Faculty Opinions recommendation of Clinical prediction tool to identify patients with Pseudomonas aeruginosa respiratory tract infections at greatest risk for multidrug resistance.

Author(s):  
Gary Lum
2006 ◽  
Vol 51 (2) ◽  
pp. 417-422 ◽  
Author(s):  
Thomas P. Lodise ◽  
Christopher D. Miller ◽  
Jeffrey Graves ◽  
Jon P. Furuno ◽  
Jessina C. McGregor ◽  
...  

ABSTRACT Despite the increasing prevalence of multiple-drug-resistant (MDR) Pseudomonas aeruginosa, the factors predictive of MDR have not been extensively explored. We sought to examine factors predictive of MDR among patients with P. aeruginosa respiratory tract infections and to develop a tool to estimate the probability of MDR among such high-risk patients. This was a single-site, case-control study of patients with P. aeruginosa respiratory tract infections. Multiple-drug resistance was defined as resistance to four or more antipseudomonal antimicrobial classes. Clinical data on demographics, antibiotic history, and microbiology were collected. Classification and regression tree analysis (CART) was used to identify the duration of antibiotic exposure associated with MDR P. aeruginosa. Log-binomial regression was used to model the probability of MDR P. aeruginosa. Among 351 P. aeruginosa-infected patients, the proportion of MDR P. aeruginosa was 35%. A significant relationship between prior antibiotic exposure and MDR P. aeruginosa was found for all of the antipseudomonal antibiotics studied, but the duration of prior exposure associated with MDR varied between antibiotic classes; the shortest prior exposure duration was observed for carbapenems and fluoroquinolones, and the longest duration was noted for cefepime and piperacillin-tazobactam. Within the final model, the predicted MDR P. aeruginosa likelihood was most dependent upon length of hospital stay, prior culture sample collection, and number of CART-derived prior antibiotic exposures. A history of a prolonged hospital stay and exposure to antipseudomonal antibiotics predicts multidrug resistance among patients with P. aeruginosa respiratory tract infections at our institution. Identifying these risk factors enabled us to develop a prediction tool to assess the risk of resistance and thus guide empirical antibiotic therapy.


2021 ◽  
Vol 10 (35) ◽  
pp. 2964-2968
Author(s):  
Swetha Thirumurthi ◽  
Priya Kanagamuthu ◽  
Rajasekaran Srinivasan ◽  
Bhalaji Dhanasekaran

BACKGROUND The term tracheostomy refers to forming an opening in the trachea.1,2 Its advantages include easy and direct access to lower respiratory tract, reduced risk of aspiration, faster weaning from ventilation support and improved physical and psychological comfort. But a common problem in tracheostomised patients is increased risk of colonisation of lower respiratory tract by exogenous bacteria because of direct exposure.1,3 This study was done to recognise pathogens in tracheal secretions collected from tracheostomised patients and their antibiotic sensitivity to treat them with appropriate antibiotics. METHODS This prospective study was done in 138 tracheostomised patients from October 2020 to March 2021 in intensive care unit (ICU) of Chettinad Hospital and Research Institute. Under sterile aseptic precautions, Day 0 and Day 7 cultures posttracheostomy was obtained and their antibiotic sensitivity was studied. Data was analysed using Statistical Package for Social Sciences (SPSS version 19) and presented in proportion, mean and standard deviation (Descriptive statistics). RESULTS In this study, of the 56 cases who had growth in their culture and sensitivity reports on day 0, the most common organism was Pseudomonas aeruginosa (33.9 %) sensitive to imipenem (94.7 %) followed by klebsiella (25 %) sensitive to teicoplanin, vancomycin, amikacin, cefoperazone/tazobactam, linezolid and piperacillin/tazobactam. On day 7, the growth of organisms isolated in tracheal culture got reduced from 56 cases to 16 cases. The prevalence of Pseudomonas reduced to 18.8 % in day 7 whereas Klebsiella pneumonia and Acinetobacter remained almost same from day 0 to day 7. CONCLUSIONS This study concludes the predominant pathogen as Pseudomonas aeruginosa with sensitivity to imipenem followed by Klebsiella with sensitivity to teicoplanin, vancomycin, amikacin, cefoperazone/tazobactam, linezolid and piperacillin/tazobactam on day 0 with reduction in the number of organisms on day 7 due to the fact that all our patients were admitted in ICU several days prior to tracheostomy and were started on antibiotics soon after admission as per choice of the treating physician. Hence, a clear understanding of bacterial colonisation post tracheostomy and its change in course is essential for timely intervention with empirical antibiotics for reducing the incidence of lower respiratory tract infections after tracheostomy in future. KEY WORDS Tracheostomy, Lower Respiratory Tract Infections, Pseudomonas Aeruginosa, Empirical Antibiotics.


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