Aim To identify distinct phenotypic subgroups in a highly-dimensional, mixed-data cohort of individuals with heart failure (HF) with preserved ejection fraction (HFpEF) using unsupervised clustering analysis. Methods and results The study included all Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT) participants from the Americas (n = 1767). In the subset of participants with available echocardiographic data (derivation cohort, n = 654), we characterized three mutually exclusive phenogroups of HFpEF participants using penalized finite mixture model-based clustering analysis on 61 mixed-data phenotypic variables. Phenogroup 1 had higher burden of co-morbidities, natriuretic peptides, and abnormalities in left ventricular structure and function; phenogroup 2 had lower prevalence of cardiovascular and non-cardiac co-morbidities but higher burden of diastolic dysfunction; and phenogroup 3 had lower natriuretic peptide levels, intermediate co-morbidity burden, and the most favourable diastolic function profile. In adjusted Cox models, participants in phenogroup 1 (vs. phenogroup 3) had significantly higher risk for all adverse clinical events including the primary composite endpoint, all-cause mortality, and HF hospitalization. Phenogroup 2 (vs. phenogroup 3) was significantly associated with higher risk of HF hospitalization but a lower risk of atherosclerotic event (myocardial infarction, stroke, or cardiovascular death), and comparable risk of mortality. Similar patterns of association were also observed in the non-echocardiographic TOPCAT cohort (internal validation cohort, n = 1113) and an external cohort of patients with HFpEF [Phosphodiesterase-5 Inhibition to Improve Clinical Status and Exercise Capacity in Heart Failure with Preserved Ejection Fraction (RELAX) trial cohort, n = 198], with the highest risk of adverse outcome noted in phenogroup 1 participants. Conclusions Machine learning-based cluster analysis can identify phenogroups of patients with HFpEF with distinct clinical characteristics and long-term outcomes.

https://onlinelibrary.wiley.com/doi/epdf/10.1002/ejhf.1621

Phenomapping of patients with heart failure with preserved ejection fraction using machine learning-based unsupervised cluster analysis

Feature selection is the process of reducing the number of collected features to a relevant subset of features and is often used to combat the curse of dimensionality. This paper provides a review of the literature on feature selection techniques specifically designed for Gaussian mixture models (GMMs) and hidden Markov models (HMMs), two common parametric latent variable models. The primary contribution of this work is the collection and grouping of feature selection methods specifically designed for GMMs and for HMMs. An additional contribution lies in outlining the connections between these two groups of feature selection methods. Often, feature selection methods for GMMs and HMMs are treated as separate topics. In this survey, we propose that methods developed for one model can be adapted to the other model. Further, we find that the number of feature selection methods for GMMs outweighs the number of methods for HMMs and that the proportion of methods for HMMs that require supervised data is larger than the proportion of GMM methods that require supervised data. We conclude that further research into unsupervised feature selection methods for HMMs is required and that established methods for GMMs could be adapted to HMMs. It should be noted that feature selection can also be referred to as dimensionality reduction, variable selection, attribute selection, and variable subset reduction. In this paper, we make a distinction between dimensionality reduction and feature selection. Dimensionality reduction, which we do not consider, is any process that reduces the number of features used in a model and can include methods that transform features in order to reduce the dimensionality. Feature selection, by contrast, is a specific form of dimensionality reduction that eliminates feature as inputs into the model. The primary difference is that dimensionality reduction can still require the collection of all the data sources in order to transform and reduce the feature set, while feature selection eliminates the need to collect the irrelevant data sources.

A survey of feature selection methods for Gaussian mixture models and hidden Markov models

Rationale Adaptive servo ventilation (ASV) is contraindicated in patients with systolic heart failure (HF) who have a left ventricular ejection fraction (LVEF) below 45% and predominant central sleep apnoea (CSA). However, the effects of ASV in other HF subgroups have not been clearly defined. Objective The European, multicentre, prospective, observational cohort trial, FACE, evaluated the effects of ASV therapy on morbidity and mortality in patients with HF with sleep-disordered breathing (SDB); 3-month outcomes in patient subgroups defined using latent class analysis (LCA) are presented. Methods Consecutive patients with HF with predominant CSA (±obstructive sleep apnoea) indicated for ASV were included from 2009 to 2018; the non-ASV group included patients who refused/were noncompliant with ASV. The primary endpoint was time to composite first event (all-cause death, lifesaving cardiovascular intervention or unplanned hospitalisation for worsening of chronic HF). Measurements and main results Baseline assessments were performed in 503 patients, and 482 underwent 3-month follow-up. LCA identified six discrete patient clusters characterised by variations in LVEF, SDB type, age, comorbidities and ASV acceptance. The 3- month rate of primary outcome events was significantly higher in cluster 1 patients (predominantly men, low LVEF, severe HF, CSA; 13.9% vs 1.5%–5% in other clusters, p Conclusion For the first time, our data identified homogeneous patient clusters representing clinically relevant subgroups relating to SDB management in patients with HF with different ASV usage, each with a different prognosis. This may improve patient phenotyping in clinical practice and allow individualisation of therapy.

https://thorax.bmj.com/content/thoraxjnl/early/2021/07/05/thoraxjnl-2021-217205.full.pdf

Adaptive servo ventilation for sleep apnoea in heart failure: the FACE study 3-month data.

Benchmark datasets with predefined cluster structures and high-dimensional biomedical datasets outline the challenges of cluster analysis: clustering algorithms are limited in their clustering ability in the presence of clusters defining distance-based structures resulting in a biased clustering solution. Data sets might not have cluster structures. Clustering yields arbitrary labels and often depends on the trial, leading to varying results. Moreover, recent research indicated that all partition comparison measures can yield the same results for different clustering solutions. Consequently, algorithm selection and parameter optimization by unsupervised quality measures (QM) are always biased and misleading. Only if the predefined structures happen to meet the particular clustering criterion and QM, can the clusters be recovered. Results are presented based on 41 open-source algorithms which are particularly useful in biomedical scenarios. Furthermore, comparative analysis with mirrored density plots provides a significantly more detailed benchmark than that with the typically used box plots or violin plots.

/pdf/distance-based-clustering-challenges-for-unbiased-1pv0whoqke.pdf

Distance-based clustering challenges for unbiased benchmarking studies.

Type 2 diabetes is a heterogeneous disease process with variable trajectories of CVD risk. We aimed to evaluate four phenomapping strategies and their ability to stratify CVD risk in individuals with type 2 diabetes and to identify subgroups who may benefit from specific therapies. Participants with type 2 diabetes and free of baseline CVD in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial were included in this study (N = 6466). Clustering using Gaussian mixture models, latent class analysis, finite mixture models (FMMs) and principal component analysis was compared. Clustering variables included demographics, medical and social history, laboratory values and diabetes complications. The interaction between the phenogroup and intensive glycaemic, combination lipid and intensive BP therapy for the risk of the primary outcome (composite of fatal myocardial infarction, non-fatal myocardial infarction or unstable angina) was evaluated using adjusted Cox models. The phenomapping strategies were independently assessed in an external validation cohort (Look Action for Health in Diabetes [Look AHEAD] trial: n = 4211; and Bypass Angioplasty Revascularisation Investigation 2 Diabetes [BARI 2D] trial: n = 1495). Over 9.1 years of follow-up, 789 (12.2%) participants had a primary outcome event. FMM phenomapping with three phenogroups was the best-performing clustering strategy in both the derivation and validation cohorts as determined by Bayesian information criterion, Dunn index and improvement in model discrimination. Phenogroup 1 (n = 663, 10.3%) had the highest burden of comorbidities and diabetes complications, phenogroup 2 (n = 2388, 36.9%) had an intermediate comorbidity burden and lowest diabetes complications, and phenogroup 3 (n = 3415, 52.8%) had the fewest comorbidities and intermediate burden of diabetes complications. Significant interactions were observed between phenogroups and treatment interventions including intensive glycaemic control (p-interaction = 0.042) and combination lipid therapy (p-interaction < 0.001) in the ACCORD, intensive lifestyle intervention (p-interaction = 0.002) in the Look AHEAD and early coronary revascularisation (p-interaction = 0.003) in the BARI 2D trial cohorts for the risk of the primary composite outcome. Favourable reduction in the risk of the primary composite outcome with these interventions was noted in low-risk participants of phenogroup 3 but not in other phenogroups. Compared with phenogroup 3, phenogroup 1 participants were more likely to have severe/symptomatic hypoglycaemic events and medication non-adherence on follow-up in the ACCORD and Look AHEAD trial cohorts. Clustering using FMMs was the optimal phenomapping strategy to identify replicable subgroups of patients with type 2 diabetes with distinct clinical characteristics, CVD risk and response to therapies.

Development and validation of optimal phenomapping methods to estimate long-term atherosclerotic cardiovascular disease risk in patients with type 2 diabetes

Variable selection in cluster analysis is important yet challenging. It can be achieved by regularization methods, which realize a trade-off between the clustering accuracy and the number of selected variables by using a lasso-type penalty. However, the calibration of the penalty term can suffer from criticisms. Model selection methods are an efficient alternative, yet they require a difficult optimization of an information criterion which involves combinatorial problems. First, most of these optimization algorithms are based on a suboptimal procedure (e.g. stepwise method). Second, the algorithms are often greedy because they need multiple calls of EM algorithms. Here we propose to use a new information criterion based on the integrated complete-data likelihood. It does not require any estimate and its maximization is simple and computationally efficient. The original contribution of our approach is to perform the model selection without requiring any parameter estimation. Then, parameter inference is needed only for the unique selected model. This approach is used for the variable selection of a Gaussian mixture model with conditional independence assumption. The numerical experiments on simulated and benchmark datasets show that the proposed method often outperforms two classical approaches for variable selection.

Variable selection for model-based clustering using the integrated complete-data likelihood

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Variable selection for model-based clustering using the integrated complete-data likelihood