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.
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