Objective To investigate the relationship between antibiotic use and antimicrobial resistance in Acinetobacter bau-mannii for rational use of antibiotics.Methods Antibiotic use density (AUD)of common antibiotics in hospitalized patients were collected in a tertiary hospital between 2006 and 2010.Clinical isolates of A.baumannii from those patients were collect-ed.The resistance to common antimicrobial agents were tested by disk diffusion method according to Clinical and Laboratory Standards Institute (CLSI,2012)guidelines.Relationship between antibiotic use and antimicrobial resistance in A.baumannii was analysed by SPSS 16.0.Results The resistant rates of A.baumannii isolated from inpatients were high.Consumption of cephalosporins and quinolones were large.There was a positive correlation between the resistant rate of A.baumannii to imi-penem and AUD of carbapenems (r=0.975,P <0.05).The resistant rate of A.baumannii to meropenem showed significantly positive relation to AUD of carbapenems (r= 0.975,P <0.05).Resistant rates of aminoglycosides,quinolones,cephalospo-rins and beta-lactamase inhibitors was not correlated to AUD of those antibiotics.Conclusions We should pay more attention to the high prevalence of resistant A.baumannnii strains.Application of imipenem and meropenem should be strictly controlled.Amikacin and beta-lactamase inhibitors are better choice for empirical antibiotic therapy in the treatment of infections caused by A. baumannii.

Objective To investigate antibiotic resistance and molecular epidemiology profile of methicillin-resistant Staphylococcus aureus (MRSA) in Shanghai. Methods The antibiograms of 140 MRSA isolates from 5 hospitals for 13 drugs were analyzed by agar dilution and broth dilution. The PVL gene and SCCmec were detected by PCR; The clonal relatedness of 140 isolates were determined by PFGE and 39 strains were chosen to be characterized further by spa typing. Results All 140 MRSA are PVL negative and most of them were identified as SCCmec Ⅲ [45.7% (64/140)], followed by SCCmec Ⅲ a [25.0% (35/140)], SCCmecⅢb [14.3% (20/140)], SCCmecⅡ [10.7% (15/140)] and SCCmecⅣ [4.3% (6/140)]. All isolates were susceptible to vancomycin, teicoplanin and daptomycin. The resistance to gentamicin, sulphamethoxazole and clindamycin was 98. 6% (138/140), 98. 6% (137/140) and 97. 9% (137/140), respectively. Resistance to erythromycin, ciprofloxacin and tetracycline was above 80%, and resistance to rifampicin was 10. 7% (15/140). Sixteen different PFGE patterns(A-P) were found and most of MRSA belonged to group C[30. 7% (43/140)] ,B[13.6% (19/140)]and Ⅰ [10. 7% (15/140)]. Among 39 strains with prevalent PFGE patterns, 4 spa genotypes were identified: t002133. 33% (13/39)] ,t030 [12. 82% (5/39)] ,t037[51.28% (20/39)]and t459[2. 57% (1/39)]. Conclusions Sixteen different PFGE patterns and 4 spa genotypos were found from 5 hospitals in Shanghai. The most popular MRSA clone is PVL negative, SCCmec Ⅲ, with resistant profile of erythromycin, ciprofloxacin,clindamycin,etracycline, gentamicin,and sulphamethoxazole [E-C-L-T-G-M-]. This result suggests that hospital infection control and reasonable antibiotic usage are critical.

OBJECTIVE To investigate antimicrobial resistance and molecular epidemiology profiles of meticillin-resistant Staphylococcus aureus(MRSA)sampled from lower respiratory tract.METHODS Totally 107 MRSA strains were isolated from lower respiratory tract specimens at Shanghai Pulmonary Hospital between Dec 2005 and Dec 2006.PVL genes were detected by PCR.The genotypes of SCCmec were identified by multiplex PCR.The antimicrobial resistance of MRSA were tested by Kirby-Bauer agar dilution.We also performed the homology of 32 MRSA strains using pulsed-field gel electrophoresis(PFGE).RESULTS All of the 107 MRSA strains were negative in the PVL locus detection and the most frequent SCCmec types were type Ⅲ(81.3%),the others including type Ⅱ(15.9%),type Ⅳ(2.8%),type Ⅰ and type Ⅴ were not found in this group.Those 3 different types of SCCmec were all resistant to ?-lactam antibiotics,less resistant to rifampin,and susceptible to vancomycin,teicoplanin and daptomycin.The resistant rate of those 3 types were different to the non-?-lactam antimicrobial drugs such as trimethoprim/sulfamethoxazole,clindamycin,erythromycin,gentamicin,levofloxacin,and tetracycline,the resistant rate in the types Ⅱ and Ⅲ was significantly higher than the type Ⅳ.PFGE analyses assorted the 32 MRSA strains into 4 PFGE patterns:pulsotype A(25 strains),including subtypes A1(17strains),A2(1 strain)and A3(7 strains);pulsotype B(5 strains),pulsotype C(1 strain),and pulsotype D(1 strain).CONCLUSIONS This study does not found positive PVL locus in the MRSA strains in our hospital,the most frequent SCCmec types are type Ⅲ and some are type Ⅱ.PFGE presented that there are outbreaks of MRSA in ICU ward and TB ward No 5 at that time and the pandemic strains are subtypes A1 and A3,most of these MRSA strains are multiple resistant,which deserves attention from both the clinical staff and infection-control department of the hospital.

Objective:To develop nursing quality evaluation indicator system for perioperative patients with Da Vinci robotic under enhanced recovery after surgery(ERAS), in order to provide references for clinical evaluation of nursing quality for lobectomy patients.Methods:Based on the theory of structure-process-outcome quality model and ERAS, literature review, semi-structured interviews were adopted to determine nursing quality indicator system for perioperative patients. Delphi method was used in two rounds of consultation among 21 experts.Results:The positive coefficient was 100%, authority coefficient was 0.839,0.901, and the coefficients of concordance were 0.317,0.335. The final indicator system consisted of 3 first-level indicators, 14 second-level indicators, and 52 third-level indicators.Conclusion:The nursing quality evaluation indicator system for lobectomy patients with Da Vinci robotic was scientific and reliable, which can provide scientific references for evaluating nursing quality for perioperative patients with Da Vinci robotic under ERAS.

Objective:To construct nursing-sensitive indicators system of perioperative pulmonary rehabilitation for patients in thoracicsurgery, so as to provide scientific monitoring standards for nursing quality of pulmonary rehabilitation.Methods:Based on the theory of enhanced recovery after surgery (ERAS) , evidence synthesis was conducted based on Johns Hopkins evidence-based nursing method for laying the foundation for nursing-sensitive quality indicators system of perioperative pulmonary rehabilitation.Based on the analysis results of the quality of nursing in the past two years, and though group discussion, appropriate indicators feasible to application were determined, and nursing-sensitive quality indicators, calculation formulas were preliminarily decided. Through two rounds of experts consultation, the nursing-sensitive quality indicators system of pulmonary rehabilitation was improved and established.Results:After two rounds of expert consultations, the constructed nursing-sensitive quality indicators system of pulmonary rehabilitation included 3 first-class indicators, 6 second-class indicators, and 31 third-class indicators. Positive coefficients of two rounds of expert consultation were both 100%, authority coefficients were 0.827 and 0.861, respectively, and the coordination coefficients were 0.309 and 0.372.Conclusion:The nursing-sensitive quality indicators system was scientific and practical and it was beneficial to regulate the behavior of nurses and improve the nursing quality of pulmonary rehabilitation.

Objective:To explore the independent risk factors of poor healing of surgical incisions in patients with drainage tube removal after thoracic and abdominal surgery and establish a risk prediction model for poor healing of surgical incisions.Methods:Using the convenient sampling method, a total of 545 patients who underwent thoracic and abdominal surgery in the Affiliated Hospital of Qingdao University were selected from July to December 2020. The patients were divided into the poor wound healing group ( n=87) and the non-incision poor healing group ( n=458) according to whether they had poor wound healing. Logistic regression analysis was used to analyze the risk factors of poor healing of surgical incisions and build a risk prediction model. The receiver operating characteristic (ROC) area under the curve was used to test the model to predict the effect and 230 patients were selected to verify the model prediction effect. Results:In this study, 5 factors including duration of exudation, serum albumin, incision infection, the volume of exudation during catheterization and catheterization time were finally included to construct a risk prediction model. The model formula was Z=4.608+4.855× duration of exudation +3.173× serum albumin +3.739× infection of the incision +2.271×the volume of exudation during catheterization + 0.466× catheterization time. The area under ROC curve of this model was 0.773 (95% CI: 0.678 - 0.868). The maximum value of Youden index was 0.549, the sensitivity was 0.742 and the specificity was 0.807. Conclusions:The risk prediction model of poor incision healing after drainage tube removal for patients undergoing thoracic and abdominal surgery can better predict the risk of poor incision healing and provide a basis for clinical medical staff to take preventive management measures for high-risk patients in time.