Ma Shaojun, Chen Xu, Zhai Yurun, Sun Xinyi, Sheng Jing, Sun Yun, Wang Haiya
Department of Geriatrics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Ann Med. 2025 Dec;57(1):2448274. doi: 10.1080/07853890.2024.2448274. Epub 2025 Jan 2.
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.
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.
We used a univariate logistic regression model to explore the 62 potential risk factors and included 21 factors in a multivariate model (all -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 -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.
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.
拔牙是心血管事件的一个风险因素，尤其在老年患者中。然而，迄今为止尚未开发出临床工具来预测拔牙期间不良事件（AE）的风险。
我们前瞻性纳入了774例年龄≥60岁的患有心血管疾病（CVD）且计划于2021年1月至2022年7月在上海第九人民医院口腔科进行拔牙的老年患者。为了确定AE的预测风险因素，我们收集并记录了62项关于一般特征、临床信息、体格和影像学检查、心理测试、围手术期特征及手术特征的因素。
我们使用单因素逻辑回归模型探索这62个潜在风险因素，并将21个因素纳入多因素模型（所有P值<0.05）。经过逐步选择，11个因素，包括年龄、收缩压、重度高血压、起搏器使用史、中风、射血分数、瓣膜关闭不全、房性早搏、室性早搏、拔除多颗牙齿及一般健康问卷-28评分，被纳入预测模型（所有P值<0.05）。在测试组中，曲线下面积为0.893（0.866，0.919），敏感性为0.878（0.827，0.93），特异性为0.735（0.697，0.773），准确性为0.768（0.736，0.800）。在验证组中，这些值分别为0.857（0.760，0.954）、0.938（0.819，1.056）和0.524（0.417，0.631）。我们创建了一个列线图来预测拔牙期间AE的风险因素。精神状态在不良反应风险中起关键作用，血压对不良反应的预测也有重要影响。
我们开发并验证了一个包含11个临床因素的预测模型，用于高效预测老年CVD患者拔牙期间的AE。
