Using prognostic factors from case series and cohort studies to identify individuals with poor long-term outcomes during periodontal maintenance.

Abstract

The accuracy of applying prognostic factors to individual patients is uncertain. The aim was to apply prognostic factors from several outcome studies (case series and cohort) to identify: (1) patients who lost a tooth/teeth during periodontal maintenance; (2) patients who were non-responding to treatment; (3) patients needing re-treatment during periodontal maintenance. In addition, tooth loss was related to initial prognosis and it was determined which of the prognostic factors were also risk factors. Chi squared analysis was carried out for the outcomes of patients with-, and without prognostic factors. Significance level was set at p≤0.05. Sensitivity and specificity was calculated for patients with and without prognostic factors. The prognostic factors only identified a small proportion of patients who lost teeth (34-38%). Combining the prognostic factors resulted in a lower accuracy. A higher proportion of patients with no prognostic factors lost teeth (53.8-96.2%). The chance of identifying a non-responding patient based on family history was 5.9%, for stress 32.4%, and for heavy smoking 8.7%. Significantly more patients (29/40 , χ²=16.2 p<0.05) with initial uncertain/poor prognosis and significantly fewer patients (11/40, χ²=16.2, p<0.05) with erratic/no compliance needing re-treatment were identified. 21 of 40 patients (52.5%) (p=0.655) with family history needing retreatment were identified. Combining the prognostic factors identified 5-22% out of a total of 40% of patients needing re-treatment. six out of nine (67%) teeth with an initial hopeless prognosis were lost, 10/109 (9%) teeth with a poor prognosis were lost, 11/346 (3%) teeth with a moderate prognosis were lost and 9/1972 (0.46%) of teeth with a good prognosis were lost. None of the prognostic factors was found also to be a risk factor for developing periodontal diseases. Applying prognostic factors to identify individual patients with poor long-term outcomes is associated with low accuracy.