Predictive model and risk engine web application for surgical site infection risk in perioperative patients with type 2 diabetes
Predictive model and risk engine web application for surgical site infection risk in perioperative patients with type 2 diabetes
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2型糖尿病围手术期患者手术部位感染风险的预测模型和风险引擎网络应用
DOI：
10.1007/s13340-022-00587-w
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发表时间：
2022
影响因子：
2.2
通讯作者：
Yokote Koutaro
中科院分区：
文献类型：
--
作者：
Koshizaka Masaya;Ishibashi Ryoichi;Maeda Yukari;Ishikawa Takahiro;Maezawa Yoshiro;Takemoto Minoru;Yokote Koutaro
AimTo identify predictive factors for surgical site infection (SSI) in patients with type 2 diabetes and develop a prediction tool.Materials and methodsWe retrospectively analyzed the perioperative blood glucose management of 105 patients with type 2 diabetes treated from 2016 to 2018 at Chiba University Hospital. The primary outcome was SSI onset within 30 postoperative days; moreover, predictive factors were identified using univariate analysis. Principal component analysis and logistic regression analysis were performed to prepare SSI predictive model using the identified predictive factors. The area under the receiver operating characteristic curve (AUC) was evaluated. Based on the predictive model, we developed a risk engine for SSI prediction.ResultsCompared 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. 140.1 ± 56.6,P= 0.036), preoperative mean blood glucose levels (178.3 ± 48.4 mg/dL vs. 155.2 ± 39.7,P= 0.009), preoperative maximum blood glucose levels (280.4 ± 87.3 mg/dL vs. 230.3 ± 92.4,P= 0.009), preoperative blood glucose fluctuations (54.9 ± 24.1 mg/dL vs. 37.7 ± 23.1,P= 0.001), percentage of hospitalization at referral (54.3% vs. 20.0,P< 0.001); longer operation time (432.5 ± 179.6 min vs. 282.5 ± 178.3,P< 0.001); and greater bleeding volume (972.3 ± 920.1 mg/dL vs. 436.4 ± 795.8,P< 0.001). Logistic regression analysis revealed preoperative blood glucose fluctuation and operation time as the most reliable predictive factors. The predictive model had high prediction accuracy (AUC of 0.801). The risk engine prototype for SSI prediction can be accessed at https://www.dm-ope-riskengine.org/.ConclusionsThe predictive model developed in this study could screen high-risk patients. It may be useful to prevent SSI in such patients.
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糖尿病的医疗标准 - 2010年。
影响因子：
16.2
作者：
American Diabetes Association
通讯作者：
American Diabetes Association
早期识别接受择期手术的糖尿病控制不佳的个体：改进术前的 A1C 测试。
DOI：
10.4158/ep14228.or
发表时间：
2015
期刊：
Endocrine practice : official journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists
影响因子：
--
作者：
Underwood,Patricia;Seiden,Johanna;Carbone,Kyle;Chamarthi,Bindu;Turchin,Alexander;Bader,AngelaM;Garg,Rajesh
通讯作者：
Garg,Rajesh
高血糖与普通手术和血管手术患者的手术部位感染相关
DOI：
10.1177/000313481708301019
发表时间：
2017
期刊：
The American surgeon
影响因子：
--
作者：
Amy Showen;T. Russell;Stephanie Young;Sachin Gupta;M. Gibbons
通讯作者：
M. Gibbons
影响因子：
1.2
作者：
Gabriel, Rodney A.;Hylton, Diana J.;Waterman, Ruth S.
通讯作者：
Waterman, Ruth S.
影响因子：
2
作者：
Cheng H;Chen BP;Soleas IM;Ferko NC;Cameron CG;Hinoul P
通讯作者：
Hinoul P