Objective To analyze the trend of standardized infection ratio (SIR) of surgical site infection (SSI) in small bowel surgery, objectively evaluate the effect of infection control, and provide evidence-based strategies for SSI prevention. Methods According to Centers for Disease Control and Prevention (CDC) / National Healthcare Safety Network (NHSN) surveillance definitions for specific types of infections and the monitoring methods of SSI events published by NHSN, the SSI and related risk factors of adult inpatients undergoing small bowel surgery in Yichang Central People’s Hospital between January 1, 2016 and December 31, 2022 were prospectively monitored. The inpatients undergoing small bowel surgery that meets the definition of International Classification of Diseases, 10th Revision Clinical Modifications/Procedure Coding System (ICD-10-CM/PCS), a multivariate binary logistic regression model was used to calculate the predicted infections in each year, the model included the risk factors for small bowel surgery in NHSN Complex Admission/Readmission (A/R) SSI Model with 7 years of surveillance data as the baseline. The SIR was calculated by dividing the number of observed SSI by the number of predicted SSI in each year. The Mid-P method was used to test the difference of SIR compared to the previous year, and the linear regression model was used to analyze the trend of SIR. Results A total of 2 436 patients were included, with 48 cases of deep incision infection and 49 cases of organ/cavity infection, and the overall incidence rate of infection was 4.0%. From 2016 to 2022, there were 151, 244, 222, 260, 320, 408, and 831 patients who underwent small bowel surgery, respectively. The Mid-P test showed that there was a significant difference in SIR from 2016 to 2019 (P<0.05), and there was an increase in 2018 compared with 2017. There was no significant difference in SIR compared to the previous year from 2019 to 2022 (P>0.05), and there was no significant difference in the trend of SIR of SSI (P=0.065). Conclusions From January 1, 2017, to December 31, 2022, advances have been made in SSI control practices of small bowel surgery in six consecutive years, except for 2018, but there was no annual downward trend from 2020 to 2022. The use of SIR provides a new approach for evaluating the quality of infection control.
Objective To study the influence factors of surgical site infection (SSI) after hepatobiliary and pancreatic surgery. Methods Fifty patients suffered from SSI after hepatobiliary and pancreatic surgery who treated in Feng,nan District Hospital of Tangshan City from April 2010 and April 2015 were retrospectively collected as observation group, and 102 patients who didn’t suffered from SSI after hepatobiliary and pancreatic surgery at the same time period were retrospectively collected as control group. Then logistic regression was performed to explore the influence factors of SSI. Results Results of univariate analysis showed that, the ratios of patients older than 60 years, combined with cardiovascular and cerebrovascular diseases, had abdominal surgery history, had smoking history, suffered from the increased level of preoperative blood glucose , suffered from preoperative infection, operative time was longer than 180 minutes, American Societyof Anesthesiologists (ASA) score were 3-5, indwelled drainage tube, without dressing changes within 48 hours after surgery, and new injury severity score (NISS) were 2-3 were higher in observation group (P＜0.05). Results of logistic regression analysis showed that, patients had history of abdominal surgery (OR=1.92), without dressing changes within 48 hours after surgery (OR=2.07), and NISS were 2-3 (OR=2.27) had higher incidence of SSI (P＜0.05). Conclusion We should pay more attention on the patient with abdominal surgery history and with NISS of 2-3, and give dressing changes within 48 hours after surgery, to reduce the incidence of SSI.
It has been certificated that hip and knee arthroplasty can improve quality of life and relieving pain and discomfort for ageing population and patients with muscloskeletal disorders. However, the outcomes of prosthetic joint infections (PJI) after arthroplasty usually are disastrous. The incidence of PJI is lower, but the number of this population is huge, which makes the strong impacts on quality of life for patients and healthcare economics. This review discusses the prevention strategies of PJI based on clinical epidemiology, diagnostic definition, pathogenesis, microbiology and risk factors, combined with some guidelines for prevention surgical site infections published recently.
目的 分析外科手术部位感染率过低的原因，掌握手术部位感染诊断标准，减少医院感染漏报，及时发现医院感染流行趋势，采取控制措施，防止医院感染暴发。 方法 选择开展较多、手术部位一旦发生感染对患者安全威胁性较大的手术：包括胆囊切除或（和）胆管手术，结肠、直肠切除术，阑尾切除术，疝手术，乳房切除术，剖宫产，子宫切除术及附件切除术，全髋关节置换术，食道贲门手术，腰椎间盘摘除术，监测时间为2011年1月1日－6月30日及2012年1月1日－6月30日，共监测1 180例手术，对手术部位感染率进行对比分析。 结果 2011年半年监测手术部位感染率1.99%，调整感染率4.74%；比国内报道低6～9倍；通过分析原因，对医院感染诊断标准再培训、加强病原微生物送检等，2012年半年监测手术部位感染率4.68%，调整感染率32.12%；与2011年比较差异有统计学意义（χ2=141.841，P=0.000）。 结论 手术部位感染率偏低的原因是医生漏报所致；采取整改措施后，提高了手术部位感染的识别能力，减少了漏报，对及时发现医院感染暴发具有重要意义。
ObjectiveTo study the effects of PDCA cycle in the control of surgical site infection (SSI). MethodsA total of 1 761 surgeries between January 2012 and December 2013 were chosen to be monitored. PDCA cycle was used as a tool of total quality management evaluation to enhance the control of SSI. ResultsAfter 2 to 4 cycles of PDCA, the preventive medication rate of ClassⅠ operation incision was decreased significantly (χ2=309.513，P<0.001) and the postoperative incision infection rate did not change significantly (χ2=1.474，P=0.669). ConclusionUsing PDCA cycle can increase SSI management level and quality significantly and total quality management can be operated effectively.
Surgical site infections are the common healthcare-associated infections. This article introduced the guidelines on the prevention and control of surgical site infection in using from background, making progress, and recommendations, to give directions for clinicians and infection prevention and control professionals choosing appropriately for decreasing surgical site infection risks.
Objective To review the adverse event of hysterectomy caused by postoperative infection after cesarean section, formulate prevention and control strategies in combination with risk assessment tools, promote the standardization of perioperative management, reduce the medical burden on pregnant women, and improve patient satisfaction. Methods The two adverse events of hysterectomy caused by postoperative infection after cesarean section that occurred in the obstetrics ward between October and November 2024 were selected as the research objects. A root cause analysis and risk assessment team composed of personnel from multiple departments was established. Through interviews, observations, and data review, the potential failure modes and causes were sorted out. The risk priority number (RPN) was calculated to determine the high-risk factors. Improvement strategies were formulated and implemented. After two-month implementation, the RPN scores and the compliance of various measures before and after the implementation were compared. Results Before the improvement, the total RPN of the healthcare failure mode and effects analysis was 367.8. When rechecked in January 2025, the total RPN after the improvement dropped to 105.7, and no serious adverse events occurred again. The compliance and passing rates of various operations significantly increased: the intervention rate for maternal malnutrition rose from 17.5% to 48.6%, the passing rate of appropriate timing for prophylactic use of antimicrobial agents before surgery increased from 50.5% to 81.0%, the compliance rate of scrubbing the vagina with disinfectant before surgery increased from 15.0% to 60.0%, the implementation rate of standardized skin disinfection during surgery rose from 66.7% to 95.2%, the passing rate of aseptic techniques and hand hygiene operations during surgery increased from 75.0% to 95.2%, and the timely submission rate of specimens from infected patients increased from 29.4% to 47.6%, and all these differences were statistically significant (P<0.05). Conclusion The combination of healthcare failure mode and effect analysis and root cause analysis can effectively improve adverse events during the perioperative period, optimize the perioperative management of cesarean section, and reduce the risk of infection.
ObjectiveTo establish a predictive model of surgical site infection (SSI) following colorectal surgery using machine learning.MethodsMachine learning algorithm was used to analyze and model with the colorectal data set from Duke Infection Control Outreach Network Surveillance Network. The whole data set was divided into two parts, with 80% as the training data set and 20% as the testing data set. In order to improve the training effect, the whole data set was divided into two parts again, with 90% as the training data set and 10% as the testing data set. The predictive result of the model was compared with the actual infected cases, and the sensitivity, specificity, positive predictive value, and negative predictive value of the model were calculated, the area under receiver operating characteristic (ROC) curve was used to evaluate the predictive capacity of the model, odds ratio (OR) was calculated to tested the validity of evaluation with a significance level of 0.05.ResultsThere were 7 285 patients in the whole data set registered from January 15th, 2015 to June 16th, 2016, among whom 234 were SSI cases, with an incidence of SSI of 3.21%. The predictive model was established by random forest algorithm, which was trained by 90% of the whole data set and tested by 10% of that. The sensitivity, specificity, positive predictive value, and negative predictive value of the model were 76.9%, 59.2%, 3.3%, and 99.3%, respectively, and the area under ROC curve was 0.767 [OR=4.84, 95% confidence interval (1.32, 17.74), P=0.02].ConclusionThe predictive model of SSI following colorectal surgery established by random forest algorithm has the potential to realize semi-automatic monitoring of SSIs, but more data training should be needed to improve the predictive capacity of the model before clinical application.