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Background/purpose: History of periodontitis is a well-documented risk indicator of peri-implantitis. However, the influence of severity of periodontitis is still unclear, especially for severe periodontitis. This study was aimed to investigate the prevalence of peri-implant disease and analyze the risk indicators in patients with treated severe periodontitis. Materials and methods: A total of 182 implants from 88 patients (44 males and 44 females) with severe periodontitis with a mean fellow-up period of 76.5 months were enrolled in this study. Patient and implant information, and periodontal and peri-implant conditions were collected to evaluate the prevalence of peri-implant disease and risk indicators. Results: The prevalence of peri-implantitis was 9.1% and 6.6% at the patient-level and implant-level. The prevalence of peri-implant mucositis was 76.1% and 51.1% at the patient-level and implant-level. Risk indicators of peri-implantitis included older age (OR: 1.132), poor proximal cleaning habits (OR: 14.218), implants in anterior area (OR: 10.36), poor periodontal disease control (OR: 12.76), high peri-implant plaque index (OR: 4.27), and keratinized tissue width (KTW)ï¼2 mm (OR: 19.203). Conclusion: Implants in patients with severe periodontitis after periodontal treatment and maintenance show a low prevalence (9.1%) of peri-implantitis and a relatively high prevalence (76.2%) of peri-implant mucositis. Patient age, peri-implant proximal cleaning habits, implant position, periodontal disease control, peri-implant plaque index, and KTW are associated with prevalence of peri-implantitis.
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Background/purpose: Computer-assisted dynamic navigation surgery could provide accurate implant placement. However, its low efficiency was always criticized by dental surgeons. The purpose of this study was to evaluate the accuracy and efficiency of a calibration approach with reflective wafers in dynamic navigation for implant placement. Materials and methods: Eighty implants were placed in the standardized polyurethane mandibular models under dynamic navigation and divided into 2 groups according to the calibration methods (n = 40). The U-shaped tube (UT) group used a prefabricated U-shaped tube embedded with radiopaque markers. The reflective wafers (RW) group used a fixation with 3 round reflective wafers as markers. Postoperative cone beam computed tomography images were obtained for implants deviation analyses. The calibration time was used to evaluate the efficiency of the 2 methods. Results: Significant differences were found in the trueness and efficiency between the 2 groups (P < 0.05). The 3D deviations at the implant platform and apex were smaller in UT group (0.89 ± 0.28 and 0.79 ± 0.30 mm, respectively) than in the RW group (0.99 ± 0.28 and 0.98 ± 0.30 mm, respectively). The angular deviation was larger in the UT group (2.16 ± 1.12°) than in the RW group (1.53 ± 0.88°). The calibration approach of RW group was more efficient than the UT group (2.05 ± 0.55 and 7.50 ± 0.71 min, respectively). Conclusion: The calibration method of RW improved the efficiency significantly and achieved equivalent trueness with UT for dynamic navigation during implant placement.
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STATEMENT OF PROBLEM: Dynamic navigation for implant placement has been reported to be more accurate than freehand surgery. However, the accuracy of the calibration methods used for navigation in partially edentulous individuals with distal extensions remains unknown. PURPOSE: The purpose of this in vitro study on dental models was to evaluate the accuracy of 3 calibration methods of dynamic navigation for implant placement in the distal extension of partially edentulous arches. MATERIAL AND METHODS: Eleven standardized polyurethane mandibular models with distal extensions were prepared. The left first molar, second molar, and second premolar from each model (33 tooth sites) were randomly assigned to 1 of the 3 calibration methods: U-shaped tube embedded with radiopaque markers, anatomic tooth cusps, and bone markers with the random number table method. Preoperative and postoperative cone beam computed tomography images were obtained for deviation analyses. The primary outcomes were 3-dimensional (3D) deviation at the implant platform and apex and angular deviation. Differences among the test groups were analyzed by using a 1-way analysis of variance (ANOVA) and the least significant difference (LSD) post hoc test (α=.05). RESULTS: The mean ±standard deviation 3D deviations were 0.78 ±0.34, 1.86 ±0.91, and 1.44 ±0.57 mm at the implant platform and 0.79 ±0.35, 2.19 ±1.01, and 1.49 ±0.50 mm at the apex in the U-shaped tube, tooth cusp, and bone marker groups, respectively. The 3D deviations at the implant platform and apex were significantly different among the groups (P<.01). The angular deviation was 1.36 ±0.54, 2.95 ±2.07, and 2.92 ±2.45 degrees, with no significant differences among the groups (P=.092). CONCLUSIONS: In the dynamic navigation of implant placement in the distal extension of partially edentulous arches, the U-shaped tube calibration with radiopaque markers was more accurate than the anatomic tooth cusp or bone marker calibration.
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BACKGROUND: Based on the 2018 classification, we aimed to determine the prevalence, distribution, and progression of periodontitis in the rural Chinese population without access to dental care. METHODS: In all, 404 subjects (28.7 ± 8.9 years, M:F = 182:222) were randomly enrolled in 1992 and re-called in 1996. With the new classification, the prevalence and distribution of stage, grade, and extent were characterized. Stage progression was compared with the progression of clinical attachment loss (CAL) and radiographic bone loss (RBL). RESULTS: At baseline, 94.1% villagers suffered from periodontitis, of whom 53.7% were in Stage III/IV. The prevalence of Stage III/IV increased from 18.2% in the age group of 15 to 24 years to 60.9% in 25 to 34-year-old group and 88.7% in the 35 to 44-year-old group. Significantly more Stage III/IV, generalized, and Grade C periodontitis were found in male villagers than female villagers. In 1996, the prevalence rate of periodontitis increased to 98.5%, with 80.0% in Stage III/IV. Further, 84.2% villagers presented with Grade C periodontitis based on longitudinal ΔCAL. The rate of progression (≥1 site with ΔCAL ≥3 mm) was 63.7%. Stage progression correlated significantly with CAL and RBL progression in Stage I/II, but this association was not found in Stage III/IV. Among subjects with disease progression in Stage III/IV, 90.4% shifted from localized to generalized cases. Furthermore, ceiling effects were observed in Stage III/IV. CONCLUSIONS: In villagers without access to dental care, 94.1% suffered from periodontitis, with more than half having Stage III/IV disease based on the 2018 classification. The majority cases presented with rapid periodontal progression. Although stage progression correlated significantly with CAL and RBL progression in Stage I/II, ceiling effects existed in Stage III/IV.