インプラント周囲炎予測モデルの構築：ロジスティック回帰と影響因子分析
1Department of Stomatology, The Affiliated Suzhou Hospital of Nanjing University Medical School. Suzhou 215100, China.
Purpose:
To explore the influencing factors of peri-implantitis in patients with dentition defects after oral implant surgery, and to construct and validate a personalized prediction model.
Methods:
Patients who underwent oral implant surgery in the Affiliated Suzhou Hospital of Nanjing University Medical School from September 2021 to March 2025 were included retrospectively. Through 1∶1 propensity score matching, 100 cases in the infected group (diagnosed with peri-implantitis) and 100 cases in the non-infected group were finally included. The baseline data of patients and serum interleukin-17A (IL-17A) levels were collected. Binary logistic regression was used to analyze the influencing factors. A nomogram prediction model was constructed based on the screening results, and the prediction performance was evaluated by Bootstrap validation, receiver operating characteristic(ROC) curve and decision tree model.
Results:
The proportions of diabetes, smoking history, chronic periodontitis history, poor alveolar bone around implants, and IL-17A levels in the infected group were significantly higher than those in the non-infected group(P＜0.05). Logistic regression showed that diabetes, smoking history, chronic periodontitis history, poor alveolar bone around implants, and elevated IL-17A were independent risk factors for peri-implantitis(OR＞1, P＜0.05). The C-index of the nomogram model was 0.905, the area under the ROC curve(AUC) was 0.905 (95%CI: 0.865-0.946, P＜0.001), and the optimal cut-off value was 48.80 points (specificity was 0.880, sensitivity was 0.820, Youden index was 0.700). The decision tree model showed that IL-17A was the primary predictor; when IL-17A ＞ 14.380 ng/L, the incidence of peri-implantitis reached 87.50%.
Conclusions:
Diabetes, smoking history, chronic periodontitis history, alveolar bone condition around implants and IL-17A level are key influencing factors of peri-implantitis. The constructed nomogram model has excellent prediction performance and can be used for preoperative individualized risk assessment.
