Predicting Patient-Reported Outcomes of Dental Implant Treatment

To investigate whether pretreatment patient-related factors are associated with the outcomes of dental implant treatment through multidimensional evaluations of oral health-related quality of life and health-related quality of life using the Oral Health Impact Profile (OHIP) and the 36-Item Short Form Health Survey (SF-36) developed from the Medical Outcomes Study, respectively.

Oral health-related quality of life was evaluated using the Japanese version of the OHIP, and health-related quality of life was evaluated using scores for the mental component summary (MCS) and physical component summary (PCS) dimensions of the SF-36. Data were collected before treatment (baseline) and 1 month after delivery of the prostheses (follow-up). Stepwise multivariate regression analysis was used to explore the associations of posttreatment OHIP and SF-36 scores and changes in these scores after treatment (dependent variables) with age, sex, educational background, number of missing teeth, Eichner's classification, pretreatment state, site of the embedded implant, number of implants, types of definitive prostheses, and baseline OHIP and SF-36 scores (independent variables).

Data from 150 consecutive patients (mean age: 58.1 ± 11.5 years) who underwent dental implant treatment between April 2008 and April 2016 were analyzed. The mean OHIP summary score, OHIP dimension scores (oral function, orofacial pain, orofacial appearance, and psychosocial impact), and MCS score showed significant improvements after treatment (P < .05). The baseline OHIP and SF-36 scores were consistently and significantly associated with the treatment outcome (ie, dependent) variables (P < .05). Moreover, the site of the embedded implant was identified as a significant predictor of the posttreatment status and changes in the OHIP summary score, oral function and orofacial appearance dimension scores (OHIP), and MCS score.

These results suggest that patient perceptions of health and oral health conditions may help in the prediction of the outcomes of dental implant treatment.