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J Prosthodont Res. 2017 Jan;61(1):43-53. doi: 10.1016/j.jpor.2016.03.004. Epub 2016 Mar 29.

Attributable fractions, modifiable risk factors and risk stratification using a risk score for peri-implant pathology.

Author information

1
Faculty of Medicine, University of Lisbon, Portugal; Malo Clinic Lisbon, Portugal. Electronic address: mignobre@gmail.com.
2
Integrated Clinic or Oral Medicine, Lisbon, Portugal.
3
Faculty of Medicine, University of Lisbon, Portugal.
4
Malo Clinic Lisbon, Portugal.

Abstract

PURPOSE:

This study aimed to estimate the impact of risk factors for peri-implant pathology, to identify potentially modifiable factors, and to evaluate the accuracy of the risk algorithm, risk scores and risk stratification.

METHODS:

This retrospective case-control study with 1275 patients (255 cases; 1020 controls) retrieved a model according to the predictors: history of Periodontitis, bacterial plaque, bleeding, bone level, lack of passive fit or non-optimal screw joint, metal-ceramic restoration, proximity to other implants/teeth, and smoking habits. Outcome measures were the attributable fraction; the positive and negative likelihood ratios at different disease cut-off points illustrated by the area under the curve statistic.

RESULTS:

Six predictors may be modified or controlled directly by either the patient or the clinician, accounting for a reduction in up to 95% of the peri-implant pathology cases. The positive and negative likelihood ratios were 9.69 and 0.13, respectively; the area under the curve was 0.96; a risk score was developed, making the complex statistical model useful to clinicians.

CONCLUSIONS:

Based on the results, six predictors for the incidence of peri-implant pathology can be modified to significantly improve the outcome. It was possible to stratify patients per risk category according to the risk score, providing a tool for clinicians to support their decision-making process.

KEYWORDS:

Dental implants; Pathology; Peri-implant pathology; Peri-implantitis; Risk factors; Risk prediction; Risk scores

PMID:
27032718
DOI:
10.1016/j.jpor.2016.03.004
[Indexed for MEDLINE]

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