Department of Geriatrics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Department of Oral Surgery, Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Gerontology. 2019;65(6):591-598. doi: 10.1159/000497424. Epub 2019 May 2.
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.
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.
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.
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.
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.
随着老年人口的快速增长，老年心血管疾病（CVD）患者的拔牙变得非常普遍。如何确保接受拔牙的老年 CVD 患者的安全是多年来困扰牙医和内科医生的问题。为该人群制定合适的风险预测工具非常重要。
本回顾性观察研究旨在建立和验证一种基于随机森林（RF）算法的预测模型，用于预测老年 CVD 患者拔牙后发生心脏并发症的风险。
2017 年 8 月至 2018 年 5 月，共有 603 名符合纳入标准的患者用于创建训练集。一个独立的测试集包含 2018 年 6 月至 7 月期间的 230 名患者。检索了与临床参数、实验室检查、拔牙前临床检查和 1 周随访相关的数据。使用逻辑回归（LR）与惩罚 LASSO（最小绝对值收缩和选择算子）变量选择确定预测因子。然后，通过使用 5 折交叉验证方法，基于 RF 算法构建预测模型。
基于 603 名参与者的训练集，包括 282 名男性和 321 名女性，参与者的平均年龄为 72.38 ± 8.31 岁。使用特征选择方法，筛选出 11 个与心脏并发症风险相关的预测因子。当构建 RF 模型时，其在最优截断值为 18.5%时的整体分类准确性为 0.82。与 LR 模型相比，RF 模型显示出更好的预测性能。RF 和 LR 模型在独立测试集中的 AUROC（接收者操作特征曲线下的面积）评分分别为 0.83 和 0.80。RF 和 LR 模型在独立测试集中的 AUPRC（精准召回曲线下的面积）评分分别为 0.56 和 0.35。
基于 RF 的预测模型有望用于术前临床评估，以预防老年 CVD 患者拔牙后的心脏并发症。研究结果可能有助于医生和牙医为该患者群体做出更明智的建议，以预防心脏并发症。
