Classification model based on strain measurements to identify patients with arrhythmogenic cardiomyopathy with left ventricular involvement. (May 2020)

• Record Type: Journal Article
• Authors: Vives-Gilabert, Yolanda; Zorio, Esther; Sanz-Sánchez, Jorge; Calvillo-Batllés, Pilar; Millet, José; Castells, Francisco
• Is Part Of: Computer methods and programs in biomedicine. Volume 188(2020)
• Publication Date: 2020-05
• Subjects: Cardiac magnetic resonance imaging -- Clustering -- Naïve Bayes classification; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2019.105296
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: This study applies PCA to consider both the amplitude and the shape of the LV strain time series in patients with AC. The patient group was heterogeneous, thus two different strain behaviors were identified (mild and severe impairment). Our novel classification model can assess the degree of LV strain impairment with a global accuracy of 82.76%. Regarding extreme values, patients with a severe strain impairment were effectively identified by the model (100% accuracy%). Moreover, LV strain was also clinically helpful in the grey zone to discriminate patients with mild impairment from controls. Abstract: Background and objective: A heterogenous expression characterizes arrhythmogenic cardiomyopathy (AC). The evaluation of regional wall movement included in the current Task Force Criteria is only qualitative and restricted to the right ventricle. However, a strain-based approach could precisely quantify myocardial deformation in both ventricles. We aim to define and modelize the strain behavior of the left ventricle in AC patients with left ventricular (LV) involvement by applying algorithms such as Principal Component Analysis (PCA), clustering and naïve Bayes (NB) classifiers. Methods: Thirty-six AC patients with LV involvement and twenty-three non-affected family members (controls) were enrolled. Feature-tracking analysis was applied to cine cardiac magnetic resonance imaging to assess strain time series from a 3D approach, to which PCA was applied. A Two-Step clustering algorithm separated the patients' group into clusters according to their level of LV strain impairment. A statistical characterization between controls and the new AC subgroups was done. Finally, a NB classifier was built and new data from a small evolutive dataset was predicted. Results: 60% of AC-LV patients showed mildly affected strain and 40% severely affected strain. Both groups and controls exhibited statistically significant differences, especially when comparing controls and severely affected AC-LV patients. The classification accuracy of the strain NB classifier reached 82.76%. The model performance was as good as to classify the individuals with a 100% sensitivity and specificity for severely impaired strain patients, 85.7% and 81.1% for mildly impaired strain patients, and 69.9% and 91.4% for normal strain, respectively. Even when the severely affected LV-AC group was excluded, LV strain showed a good accuracy to differentiate patients and controls. The prediction of the evolutive dataset revealed a progressive alteration of strain in time. Conclusions: Our LV strain classification model may help to identify AC patients with LV involvement, at least in a setting of a high pretest probability, such as family screening. Graphical abstract: Image, graphical abstract