drug-Resistant Bacterial Infection in Hospitalized Patients at the Respiratory Department During the COVID-19 Pandemic

Abstract

Objective To investigate the distribution and drug resistance of pathogens among hospitalized patients in the respiratory unit during the COVID-19 pandemic, analyze the risk factors of drug resistance, construct a risk prediction model. Methods This study isolated 791 strains from 489 patients admitted to the Affiliated Hospital of Chengdu University, who were retrospectively enrolled between December 2019 and June 2021. The patients were divided into training and validation sets based on a random number table method (8:2). The baseline information, clinical characteristics, and culture results were collected using an electronic database and WHONET 5.6 software and compared between the two groups. A risk prediction model for drug-resistant bacteria was constructed using multi-factor logistic regression. Results K. pneumoniae (24.78%), P. aeruginosa (17.19%), A. baumannii (10.37%), and E. coli (10.37%) were the most abundant bacterial isolates. 174 isolates of drug-resistant bacteria were collected, ie, Carbapenem-resistant organism-strains, ESBL-producing strains, methicillin-resistant S. aureus , multi-drug resistance constituting 38.51%, 50.57%, 6.32%, 4.60%, respectively. The nosocomial infection prediction model of drug-resistant bacteria was developed based on the combined use of antimicrobials, pharmacological immunosuppression, PCT>0.5 ng/mL, CKD stage 4–5, indwelling catheter, and age > 60 years. The AUC under the ROC curve of the training and validation sets were 0.768 (95% CI: 0.624–0.817) and 0.753 (95% CI: 0.657–0.785), respectively. Our model revealed an acceptable prediction demonstrated by a non-significant Hosmer-Lemeshow test (training set, p=0.54; validation set, p=0.88). Conclusion K. pneumoniae, P. aeruginosa, A. baumannii , and E. coli were the most abundant bacterial isolates. Antimicrobial resistance among the common isolates was high for most routinely used antimicrobials and carbapenems. COVID-19 did not increase the drug resistance pressure of the main strains. The risk prediction model of drug-resistant bacterial infection is expected to improve the prevention and control of antibacterial-resistant bacterial infection in hospital settings.

Frequency of Multidrug-Resistant Microorganisms Associated with Bacterial Coinfections in Hospitalized Patient with Covid-19: A Literature Review

“…In line with these findings, Obeidat et al [22] reported, in a study involving the distribution of reported microorganisms, that gram-negative and gram-positive bacteria were isolated in 72% and 8.5% of cases, respectively. This finding differed from a Chinese study, where gram-negative bacteria accounted for 87.5%, and gram-positive isolates were identified in 12.0% of cases [40].…”

The impact of COVID-19 pandemic on healthcare associated infections: A teaching hospital experience

“…This finding was different from a Chinese study, where gram-negative bacteria accounted for 87.5% and 12.5% for gram-positive isolates. [24] This study showed that gram-negative bacterial infections had the highest drug resistant rate, where A baumannii was the common isolated microorganism, followed by E coli, K pneumonia, and P aeruginosa. Furthermore, these isolates were associated with the hospital's ICUs and non-COVID-19 admitted patients.…”

“…In addition, neither XDR, nor PDR were reported in contrast to the findings of this study. [24] The resistance rates to tigecycline, amikacin and tobramycin were 22%, 46%, and 49%, respectively. These antibiotics have a lower resistance rate compared with the other antibiotics.…”

“…This finding was different from a Chinese study, where gram-negative bacteria accounted for 87.5% and 12.5% for gram-positive isolates. [ 24 ]…”

Establishment and Validation of Models for the Risk of Multi-Drug Resistant Bacteria Infection and Prognosis in Elderly Patients with Pulmonary Infection: A Multicenter Retrospective Study

“…Nomogram is effective complementary tool to help clinicians make clinical decisions, and there are many models on drug-resistant bacteria and lung infections worldwide, such as Hui Zhang et al 32 built a model to predict the risk of death in patients with Acinetobacter baumannii infection; Wei et al 5 constructed a risk prediction model for drug-resistant bacterial infections in respiratory inpatients during the COVID-19 epidemic, and Gonzalez et al 16 constructed and validated a model for predicting the risk of multidrug-resistant microbial infections in the emergency medicine population in the Spanish region. It’s no doubt that these predictive models bring great benefits to medicine, but our model has some significant advantages over these models.…”