Logistic regression with recursive feature elimination had modest ability in predicting the need for permanent pacemaker implantation after TAVR, achieving an ROC of 0.63 on the test set.
Observational (n=513)
No
Can a logistic regression model using Recursive Feature Elimination accurately predict the need for permanent pacemaker implantation in patients undergoing TAVR?
A machine learning model using recursive feature elimination identified pre-operative RBBB, prolonged QRS duration, and valve size as key predictors for PPMI after TAVR, demonstrating modest predictive ability.
Effect estimate: ROC 0.63
Introduction: TAVR is increasingly being performed because of several recent large-scale clinical trials supporting its use across a range of patient sub-classes. Although it provides life-altering relief of severe aortic stenosis, adverse outcomes are not uncommon including paravalvular leak, life-threatening bleeding, acute kidney injury, stroke, and PPMI. RFE can help certain classification models like logistic regression in better predicting binary variables such as PPMI by creating a model based upon a set of predictors and then progressively eliminating variables to optimize the model. Objective: To determine whether RFE when applied to logistic regression would result in discriminatory ability in the prediction of PPM implantation in patients undergoing TAVR Methods: Pre- and postoperative data from a single institution were collected for all patients undergoing TAVR without a history of PPMI between January 2016 and December 2019. EKG data obtained included QRS duration, presence of atrioventricular block, left anterior and posterior fasicular block and right bundle branch block(RBBB). Data was imported into Python and a stratified 5 fold cross validation with SMOTE oversampling was run with RFE running at every fold to avoid overfitting. Upon completion, a rank score was tabulated for each predictor and a final logistic regression model with the highest optimized receiver under the operator curve was exported and applied to a test set. The receiver under the operator score was calculated for the training and test sets and the variables of importance were identified using RFE. Results: The total sample size for this cohort was 513 patients, with a PPMI incidence of 8.58%. The training set consisted of 40 variables and 384 patients, and the test set had 129 patients. The final optimized model on the training set had an ROC of 0.75 and utilized three features out of forty. The three features identified by RFE were: implanted TAVR valve size, QRS duration, and presence of RBBB. The model had a ROC of 0.63 on the test set. Conclusions: Our results show that presence of pre-op RBBB, prolonged QRS duration and valve size were risk factors for PPMI. Logistic regression classification had modest ability in predicting the need for PPMI after TAVR.
Ilyas et al. (Thu,) conducted a observational in Patients undergoing TAVR (n=513). Logistic regression with Recursive Feature Elimination (RFE) was evaluated on Prediction of permanent pacemaker implantation (PPMI) (ROC 0.63). Logistic regression with recursive feature elimination had modest ability in predicting the need for permanent pacemaker implantation after TAVR, achieving an ROC of 0.63 on the test set.