Jun Zhang, Song Luo, Li Qi, Shutian Xu, Dongna Yi, Yue Jiang, Xiang Kong, Tongyuan Liu, Weiqiang Dou, Jun Cai, Long Jiang Zhang
{"title":"Cardiovascular magnetic resonance feature tracking derived strain analysis can predict return to training following exertional heatstroke.","authors":"Jun Zhang, Song Luo, Li Qi, Shutian Xu, Dongna Yi, Yue Jiang, Xiang Kong, Tongyuan Liu, Weiqiang Dou, Jun Cai, Long Jiang Zhang","doi":"10.1016/j.jocmr.2024.101076","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Exertional heatstroke (EHS) is increasingly common in young trained soldiers. However, prognostic markers in EHS patients remain unclear. The objective of this study was to evaluate cardiovascular magnetic resonance (CMR) feature tracking derived left ventricle (LV) strain as a biomarker for return to training (RTT) in trained soldiers with EHS.</p><p><strong>Methods: </strong>Trained soldiers (participants) with EHS underwent CMR cine sequences between June 2020 and August 2023. Two-dimensional (2D) LV strain parameters were derived. At 3 months after index CMR, the participants with persistent cardiac symptoms including chest pain, dyspnea, palpitations, syncope, and recurrent heat-related illness were defined as non-RTT. Multivariable logistic regression analysis was used to develop a predictive RTT model. The performance of different models was compared using the area under curve (AUC).</p><p><strong>Results: </strong>A total of 80 participants (median age, 21 years; interquartile range (IQR), 20-23 years) and 27 health controls (median age, 21 years; IQR, 20-22 years) were prospectively included. Of the 77 participants, 32 had persistent cardiac symptoms and were not able to RTT at 3 months follow-up after experiencing EHS. The 2D global longitudinal strain (GLS) was significantly impaired in EHS participants compared to the healthy control group (-15.8 ± 1.7% vs -16.9 ± 1.2%, P = 0.001), which also showed significant statistical differences between participants with RTT and non-RTT (-15.0 ± 3.5% vs -16.5 ± 1.4%, P < 0.001). 2D-GLS (≤ -15.0%) (odds ratio, 1.53; 95% confidence interval: 1.08, 2.17; P = 0.016) was an independent predictor for RTT even after adjusting known risk factors. 2D-GLS provided incremental prognostic value over the clinical model and conventional CMR parameters model (AUCs: 0.72 vs 0.88, P = 0.013; 0.79 vs 0.88, P = 0.023; respectively).</p><p><strong>Conclusion: </strong>Two-dimensional global longitudinal strain (≤ -15.0%) is an incremental prognostic CMR biomarker to predict RTT in soldiers suffering from EHS.</p>","PeriodicalId":15221,"journal":{"name":"Journal of Cardiovascular Magnetic Resonance","volume":null,"pages":null},"PeriodicalIF":4.2000,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11417221/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Cardiovascular Magnetic Resonance","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.jocmr.2024.101076","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CARDIAC & CARDIOVASCULAR SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Exertional heatstroke (EHS) is increasingly common in young trained soldiers. However, prognostic markers in EHS patients remain unclear. The objective of this study was to evaluate cardiovascular magnetic resonance (CMR) feature tracking derived left ventricle (LV) strain as a biomarker for return to training (RTT) in trained soldiers with EHS.
Methods: Trained soldiers (participants) with EHS underwent CMR cine sequences between June 2020 and August 2023. Two-dimensional (2D) LV strain parameters were derived. At 3 months after index CMR, the participants with persistent cardiac symptoms including chest pain, dyspnea, palpitations, syncope, and recurrent heat-related illness were defined as non-RTT. Multivariable logistic regression analysis was used to develop a predictive RTT model. The performance of different models was compared using the area under curve (AUC).
Results: A total of 80 participants (median age, 21 years; interquartile range (IQR), 20-23 years) and 27 health controls (median age, 21 years; IQR, 20-22 years) were prospectively included. Of the 77 participants, 32 had persistent cardiac symptoms and were not able to RTT at 3 months follow-up after experiencing EHS. The 2D global longitudinal strain (GLS) was significantly impaired in EHS participants compared to the healthy control group (-15.8 ± 1.7% vs -16.9 ± 1.2%, P = 0.001), which also showed significant statistical differences between participants with RTT and non-RTT (-15.0 ± 3.5% vs -16.5 ± 1.4%, P < 0.001). 2D-GLS (≤ -15.0%) (odds ratio, 1.53; 95% confidence interval: 1.08, 2.17; P = 0.016) was an independent predictor for RTT even after adjusting known risk factors. 2D-GLS provided incremental prognostic value over the clinical model and conventional CMR parameters model (AUCs: 0.72 vs 0.88, P = 0.013; 0.79 vs 0.88, P = 0.023; respectively).
Conclusion: Two-dimensional global longitudinal strain (≤ -15.0%) is an incremental prognostic CMR biomarker to predict RTT in soldiers suffering from EHS.
期刊介绍:
Journal of Cardiovascular Magnetic Resonance (JCMR) publishes high-quality articles on all aspects of basic, translational and clinical research on the design, development, manufacture, and evaluation of cardiovascular magnetic resonance (CMR) methods applied to the cardiovascular system. Topical areas include, but are not limited to:
New applications of magnetic resonance to improve the diagnostic strategies, risk stratification, characterization and management of diseases affecting the cardiovascular system.
New methods to enhance or accelerate image acquisition and data analysis.
Results of multicenter, or larger single-center studies that provide insight into the utility of CMR.
Basic biological perceptions derived by CMR methods.