Predictive risk factors for adverse events during tooth extraction among elderly patients with cardiovascular diseases - Shaojun Ma; Xu Chen; Yurun Zhai; Xinyi Sun; Jing Sheng; Yun Sun; Haiya Wang (Annals of Medicine)
Background Tooth extraction is a risk factor for cardiovascular events, particularly in elderly patients. However, no clinical tool has been developed to date to predict the risk of adverse events (AEs) during tooth extraction.Materials and Methods We prospectively enrolled 774 elderly patients (aged ≥ 60 years) with cardiovascular disease (CVD) who were scheduled to undergo tooth extraction at the dental surgery department of Shanghai Ninth People’s Hospital from January 2021 to July 2022. To determine the predictive risk factors for AEs, we collected and recorded 62 factors on general characteristics, clinical information, physical and imaging examinations, psychological tests, perioperative characteristics, and surgical characteristics.Results We used a univariate logistic regression model to explore the 62 potential risk factors and included 21 factors in a multivariate model (all P-values < 0.05). After stepwise selection, 11 factors, including age, systolic blood pressure, severe hypertension, history of pacemaker use, stroke, ejection fraction, valvular insufficiency, atrial premature beats, ventricular premature beats, extraction of more than one tooth and the General Health Questionnaire-28 score, were included in the predictive model (all P-values < 0.05). In the test group, the area under the curve was 0.893 (0.866, 0.919), sensitivity was 0.878 (0.827, 0.93), specificity was 0.735 (0.697, 0.773) and accuracy was 0.768 (0.736, 0.800). In the validation group, these values were 0.857 (0.760, 0.954), 0.938 (0.819, 1.056) and 0.524 (0.417, 0.631), respectively. We created a nomogram to predict the risk factors for AEs during tooth extraction. Mental status plays a critical role in the risk of adverse effects, and the blood pressure also has a key influence on the prediction of adverse effects.Conclusions We developed and validated a predictive model with 11 clinical factors for the AEs during tooth extraction in elderly patients with CVD with well efficiency.