Predictive model and risk engine web application for surgical site infection risk in perioperative patients with type 2 diabetes

Masaya Koshizaka; Ryoichi Ishibashi; Yukari Maeda; Takahiro Ishikawa; Yoshiro Maezawa; Minoru Takemoto; Koutaro Yokote

doi:10.1007/s13340-022-00587-w

Predictive model and risk engine web application for surgical site infection risk in perioperative patients with type 2 diabetes

Diabetol Int. 2022 May 19;13(4):657-664. doi: 10.1007/s13340-022-00587-w. eCollection 2022 Oct.

Authors

Masaya Koshizaka^{1

2}, Ryoichi Ishibashi^{2

3}, Yukari Maeda^{1

2}, Takahiro Ishikawa^{1

2}, Yoshiro Maezawa^{1

2}, Minoru Takemoto^{2

4}, Koutaro Yokote^{1

2}

Affiliations

¹ Department of Medicine, Division of Diabetes, Metabolism, and Endocrinology, Chiba University Hospital, Chiba, Japan.
² Division of Diabetes, Metabolism and Endocrinology, Department of Endocrinology, Hematology, and Gerontology, Chiba University Hospital, Chiba University Graduate School of Medicine, 1-8-1 Inohana, Chuo-ku, Chiba, 260-8670 Japan.
³ Department of Medicine, Division of Diabetes, Endocrinology and Metabolism, Kimitsu Chuo Hospital, Kisarazu, Chiba Japan.
⁴ Department of Medicine, Division of Diabetes, Metabolism and Endocrinology, International University of Health and Welfare, Chiba, Japan.

Abstract

Aim: To identify predictive factors for surgical site infection (SSI) in patients with type 2 diabetes and develop a prediction tool.

Materials and methods: We 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.

Results: Compared 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/.

Conclusions: The predictive model developed in this study could screen high-risk patients. It may be useful to prevent SSI in such patients.

Supplementary information: The online version contains supplementary material available at 10.1007/s13340-022-00587-w.

Keywords: Predictive model; Risk engine web application; Surgical site infection; Type 2 diabetes.