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Psychological risk indicators for peri‐implantitis: A cross‐sectional study
Author(s) -
Strooker Hans,
Waal Yvonne Catharina Maria,
Bildt Miriam Margot
Publication year - 2022
Publication title -
journal of clinical periodontology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.456
H-Index - 151
eISSN - 1600-051X
pISSN - 0303-6979
DOI - 10.1111/jcpe.13645
Subject(s) - medicine , logistic regression , cross sectional study , peri implantitis , depression (economics) , multivariate analysis , univariate analysis , checklist , confounding , psychology , surgery , pathology , implant , economics , cognitive psychology , macroeconomics
Aim The aim of this analytical cross‐sectional study was to evaluate the association between peri‐implantitis and psychological distress, and potentially related/mediating factors such as general health, bruxism, and lifestyle factors. Materials and Methods Patients who received dental implants at a private practice in the Netherlands between January 2011 and January 2014 were recalled on a 5‐year clinical and radiographic follow‐up examination. Presence of peri‐implantitis was examined, and patients completed questionnaires measuring psychological distress (Symptom Checklist [SCL]‐90), bruxism, general health, and lifestyle factors. Associations between the self‐reported factors and peri‐implantitis were analysed with univariate and multivariate logistic regression models. Results A total of 230 patients (with 347 implants) were included in the analysis. Prevalence of (mild to severe) peri‐implantitis was 30% (69 patients). Variables that showed a significant univariable association with peri‐implantitis ( p < .10) were the SCL‐90 subdomain depression, smoking, current medical treatment, and lung problems. In the multivariate regression analysis, depression was the only variable that was significantly associated with peri‐implantitis ( p < .05). Conclusions The presence of depressive symptoms is a risk indicator for peri‐implantitis. Recognizing the potential negative impact of depressive symptoms may allow for better identification of high‐risk patients.