Development and Retrospective Validation of the Pterygoid Implant Placement Success Index (PIPSI)©: A Novel Scoring System for Evaluating Implant Outcomes

翼突种植体植入成功指数(PIPSI)©的开发和回顾性验证:一种评估种植体效果的新型评分系统

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Abstract

INTRODUCTION: Pterygoid implants offer a reliable alternative for rehabilitating the atrophic posterior maxilla by avoiding complex grafting procedures. Despite their increasing use, there is no standardized framework for assessing their clinical success. This study introduces the Pterygoid Implant Placement Success Index (PIPSI), a comprehensive scoring system to evaluate pterygoid implants across preoperative, intraoperative, and postoperative parameters. MATERIALS AND METHODS: PIPSI was developed using eight critical parameters: bone density, implant length, angulation accuracy, primary stability, loading protocol, bone resorption, postoperative complications, and prosthetic success. Each parameter was weighted based on clinical relevance, culminating in a total score of 80. Scores were stratified into four outcome categories: Excellent (70-80), Good (55-69), Moderate (40-54), and Poor (< 40). Validation involved inter-observer reliability, internal consistency (Cronbach's alpha), and predictive validity using logistic regression and ROC curve analysis. RESULTS: PIPSI scores demonstrated strong internal consistency and significant correlation with implant survival and prosthetic outcomes. Higher scores were associated with fewer complications and better long-term stability. Stratification revealed distinct survival curves across outcome groups, confirmed by Kaplan-Meier analysis and log-rank testing (p < 0.05). ROC analysis yielded high discriminative accuracy for predicting implant success. DISCUSSION: The PIPSI offers a robust, objective method for assessing pterygoid implant success, integrating anatomical, surgical, and prosthetic dimensions. It facilitates clinical decision-making and standardized reporting. Limitations include the need for prospective validation, standardization of imaging metrics, and incorporation of patient-reported outcomes. Future enhancements may include digital integration and AI-driven predictive modelling.

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