The risk of moderate‐to‐severe post‐operative pain following the placement of dental implants

• Published in: Journal of oral rehabilitation 2019-09, Vol.46 (9), p.836-844
• Main Authors: Wang, Meijie, Li, Yiyuan, Li, Junying, Fan, Linli, Yu, Haiyang
• Format: Article
• Language: English
• Subjects: Anxiety; Dental Implantation; Dental Implantation, Endosseous; Dental Implants; Dental prosthetics; Dental Restoration Failure; Dentures; Humans; Pain; Pain, Postoperative; Postoperative period; post‐operative; Prediction models; Reproducibility of Results; risk assessment; Risk factors; Surgery; Transplants & implants
• ISSN: 0305-182X; 1365-2842
• DOI: 10.1111/joor.12815

In recent years, the implant‐supported dentures have risen rapidly, and thus, more attention was paid to post‐operative pain, following dental implantation. To explore risk factors and establish as well as validate a risk prediction model for moderate‐to‐severe post‐operative pain following dental implantation. A observational study of 352 patients with 563 implants was carried out. The following candidate predictors were collected: age, gender, pain sensitivity, anxiety, pain expectation, operator experience, position, length and number of placed implants, duration of surgery and surgery procedures. The outcome was the presence of moderate‐to‐severe post‐operative pain within the 24 hours post‐surgery. Multivariate logistic regression in combination with bootstrapping techniques was used to explore independent risk factors and establish a prediction model. The mean pain intensity score was 4.21 within 24 hours post‐operatively, while the incidence of moderate‐to‐severe pain was 61.9%. Independent risk factors of moderate‐to‐severe post‐operative pain were flap surgery, surgical template, the interaction between anxiety state and pain sensitivity, the interaction between pain sensitivity and pain expectation and the interaction between implant length and immediate implant. The area under the receiver operator characteristic curve was 0.72. The model's sensitivity was 75.7%, and the specificity was 64.2%. The model reliability was good (Nagelkerke's R2 0.226). The risk factors and the prediction model (needs further improvement) can help dentists to identify patients at increased risk of moderate‐to‐severe post‐operative pain following dental implantation.