A New Equation for Predicting Evolution of Oral Pain in Orthodontic Treatment: A Longitudinal, Prospective Cohort Study.

AIMS: To develop an equation capable of relating the evolution of oral pain to the time elapsed, measured from the moment of dental archwire fitting and identifying when pain begins, peaks, and ends; and secondly, to compare pain during orthodontic treatment in relation to archwire material (steel or nickel-titanium [Ni-Ti]) and position (maxillary or mandibular) and patient age (child, teenager, or adult) and gender (male or female). METHODS: A longitudinal prospective cohort study was conducted of 112 patients who filled in a scale to evaluate pain, noting the times when the pain occurred. The total sample consisted of 60 males and 52 females with a mean (+/- standard deviation [SD]) age of 19.8 +/- 6.2 years. The sample was divided into five groups depending on archwire material and position, and patient age and gender. A univariate four-way ANOVA model was performed to compare mean pain levels between groups. Bonferroni test was used for multiple comparisons. A univariate nonlinear regression model was carried out for pain level, 95% confidence intervals (95% CI) were calculated, and the statistic R(2) was used. RESULTS: An equation was developed based on pain levels in relation to time elapsed, measured from the moment when the archwire had been fitted in the mouth. The equation had three coefficients related to mean pain values: overall pain, peak pain, and how pain decreased. It fitted all study groups with a correlation coefficient > 0.9. The model showed that pain levels were influenced by archwire material and patient gender and age, but not archwire position. CONCLUSION: The equation reproduced the data registered and can be applied to studies of pain derived from archwires, and this methodology could be used for other external agents fitted in the mouth. Patients receiving dental treatment involving external agents can be made aware of the pain they can expect to experience. This will enable them to distinguish expected pain from other pain, which will help them identify other pathologies requiring medical attention and to approach treatment with better motivation since the pattern of pain evolution is known in advance.