Effectiveness of Implant Therapy in Patients With and Without a History of Periodontitis: A Systematic Review With Meta‐Analysis of Prospective Cohort Studies
Bibliographic record
Abstract
ABSTRACT Aim This systematic review investigates the effectiveness of implant therapy in patients with and without a history of periodontitis in terms of implant loss, peri‐implant marginal bone loss (MBL), and occurrence of peri‐implant diseases. Methods The protocol of the present meta‐analysis was registered on PROSPERO (CRD42021264980). An electronic search was conducted up to April 2024. All prospective cohort studies reporting implant loss, MBL, and occurrence of peri‐implant diseases in both patients with a history of periodontitis (HP) and patients with no history of periodontitis (NHP) after at least 36‐month follow‐up were included. The risk of bias was evaluated using the Newcastle‐Ottawa Scale and the quality of the evidence was also assessed. A meta‐analysis was performed on the selected outcomes at the available follow‐up time points. Subgroup analyses were conducted based on follow‐up time, rate of progression and severity of periodontitis, and implant surface characteristics. Publication bias was evaluated using the Funnel plot and Egger's test. Results From 13 761 initial records, 14 studies (17 articles) were finally included. Eight studies had a low risk of bias level, and six had a medium risk of bias level. Meta‐analysis showed that HP patients had a significantly greater risk for implant loss (HR: 1.75; 95% CI: 1.28–2.40; p = 0.0005; I 2 = 0%), MBL (MD: 0.41 mm; 95% CI 0.19, 0.63; p = 0.0002; I 2 = 54%), and peri‐implantitis (3.24; 95% CI: 1.58–6.64; p = 0.001; I 2 = 57%) compared to NHP, whereas no significant intergroup difference for peri‐implant mucositis was found. Subgroup analyses revealed a particularly greater risk for implant loss for HP patients over a ≥ 10‐year follow‐up (HR: 2.02; 95% CI: 1.06–3.85; p = 0.03; I 2 = 0%) and for patients with a history of grade C (formerly aggressive) periodontitis (HR: 6.16; 95% CI: 2.53–15.01; p < 0.0001; I 2 = 0%). A greater risk for implant loss for stages III–IV (severe) periodontitis, and implants with rough surfaces was also found. Conclusions Within the limits of heterogeneous case definitions and methods of assessment, a history of periodontitis has been proved to significantly increase the risk for implant loss, particularly at long follow‐up (≥ 10 years) and in case of rapidly progressive forms (grade C), and for MBL and peri‐implantitis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.049 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".