Global longitudinal strain in heart failure with reduced ejection fraction: Prognostic relevance across disease severity as assessed by automated cluster analysis

Identifying High-risk Fontan Phenotypes Using K-means Clustering of CMR-based Dyssynchrony Metrics

BACKGROUND

Individuals with a Fontan circulation encompass a heterogenous group with adverse outcomes linked to ventricular dilation, dysfunction, and dyssynchrony. The purpose of this study was to assess if unsupervised machine learning cluster analysis of cardiac magnetic resonance (CMR)-derived dyssynchrony metrics can separate ventricles in the Fontan circulation from normal control left ventricles and identify prognostically distinct subgroups within the Fontan cohort.

MATERIALS AND METHODS

This single-center, retrospective study used 503 CMR studies from Fontan patients (median age 15y) and 42 from age-matched controls from January 2005 to May 2011. Feature tracking on short-axis cine stacks assessed radial and circumferential strain, strain rate, and displacement. Unsupervised K-means clustering was applied to 24 mechanical dyssynchrony metrics derived from these deformation measurements. Clusters were compared for demographic, anatomical, and composite outcome of death, or heart transplantation.

RESULTS

Four distinct phenotypic clusters were identified. Over a median follow-up of 4.2y (IQR 1.7-8.8y), 58 (11.5%) patients met the composite outcome. The highest risk cluster (largely comprised of right or mixed ventricular morphology and dilated, dyssynchronous ventricles) exhibited a higher hazard for the composite outcome compared to the lowest-risk cluster while controlling for ventricular morphology (HR 6.4; 95% CI 2.1-19.3; P value 0.001) and higher indexed end-diastolic volume (HR 3.2; 95% CI 1.04-10.0; P value 0.043) per 10ml/m2.

CONCLUSIONS

Unsupervised machine learning using CMR-derived dyssynchrony metrics identified four distinct clusters of patients with Fontan circulation and healthy controls with varying clinical characteristics and risk profiles. This technique can be used to guide future studies and identify more homogeneous subsets of patients from an overall heterogeneous population.

Diastology in the intensive care unit: Challenges for the assessment and future directions

Myocardial dysfunction is common in patients admitted to the intensive care unit (ICU). Septic disease frequently results in cardiac dysfunction, and sepsis represents the most common cause of admission and death in the ICU. The association between left ventricular (LV) systolic dysfunction and mortality is not clear for critically ill patients. Conversely, LV diastolic dysfunction (DD) seems increasingly recognized as a factor associated with poor outcomes, not only in sepsis but also more generally in critically ill patients. Despite recent attempts to simplify the diagnosis and grading of DD, this remains relatively complex, with the need to use several echocardiographic parameters. Furthermore, the current guidelines have several intrinsic limitations when applied to the ICU setting. In this manuscript, we discuss the challenges in DD classification when applied to critically ill patients, the importance of left atrial pressure estimates for the management of patients in ICU, and whether the study of cardiac dysfunction spectrum during critical illness may benefit from the integration of left ventricular and left atrial strain data to improve diagnostic accuracy and implications for the treatment and prognosis.

Left Atrial Strain in the Assessment of Diastolic Function in Heart Failure: A Machine Learning Approach

BACKGROUND

Diastolic dysfunction (DD) assessment in heart failure is still challenging. Peak atrial longitudinal strain (PALS) is strongly related to end-diastolic pressure and prognosis, but it is still not part of standard DD assessment. We tested the hypothesis that a machine learning approach would be useful to include PALS in DD classification and refine prognostic stratification.

METHODS

In a derivation cohort of 864 heart failure patients in sinus rhythm (age, 66.6±12 years; heart failure with reduced ejection fraction, n=541; heart failure with mildly reduced ejection fraction, n=129; heart failure with preserved ejection fraction, n=194), machine learning techniques were retrospectively applied to PALS and guideline-recommended diastolic variables. Outcome (death/heart failure rehospitalization) of the identified DD-clusters was compared with that by guidelines-based classification. To identify the best combination of variables able to classify patients in one of the identified DD-clusters, classification and regression tree analysis was applied (with DD-clusters as dependent variable and PALS plus guidelines-recommended diastolic variables as explanatory variables). The algorithm was subsequently validated in a prospective cohort of 189 heart failure outpatients (age, 65±13 years).

RESULTS

Three distinct echocardiographic DD-clusters were identified (cluster-1, n=212; cluster-2, n=376; cluster-3 DD, n=276), with modest agreement with guidelines-recommended classification (kappa=0.40; P<0.001). DD-clusters were predicted by a simple algorithm including E/A ratio, left atrial volume index, E/e' ratio, and PALS. After 36.5±29.4 months follow-up, 318 events occurred. Compared to guideline-based classification, DD-clusters showed a better association with events in multivariable models (C-index 0.720 versus 0.733, P=0.033; net reclassification improvement 0.166 [95% CI, 0.035-0.276], P=0.013), without interaction with ejection fraction category. In the validation cohort (median follow-up: 18.5 months), cluster-based classification better predicted outcome than guideline-based classification (C-index 0.80 versus 0.78, P=0.093).

CONCLUSIONS

Integrating PALS by machine learning algorithm in DD classification improves risk stratification over recommended current criteria, regardless of ejection fraction status. This proof of concept study needs further validation of the proposed algorithm to assess generalizability to other populations.

Identifying novel subgroups in heart failure patients with unsupervised machine learning: A scoping review

Background

Heart failure is currently divided into three main forms, HFrEF, HFpEF, and HFmrEF, but its etiology is diverse and highly heterogeneous. Many studies reported a variety of novel subgroups in heart failure patients, with unsupervised machine learning methods. The aim of this scoping review is to provide insights into how these techniques can diagnose and manage HF faster and better, thus providing direction for future research and facilitating its routine use in clinical practice.

Methods

The review was performed following PRISMA-SCR guideline. We searched the PubMed database for eligible publications. Studies were included if they defined new subgroups in HF patients using clustering analysis methods, and excluded if they are (1) Reviews, commentary, or editorials, (2) Studies not about defining new sub-types, or (3) Studies not using unsupervised algorithms. All study screening and data extraction were conducted independently by two investigators and narrative integration of data extracted from included studies was performed.

Results

Of the 498 studies identified, 47 were included in the analysis. Most studies (61.7%) were published in 2020 and later. The largest number of studies (46.8%) coming from the United States, and most of the studies were authored and included in the same country. The most commonly used machine learning method was hierarchical cluster analysis (46.8%), the most commonly used cluster variable type was comorbidity (61.7%), and the least used cluster variable type was genomics (12.8%). Most of the studies used data sets of less than 500 patients (48.9%), and the sample size had negative correlation with the number of clustering variables. The majority of studies (85.1%) assessed the association between cluster grouping and at least one outcomes, with death and hospitalization being the most commonly used outcome measures.

Conclusion

This scoping review provides an overview of recent studies proposing novel HF subgroups based on clustering analysis. Differences were found in study design, study population, clustering methods and variables, and outcomes of interests, and we provided insights into how these studies were conducted and identify the knowledge gaps to guide future research.

Effects of SGLT2 inhibitors on cardiac structure and function

SGLT2 inhibitors reduce cardiovascular death or hospitalization for heart failure, regardless of the presence or absence of diabetes in patients at high cardiovascular risk and in those with heart failure and reduced ejection fraction (HFrEF). In patients with HF and preserved EF, empagliflozin also showed favorable effects mainly related to the reduction of hospitalization for heart failure. These favorable effects are beyond the reduction of glycemic levels and mainly related to beneficial hemodynamic and anti-inflammatory effects of these drugs and improved cardiac energy metabolism. In this review, we aimed to evaluate the effects of SGLT2 inhibitor on cardiac remodeling and function, which is still incompletely clear.

Circulating DHEA-S levels and major cardiovascular outcomes in chronic Chagas cardiomyopathy: A prospective cohort study

OBJECTIVE

To analyze the association of circulating dehydroepiandrosterone sulfate (DHEA-S) levels with cardiovascular outcomes in patients with chronic Chagas cardiomyopathy (CCM) diagnosis.

BACKGROUND

DHEA-S is among the main endogenous steroid hormones. Some studies have suggested a relevant role of this hormone in infections and the setting of CCM. Nevertheless, no study has evaluated the prognostic role of DHEA-S in CCM patients.

METHODS

Prospective cohort study. Patients with CCM and reduced ejection fraction were included. We explored the association of DHEA-S levels with NT-proBNP levels and echocardiographic variables using linear regression models. Next, by using Cox Proportional Hazard models, we examined whether levels of DHEA-S could predict a composite outcome (CO) including all-cause mortality, cardiac transplantation, and implantation of a left ventricular assist device (LVAD).

RESULTS

Seventy-four patients were included (59% males, median age: 64 years). After adjustment for confounding factors, high DHEA-S levels were associated with better LVEF, lower left atrium volume, end-systolic volume of the left ventricle and lower NT-proBNP levels. 43% of patients experienced the CO during a median follow-up of 40 months. Increased levels of DHEA-S were associated with a lower risk of developing the CO (HR 0.43; 95%CI 0.21-0.86). Finally, adding DHEA-S to the multivariate model did not improve the prediction of the CO, but substituting NT-proBNP in the model with DHEA-S showed similar performance.

CONCLUSIONS

In patients with CCM, higher DHEA-S levels were associated with lower mortality, heart transplantation, and LVAD implantation. Further larger studies are required to confirm our results and assess causality.