One-year mortality prognosis in heart failure: A neural network approach based on echocardiographic data

Abstract

The artificial neural network method has proved to be reliable for implementing quantitative prognosis of mortality in patients with heart failure. Additional studies with larger numbers of patients are required to better assess the usefulness of artificial neural networks.

Ambulatory blood pressure monitoring of patients with heart failure: a new prognosis marker

“…Analysis of myocardial impairment ith echo-Doppler cardiography is one of the most used because it is a noninvasive examination, inexpensive, and easy to perform. Left ventricular ejection fraction and diastolic diameter used in the present sample are variables routinely used in the anatomical-functional and prognostic evaluation of patients with congestive heart failure, but nonetheless, are subject to criticism [12][13][14][15][16] . In clinical experience, it is evident that patients with larger ventricular dilation and smaller ejection fraction have a worse prognosis; however, it is not uncommon for patients with important compromise to have irrelevant clinical repercussion over a long evolutionary period.…”

“…, it is important to emphasize that, in other studies, it was characterized as a variable, identifying patients with distinct evolutionary potentials 15,17,18 . The studies report that ventricular function indexes less influenced by loading conditions, such as the relation between final systolic pressure (or stress)/ final systolic volume (or diameter), and the relation between ejection fraction (or shortening percentage)/wall final systolic stress, are better mortality predictors than the classical indexes of ejection phase, such as ejection fraction, percentage of shortening of myocardial fiber, and velocity of circumferencial shortening 20 .…”

Machine Learning and Deep Neural Networks in Thoracic and Cardiovascular Imaging

“…Ortiz et al 30 used neural networks to analyze cardiac contractility to predict 1-year mortality in patients with heart failure. Since this early work, supervised machine learning 26 RV, LV endocardium and epicardium CNN Tan et al 27 LV segmentation ANN Baessler et al 28 Myocardial scar detection Random forests Dawes et al 29 Pulmonary hypertension prognosis PCA ECHO Ortiz et al 30 HF prognosis ANN Narula et al 31 HCM vs athlete's heart SVM, Random forests, ANN Sengupta et al 32 Constrictive pericarditis vs restrictive cardiomyopathy AMC, random forest, k-NN, SVM Sengur 33 Valvular disease SVM Moghaddasi and Nourian 34 MR severity SVM Vidya et al 35 MI detection SVM CT Wolterink et al 36 CAC scoring CNN Isgum et al 37 CAC scoring k-NN, SVM Itu et al 38 FFR estimation deep neural network Motwani et al 39 Prognosis Logistic regression Mannil et al 40 MI detection Decision tree, k-NN, random forest, ANN 32 diagnose valvular heart disease, 33 grade severity of mitral valve regurgitation, 34 automate ejection fraction measurement, 53 and detect the presence of myocardial infarction. 35,54 Several machine learning applications have also been developed to assist in the interpretation of CT. For example, algorithms have been developed for the automation of coronary artery calcium scoring 36,37,55,56 and assessment of the functional significance of coronary lesions.…”

Use of Multiple Biomarkers in Heart Failure

“…This could be accomplished through handheld computers with applications for interpretation of multiple data points or through the presentation of risk model results as part of the routine laboratory report. As the field moves forward, one option is to take advantage of advances in the field of computation biology by using artificial neural networks to analyze complex changes in multiple biomarkers simultaneously [43][44][45]. The main advantage of neural networks over traditional statistical techniques is that the model does not have to be explicitly defined before beginning the analysis.…”