Ernest Spitzer, Benjamin Camacho, Blaz Mrevlje, Hans-Jelle Brandendburg, Claire B Ren
{"title":"超声心动图核心实验室验证的一个新的供应商独立的基于网络的软件评估左心室整体纵向应变。","authors":"Ernest Spitzer, Benjamin Camacho, Blaz Mrevlje, Hans-Jelle Brandendburg, Claire B Ren","doi":"10.4250/jcvi.2022.0130","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Global longitudinal strain (GLS) is an accurate and reproducible parameter of left ventricular (LV) systolic function which has shown meaningful prognostic value. Fast, user-friendly, and accurate tools are required for its widespread implementation. We aim to compare a novel web-based tool with two established algorithms for strain analysis and test its reproducibility.</p><p><strong>Methods: </strong>Thirty echocardiographic datasets with focused LV acquisitions were analyzed using three different semi-automated endocardial GLS algorithms by two readers. Analyses were repeated by one reader for the purpose of intra-observer variability. CAAS Qardia (Pie Medical Imaging) was compared with 2DCPA and AutoLV (TomTec).</p><p><strong>Results: </strong>Mean GLS values were -15.0 ± 3.5% from Qardia, -15.3 ± 4.0% from 2DCPA, and -15.2 ± 3.8% from AutoLV. Mean GLS between Qardia and 2DCPA were not statistically different (p = 0.359), with a bias of -0.3%, limits of agreement (LOA) of 3.7%, and an intra-class correlation coefficient (ICC) of 0.88. Mean GLS between Qardia and AutoLV were not statistically different (p = 0.637), with a bias of -0.2%, LOA of 3.4%, and an ICC of 0.89. The coefficient of variation (CV) for intra-observer variability was 4.4% for Qardia, 8.4% 2DCPA, and 7.7% AutoLV. The CV for inter-observer variability was 4.5%, 8.1%, and 8.0%, respectively.</p><p><strong>Conclusions: </strong>In echocardiographic datasets of good image quality analyzed at an independent core laboratory using a standardized annotation method, a novel web-based tool for GLS analysis showed consistent results when compared with two algorithms of an established platform. Moreover, inter- and intra-observer reproducibility results were excellent.</p>","PeriodicalId":15229,"journal":{"name":"Journal of Cardiovascular Imaging","volume":"31 3","pages":"135-141"},"PeriodicalIF":0.0000,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/19/7f/jcvi-31-135.PMC10374390.pdf","citationCount":"1","resultStr":"{\"title\":\"Echocardiography Core Laboratory Validation of a Novel Vendor-Independent Web-Based Software for the Assessment of Left Ventricular Global Longitudinal Strain.\",\"authors\":\"Ernest Spitzer, Benjamin Camacho, Blaz Mrevlje, Hans-Jelle Brandendburg, Claire B Ren\",\"doi\":\"10.4250/jcvi.2022.0130\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Global longitudinal strain (GLS) is an accurate and reproducible parameter of left ventricular (LV) systolic function which has shown meaningful prognostic value. Fast, user-friendly, and accurate tools are required for its widespread implementation. We aim to compare a novel web-based tool with two established algorithms for strain analysis and test its reproducibility.</p><p><strong>Methods: </strong>Thirty echocardiographic datasets with focused LV acquisitions were analyzed using three different semi-automated endocardial GLS algorithms by two readers. Analyses were repeated by one reader for the purpose of intra-observer variability. CAAS Qardia (Pie Medical Imaging) was compared with 2DCPA and AutoLV (TomTec).</p><p><strong>Results: </strong>Mean GLS values were -15.0 ± 3.5% from Qardia, -15.3 ± 4.0% from 2DCPA, and -15.2 ± 3.8% from AutoLV. Mean GLS between Qardia and 2DCPA were not statistically different (p = 0.359), with a bias of -0.3%, limits of agreement (LOA) of 3.7%, and an intra-class correlation coefficient (ICC) of 0.88. Mean GLS between Qardia and AutoLV were not statistically different (p = 0.637), with a bias of -0.2%, LOA of 3.4%, and an ICC of 0.89. The coefficient of variation (CV) for intra-observer variability was 4.4% for Qardia, 8.4% 2DCPA, and 7.7% AutoLV. The CV for inter-observer variability was 4.5%, 8.1%, and 8.0%, respectively.</p><p><strong>Conclusions: </strong>In echocardiographic datasets of good image quality analyzed at an independent core laboratory using a standardized annotation method, a novel web-based tool for GLS analysis showed consistent results when compared with two algorithms of an established platform. Moreover, inter- and intra-observer reproducibility results were excellent.</p>\",\"PeriodicalId\":15229,\"journal\":{\"name\":\"Journal of Cardiovascular Imaging\",\"volume\":\"31 3\",\"pages\":\"135-141\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/19/7f/jcvi-31-135.PMC10374390.pdf\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Cardiovascular Imaging\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4250/jcvi.2022.0130\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Cardiovascular Imaging","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4250/jcvi.2022.0130","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Medicine","Score":null,"Total":0}
引用次数: 1
摘要
背景:全局纵向应变(GLS)是一种准确、可重复的左室收缩功能参数,具有重要的预后价值。它的广泛实施需要快速、用户友好和准确的工具。我们的目标是比较一个新的基于网络的工具与两种既定的算法应变分析和测试其再现性。方法:采用三种不同的半自动心内膜GLS算法,对30个超声心动图数据集进行分析。为了观察观察者内部的可变性,分析由一位读者重复进行。将CAAS Qardia (Pie Medical Imaging)与2DCPA和AutoLV (TomTec)进行比较。结果:Qardia组GLS平均值为-15.0±3.5%,2DCPA组为-15.3±4.0%,AutoLV组为-15.2±3.8%。Qardia和2DCPA的平均GLS无统计学差异(p = 0.359),偏倚为-0.3%,一致限(LOA)为3.7%,类内相关系数(ICC)为0.88。Qardia和AutoLV的平均GLS无统计学差异(p = 0.637),偏倚为-0.2%,LOA为3.4%,ICC为0.89。Qardia的观察者内部变异系数(CV)为4.4%,2DCPA为8.4%,AutoLV为7.7%。观察者间变异的CV分别为4.5%、8.1%和8.0%。结论:在独立核心实验室使用标准化注释方法分析的良好图像质量的超声心动图数据集中,一种新型的基于网络的GLS分析工具与已建立平台的两种算法相比,结果一致。此外,观察者之间和观察者内部的可重复性结果非常好。
Echocardiography Core Laboratory Validation of a Novel Vendor-Independent Web-Based Software for the Assessment of Left Ventricular Global Longitudinal Strain.
Background: Global longitudinal strain (GLS) is an accurate and reproducible parameter of left ventricular (LV) systolic function which has shown meaningful prognostic value. Fast, user-friendly, and accurate tools are required for its widespread implementation. We aim to compare a novel web-based tool with two established algorithms for strain analysis and test its reproducibility.
Methods: Thirty echocardiographic datasets with focused LV acquisitions were analyzed using three different semi-automated endocardial GLS algorithms by two readers. Analyses were repeated by one reader for the purpose of intra-observer variability. CAAS Qardia (Pie Medical Imaging) was compared with 2DCPA and AutoLV (TomTec).
Results: Mean GLS values were -15.0 ± 3.5% from Qardia, -15.3 ± 4.0% from 2DCPA, and -15.2 ± 3.8% from AutoLV. Mean GLS between Qardia and 2DCPA were not statistically different (p = 0.359), with a bias of -0.3%, limits of agreement (LOA) of 3.7%, and an intra-class correlation coefficient (ICC) of 0.88. Mean GLS between Qardia and AutoLV were not statistically different (p = 0.637), with a bias of -0.2%, LOA of 3.4%, and an ICC of 0.89. The coefficient of variation (CV) for intra-observer variability was 4.4% for Qardia, 8.4% 2DCPA, and 7.7% AutoLV. The CV for inter-observer variability was 4.5%, 8.1%, and 8.0%, respectively.
Conclusions: In echocardiographic datasets of good image quality analyzed at an independent core laboratory using a standardized annotation method, a novel web-based tool for GLS analysis showed consistent results when compared with two algorithms of an established platform. Moreover, inter- and intra-observer reproducibility results were excellent.