Objective To investigate the antibiotic resistance and their genetic homology of stenotrophomonas maltophilia isolated from January 2005 to February 2006 at intensive care unit（ICU） of 6 hospitals in Bejing area.Methods The minimal inhibitory concentration（MIC） of 12 antibiotics against 82 strenotrophomonas maltophilia was determined by broth dilution method.PFGE was used to analyze the homology of 82 stenotrophomonas maltophilia.Results The drug sensitivity tests in vitro showed these strains were resistance to commonly-used antibiotics.Antibiotics with sensitive rate over 50% included Doxycycline, gatifloxacin,cefoperazone-sulbactam,levofloxacin,Compound sulfamethoxazole,Ceftazidime and ticarcillin- clavulanate. 7-18 DNA bands of different size were present in the gel and different homology was shown among the 82 strains.Four couples with homology over 85% were isolated from the same ICU.Three strain were same clones in PLA General Hospitals first hospital.2 couples from the different wards had homology of 80.6% and 79.6% of，respictively.Others strains had either poor or no homology.Conclusions No clonal outbreak is certified at ICU of 6 hospitals in Beijing area. There are only vertical dissemination of single clone in 6 ICU wards.PFGE is an effective approach for drug resistance test and epidemic analysis.
Objective To investigate the distribution and antibiotic resistance of pathogens isolated fromlower respiratory tract in mechanically ventilated patients with acute exacerbation of chronic obstructive pulmonary disease ( AECOPD) . Methods The patients with AECOPD, who were hospitalized in RICU from January 2008 to November 2009, were divided into a community infection group and a nosocomial infection group. Lower respiratory tract isolates were collected by bronchoscopic protected specimen brush for bacterial identification and susceptibility test. Results 134 cases were enrolled in the study, with 75 cases in thecommunity infection group and 59 cases in the nosocomial infection group. The positive detection rate in the nosocomial infection group was significantly higher than that in the community infection group [ 81. 4%( 48/59) vs. 54. 7% ( 41/75) ] . In the community infection group, 49 strains were isolated, in which gramnegativebacteria, gram-positive bacteria, and fungi accounted for 55. 1% , 28. 6% , and 16. 3% , respectively.In the nosocomial infection group, 55 strains were isolated, in which gram-negative bacteria, gram-positive bacteria, and fungi accounted for 61. 8% , 21. 8% , and 16. 4%, respectively. There was no significant difference in the microbial distribution between the two groups ( P gt; 0. 05) . The detection rate of ESBLs producing strains in the nosocomial infection group was significantly higher than that in the community infection group ( 58. 8% vs. 37% ) . The resistance rates in the nosocomial groups were higher than those in the community infection group. Conclusions Antibiotic resistance is serious in mechanically ventilated patients with AECOPD, especially in the nosocomial infection patients. The increased fungi infection and drug resistance warrant clinicians to pay more attention to rational use of antibiotics, and take effective control measures.
Objective To investigate the species distribution and antibiotic resistance of pathogens fromcatheter-related bloodstream infections ( CRBSI) in intensive care unit( ICU) , to provide evidence for the guidance of clinical rational administration.Methods A retrospective analysis was performed to review the microbiological and susceptibility test data of all CRBSI patients in ICU from January 2009 to December 2011. The patterns of antibiotic resistance among the top seven bacteria were compared. Results 67 cases of CRBSI were detected with 81 strains, including 40 Gram-positive ( G+ ) bacteria( 49.4% ) , 38 Gram-negative( G- ) bacteria ( 46.9% ) , and 3 fungi ( 3.7% ) . The main pathogens causing CRBSI were coagulase negative Staphylococci ( 27 strains, 33.3%) , Acinetobacter baumannii ( 12 strains, 14.8% ) , Klebsiella pneumoniae( 9 strains, 11. 1% ) , Staphylococcus aureus ( 8 strains, 9. 9% ) , Pseudomonas aeruginosa ( 7 strains, 8. 6% ) , Escherichia coli ( 6 strains, 7.4% ) , suggesting that Staphylococcus epidermidis was predominant pathogenic G+ bacteria, and Acinetobacter baumannii was predominant G- bacteria. The antibiotic resistance tests demonstrated that isolated G- bacillus was highly sensitive to carbopenem, while vancomycin-resistant G+ bacteria were not found. Conclusions Within the latest 3 years, the predominant pathogens of CRBSI in ICU are Staphylococcus epidermidis and Acinetobacter baumannii. Acinetobacter baumannii exhibited high drug resistance to all antibiotics.
Objective To analyze the distribution of pathogens, drug susceptibility and multi-drug resistant bacteria (MDRB) in elderly patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) complicated with pneumonia. Methods The clinical data of patients whose discharge diagnosis included AECOPD with pneumonia or pulmonary infection from January 2012 to December 2015 were retrospectively analyzed. Strain identification and drug sensitivity analysis were performed in the pathogenic bacterias isolated from sputum culture. Results A total of 1 978 patients were enrolled in this study, and pathogenic bacterias were isolated from the sputum of 708 patients, including 485 cases of community-acquired pneumonia (CAP) and 223 cases of hospital-acquired pneumonia (HAP); and 786 strains of pathogens were isolated (501 strains from CAP cases, 285 strains from HAP cases), including 448 strains of Gram-negative (G–) bacilli (57.0%), 117 strains of Gram-positive (G+) cocci (14.9%), and 221 strains of fungi (28.1%). Susceptibility testing results showed that G– bacilli were highly resistant to penicillins, third generation cephalosporins, ciprofloxacin, gentamicin, etc., and G+ cocci were highly resistant to penicillin, clindamycin and erythromycin. There were 238 strains of MDRB, mainly including 69 strains of Acinetobacter baumanii [multiple drug resistance rate (MDRR)=67.6%], 27 strains of Escherichia coli (MDRR=52.9%), 25 strains of Klebsiella pneumoniae (MDRR=34.2%), 33 strains of Pseudomonas aeruginosa (MDRR=33.0%) and 24 strains of Stenotrophomonas maltophilia (MDRR=100.0%). MDRR of Enterococcus genus and methicillin-resistant Staphylococcus aureus was 50.0% and 48.0%, respectively. Conclusions The pathogenic bacterias in elderly AECOPD patients complicated with pneumonia are mainly G– bacterias, and the proportion of fungal infection tends to increase. Bacterial drug resistance is serious and the MDRB tends to increase, especially in patients with HAP. Physicians should early find out the characteristics of local pathogenic bacteria and drug sensitivity, rationally select antibiotics, reduce the occurrence of drug-resistant strains and superinfection when treating the elderly patients with AECOPD complicated with pneumonia.
ObjectiveTo systematically review the health economic evaluation studies in which externalities of antibacterial drug uses were identified.MethodsPubMed, EMbase, The Cochrane Library, CNKI, WanFang Data and VIP databases were electronically searched to collect health economic evaluation studies in which externalities of antibacterial drug uses were identified from inception to December 31st, 2020. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies. Descriptive analysis was then performed.ResultsA total of 14 studies were included. Negative externalities and their impacts on costs and/or effectiveness were examined in 13 literature, and positive externalities in terms of an improvement in disease control were included in only one study. No study was found in which both negative and positive externalities were included. The methods used to quantify negative externalities included: only costs associated with drug resistance per prescription or per unit were calculated; both costs and health impacts associated with the second/third line treatments followed a treatment failure (due to drug resistance) were calculated using a decision tree. In one study in which positive externalities were measured, both health gain and cost reduction from an improvement in disease control (as a benefit of antibacterial drug uses) were calculated by constructing a dynamic model at the population level.ConclusionsWe propose that both the positive and negative externalities should be included in health economic evaluation. This can be achieved by measuring the relevant costs and health impacts in a broader perspective, using a disease-transmission dynamic model. In addition, to achieve an improved health utility measurement, disability-adjusted-life years rather than quality-adjusted-life years should be encouraged for use. Finally, both costs and effectiveness should be discounted.