Subphenotyping of Patients With Aortic Stenosis by Unsupervised Agglomerative Clustering of Echocardiographic and Hemodynamic Data

Lachmann, Mark; Rippen, Elena; Schuster, Tibor; Xhepa, Erion; Scheidt, Moritz von; Pellegrini, Costanza; Trenkwalder, Teresa; Rheude, Tobias; Stundl, Anja; Thalmann, Ruth; Harmsen, Gerhard; Yuasa, Shinsuke; Schunkert, Heribert; Kastrati, Adnan; Laugwitz, Karl Ludwig; Kupatt, Christian

doi:10.1016/j.jcin.2021.08.034

Cited by 24 publications

(21 citation statements)

References 21 publications

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“…The methodology for unsupervised clustering of patients and subsequent training of an ANN has been extensively described elsewhere. 3 In summary, the previous work performed a two-step experiment with the paramount goal of establishing a man-machine interaction-based phenotyping approach for patients undergoing TAVR for severe AS as follows:…”

Section: Methodsmentioning

confidence: 99%

“…A novel classification system based on unsupervised agglomerative, hierarchical clustering in conjunction with an artificial neural network (ANN) was therefore established to comprehensively capture the complexity of cardiopulmonary impairments, without inferring causality nor hypothesising a sequential progression of accumulated pathologies upstream of the causative AS. 3 …”

Section: Introductionmentioning

confidence: 99%

“…The aim of this study was therefore to shed light on the trajectory of cardiopulmonary impairments after TAVR in accordance with the previously established classification system based on unsupervised clustering. 3 Ideally, this artificial intelligence-based phenotyping approach could serve the paramount aim to preprocedurally identify patients, who will suffer from persistent right heart dysfunction after TAVR and who will hence decease earlier unless specifically treated.…”

Section: Introductionmentioning

confidence: 99%

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Artificial intelligence-enabled phenotyping of patients with severe aortic stenosis: on the recovery of extra-aortic valve cardiac damage after transcatheter aortic valve replacement

et al. 2022

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ObjectiveA novel artificial intelligence-based phenotyping approach to stratify patients with severe aortic stenosis (AS) prior to transcatheter aortic valve replacement (TAVR) has been proposed, based on echocardiographic and haemodynamic data. This study aimed to analyse the recovery of extra-aortic valve cardiac damage in accordance with this novel stratification system following TAVR.MethodsThe proposed phenotyping approach was previously established employing data from 366 patients with severe AS from a bicentric registry. For this consecutive study, echocardiographic follow-up data, obtained on day 147±75.1 after TAVR, were available from 247 patients (67.5%).ResultsCorrection of severe AS by TAVR significantly reduced the proportion of patients suffering from concurrent severe mitral regurgitation (from 9.29% to 3.64%, p value: 0.0015). Moreover, pulmonary artery pressures were ameliorated (estimated systolic pulmonary artery pressure: from 47.2±15.8 to 43.3±15.1 mm Hg, p value: 0.0079). However, right heart dysfunction as well as the proportion of patients with severe tricuspid regurgitation remained unchanged. Clusters with persistent right heart dysfunction ultimately displayed 2-year survival rates of 69.2% (95% CI 56.6% to 84.7%) and 74.6% (95% CI 65.9% to 84.4%), which were significantly lower compared with clusters with little or no persistent cardiopulmonary impairment (88.3% (95% CI 83.3% to 93.5%) and 85.5% (95% CI 77.1% to 94.8%)).ConclusionsThis phenotyping approach preprocedurally identifies patients with severe AS, who will not recover from extra-aortic valve cardiac damage following TAVR and whose survival is therefore significantly reduced. Importantly, not the degree of pulmonary hypertension at initial presentation, but the irreversibility of right heart dysfunction determines prognosis.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Artificial intelligence-enabled phenotyping of patients with severe aortic stenosis: on the recovery of extra-aortic valve cardiac damage after transcatheter aortic valve replacement

et al. 2022

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“…In an agglomerative clustering model, the clustering initiates with individual collections of every data point [55]. AGNES has been extensively used various medical domains [56,57], such as categorizing patients with severe aortic stenosis [58], and mapping molecular substructures [59]. However, AGNES has been ineffective in some problems since finding the nearest pair of clusters can be challenging when data is sparse and noisy [60].…”

Section: Agglomerative Clusteringmentioning

confidence: 99%

Unsupervised machine learning framework for discriminating major variants of concern during COVID-19

Mingyue¹,

Vasan²,

Wilson³

et al. 2022

Preprint

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Due to the rapid evolution of the SARS-CoV-2 (COVID-19) virus, a number of mutations emerged with variants such as Alpha, Gamma, Delta and Omicron which created massive impact to the world economy. Unsupervised machine learning methods have the ability to compresses, characterize and visualises unlabelled data. In this paper, we present a framework that utilizes unsupervised machine learning methods that includes combination of selected dimensional reduction and clustering methods to discriminate and visualise the associations with the major COVID-19 variants based on genome sequences. The framework utilises k-mer analysis for processing the genome (RNA) sequences and compares different dimensional reduction methods, that include principal component analysis (PCA), and t-distributed stochastic neighbour embedding (t-SNE), and uniform manifold approximation projection (UMAP). Furthermore, the framework employs agglomerative hierarchical clustering methods and provides a visualisation using a dendogram. We find that the proposed framework can effectively distinguish the major variants and hence can be used for distinguishing emerging variants in the future.

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“…With the advancement of electronic medical record (EMR) and artificial intelligence, machine learning (ML) approaches have been developed as part of precision medicine to assist in clinical decision-making, including disease detection, medical imaging, and explainable risk prediction [21][22][23][24][25][26][27][28][29]. In recent years, unsupervised ML algorithms have been utilized to reveal the patterns of diseases such as diabetes and cardiovascular diseases [30][31][32][33]. Consensus clustering is an unsupervised ML technique used to identify patterns of data, and provides a visualization tool to inspect cluster numbers, membership, and boundaries [34].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Consensus Clustering Approach for Hospitalized Patients with Dysmagnesemia

et al. 2021

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Background: The objectives of this study were to classify patients with serum magnesium derangement on hospital admission into clusters using unsupervised machine learning approach and to evaluate the mortality risks among these distinct clusters. Methods: Consensus cluster analysis was performed based on demographic information, principal diagnoses, comorbidities, and laboratory data in hypomagnesemia (serum magnesium ≤ 1.6 mg/dL) and hypermagnesemia cohorts (serum magnesium ≥ 2.4 mg/dL). Each cluster’s key features were determined using the standardized mean difference. The associations of the clusters with hospital mortality and one-year mortality were assessed. Results: In hypomagnesemia cohort (n = 13,320), consensus cluster analysis identified three clusters. Cluster 1 patients had the highest comorbidity burden and lowest serum magnesium. Cluster 2 patients had the youngest age, lowest comorbidity burden, and highest kidney function. Cluster 3 patients had the oldest age and lowest kidney function. Cluster 1 and cluster 3 were associated with higher hospital and one-year mortality compared to cluster 2. In hypermagnesemia cohort (n = 4671), the analysis identified two clusters. Compared to cluster 1, the key features of cluster 2 included older age, higher comorbidity burden, more hospital admissions primarily due to kidney disease, more acute kidney injury, and lower kidney function. Compared to cluster 1, cluster 2 was associated with higher hospital mortality and one-year mortality. Conclusion: Our cluster analysis identified clinically distinct phenotypes with differing mortality risks in hospitalized patients with dysmagnesemia. Future studies are required to assess the application of this ML consensus clustering approach to care for hospitalized patients with dysmagnesemia.

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Subphenotyping of Patients With Aortic Stenosis by Unsupervised Agglomerative Clustering of Echocardiographic and Hemodynamic Data

Cited by 24 publications

References 21 publications

Artificial intelligence-enabled phenotyping of patients with severe aortic stenosis: on the recovery of extra-aortic valve cardiac damage after transcatheter aortic valve replacement

Artificial intelligence-enabled phenotyping of patients with severe aortic stenosis: on the recovery of extra-aortic valve cardiac damage after transcatheter aortic valve replacement

Unsupervised machine learning framework for discriminating major variants of concern during COVID-19

Machine Learning Consensus Clustering Approach for Hospitalized Patients with Dysmagnesemia

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