A logistic regression model for the decision to perform access surgery

Abstract

Access surgery may be recommended to about 80% of patients who present with advanced forms of periodontal disease. In this report, a multivariate logistic regression analysis which incorporated several clinical parameters for each tooth examined, i.e., tooth type, furcation involvement, bleeding on probing, attachment level, probing depth, mobility and BANA test score, was conducted using generalized estimating equations (GEE). This approach identified parameters that were significantly associated at p < 0.05 level with the need for access surgery or extraction for periodontal purposes. The estimated probabilities derived from the GEE model were plotted over the complete spectrum of operating conditions to obtain a receiver-operator characteristic (ROC) curve. At a probability cutpoint of 0.8, the decision threshold for surgery/extraction at the pretreatment examination would have a sensitivity of 76.1% and a specificity of 75.3%. We have taken this 0.8 cut point to look at specific clinical decisions made by our examiners after the patients had received scaling and root planing plus 2 weeks unsupervised usage of systemic antimicrobials. The clinicians' decision was taken as the primary reference standard. The model's estimated decision agreed with the clinicians' decision in 226 of the 284 teeth, for an accuracy of 80%. The specificity was 90% and the sensitivity was 43%.