Arrhythmic Mitral Valve Prolapse Phenotype: An Unsupervised Machine Learning Analysis Using a Multicenter Cardiac MRI Registry.

Purpose To use unsupervised machine learning to identify phenotypic clusters with increased risk of arrhythmic mitral valve prolapse (MVP). Materials and Methods This retrospective study included patients with MVP without hemodynamically significant mitral regurgitation or left ventricular (LV) dysfunction undergoing late gadolinium enhancement (LGE) cardiac MRI between October 2007 and June 2020 in 15 European tertiary centers. The study end point was a composite of sustained ventricular tachycardia, (aborted) sudden cardiac death, or unexplained syncope. Unsupervised data-driven hierarchical k-mean algorithm was utilized to identify phenotypic clusters. The association between clusters and the study end point was assessed by Cox proportional hazards model. Results A total of 474 patients (mean age, 47 years ± 16 [SD]; 244 female, 230 male) with two phenotypic clusters were identified. Patients in cluster 2 (199 of 474, 42%) had more severe mitral valve degeneration (ie, bileaflet MVP and leaflet displacement), left and right heart chamber remodeling, and myocardial fibrosis as assessed with LGE cardiac MRI than those in cluster 1. Demographic and clinical features (ie, symptoms, arrhythmias at Holter monitoring) had negligible contribution in differentiating the two clusters. Compared with cluster 1, the risk of developing the study end point over a median follow-up of 39 months was significantly higher in cluster 2 patients (hazard ratio: 3.79 [95% CI: 1.19, 12.12], P = .02) after adjustment for LGE extent. Conclusion Among patients with MVP without significant mitral regurgitation or LV dysfunction, unsupervised machine learning enabled the identification of two phenotypic clusters with distinct arrhythmic outcomes based primarily on cardiac MRI features. These results encourage the use of in-depth imaging-based phenotyping for implementing arrhythmic risk prediction in MVP. Keywords: MR Imaging, Cardiac, Cardiac MRI, Mitral Valve Prolapse, Cluster Analysis, Ventricular Arrhythmia, Sudden Cardiac Death, Unsupervised Machine Learning Supplemental material is available for this article. © RSNA, 2024.

Predictors of in-hospital mortality in critically ill patients with COVID-19: a large dual tertiary centre study.

OBJECTIVES: The aim of this study was to investigate the relationship of echocardiographic parameters, laboratory findings and clinical characteristics with in-hospital mortality in adult patients with COVID-19 admitted to the intensive care units (ICU) in two large collaborating tertiary UK centres. DESIGN: Observational retrospective study. SETTING: The study was conducted in patients admitted to the ICU in two large tertiary centres in London, UK. PARTICIPANTS: Inclusion criteria were: (1) patients admitted to the ICU with a COVID-19 diagnosis over a period of 16 weeks. and (2) underwent a transthoracic echocardiogram on the first day of ICU admission as clinically indicated.No exclusion criteria applied.Three hundred patients were enrolled and completed the follow-up. PRIMARY AND SECONDARY OUTCOME MEASURES: The outcome measure in this study was in-hospital mortality in patients admitted to the ICU with COVID-19 infection. RESULTS: Older age (HR: 1.027, 95% CI 1.007 to 1.047; p=0.008), left ventricular (LV) ejection fraction<35% (HR: 5.908, 95% CI 2.609 to 13.376; p<0.001), and peak C reactive protein (CRP) (HR: 1.002, 95% CI 1.001 to 1.004, p=0.001) were independently correlated with mortality in a multivariable Cox regression model. Following multiple imputation of variables with more than 5% missing values, random forest analysis was applied to the imputed data. Right ventricular (RV) basal diameter (RVD1), RV mid-cavity diameter (RVD2), tricuspid annular plane systolic excursion, RV systolic pressure, hypertension, RV dysfunction, troponin level on admission, peak CRP, creatinine level on ICU admission, body mass index and age were found to have a high relative importance (> 0.7). CONCLUSIONS: In patients with COVID-19 in the ICU, both severely impaired LV function and impaired RV function may have adverse prognostic implications, but older age and inflammatory markers appear to have a greater impact. A combination of echocardiographic and laboratory investigations as well as demographic and clinical characteristics appears appropriate for risk stratification in patients with COVID-19 who are admitted to the ICU.

First-Phase Ejection Fraction, a Measure of Preclinical Heart Failure, Is Strongly Associated With Increased Mortality in Patients With COVID-19.

Presence of heart failure is associated with a poor prognosis in patients with coronavirus disease 2019 (COVID-19). The aim of the present study was to examine whether first-phase ejection fraction (EF1), the ejection fraction measured in early systole up to the time of peak aortic velocity, a sensitive measure of preclinical heart failure, is associated with survival in patients hospitalized with COVID-19. A retrospective outcome study was performed in patients hospitalized with COVID-19 who underwent echocardiography (n=380) at the West Branch of the Union Hospital, Wuhan, China and in patients admitted to King's Health Partners in South London, United Kingdom. Association of EF1 with survival was performed using Cox proportional hazards regression. EF1 was compared in patients with COVID-19 and in historical controls with similar comorbidities (n=266) who had undergone echocardiography before the COVID-19 pandemic. In patients with COVID-19, EF1 was a strong predictor of survival in each patient group (Wuhan and London). In the combined group, EF1 was a stronger predictor of survival than other clinical, laboratory, and echocardiographic characteristics including age, comorbidities, and biochemical markers. A cutoff value of 25% for EF1 gave a hazard ratio of 5.23 ([95% CI, 2.85-9.60]; P<0.001) unadjusted and 4.83 ([95% CI, 2.35-9.95], P<0.001) when adjusted for demographics, comorbidities, hs-cTnI (high-sensitive cardiac troponin), and CRP (C-reactive protein). EF1 was similar in patients with and without COVID-19 (23.2±7.3 versus 22.0±7.6%, P=0.092, adjusted for prevalence of risk factors and comorbidities). Impaired EF1 is strongly associated with mortality in COVID-19 and probably reflects preexisting, preclinical heart failure.

Impact and Determinants of High-Sensitivity Cardiac Troponin-T Concentration in Patients With COVID-19 Admitted to Critical Care.

Cardiac Troponin (hs-TnT) elevation has been reported in unselected patients hospitalized with COVID-19 however the mechanism and relationship with mortality remain unclear. Consecutive patients admitted to a high-volume intensive care unit (ICU) in London with severe COVID-19 pneumonitis were included if hs-TnT concentration at admission was known. Kaplan-Meier survival analysis performed, with cohorts classified a priori by multiples of the upper limit of normal (ULN). 277 patients were admitted during a 7-week period in 2020; 176 were included (90% received invasive ventilation). hs-TnT at admission was 16.5 (9.0 to 49.3) ng/L, 56% had concentrations >ULN. 56 patients (31.8%) died during the index admission. Admission hs-TnT level was lower in survivors (12.0 (8.0-27.8) vs 28.5 (14.0 to 81.0) ng/L, p = 0.001). Univariate predictors of mortality were age, APACHE-II Score and admission hs-TnT (HR 1.73, p = 0.007). By multivariate regression, only age (HR 1.33, CI: 1.16.to 1.51, p < 0.01) and admission hs-TnT (HR 1.94, CI: 1.22 to 3.10, p = 0.006) remained predictive. Survival was significantly lower when admission hs-TnT was >ULN (log-rank p-value<0.001). Peak hs-TnT was higher in those who died but was not predictive of death after adjustment for other factors. In conclusion, in critically ill patients with COVID-19 pneumonitis, the hs-TnT level at admission is a powerful independent predictor of the likelihood of surviving to discharge from ICU. In most cases, hs-TnT elevation does not represent major myocardial injury but acts as a sensitive integrated biomarker of global stress. Whether stratification based on admission Troponin level could be used to guide prognostication and management warrants further evaluation.