Cargando…
Understanding post-surgical decline in left ventricular function in primary mitral regurgitation using regression and machine learning models
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 incr...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10160646/ https://www.ncbi.nlm.nih.gov/pubmed/37153472 http://dx.doi.org/10.3389/fcvm.2023.1112797 |
_version_ | 1785037325974110208 |
---|---|
author | Zheng, Jingyi Li, Yuexin Billor, Nedret Ahmed, Mustafa I. Fang, Yu-Hua Dean Pat, Betty Denney, Thomas S. Dell’Italia, Louis J. |
author_facet | Zheng, Jingyi Li, Yuexin Billor, Nedret Ahmed, Mustafa I. Fang, Yu-Hua Dean Pat, Betty Denney, Thomas S. Dell’Italia, Louis J. |
author_sort | Zheng, Jingyi |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10160646 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-101606462023-05-06 Understanding post-surgical decline in left ventricular function in primary mitral regurgitation using regression and machine learning models Zheng, Jingyi Li, Yuexin Billor, Nedret Ahmed, Mustafa I. Fang, Yu-Hua Dean Pat, Betty Denney, Thomas S. Dell’Italia, Louis J. Front Cardiovasc Med Cardiovascular Medicine 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. Frontiers Media S.A. 2023-04-21 /pmc/articles/PMC10160646/ /pubmed/37153472 http://dx.doi.org/10.3389/fcvm.2023.1112797 Text en © 2023 Zheng, Li, Billor, Ahmed, Fang, Pat, Denney and Dell'Italia. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (https://creativecommons.org/licenses/by/4.0/) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Cardiovascular Medicine Zheng, Jingyi Li, Yuexin Billor, Nedret Ahmed, Mustafa I. Fang, Yu-Hua Dean Pat, Betty Denney, Thomas S. Dell’Italia, Louis J. Understanding post-surgical decline in left ventricular function in primary mitral regurgitation using regression and machine learning models |
title | Understanding post-surgical decline in left ventricular function in primary mitral regurgitation using regression and machine learning models |
title_full | Understanding post-surgical decline in left ventricular function in primary mitral regurgitation using regression and machine learning models |
title_fullStr | Understanding post-surgical decline in left ventricular function in primary mitral regurgitation using regression and machine learning models |
title_full_unstemmed | Understanding post-surgical decline in left ventricular function in primary mitral regurgitation using regression and machine learning models |
title_short | Understanding post-surgical decline in left ventricular function in primary mitral regurgitation using regression and machine learning models |
title_sort | understanding post-surgical decline in left ventricular function in primary mitral regurgitation using regression and machine learning models |
topic | Cardiovascular Medicine |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10160646/ https://www.ncbi.nlm.nih.gov/pubmed/37153472 http://dx.doi.org/10.3389/fcvm.2023.1112797 |
work_keys_str_mv | AT zhengjingyi understandingpostsurgicaldeclineinleftventricularfunctioninprimarymitralregurgitationusingregressionandmachinelearningmodels AT liyuexin understandingpostsurgicaldeclineinleftventricularfunctioninprimarymitralregurgitationusingregressionandmachinelearningmodels AT billornedret understandingpostsurgicaldeclineinleftventricularfunctioninprimarymitralregurgitationusingregressionandmachinelearningmodels AT ahmedmustafai understandingpostsurgicaldeclineinleftventricularfunctioninprimarymitralregurgitationusingregressionandmachinelearningmodels AT fangyuhuadean understandingpostsurgicaldeclineinleftventricularfunctioninprimarymitralregurgitationusingregressionandmachinelearningmodels AT patbetty understandingpostsurgicaldeclineinleftventricularfunctioninprimarymitralregurgitationusingregressionandmachinelearningmodels AT denneythomass understandingpostsurgicaldeclineinleftventricularfunctioninprimarymitralregurgitationusingregressionandmachinelearningmodels AT dellitalialouisj understandingpostsurgicaldeclineinleftventricularfunctioninprimarymitralregurgitationusingregressionandmachinelearningmodels |