American journal of translational research · 2024
Risk factors and prediction model of peri-implantitis in post operative periodontitis patients.
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Gao Xinyu, Li Dongdong, Duan Yanling, Wu Liling
Аннотация
OBJECTIVES: To analyze the risk factors for peri-implantitis (PI) in patients with periodontitis after dental implantation and to establish a prediction model.
METHODS: A retrospective analysis was conducted using clinical data from 208 patients with periodontitis who required implant restoration due to tooth loss from various causes. These patients, meeting the indications for dental implantation, were treated at the Third People's Hospital of Shenzhen from January 2019 to December 2023. The dataset was divided into training and validation sets in a 7:3 ratio. Logistic regression was used to identify risk factors for PI in these patients. Significant variables from the regression analysis were incorporated into the prediction model. The model's accuracy was evaluated using Receiver Operating Characteristic (ROC) and calibration curves. A decision curve was also drawn to assess the clinical utility of the model. The model's performance was evaluated using the Area Under the Curve (AUC), accuracy, sensitivity, and specificity.
RESULTS: Among the 208 patients, 68 developed PI, resulting in an incidence rate of 32.69%. Independent risk factors for PI included smoking history, diabetes, irregular periodontal treatment, high alveolar bone resorption, and a high plaque index score (all P < 0.05). Based on these risk factors, a logistic regression model was constructed to predict the occurrence of PI. The AUC of the logistic regression model was 0.911 for the training set and 0.823 for the validation set. The calibration curve indicated that the predicted probabilities closely matched the actual probabilities. The decision curve showed that the threshold probabilities for the training and validation sets were 0.1 to 0.85 and 0.1 to 0.81, respectively, suggesting that the net benefit was maximized within these ranges.
CONCLUSION: Smoking history, diabetes, irregular periodontal treatment, high alveolar bone resorption, and a high plaque index score are significant risk factors for PI in patients with periodontitis. The logistic regression model constructed from these factors effectively predicts the probability of PI, providing a valuable reference for the prevention and management of PI.
METHODS: A retrospective analysis was conducted using clinical data from 208 patients with periodontitis who required implant restoration due to tooth loss from various causes. These patients, meeting the indications for dental implantation, were treated at the Third People's Hospital of Shenzhen from January 2019 to December 2023. The dataset was divided into training and validation sets in a 7:3 ratio. Logistic regression was used to identify risk factors for PI in these patients. Significant variables from the regression analysis were incorporated into the prediction model. The model's accuracy was evaluated using Receiver Operating Characteristic (ROC) and calibration curves. A decision curve was also drawn to assess the clinical utility of the model. The model's performance was evaluated using the Area Under the Curve (AUC), accuracy, sensitivity, and specificity.
RESULTS: Among the 208 patients, 68 developed PI, resulting in an incidence rate of 32.69%. Independent risk factors for PI included smoking history, diabetes, irregular periodontal treatment, high alveolar bone resorption, and a high plaque index score (all P < 0.05). Based on these risk factors, a logistic regression model was constructed to predict the occurrence of PI. The AUC of the logistic regression model was 0.911 for the training set and 0.823 for the validation set. The calibration curve indicated that the predicted probabilities closely matched the actual probabilities. The decision curve showed that the threshold probabilities for the training and validation sets were 0.1 to 0.85 and 0.1 to 0.81, respectively, suggesting that the net benefit was maximized within these ranges.
CONCLUSION: Smoking history, diabetes, irregular periodontal treatment, high alveolar bone resorption, and a high plaque index score are significant risk factors for PI in patients with periodontitis. The logistic regression model constructed from these factors effectively predicts the probability of PI, providing a valuable reference for the prevention and management of PI.