Phenotypic Clustering of Beta-Thalassemia Intermedia Patients Using Cardiovascular Magnetic Resonance

We employed an unsupervised clustering method that integrated demographic, clinical, and cardiac magnetic resonance (CMR) data to identify distinct phenogroups (PGs) of patients with beta-thalassemia intermedia (β-TI). We considered 138 β-TI patients consecutively enrolled in the Myocardial Iron Overload in Thalassemia (MIOT) Network who underwent MR for the quantification of hepatic and cardiac iron overload (T2* technique), the assessment of biventricular size and function and atrial dimensions (cine images), and the detection of replacement myocardial fibrosis (late gadolinium enhancement technique). Three mutually exclusive phenogroups were identified based on unsupervised hierarchical clustering of principal components: PG1, women; PG2, patients with replacement myocardial fibrosis, increased biventricular volumes and masses, and lower left ventricular ejection fraction; and PG3, men without replacement myocardial fibrosis, but with increased biventricular volumes and masses and lower left ventricular ejection fraction. The hematochemical parameters and the hepatic and cardiac iron levels did not contribute to the PG definition. PG2 exhibited a significantly higher risk of future cardiovascular events (heart failure, arrhythmias, and pulmonary hypertension) than PG1 (hazard ratio-HR = 10.5; p = 0.027) and PG3 (HR = 9.0; p = 0.038). Clustering emerged as a useful tool for risk stratification in TI, enabling the identification of three phenogroups with distinct clinical and prognostic characteristics.

Biomarker Phenotypes in Heart Failure with Preserved Ejection Fraction Using Hierarchical Clustering-A Pilot Study

OBJECTIVES

We hypothesized the existence of distinct phenotype-based groups within the very heterogeneous population of patients of heart failure with preserved ejection fraction (HFpEF) and using an unsupervised hierarchical clustering applied to plasma concentration of various biomarkers. We sought to characterize them as "biomarker phenotypes" and to conclude differences in their overall characteristics.

SUBJECTS AND METHODS

A cross-sectional study was conducted on 75 patients with HFpEF. An agglomerative hierarchical clustering was performed using the concentrations of cardiac remodeling biomarkers, BNP and cystatin C.

RESULTS

According to the obtained heat map of this analysis, we concluded two distinctive biomarker phenotypes within the HFpEF. The "remodeled phenotype" presented with significantly higher concentrations of cardiac remodeling biomarkers and cystatin C (p < 0.001), higher prevalence of myocardial infarction (p = 0.047), STEMI (p = 0.045), atrial fibrillation (p = 0.047) and anemia: lower erythrocytes count (p=0.037), hemoglobin concentration (p = 0.034) and hematocrit (p = 0.046), compared to "non-remodeled phenotype". Echocardiography showed that patients within "remodeled phenotype" had significantly increased parameters of left ventricular remodeling: left ventricular mass index (p < 0.001), left ventricular mass (p = 0.001), diameters of the interventricular septum (p = 0.027) and posterior wall (p = 0.003) and function alterations, intermediate pauses duration >2.0 seconds (p < 0.006).

CONCLUSION

Unsupervised hierarchical clustering applied to plasma concentration of various biomarkers in patients with HFpEF enables the identification of two biomarker phenotypes, significantly different in clinical characteristics and cardiac structure and function, whereas one phenotype particularly relates to patients with reduced ejection fraction. These findings imply distinct underlying pathophysiology within a unique cohort of HFpEF.

Penalized Model-Based Unsupervised Phenomapping Unravels Distinctive HFrEF Phenotypes With Improved Outcomes Discrimination From Sacubitril/Valsartan Treatment Independent of MAGGIC Score

Background The angiotensin receptor-neprilysin inhibitor (LCZ696) has emerged as a promising pharmacological intervention against renin-angiotensin system inhibitor in reduced ejection fraction heart failure (HFrEF). Whether the therapeutic benefits may vary among heterogeneous HFrEF subgroups remains unknown. Methods and Results This study comprised a pooled 2-center analysis including 1103 patients with symptomatic HFrEF with LCZ696 use and another 1103 independent HFrEF control cohort (with renin-angiotensin system inhibitor use) matched for age, sex, left ventricular ejection fraction, and comorbidity conditions. Three main distinct phenogroup clusterings were identified from unsupervised machine learning using 29 clinical variables: phenogroup 1 (youngest, relatively lower diabetes prevalence, highest glomerular filtration rate with largest left ventricular size and left ventricular wall stress); phenogroup 2 (oldest, lean, highest diabetes and vascular diseases prevalence, lowest highest glomerular filtration rate with smallest left ventricular size and mass), and phenogroup 3 (lowest clinical comorbidity with largest left ventricular mass and highest hypertrophy prevalence). During the median 1.74-year follow-up, phenogroup assignment provided improved prognostic discrimination beyond Meta-Analysis Global Group in Chronic Heart Failure risk score risk score (all net reclassification index P<0.05) with overall good calibrations. While phenogroup 1 showed overall best clinical outcomes, phenogroup 2 demonstrated highest cardiovascular death and worst renal end point, with phenogroup 3 having the highest all-cause death rate and HF hospitalization among groups, respectively. These findings were broadly consistent when compared with the renin-angiotensin system inhibitor control as reference group. Conclusions Phenomapping provided novel insights on unique characteristics and cardiac features among patients with HFrEF with sacubitril/valsartan treatment. These findings further showed potentiality in identifying potential sacubitril/valsartan responders and nonresponders with improved outcome discrimination among patients with HFrEF beyond clinical scoring.

Phenomapping in heart failure with preserved ejection fraction: insights, limitations, and future directions

Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous entity with complex pathophysiology and manifestations. Phenomapping is the process of applying statistical learning techniques to patient data to identify distinct subgroups based on patterns in the data. Phenomapping has emerged as a technique with potential to improve the understanding of different HFpEF phenotypes. Phenomapping efforts have been increasing in HFpEF over the past several years using a variety of data sources, clinical variables, and statistical techniques. This review summarizes methodologies and key takeaways from these studies, including consistent discriminating factors and conserved HFpEF phenotypes. We argue that phenomapping results to date have had limited implications for clinical care and clinical trials, given that the phenotypes, as currently described, are not reliably identified in each study population and may have significant overlap. We review the inherent limitations of aggregating and utilizing phenomapping results. Lastly, we discuss potential future directions, including using phenomapping to optimize the likelihood of clinical trial success or to drive discovery in mechanisms of the disease process of HFpEF.

Phenotypic Clustering of Patients With Newly Diagnosed Coronary Artery Disease Using Cardiovascular Magnetic Resonance and Coronary Computed Tomography Angiography

Background: Epidemiological characteristics and prognostic profiles of patients with newly diagnosed coronary artery disease (CAD) are heterogeneous. Therefore, providing individualized cardiovascular (CV) risk stratification and tailored prevention is crucial.

Objective: Phenotypic unsupervised clustering integrating clinical, coronary computed tomography angiography (CCTA), and cardiac magnetic resonance (CMR) data were used to unveil pathophysiological differences between subgroups of patients with newly diagnosed CAD.

Materials and Methods: Between 2008 and 2020, consecutive patients with newly diagnosed obstructive CAD on CCTA and further referred for vasodilator stress CMR were followed for the occurrence of major adverse cardiovascular events (MACE), defined by cardiovascular death or non-fatal myocardial infarction. For this exploratory work, a cluster analysis was performed on clinical, CCTA, and CMR variables, and associations between phenogroups and outcomes were assessed.

Results: Among 2,210 patients who underwent both CCTA and CMR, 2,015 (46% men, mean 70 ± 12 years) completed follow-up [median 6.8 (IQR 5.9–9.2) years], in which 277 experienced a MACE (13.7%). Three mutually exclusive and clinically distinct phenogroups (PG) were identified based upon unsupervised hierarchical clustering of principal components: (PG1) CAD in elderly patients with few traditional risk factors; (PG2) women with metabolic syndrome, calcified plaques on CCTA, and preserved left ventricular ejection fraction (LVEF); (PG3) younger men smokers with proximal non-calcified plaques on CCTA, myocardial scar, and reduced LVEF. Using survival analysis, the occurrence of MACE, cardiovascular mortality, and all-cause mortality (all p < 0.001) differed among the three PG, in which PG3 had the worse prognosis. In each PG, inducible ischemia was associated with MACE [PG1, Hazards Ratio (HR) = 3.09, 95% CI, 1.70–5.62; PG2, HR = 3.62, 95% CI, 2.31–5.7; PG3, HR = 3.55, 95% CI, 2.3–5.49; all p < 0.001]. The study presented some key limitations that may impact generalizability.

Conclusions: Cluster analysis of clinical, CCTA, and CMR variables identified three phenogroups of patients with newly diagnosed CAD that were associated with distinct clinical and prognostic profiles. Inducible ischemia assessed by stress CMR remained associated with the occurrence of MACE within each phenogroup. Whether automated unsupervised phenogrouping of CAD patients may improve clinical decision-making should be further explored in prospective studies.

Precision Phenomapping of Acute Coronary Syndromes to Improve Patient Outcomes

Acute coronary syndromes (ACS) are a global leading cause of death. These syndromes show heterogeneity in presentation, mechanisms, outcomes and responses to treatment. Precision medicine aims to identify and synthesize unique features in individuals, translating the acquired data into improved personalised interventions. Current precision treatments of ACS include immediate coronary revascularisation driven by ECG ST-segment elevation, early coronary angiography based on elevated blood cardiac troponins in patients without ST-segment elevation, and duration of intensified antithrombotic therapy according to bleeding risk scores. Phenotypically stratified analyses of multi-omic datasets are urgently needed to further refine and couple the diagnosis and treatment of these potentially life-threatening conditions. We provide definitions, examples and possible ways to advance precision treatments of ACS.

Clinical phenomapping and outcomes after heart transplantation

BACKGROUND

Survival after heart transplantation (HTx) is limited by complications related to alloreactivity, immune suppression, and adverse effects of pharmacologic therapies. We hypothesize that time-dependent phenomapping of clinical and molecular data sets is a valuable approach to clinical assessments and guiding medical management to improve outcomes.

METHODS

We analyzed clinical, therapeutic, biomarker, and outcome data from 94 adult HTx patients and 1,557 clinical encounters performed between January 2010 and April 2013. Multivariate analyses were used to evaluate the association between immunosuppression therapy, biomarkers, and the combined clinical end point of death, allograft loss, retransplantation, and rejection. Data were analyzed by K-means clustering (K = 2) to identify patterns of similar combined immunosuppression management, and percentile slopes were computed to examine the changes in dosages over time. Findings were correlated with clinical parameters, human leucocyte antigen antibody titers, and peripheral blood mononuclear cell gene expression of the AlloMap (CareDx, Inc., Brisbane, CA) test genes. An intragraft, heart tissue gene coexpression network analysis was performed.

RESULTS

Unsupervised cluster analysis of immunosuppressive therapies identified 2 groups, 1 characterized by a steeper immunosuppression minimization, associated with a higher likelihood for the combined end point, and the other by a less pronounced change. A time-dependent phenomap suggested that patients in the group with higher event rates had increased human leukocyte antigen class I and II antibody titers, higher expression of the FLT3 AlloMap gene, and lower expression of the MARCH8 and WDR40A AlloMap genes. Intramyocardial biomarker-related coexpression network analysis of the FLT3 gene showed an immune system-related network underlying this biomarker.

CONCLUSIONS

Time-dependent precision phenotyping is a mechanistically insightful, data-driven approach to characterize patterns of clinical care and identify ways to improve clinical management and outcomes.