Predictive model and risk engine web application for surgical site infection risk in perioperative patients with type 2 diabetes
Author:
Funder
Pfizer Health Research Foundation
Publisher
Springer Science and Business Media LLC
Subject
Endocrinology, Diabetes and Metabolism,Internal Medicine
Link
https://link.springer.com/content/pdf/10.1007/s13340-022-00587-w.pdf
Reference21 articles.
1. Totty JP, Moss JWE, Barker E, et al. The impact of surgical site infection on hospitalisation, treatment costs, and health-related quality of life after vascular surgery. Int Wound J. 2021;18:261–8. https://doi.org/10.1111/iwj.13526.
2. NICE-SUGAR Study Investigators, Finfer S, Chittock DR et al. Intensive versus conventional glucose control in critically ill patients. N Engl J Med 2009; 360: 1283–1297. https://doi.org/10.1056/NEJMoa0810625.
3. Stevens RJ, Kothari V, Adler AI et al. United Kingdom Prospective Diabetes Study (UKPDS) Group. The UKPDS risk engine: a model for the risk of coronary heart disease in Type II diabetes (UKPDS 56). Clin Sci (Lond) 2001; 101: 671–679. https://doi.org/10.1042/cs1010671.
4. Tanaka S, Tanaka S, Iimuro S, et al. Predicting macro- and microvascular complications in type 2 diabetes: the Japan diabetes complications study/the japanese elderly diabetes intervention trial risk engine. Diabetes Care. 2013;36:1193–9. https://doi.org/10.2337/dc12-0958.
5. Horan TC, Andrus M, Dudeck MA. CDC/NHSN surveillance definition of health care-associated infection and criteria for specific types of infections in the acute care setting. Am J Infect Control. 2008;36:309–32. https://doi.org/10.1016/j.ajic.2008.03.002.Erratum.In:AmJInfectControl.2008;36:655 (PMID: 18538699).
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