Frontiers in public health · 2026
Heterogeneous trajectories of oral frailty and their determinants in older patients receiving dental implants: a longitudinal study.
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Zhang Ying, Lu Nan, Liang Mengyao, Zhu Qiufeng
Аннотация
OBJECTIVE: To describe distinct trajectory patterns of oral frailty after implant-supported prosthesis delivery in older patients and to identify associated factors.
METHODS: This prospective longitudinal cohort study consecutively enrolled 211 older patients who completed implant-supported prosthesis delivery at Nantong Stomatological Hospital and Haoting Dental Clinic between January 1 and April 30, 2025. Oral frailty was assessed using the adapted seven-item version of the Oral Frailty Index-8 (OFI-8, 7-item version; score range 0-9) at baseline (T0, prosthesis delivery) and at 3, 6, 9, and 12 months post-delivery (T1-T4). Distinct trajectories were identified using latent class growth modeling (LCGM). Multivariable multinomial logistic regression was used to analyze factors associated with trajectory membership.
RESULTS: Three distinct oral frailty trajectories were identified: (1) Rapid Improvement-Stable Pattern (Class 1, n = 90, 42.7%), characterized by a sharp decline in OFI-8 scores immediately after prosthesis delivery followed by sustained low levels; (2) Gradual Improvement-Plateau Pattern (Class 2, n = 74, 35.1%), showing a gradual decline followed by a plateau; and (3) Persistent High-Risk Pattern (Class 3, n = 47, 22.2%), with scores remaining elevated throughout the 12-month observation period. Multivariable multinomial logistic regression analysis revealed that age (aOR = 1.12, 95% CI: 1.04-1.21), number of medications (aOR = 1.25, 95% CI: 1.05-1.49), and number of missing teeth (aOR = 1.09, 95% CI: 1.02-1.16) were independently associated with membership in Class 3. Bootstrap validation supported the robustness of the model. None of these variables showed statistical significance in distinguishing Class 2 from Class 1.
CONCLUSION: Oral frailty trajectories in older patients with dental implants are heterogeneous. Older age, polypharmacy, and a greater number of missing teeth were independently associated with membership in the persistent high-risk trajectory, suggesting that these factors may help identify patients requiring prioritized follow-up.
METHODS: This prospective longitudinal cohort study consecutively enrolled 211 older patients who completed implant-supported prosthesis delivery at Nantong Stomatological Hospital and Haoting Dental Clinic between January 1 and April 30, 2025. Oral frailty was assessed using the adapted seven-item version of the Oral Frailty Index-8 (OFI-8, 7-item version; score range 0-9) at baseline (T0, prosthesis delivery) and at 3, 6, 9, and 12 months post-delivery (T1-T4). Distinct trajectories were identified using latent class growth modeling (LCGM). Multivariable multinomial logistic regression was used to analyze factors associated with trajectory membership.
RESULTS: Three distinct oral frailty trajectories were identified: (1) Rapid Improvement-Stable Pattern (Class 1, n = 90, 42.7%), characterized by a sharp decline in OFI-8 scores immediately after prosthesis delivery followed by sustained low levels; (2) Gradual Improvement-Plateau Pattern (Class 2, n = 74, 35.1%), showing a gradual decline followed by a plateau; and (3) Persistent High-Risk Pattern (Class 3, n = 47, 22.2%), with scores remaining elevated throughout the 12-month observation period. Multivariable multinomial logistic regression analysis revealed that age (aOR = 1.12, 95% CI: 1.04-1.21), number of medications (aOR = 1.25, 95% CI: 1.05-1.49), and number of missing teeth (aOR = 1.09, 95% CI: 1.02-1.16) were independently associated with membership in Class 3. Bootstrap validation supported the robustness of the model. None of these variables showed statistical significance in distinguishing Class 2 from Class 1.
CONCLUSION: Oral frailty trajectories in older patients with dental implants are heterogeneous. Older age, polypharmacy, and a greater number of missing teeth were independently associated with membership in the persistent high-risk trajectory, suggesting that these factors may help identify patients requiring prioritized follow-up.