Dataset Information

Left Ventricular Noncompaction Detected by Cardiac Magnetic Resonance Screening: A Reexamination of Diagnostic Criteria.

ABSTRACT: In a previous cross-sectional screening study of 5,169 middle and high school students (mean age, 13.1 ± 1.78 yr) in which we estimated the prevalence of high-risk cardiovascular conditions associated with sudden cardiac death, we incidentally detected by cardiac magnetic resonance (CMR) 959 cases (18.6%) of left ventricular noncompaction (LVNC) that met the Petersen diagnostic criterion (noncompaction:compaction ratio >2.3). Short-axis CMR images were available for 511 of these cases (the Short-Axis Study Set). To determine how many of those cases were truly abnormal, we analyzed the short-axis images in terms of LV structural and functional variables and applied 3 published diagnostic criteria besides the Petersen criterion to our findings. The estimated prevalences were 17.5% based on trabeculated LV mass (Jacquier criterion), 7.4% based on trabeculated LV volume (Choi criterion), and 1.3% based on trabeculated LV mass and distribution (Grothoff criterion). Absent longitudinal clinical outcomes data or accepted diagnostic standards, our analysis of the screening data from the Short-Axis Study Set did not definitively differentiate normal from pathologic cases. However, it does suggest that many of the cases might be normal anatomic variants. It also suggests that cases marked by pathologically excessive LV trabeculation, even if asymptomatic, might involve unsustainable physiologic disadvantages that increase the risk of LV dysfunction, pathologic remodeling, arrhythmias, or mural thrombi. These disadvantages may escape detection, particularly in children developing from prepubescence through adolescence. Longitudinal follow-up of suspected LVNC cases to ascertain their natural history and clinical outcome is warranted.

SUBMITTER: Masso AH

PROVIDER: S-EPMC7529067 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Left Ventricular Noncompaction Detected by Cardiac Magnetic Resonance Screening: A Reexamination of Diagnostic Criteria.

Masso Anthony H AH Uribe Carlo C Willerson James T JT Cheong Benjamin Y BY Davis Barry R BR

Texas Heart Institute journal 20200601 3

In a previous cross-sectional screening study of 5,169 middle and high school students (mean age, 13.1 ± 1.78 yr) in which we estimated the prevalence of high-risk cardiovascular conditions associated with sudden cardiac death, we incidentally detected by cardiac magnetic resonance (CMR) 959 cases (18.6%) of left ventricular noncompaction (LVNC) that met the Petersen diagnostic criterion (noncompaction:compaction ratio >2.3). Short-axis CMR images were available for 511 of these cases (the Short ...[more]

PMID: 32997774

Similar Datasets

Project description:BackgroundCardiac magnetic resonance (CMR) has been used to diagnose and risk-stratify patients with left ventricular noncompaction (LVNC). The prognostic value of CMR parameters for LVNC, especially feature tracking (CMR-FT), is not well known in LVNC patients with left ventricular dysfunction. The present study aimed to investigate whether the combination of CMR-FT with traditional CMR parameters can increase the prognostic value of CMR for LVNC patients with reduced left ventricular ejection fraction (LVEF).MethodsA total of 123 candidates were retrospectively included in this multicenter study and 55 LVNC patients (mean age, 45.7 ± 16.2 years; 61.8% men) remained after applying the exclusion criteria. Clinical features, left ventricular (LV) function parameters, global and segment myocardial strain, and late gadolinium enhancement (LGE) were evaluated. The outcomes include the composite events of cardiovascular death, heart transplantation, hospitalization for heart failure, thromboembolic events, and ventricular arrhythmias.ResultsAfter a median follow-up of 5.17 years (interquartile range: 0.17 to 10.58 years), 24 (36.8%) patients experienced at least one major adverse cardiovascular event (MACE). The myocardial strain parameters of patients with events were lower than those of patients without events. In the univariable Cox analysis, LVEF, the presence of LGE, global longitudinal strain (GLS) and segmental strains, including longitudinal strain at the apical level and radial and circumferential strain at the basal level, were significantly associated with MACEs. In the multivariate analysis, LGE (hazard ratio (HR) 3.452, 95% CI 1.133 to 10.518, p = 0.029) was a strong predictor of MACEs and significantly improved the predictive value (chi-square of the model after adding LGE: 7.51 vs. 13.47, p = 0.009). However, myocardial strain parameters were not statistically significant for the prediction of MACEs after adjusting for age, body mass index, LVEF and the presence of LGE and did not increase the prognostic value (chi-square of the model after adding GLS: 13.47 vs. 14.14, p = 0.411) in the multivariate model.ConclusionsThe combination of CMR-FT with traditional CMR parameters may not increase the prognostic value of CMR in LVNC patients with reduced LVEF, while the presence of LGE was a strong independent predictor of MACEs and significantly improved the predictive value.

Project description:BackgroundAlthough there are many studies on the prognostic factors of left ventricular myocardial noncompaction (LVNC), the determinants are varied and not entirely consistent. This study aimed to build predictive models using radiomics features and machine learning to predict major adverse cardiovascular events (MACEs) in patients with LVNC.MethodsIn total, 96 patients with LVNC were included and randomly divided into training and test cohorts. A total of 105 cine cardiac magnetic resonance (CMR)-derived radiomics features and 35 clinical characteristics were extracted. Five different oversampling algorithms were compared for selection of the optimal imbalanced processing. Feature importance was assessed with extreme gradient boosting (XGBoost). We compared the performance of 5 machine learning classification methods with different sample:feature ratios to determine the optimal hybrid classification strategy. Subsequently, radiomics, clinical, and combined radiomics-clinical models were developed and compared.ResultsThe machine learning pipeline included an adaptive synthetic (ADASYN) algorithm for imbalanced processing, XGBoost feature selection with a sample:feature ratio of 10, and support vector machine (SVM) modeling. The areas under the receiver operating characteristic curves (AUCs) of the radiomics model, clinical model, and combined model in the validation cohort were 0.87 (sensitivity 83.33%, specificity 64.29%), 0.65 (sensitivity 16.67%, specificity 78.57%), and 0.92 (specificity 33.33%, sensitivity 100.00%), respectively. The radiomics model performed similarly to the clinical and combined models (P=0.124 and P=0.621, respectively). The performance of the combined model was significantly better than that of the clinical model (P=0.003).ConclusionsThe machine learning-based cine CMR radiomics model performed well at predicting MACEs in patients with LVNC. Adding radiomics features offered incremental prognostic value over clinical factors alone.

Dataset Information

Left Ventricular Noncompaction Detected by Cardiac Magnetic Resonance Screening: A Reexamination of Diagnostic Criteria.

Publications

Left Ventricular Noncompaction Detected by Cardiac Magnetic Resonance Screening: A Reexamination of Diagnostic Criteria.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets