The International journal of oral & maxillofacial implants · 2025
Peri-implantitis Risk Assessment (PiRA) Part 2: Retrospective Study and Framework for an Evidence-Based Prediction Model for Clinicians.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Quirynen Marc, Tarce Mihai, Siawasch Manoetjer, Castro Ana B, Temmerman Andy, Coucke Wim, Teughels Wim
Аннотация
PURPOSE: To develop an online tool based on an evidence-based predictive model that allows clinicians to accurately predict the risk of peri-implantitis in candidates for dental implant therapy.
MATERIALS AND METHODS: A retrospective study of patients attending the University Hospital Leuven in Leuven, Belgium, was performed at the Implant Review Clinic. The presence of peri-implantitis and related risk factors were recorded, and a predictive model for peri-implantitis was then developed based on this data.
RESULTS: A total of 460 patients with 1,432 implants were included. Peri-implantitis was reported in 78 (17%) patients. For partially edentulous patients (n = 350; 60% female; average age 64.1 years), susceptibility to periodontitis (odds ratio [OR] = 0.48 [0.24;0.94]; P = .03), the number of sites with a probing pocket depth (PPD) of ≥ 5 mm (OR = 0.2 [0.10;0.40]; P < .01), and smoking (OR = 0.25 [0.09;0.66]; P < .01) were significantly associated with peri-implantitis. For fully edentulous patients (n = 50; 72% female; average age 72.2 years), implants placed in the maxilla displayed a greater risk (OR = 0.15 [0.02;0.87]; P = .03) of developing peri-implantitis. A predictive model for the development of peri-implantitis was created based on eight patient-related risk factors for partially edentulous patients (sensitivity = 90.2%; specificity = 55.0%) and four risk factors for fully edentulous patients (sensitivity = 100%; specificity = 51.3%).
CONCLUSIONS: The predictive model can be used for a preoperative risk assessment of partially edentulous patients. Further validation and refinement of the model with additional data could enable its use for fully edentulous patients and will improve its predictive power, thereby increasing its reliability.
MATERIALS AND METHODS: A retrospective study of patients attending the University Hospital Leuven in Leuven, Belgium, was performed at the Implant Review Clinic. The presence of peri-implantitis and related risk factors were recorded, and a predictive model for peri-implantitis was then developed based on this data.
RESULTS: A total of 460 patients with 1,432 implants were included. Peri-implantitis was reported in 78 (17%) patients. For partially edentulous patients (n = 350; 60% female; average age 64.1 years), susceptibility to periodontitis (odds ratio [OR] = 0.48 [0.24;0.94]; P = .03), the number of sites with a probing pocket depth (PPD) of ≥ 5 mm (OR = 0.2 [0.10;0.40]; P < .01), and smoking (OR = 0.25 [0.09;0.66]; P < .01) were significantly associated with peri-implantitis. For fully edentulous patients (n = 50; 72% female; average age 72.2 years), implants placed in the maxilla displayed a greater risk (OR = 0.15 [0.02;0.87]; P = .03) of developing peri-implantitis. A predictive model for the development of peri-implantitis was created based on eight patient-related risk factors for partially edentulous patients (sensitivity = 90.2%; specificity = 55.0%) and four risk factors for fully edentulous patients (sensitivity = 100%; specificity = 51.3%).
CONCLUSIONS: The predictive model can be used for a preoperative risk assessment of partially edentulous patients. Further validation and refinement of the model with additional data could enable its use for fully edentulous patients and will improve its predictive power, thereby increasing its reliability.