A Predictive Model for Root Caries Incidence

André V. Ritter, John S. Preisser, Chaitanya P. Puranik, Yunro Chung, James D. Bader, Daniel A. Shugars, Sonia Makhija, William M. Vollmer

Research output: Contribution to journalArticle

Abstract

This study aimed to find the set of risk indicators best able to predict root caries (RC) incidence in caries-active adults utilizing data from the Xylitol for Adult Caries Trial (X-ACT). Five logistic regression models were compared with respect to their predictive performance for incident RC using data from placebo-control participants with exposed root surfaces at baseline and from two study centers with ancillary data collection (n = 155). Prediction performance was assessed from baseline variables and after including ancillary variables [smoking, diet, use of removable partial dentures (RPD), toothbrush use, income, education, and dental insurance]. A sensitivity analysis added treatment to the models for both the control and treatment participants (n = 301) to predict RC for the control participants. Forty-nine percent of the control participants had incident RC. The model including the number of follow-up years at risk, the number of root surfaces at risk, RC index, gender, race, age, and smoking resulted in the best prediction performance, having the highest AUC and lowest Brier score. The sensitivity analysis supported the primary analysis and gave slightly better performance summary measures. The set of risk indicators best able to predict RC incidence included an increased number of root surfaces at risk and increased RC index at baseline, followed by white race and nonsmoking, which were strong nonsignificant predictors. Gender, age, and increased number of follow-up years at risk, while included in the model, were also not statistically significant. The inclusion of health, diet, RPD use, toothbrush use, income, education, and dental insurance variables did not improve the prediction performance.

Original languageEnglish (US)
Pages (from-to)271-278
Number of pages8
JournalCaries research
Volume50
Issue number3
DOIs
StatePublished - Jun 1 2016

Keywords

  • Clinical studies/trials
  • Mathematical modeling
  • Prevention
  • ROC methodology
  • Resampling
  • Risk modeling
  • Root caries prediction

ASJC Scopus subject areas

  • Dentistry(all)

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  • Cite this

    Ritter, A. V., Preisser, J. S., Puranik, C. P., Chung, Y., Bader, J. D., Shugars, D. A., Makhija, S., & Vollmer, W. M. (2016). A Predictive Model for Root Caries Incidence. Caries research, 50(3), 271-278. https://doi.org/10.1159/000445445