Real-World Data and Machine Learning to Predict Cardiac Amyloidosis

Abstract

(1) Background: Cardiac amyloidosis or “stiff heart syndrome” is a rare condition that occurs when amyloid deposits occupy the heart muscle. Many patients suffer from it and fail to receive a timely diagnosis mainly because the disease is a rare form of restrictive cardiomyopathy that is difficult to diagnose, often associated with a poor prognosis. This research analyses the characteristics of this pathology and proposes a statistical learning algorithm that helps to detect the disease. (2) Methods: The hospitalization clinical (medical and nursing ones) records used for this study are the basis of the learning and training techniques of the algorithm. The approach consisted of using the information generated by the patients in each admission and discharge episode and treating it as data vectors to facilitate their aggregation. The large volume of clinical histories implied a high dimensionality of the data, and the lack of diagnosis led to a severe class imbalance caused by the low prevalence of the disease. (3) Results: Although there are few patients with amyloidosis in this study, the proposed approach demonstrates that it is possible to learn from clinical records despite the lack of data. In the validation phase, the algorithm first acted on data from the general study population. It then was applied to a sample of patients diagnosed with heart failure. The results revealed that the algorithm detects disease when data vectors profile each disease episode. (4) Conclusions: The prediction levels showed that this technique could be useful in screening processes on a specific population to detect the disease.

Machine Learning Approaches in Diagnosis, Prognosis and Treatment Selection of Cardiac Amyloidosis

“…The findings showed that when data vectors profile each disease occurrence, the algorithm may detect disease. The prediction levels demonstrated that this method may be helpful in illness screening procedures on a particular group [ 175 ].…”

Machine Learning Methods in Real-World Studies of Cardiovascular Disease

“…A recent retrospective analysis of transversal RWS in more than 11,000 patients above 65 years of age has been performed with principal component analysis (PCA), clustering, synthetic minority oversampling, and logistic regression to diagnose cardiac amyloidosis, a rare disease in which poor diagnostic capability results in treatment delays [18]. Analyses of data with high dimensionality, low prevalence, and missing data in EHRs containing structured and unstructured records have relied on the processing and pipelines of data governance and analysis by ML algorithms to transform the investigation of cardiac amyloidosis to meet patient needs.…”

A Machine Learning Challenge: Detection of Cardiac Amyloidosis Based on Bi-Atrial and Right Ventricular Strain and Cardiac Function

“…Early CA diagnostics is usually hindered by indistinguishable clinical presentation and greater awareness of more prevalent hypertrophic diseases and heart failure syndromes. Thus, CA can be considered underdiagnosed, consequently delaying necessary therapeutic measures, reducing quality of life and worsening clinical prognosis [ 2 , 3 ].…”

“…Machine learning is a function of artificial intelligence, used as a promising alternative to overcoming the limitations of traditional prediction approaches [ 17 ]. Recent supervised and unsupervised machine learning approaches have proven highly valuable for the classification, regression and prediction of complex datasets in all industrial sectors, including the medical field of CA diagnostics [ 3 , 18 , 19 ]. For example, promising machine learning aspects have been based on CA-specific laboratory profiles of heart failure patients [ 19 ] or CA-associated ECG mapping [ 18 ].…”