| Literature DB >> 34109477 |
Sunil Kumar Nettemu1, Sowmya Nettem2, Vijendra Pal Singh1, Sheila Shirley William3, Shargunan Selvanthan Gunasekaran4, Malathi Krisnan5, Adinegara Lutfi Abas1.
Abstract
AIM: This study was to evaluate the association between peri-implant bleeding on probing in peri-implant diseases and its association with multilevel factors (site specific factors, implant factors, and patient level factors).Entities:
Keywords: Bleeding on probing; Gingival biotype; Peri-implant diseases
Year: 2021 PMID: 34109477 PMCID: PMC8190223 DOI: 10.1186/s40729-021-00315-0
Source DB: PubMed Journal: Int J Implant Dent ISSN: 2198-4034
Fig. 1Peri-implant bleeding on probing
Fig. 2Probe not visible through sulcus (thick gingival biotype)
Fig. 3Probe visible through sulcus (thin gingival biotype)
Results of descriptive statistics
| Peri-implant bleeding on probing | 42 (35.29) |
| Gingival recession (present) | 27 (22.69) |
| Gingival biotype (thin) | 29 (24.37) |
| Bone loss (present) | 13 (10.92) |
| | |
| 12–23 | 52 (43.70) |
| 24–35 | 59 (49.60) |
| 36–47 | 3 (2.51) |
| 48–59 | 3 (2.51) |
| 60–71 | 0 |
| 72–83 | 2 (1.68) |
| | |
| Anterior | 9 (7.57) |
| Posterior | 110 (92.43) |
| | |
| Female | 42 (52.5) |
| Male | 38 (47.5) |
| | |
| Chinese | 52 (65.0) |
| Malay | 16(20.0) |
| Indian | 12(15.0) |
| | |
| 20–34 | 4 (5.0) |
| 35–49 | 19 (23.75) |
| 50–64 | 45 (56.25) |
| 65–79 | 12 (15.0) |
| | |
| Hypertension | 15 (18.75) |
| Diabetes mellitus | 11 (13.75) |
| Asthma | 3 (3.75) |
| 6 (7.5) | |
Results of bivariate analysis referring to bleeding on probing
| Variable | Odds ratio | 95% CI | p value |
|---|---|---|---|
| | 1.000 | – | – |
| | 1.467 | 1.040–2.069 | 0.029 |
| | 1.000 | – | – |
| | 2.601 | 1.817–3.722 | < 0.001 |
| | 1.000 | – | – |
| | 1.778 | 1.108–2.852 | 0.017 |
| 1.975 | 1.713–2.276 | < 0.001 | |
| 0.981 | 0.966–0.997 | 0.021 | |
| | 1.000 | – | – |
| | 0.000 | 0.000 | 0.999 |
| | 1.010 | 0.636–1.605 | 0.966 |
| | 0.000 | 0.000 | 0.999 |
| | 0.584 | 0.352–0.969 | 0.037 |
| | 0.000 | 0.000 | 0.999 |
| | 2.571 | 1.363–4.850 | 0.004 |
| | 0.643 | 0.288–1.433 | 0.280 |
| | 1.000 | – | – |
| | 1.357 | 1.006–1.832 | 0.046 |
| | 1.000 | – | – |
| | 1.581 | 1.039–2.405 | 0.032 |
| | 1.185 | 0.796–1.765 | 0.402 |
| 1.327 | 0.982–1.793 | 0.065 | |
| 0.288 | 0.151–0.549 | < 0.001 | |
| 1.027 | 1.012–1.042 | < 0.001 | |
Results of multilevel logistic regression (generalized linear) after backward stepwise logistic regression (conditional) was performed
| Model term | Coefficient | Std. error | t. | Sig. | Exp (coefficient) | 95% confidence interval for exp (coefficient) | |
|---|---|---|---|---|---|---|---|
| Lower Upper | |||||||
| − 21.850 | 7,060.389 | − 0.003 | .998 | 0.000 | 0.000 | – | |
| 2.451 | 1.238 | 1.980 | .048 | 11.599 | 1.020 | 131.877 | |
| − 5.771 | 2.654 | − 2.174 | .030 | 0.003 | 0.000 | 0.571 | |
| − 0.120 | 0.056 | − 2.153 | .032 | 0.887 | 0.795 | 0.990 | |
| 3.410 | 1.066 | 3.199 | .001 | 30.273 | 3.733 | 245.499 | |
| 4.100 | 0.950 | 4.314 | .000 | 60.316 | 9.336 | 389.673 | |