![]() ![]() The Society for hospital Epidemiology of America The Association for Practitioners in Infection Control The Centers for Disease Control The Surgical Infection Society. 2010 33:S11–61.Ĭonsensus paper on the surveillance of surgical wound infections. Standards of medical care in diabetes-2010. 2012 7: e45616.Īmerican Diabetes Association. The role of pre-operative and post-operative glucose control in surgical-site infections and mortality. Preoperative hypoglycemia and hyperglycemia are related to postoperative infection rates in implant-based breast reconstruction. Proinflammatory effects of hypoglycemia in humans with or without diabetes. Ratter JM, Rooijackers HM, Tack CJ, et al. The effect of short-term hyperglycemia on the innate immune system. The association of diabetes and glucose control with surgical-site infections among cardiothoracic surgery patients. Latham R, Lancaster AD, Covington JF, et al. ![]() The association of preoperative haemoglobin A1c with 30-day postoperative surgical site infection following non-cardiac surgery. Hyperglycemia is associated with surgical site infections among general and vascular surgery patients. Early identification of individuals with poorly controlled diabetes undergoing elective surgery: Improving A1C testing in the preoperative period. Multivariate analysis of incision infection after posterior lumbar surgery in diabetic patients: a single-center retrospective analysis. Increased postoperative glucose variability is associated with adverse outcomes following total joint arthroplasty. Shohat N, Restrepo C, Allierezaie A, et al. CDC/NHSN surveillance definition of health care-associated infection and criteria for specific types of infections in the acute care setting. Predicting macro- and microvascular complications in type 2 diabetes: the Japan diabetes complications study/the japanese elderly diabetes intervention trial risk engine. The UKPDS risk engine: a model for the risk of coronary heart disease in Type II diabetes (UKPDS 56). United Kingdom Prospective Diabetes Study (UKPDS) Group. Intensive versus conventional glucose control in critically ill patients. NICE-SUGAR Study Investigators, Finfer S, Chittock DR et al. The impact of surgical site infection on hospitalisation, treatment costs, and health-related quality of life after vascular surgery. It may be useful to prevent SSI in such patients. The predictive model developed in this study could screen high-risk patients. The risk engine prototype for SSI prediction can be accessed at. The predictive model had high prediction accuracy (AUC of 0.801). Logistic regression analysis revealed preoperative blood glucose fluctuation and operation time as the most reliable predictive factors. ResultsĬompared with patients without SSI ( n = 70), those with SSI ( n = 35) had significantly higher fasting blood glucose levels at referral (169.1 ± 61.8 mg/dL vs. Based on the predictive model, we developed a risk engine for SSI prediction. The area under the receiver operating characteristic curve (AUC) was evaluated. Principal component analysis and logistic regression analysis were performed to prepare SSI predictive model using the identified predictive factors. The primary outcome was SSI onset within 30 postoperative days moreover, predictive factors were identified using univariate analysis. We retrospectively analyzed the perioperative blood glucose management of 105 patients with type 2 diabetes treated from 2016 to 2018 at Chiba University Hospital. To identify predictive factors for surgical site infection (SSI) in patients with type 2 diabetes and develop a prediction tool.
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