TY - JOUR
T1 - Understanding post-surgical decline in left ventricular function in primary mitral regurgitation using regression and machine learning models
AU - Zheng, Jingyi
AU - Li, Yuexin
AU - Billor, Nedret
AU - Ahmed, Mustafa I.
AU - Fang, Yu Hua Dean
AU - Pat, Betty
AU - Denney, Thomas S.
AU - Dell’Italia, Louis J.
N1 - © 2023 Zheng, Li, Billor, Ahmed, Fang, Pat, Denney and Dell'Italia.
PY - 2023
Y1 - 2023
N2 - BACKGROUND: Class I echocardiographic guidelines in primary mitral regurgitation (PMR) risks left ventricular ejection fraction (LVEF) < 50% after mitral valve surgery even with pre-surgical LVEF > 60%. There are no models predicting LVEF < 50% after surgery in the complex interplay of increased preload and facilitated ejection in PMR using cardiac magnetic resonance (CMR).OBJECTIVE: Use regression and machine learning models to identify a combination of CMR LV remodeling and function parameters that predict LVEF < 50% after mitral valve surgery.METHODS: CMR with tissue tagging was performed in 51 pre-surgery PMR patients (median CMR LVEF 64%), 49 asymptomatic (median CMR LVEF 63%), and age-matched controls (median CMR LVEF 64%). To predict post-surgery LVEF < 50%, least absolute shrinkage and selection operator (LASSO), random forest (RF), extreme gradient boosting (XGBoost), and support vector machine (SVM) were developed and validated in pre-surgery PMR patients. Recursive feature elimination and LASSO reduced the number of features and model complexity. Data was split and tested 100 times and models were evaluated
via stratified cross validation to avoid overfitting. The final RF model was tested in asymptomatic PMR patients to predict post-surgical LVEF < 50% if they had gone to mitral valve surgery.
RESULTS: Thirteen pre-surgery PMR had LVEF < 50% after mitral valve surgery. In addition to LVEF (
P = 0.005) and LVESD (
P = 0.13), LV sphericity index (
P = 0.047) and LV mid systolic circumferential strain rate (
P = 0.024) were predictors of post-surgery LVEF < 50%. Using these four parameters, logistic regression achieved 77.92% classification accuracy while RF improved the accuracy to 86.17%. This final RF model was applied to asymptomatic PMR and predicted 14 (28.57%) out of 49 would have post-surgery LVEF < 50% if they had mitral valve surgery.
CONCLUSIONS: These preliminary findings call for a longitudinal study to determine whether LV sphericity index and circumferential strain rate, or other combination of parameters, accurately predict post-surgical LVEF in PMR.
AB - BACKGROUND: Class I echocardiographic guidelines in primary mitral regurgitation (PMR) risks left ventricular ejection fraction (LVEF) < 50% after mitral valve surgery even with pre-surgical LVEF > 60%. There are no models predicting LVEF < 50% after surgery in the complex interplay of increased preload and facilitated ejection in PMR using cardiac magnetic resonance (CMR).OBJECTIVE: Use regression and machine learning models to identify a combination of CMR LV remodeling and function parameters that predict LVEF < 50% after mitral valve surgery.METHODS: CMR with tissue tagging was performed in 51 pre-surgery PMR patients (median CMR LVEF 64%), 49 asymptomatic (median CMR LVEF 63%), and age-matched controls (median CMR LVEF 64%). To predict post-surgery LVEF < 50%, least absolute shrinkage and selection operator (LASSO), random forest (RF), extreme gradient boosting (XGBoost), and support vector machine (SVM) were developed and validated in pre-surgery PMR patients. Recursive feature elimination and LASSO reduced the number of features and model complexity. Data was split and tested 100 times and models were evaluated
via stratified cross validation to avoid overfitting. The final RF model was tested in asymptomatic PMR patients to predict post-surgical LVEF < 50% if they had gone to mitral valve surgery.
RESULTS: Thirteen pre-surgery PMR had LVEF < 50% after mitral valve surgery. In addition to LVEF (
P = 0.005) and LVESD (
P = 0.13), LV sphericity index (
P = 0.047) and LV mid systolic circumferential strain rate (
P = 0.024) were predictors of post-surgery LVEF < 50%. Using these four parameters, logistic regression achieved 77.92% classification accuracy while RF improved the accuracy to 86.17%. This final RF model was applied to asymptomatic PMR and predicted 14 (28.57%) out of 49 would have post-surgery LVEF < 50% if they had mitral valve surgery.
CONCLUSIONS: These preliminary findings call for a longitudinal study to determine whether LV sphericity index and circumferential strain rate, or other combination of parameters, accurately predict post-surgical LVEF in PMR.
KW - LV circumferential strain rate
KW - machine learning
KW - mitral regurgitation (MR)
KW - post-surgical LVEF
KW - predictive models
UR - http://www.scopus.com/inward/record.url?scp=85158093323&partnerID=8YFLogxK
U2 - 10.3389/fcvm.2023.1112797
DO - 10.3389/fcvm.2023.1112797
M3 - 文章
C2 - 37153472
AN - SCOPUS:85158093323
SN - 2297-055X
VL - 10
SP - 1112797
JO - Frontiers in Cardiovascular Medicine
JF - Frontiers in Cardiovascular Medicine
M1 - 1112797
ER -