Date

2016-01-01

Author

KAYAOĞLU, GÜVEN
Gurel, Mugem
Saricam, Esma
İLHAN, MUSTAFA NECMİ
İlk Dağ, Özlem

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Introduction: This observational study sought to assess the incidence of intraoperative pain (10P) among patients receiving endodontic treatment and to construct a model for predicting the probability of 10P. Methods: All patients attending the endodontic training clinic at Gazi University, Ankara, Turkey, during the spring term of 2014 were examined (N = 2785 patients; observation completed in 1435 patients; male: 628, female: 807; mean age: 39 years; 1655 teeth total). Demographic and clinical variables were recorded for patients requiring primary endodontic treatment. Local anesthesia was administered and routine endodontic treatment commenced. After the working length was established, each patient was asked to report any pain according to a visual analog scale. Supplementary local infiltration anesthesia was administered if necessary. If pain continued despite supplementary anesthesia, then the pain score was immediately assessed. A visual analog scale score corresponding to more than mild pain indicated 10P. A predictive model was constructed with multiple logistic regression analysis from the data of 85% of cases, with the remaining 15% of cases being used to test the external validity of the model. Results: The incidence of 1013 was 6.1% (101/1655 cases). One tooth from each patient was randomly selected, with 1435 teeth being retained for further analysis. A multiple logistic regression model was constructed with the variables age, tooth type, arc, pulpal diagnosis, pain present within the previous 24 hours, and anesthetic solution (P <.05). Good fits were obtained for the final model and external control, with a correct classification rate (efficiency) of 0.78, sensitivity (true positive rate) of 0.63, and specificity (true negative rate) of 0.79 for the external control. Conclusions: A successful predictive model of 10P was constructed with demographic and clinical variables.

Subject Keywords

Dental anesthesia, Endodontics, Forecasting, Pain assessment, Root canal treatment, Root canal therapy

URI

https://hdl.handle.net/11511/45624

Collections

Department of Statistics, Article