GF-Predictability for Dental Implants (GF-PreDImp): A Multidomain Predictive Model for Dental Implant Success – Development, Structure, and Clinical Application

Dental implant therapy demonstrates high long-term survival; however, biological, behavioral, and technical complications remain prevalent. The objective of this study was to introduce GF-Predictability for Dental Implants (GF-PreDImp), the first multidomain predictive tool in the literature, designed to quantify implant success predictability through a structured, evidence-based scoring system. The model integrates six domains: Biological, Behavioral, Hard tissue, Soft tissue, Implant, and Prosthetic, approaching systemic, behavioral, anatomical, surgical, and prosthetic variables into a 100-point composite index. The Biological/Systemic point (20 points) involves diabetes (HbA1c), bisphosphonates, head and neck radiation, cardiovascular disease, osteoporosis, and immunosuppression; the Behavioral/External topic (20 points) approaches post-implant smoking, oral hygiene, plaque/calculus index, brushing performance, alcohol usage, and patient’s compliance; the Hard Tissue (20 points) analyzed bone quality (densities: D1–D4), bone quantity, arch position, guided-bone regeneration (GBR) need, sinus lift, cone beam computed tomography (CBCT) height/width; the Soft Tissue evolution (15 points) observes keratinized mucosa width (KMW), periodontal history, gingival phenotype, bleeding on probing (BoP), and probing depth (PD); the Implant Parameters topic (15 points) assessed tooth position, loading timing, primary stability (ISQ), length/diameter, and surface treatment; and the last point analyzed, Prosthetic/Surgical (10 points), appraisal bruxism characteristic, occlusal contacts, crown-to-implant ratio, cantilever, surgeon experience, and antibiotic protocol. The final GF-PreDImp score could be excellent (≥ 85), good (70 – 84), moderate to guarded (55-69), guarded to high risk (40-54), and poor (<40). Results: Predictors were derived from literature on implant failure, peri-implant disease, and risk assessment. The tool generates dynamic visual outputs, including radar charts and domain-specific scores, enabling real-time clinical interpretation. Each domain can achieve up to 100%, and the average results predict the predictability of dental implant therapy. The final screen of the GF-PreDImp outcome presents a summary of the worst areas to clarify possible risks for clinicians and patients. The graphic and result can be printed for electronic filing and/or shown and given to the patient. The GF-PreDImp system can provide a comprehensive framework for individualized risk stratification and treatment optimization. Its implementation can improve clinical decision-making and enhance long-term implant outcomes. Further clinical assessments must be done to confirm the findings in future studies.