Shanghai Journal of Stomatology ›› 2021, Vol. 30 ›› Issue (5): 517-521.doi: 10.19439/j.sjos.2021.05.013

• Original Articles • Previous Articles     Next Articles

Establishment of a nomogram model to predict the risk of peri-implantitis in patients with diabetes mellitus

ZHANG Rui, MA Jun-tao   

  1. Department of Stomatology, First Affiliated Hospital of Dalian Medical University. Dalian 116000, Liaoning Province, China
  • Received:2020-07-09 Revised:2020-11-20 Online:2021-10-25 Published:2021-11-08

Abstract: PURPOSE: To explore the risk factors of peri-implantitis after dental implants in diabetic patients, and establish a related nomogram prediction model. METHODS: The clinical data of diabetic patients undergoing dental implant restoration from January 2016 to December 2018 were retrospectively analyzed, and cluster random sampling method was used to divide the data into training set (n=153) and validation set (n=104). Univariate and Logistic regression were used to analyze the independent risk factors of peri-implantitis in diabetic patients. At the same time, the relevant nomogram prediction model was established, and the model was internally verified by Bootstrap method. The external verification was completed by self sampling method of verification set. SPSS 22.0 software package was used for statistical analysis. RESULTS: Smoking index>200 cigarettes(OR=6.364, 95%CI: 1.943-20.840), HbA1c>7%(OR=4.680, 95%CI: 1.497-14.628), periodontal history (OR=3.779, 95%CI: 1.359-10.507), anterior teeth implantation(OR=7.183, 95%CI: 2.371-21.756), tooth brushing frequency ≤1 time/day (OR=4.796, 95%CI: 1.471-15.637) and unscheduled cleaning (OR=4.994, 95%CI: 1.745-14.295) were independent risk factors for peri-implantitis after dental implantation in diabetic patients (P<0.05). Based on the above 6 risk factors, a nomogram model to predict the occurrence of peri-implantitis in diabetic patients was established. The calibration curve verification showed that the predicted values of the training set and the verification set were basically the same as the actual measured values, and ROC curve verification showed C-index indexes of the training set and the verification set were 0.867 and 0.822, respectively, indicating that the nomogram model had good prediction accuracy. CONCLUSIONS: Smoking index>200 cigarettes, HbA1c>7%, periodontal history, anterior dental implantation, brushing frequency ≤1 time/day and unscheduled cleaning are independent risk factors for peri-implantitis in diabetic patients based on the above risk factors. The line graph model can intuitively and accurately predict the probability of peri-implantitis in diabetic patients.

Key words: Diabetes, Dental implant, Peri-implantitis, Nomogram model

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