Risk factors and prediction model for inpatient surgical site infection after elective abdominal surgery

BACKGROUND Surgical site infections(SSIs) are the commonest healthcare-associated infection. In addition to increasing mortality, it also lengthens the hospital stay and raises healthcare expenses. SSIs are challenging to predict, with most models having poor predictability. Therefore, we developed a prediction model for SSI after elective abdominal surgery by identifying risk factors.AIM To analyse the data on inpatients undergoing elective abdominal surgery to identify risk factors and develop predictive models that will help clinicians assess patients preoperatively.METHODS We retrospectively analysed the inpatient records of Shaanxi Provincial People’s Hospital from January 1, 2018 to January 1, 2021. We included the demographic data of the patients and their haematological test results in our analysis. The attending physicians provided the Nutritional Risk Screening 2002(NRS 2002)scores. The surgeons and anaesthesiologists manually calculated the National Nosocomial Infections Surveillance(NNIS) scores. Inpatient SSI risk factors were evaluated using univariate analysis and multivariate logistic regression. Nomograms were used in the predictive models. The receiver operating characteristic and area under the curve values were used to measure the specificity and accuracy of the model.RESULTS A total of 3018 patients met the inclusion criteria. The surgical sites included the uterus(42.2%), the liver(27.6%), the gastrointestinal tract(19.1%), the appendix(5.9%), the kidney(3.7%), and the groin area(1.4%). SSI occurred in 5% of the patients(n = 150). The risk factors associated with SSI were as follows: Age;gender;marital status;place of residence;history of diabetes;surgical season;surgical site;NRS 2002 score;preoperative white blood cell, procalcitonin(PCT), albumin, and low-density lipoprotein cholesterol(LDL) levels;preoperative antibiotic use;anaesthesia method;incision grade;NNIS score;intraoperative blood loss;intraoperative drainage tube placement;surgical operation items. Multivariate logistic regression revealed the following independent risk factors: A history of diabetes [odds ratio(OR) = 5.698, 95% confidence interval(CI): 3.305-9.825, P = 0.001], antibiotic use(OR = 14.977, 95%CI: 2.865-78.299, P = 0.001), an NRS 2002 score of ≥ 3(OR = 2.426, 95%CI: 1.199-4.909, P = 0.014), general anaesthesia(OR = 3.334, 95%CI: 1.134-9.806, P = 0.029), an NNIS score of ≥ 2(OR = 2.362, 95%CI: 1.019-5.476, P = 0.045), PCT ≥ 0.05 μg/L(OR = 1.687, 95%CI: 1.056-2.695, P = 0.029), LDL < 3.37 mmol/L(OR = 1.719, 95%CI: 1.039-2.842, P = 0.035), intraoperative blood loss ≥ 200 mL(OR = 29.026, 95%CI: 13.751-61.266, P < 0.001), surgical season(P < 0.05), surgical site(P < 0.05), and incision grade I or Ⅲ(P < 0.05). The overall area under the receiver operating characteristic curve of the predictive model was 0.926, which is significantly higher than the NNIS score(0.662).CONCLUSION The patient’s condition and haematological test indicators form the bases of our prediction model. It is a novel, efficient, and highly accurate predictive model for preventing postoperative SSI, thereby improving the prognosis in patients undergoing abdominal surgery.

尺度解析模拟在液力偶合器、液力缓速器和液力变矩器中的应用（英文）

目的:针对流体机械数值模拟过程中雷诺时均应力(RANS)方法占据主导地位但预测精度较低且缺乏对流场信息准确描述的现状,提出应用尺度解析模拟(SRS)方法来改进性能的预测精度以及加深对流动结构的理解。创新点:1.利用SRS方法,降低RANS湍流模型的选择困难,实现性能精准预测;2.建立全流道网格计算模型,充分展现单流道间瞬时流动信息的差异。方法:1.通过较少的网格划分及周期计算,对具有简单循环圆和平面叶片的液力偶合器进行计算,并与试验数据进行对比,初步筛选出较为适合的湍流模型(图6),进而在模型更为复杂、流动更加多变的液力缓速器和液力变矩器性能预测中进行验证(图15和21);2.通过对复杂的瞬态流动现象的清晰捕捉,深入展示3种液力元件的内部流动机理(图9、10、16、17、22和23),并评估SRS方法相较RANS方法在流动结构描述方面的先进性(图7和8)。结论:1.在液力偶合器、液力缓速器和液力变矩器等液力流体机械的计算流体动力学(CFD)模拟中,SRS方法可以提高性能预测精度并提供更为细致的流场信息;2. SRS方法中的混合RANS/LES(大涡模拟)模型在液力元件流场计算中的预测准确度、流场结构描述及计算成本等方面表现出色,尤其是BSLSBESDSL模型值得重点关注和发展;3.为了进一步验证SRS方法的实用性,可以在模拟中考虑工作介质物理属性的影响,细化网格并对气液两相流动及边界层流动进行详细计算。