在外科领域,预防性使用抗生素占有相当重要的地位,这主要是为了预防外科手术部位感染，包括外科手术后的切口感染和手术部位的深部腔隙或器官感染，如胸腔感染、腹腔感染、脓肿形成等。此类感染约占医院全部感染的14%～16%，占外科患者感染的38%［1］，其重要性不容忽视。
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.
ObjectiveTo analyze the relevant factors for surgical site infection. MethodsA total of 677 cases of surgery in one hospital from July 1 to December 31 in 2012 were surveyed (not including implant and cardiac intervention surgeries), which were divided into different groups according to the preoperative incision contamination level, and the postoperative healing of incisions were observed closely. After the patients were discharged, we investigated the situation of incisions by phone or periodic review, and forms were filled in on schedule. ResultsBy follow-up evaluation of the 677 cases, the incisions in 12 cases were infected and the infection rate was 1.77%. Polluted and infected (14.28%, 30.76%) incisions caused more infection than the clean and clean-polluted incisions (0.00%, 0.59%). The patients who stayed in hospital for 4 or more than 4 days before surgeries (infection rate was 4.55%) took more risk of infection than the patients whose preoperative time in hospital were 2-3 days (infection rate was 0.60%) and 1 or shorter than 1 day (0.68%). Perioperative use of antibiotics for longer than 72 hours will increase the risk of incision infection than those within 48 hours (7.69%, 0.00%; P=0.002). ConclusionSurgical site infection is related to the incision type. Shortening the preoperative in-hospital time will reduce the risk of infection. Long time use of antibiotics in perioperative period cannot prevent the postoperative infection effectively, but may increase the risk of infection.
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 infection (SSI) is a common hospital acquired infection that can increase medical burden and affect patient prognosis. Its occurrence involves multiple factors such as the patient’s basic condition and perioperative management quality. Although there is a basic consensus on SSI prevention in domestic and foreign guidelines, there are still differences between the recommendations in the guidelines and infection prevention and control management. To further promote the implementation of the guidelines, this article reviews the key preventive measures for SSI in domestic and foreign guidelines from preoperative skin preparation, intraoperative standardized operation, and postoperative incision management, and explores in depth the management strategies of SSI, in order to provide a reference for building a full process infection prevention and control system for SSI.
Objective To explore the application methods and values of using health failure mode and effect analysis (HFMEA) to prevent surgical site infection (SSI) in patients undergoing bone fracture and craniotomy surgery with class Ⅰ incision. Methods Patients undergoing bone fracture and craniotomy surgery with class Ⅰ incision at the Chengdu Pidu District People’s Hospital between January 2020 to December 2021 were selected. Based on whether receiving HFMEA-based risk management or not, the patients were divided into conventional group and intervention group. The compliance rates with infection control measures, changes in risk priority numbers (RPN) at various stages (1 month and 10 months after intervention) of HFMEA implementation, and the incidence of SSI between the conventional group and the intervention group were compared. Results A total of 884 surgeries were included. Among them, there were 399 cases in the conventional group and 485 cases in the intervention group; 16 cases SSI occurred. A total of 7 SSI prevention and control measures had been formulated. Except for proper surgical attire (P＞0.05), there were statistically significant differences in the compliance rate of the other prevention and control measures between the two groups of patients (P＜0.05). In the intervention group, the RPN values of pre-operative, intra-operative, and post-operative risk factors at the 10th month after intervention were all lower than those at the 1st month after intervention (P<0.05). Except for the incidence of SSI during craniotomy surgery (6.1% vs. 1.8%, P=0.375), there were statistically significant differences in the total SSI incidence (3.3% vs. 0.6%) and bone fracture surgery SSI incidence (2.7% vs. 0.5%) between the conventional group and the intervention group (P>0.05). Conclusion Applying HFMEA-based risk management techniques to prospectively identify, assess, analyze, manage and track the risk of SSI in bone fracture and craniotomy surgery with class Ⅰ incision can effectively enhance the adherence of preventive measures and reduce the incidence rate of SSI.
目的探讨碘伏原液浸泡对于预防阑尾Ⅲ类手术切口手术部位感染（SSI）的疗效。 方法回顾性分析笔者所在医院2012年5月至2013年5月期间施行阑尾切除术者中切口类型为Ⅲ类的92例患者的临床资料，比较以碘伏原液浸泡切口（浸泡组）和冲洗切口（常规组）处理后患者的切口愈合情况。 结果术后常规组57例患者中，有43例切口愈合等级为甲级，5例为乙级，9例为丙级，SSI发生率为24.56%（14/57）。浸泡组35例患者的切口愈合均良好，均为甲级愈合，SSI发生率为0，低于常规组（P<0.05）。 结论采用碘伏原液浸泡切口5 min能有效预防阑尾炎Ⅲ类切口SSI的发生，值得临床推广应用。
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.
ObjectiveTo evaluate existing predictive models for surgical site infection (SSI) following colorectal cancer (CRC) surgery, aiming to provide a scientific basis for refining risk prediction models and developing clinically practical and widely applicable screening tools. MethodA comprehensive review of existing literature on predictive models for SSI following CRC surgery, both domestically and internationally, were conducted. ResultsThe determination of SSI following CRC surgery primarily relied on the Centers for Disease Control and Prevention standard of USA, which presented issues of consistency and accuracy. Various predictive models had been developed, including traditional statistical models and machine learning models, with 0.991 of an area under the operating characteristic curve of predictive model. However, most studies were based on retrospective and single-center data, which limited their applicability and accuracy. ConclusionsAlthough existing models provide strong support for predicting SSI following CRC surgery, there is a need for multi-center, prospective studies to enhance the generalizability and accuracy of these models. Additionally, future research should focus on improving model interpretability to better apply them in clinical practice, providing personalized risk assessments and intervention strategies for patients.