Prediction Model of Cardiac Risk for Dental Extraction in Elderly Patients with Cardiovascular Diseases

Abstract

Background: With the rapidly increasing population of elderly people, dental extraction in elderly individuals with cardiovascular diseases (CVDs) has become quite common. The issue of how to assure the safety of elderly patients with CVDs undergoing dental extraction has perplexed dentists and internists for many years. And it is important to derive an appropriate risk prediction tool for this population. Objectives: The aim of this retrospective, observational study was to establish and validate a prediction model based on the random forest (RF) algorithm for the risk of cardiac complications of dental extraction in elderly patients with CVDs. Methods: Between August 2017 and May 2018, a total of 603 patients who fulfilled the inclusion criteria were used to create a training set. An independent test set contained 230 patients between June 2018 and July 2018. Data regarding clinical parameters, laboratory tests, clinical examinations before dental extraction, and 1-week follow-up were retrieved. Predictors were identified by using logistic regression (LR) with penalized LASSO (least absolute shrinkage and selection operator) variable selection. Then, a prediction model was constructed based on the RF algorithm by using a 5-fold cross-validation method. Results: The training set, based on 603 participants, including 282 men and 321 women, had an average participant age of 72.38 ± 8.31 years. Using feature selection methods, 11 predictors for risk of cardiac complications were screened out. When the RF model was constructed, its overall classification accuracy was 0.82 at the optimal cutoff value of 18.5%. In comparison to the LR model, the RF model showed a superior predictive performance. The AUROC (area under the receiver operating characteristic curve) scores of the RF and LR models were 0.83 and 0.80, respectively, in the independent test set. The AUPRC (area under the precision-recall curve) scores of the RF and LR models were 0.56 and 0.35, respectively, in the independent test set. Conclusion: The RF-based prediction model is expected to be applicable for preoperative clinical assessment for preventing cardiac complications in elderly patients with CVDs undergoing dental extraction. The findings may aid physicians and dentists in making more informed recommendations to prevent cardiac complications in this patient population.