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A speckle-tracking strain-based artificial neural network model to differentiate cardiomyopathy type.

基于斑点追踪应变的人工神经网络模型鉴别心肌病类型。

  • 影响因子:1.33
  • DOI:10.1080/14017431.2019.1678764
  • 作者列表:"Walsh JL","AlJaroudi WA","Lamaa N","Abou Hassan OK","Jalkh K","Elhajj IH","Sakr G","Isma'eel H
  • 发表时间:2020-04-01
Abstract

:Objectives. In heart failure, invasive angiography is often employed to differentiate ischaemic from non-ischaemic cardiomyopathy. We aim to examine the predictive value of echocardiographic strain features alone and in combination with other features to differentiate ischaemic from non-ischaemic cardiomyopathy, using artificial neural network (ANN) and logistic regression modelling. Design. We retrospectively identified 204 consecutive patients with an ejection fraction <50% and a diagnostic angiogram. Patients were categorized as either ischaemic (n = 146) or non-ischaemic cardiomyopathy (n = 58). For each patient, left ventricular strain parameters were obtained. Additionally, regional wall motion abnormality, 13 electrocardiographic (ECG) features and six demographic features were retrieved for analysis. The entire cohort was randomly divided into a derivation and a validation cohort. Using the parameters retrieved, logistic regression and ANN models were developed in the derivation cohort to differentiate ischaemic from non-ischaemic cardiomyopathy, the models were then tested in the validation cohort. Results. A final strain-based ANN model, full feature ANN model and full feature logistic regression model were developed and validated, F1 scores were 0.82, 0.79 and 0.63, respectively. Conclusions. Both ANN models were more accurate at predicting cardiomyopathy type than the logistic regression model. The strain-based ANN model should be validated in other cohorts. This model or similar models could be used to aid the diagnosis of underlying heart failure aetiology in the form of the online calculator (https://cimti.usj.edu.lb/strain/index.html) or built into echocardiogram software.

摘要

: 目标。在心力衰竭中,侵入性血管造影常被用来鉴别缺血性和非缺血性心肌病。我们的目的是使用人工神经网络 (ANN) 和 logistic 回归模型,单独检查超声心动图应变特征并结合其他特征区分缺血性和非缺血性心肌病的预测价值。设计。我们回顾性地确定了 204 例射血分数 <50% 的连续患者和诊断性血管造影。将患者分为缺血性 (n = 146) 或非缺血性心肌病 (n = 58)。对于每个患者,获得左心室应变参数。此外,检索局部室壁运动异常、 13 项心电图 (ECG) 特征和 6 项人口统计学特征进行分析。将整个队列随机分为推导队列和验证队列。利用检索到的参数,在推导队列中开发了 logistic 回归和 ANN 模型,以区分缺血性和非缺血性心肌病,然后在验证队列中测试模型。结果。开发并验证了最终的基于应变的 ANN 模型、全特征 ANN 模型和全特征 logistic 回归模型,F1 评分分别为 0.82 、 0.79 和 0.63。结论。两种 ANN 模型在预测心肌病类型方面均比 logistic 回归模型更准确。基于应变的 ANN 模型应在其他队列中进行验证。该模型或类似模型可用于以在线计算器 ( https://cimti.usj.edu.lb/strain/index.html ) 或内置于超声心动图软件中。

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发表时间:2020-01-12
DOI:10.1186/s12872-019-01311-4
作者列表:["Nattawut Wongpraparut","Sarawut Siwamogsatham","Tomorn Thongsri","Pornchai Ngamjanyaporn","Arintaya Phrommintikul","Kompoj Jirajarus","Tarinee Tangcharoen","Kid Bhumimuang","Pinij Kaewsuwanna","Rungroj Krittayaphong","Rungtiwa Pongakasira","Harvey D. White"]

METHODS:Abstract Background Ischemic cardiomyopathy is a high-cost, resource-intensive public health burden that is associated with a 1-year mortality rate of about 16% in western population. Different in patient ethnicity and pattern of practice may impact the clinical outcome. We aim to determine 1-year mortality and to identify factors that significantly predicts 1-year mortality of Thai patients with ischemic cardiomyopathy. Methods This prospective multicenter registry enrolled consecutive Thai patients that were diagnosed with ischemic cardiomyopathy at 9 institutions located across Thailand. Patients with left ventricular function  75% in the left main or proximal left anterior descending artery or coronary angiography, and/or two major epicardial coronary stenoses; 2) prior myocardial infarction; 3) prior revascularization by coronary artery bypass graft or percutaneous coronary intervention; or, 4) magnetic resonance imaging pattern compatible with ischemic cardiomyopathy. Baseline clinical characteristics, coronary and echocardiographic data were recorded. The 1-year clinical outcome was pre-specified. Results Four hundred and nineteen patients were enrolled. Thirty-nine patients (9.9%) had died at 1 year, with 27 experiencing cardiovascular death, and 12 experiencing non-cardiovascular death. A comparison between patients who were alive and patients who were dead at 1 year revealed lower baseline left ventricular ejection fraction (LVEF) (26.7 ± 7.6% vs 30.2 ± 7.8%; p = 0.021), higher left ventricular end-diastolic volume (LVEDV) (185.8 ± 73.2 ml vs 155.6 ± 64.2 ml; p = 0.014), shorter mitral valve deceleration time (142.9 ± 57.5 ml vs 182.4 ± 85.7 ml; p = 0.041), and lower use of statins (94.7% vs 99.7%; p = 0.029) among deceased patients. Patients receiving guideline-recommended β-blockers had lower mortality than patients receiving non-guideline-recommended β-blockers (8.1% vs 18.2%; p = 0.05). Conclusions The results of this study revealed a 9.9% 1-year mortality rate among Thai ischemic cardiomyopathy patients. Doppler echocardiographic parameters significantly associated with 1-year mortality were LVEF, LVEDV, mitral E velocity, and mitral valve deceleration time. The use of non-guideline-recommended β-blockers rather than guideline recommended β-blockers were associated with increased with 1-year mortality. Guidelines recommended β-blockers should be preferred. Trial registration Thai Clinical Trials Registry TCTR20190722002. Registered 22 July 2019. “Retrospectively registered”.

影响因子:4.69
发表时间:2020-01-07
DOI:10.1186/s12968-019-0590-z
作者列表:["Yao-Dan Liang","Yuan-Wei Xu","Wei-Hao Li","Ke Wan","Jia-Yu Sun","Jia-Yi Lin","Qing Zhang","Xiao-Yue Zhou","Yu-Cheng Chen"]

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影响因子:4.69
发表时间:2020-01-05
DOI:10.1186/s12968-019-0589-5
作者列表:["Yingxia Yang","Gang Yin","Yong Jiang","Lei Song","Shihua Zhao","Minjie Lu"]

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