Development of descriptive mathematical models for different domains of gingival recession using multiple linear regression analysis

Abstract

Context:Gingival recession (GR) is a common finding seen in the periodontics clinic. It has a significant functional and esthetic impact on the patient's dentition and quality of life.Aims:The current study aimed to develop the descriptive mathematical models for different domains of GR based on the data obtained from the North Indian population.Settings and Design:Cross-sectional observational study.Materials and Methods:Consecutive 130 participants were enrolled between June and August 2019. Complete case history and thorough oral examination were carried out including assessment of periodontal variables, for example, pocket depth (PD), gingival marginal level, clinical attachment level (CAL), simplified oral hygiene index, and gingival index. Prediction models for different domains of GR, namely Miller's class, severity, extent, and distribution of recession were made, and further, the best-fitted model on the basis of “coefficient of determination (R2)” was analyzed.Statistical Analysis Used:Multiple linear regression.Results:Nine factors, i.e., mean CAL, mean PD, tooth mobility, abrasion, width of attached gingiva, number of teeth present, age, type of brush, and socioeconomic status showed a significant association with different domains of the GR. In addition, a high degree of overlap was observed among factors associated with different domains of the GR.Conclusion:Diverse clinical (mean CAL, mean PD, tooth mobility, and abrasion), biological (width of attached gingiva, number of teeth present, and age), and environmental factors (type of brush and socioeconomic status) were found to have a significant association with the occurrence of GR in the North Indian population. Owing to the multifactorial etiology of GR, the identification of susceptible patients based on the presence of risk factors is an essential step in developing action plans for the prevention of the disease.