Identifying novel phenotypes of acute heart failure using cluster analysis of clinical variables

Horiuchi, Yu; Tanimoto, Shuzou; Latif, A. H. M. Mahbub; Urayama, Kevin Y.; Aoki, Jiro; Yahagi, Kazuyuki; Okuno, Taishi; Sato, Yu; Tanaka, Tetsu; Koseki, Keita; Komiyama, Kanki; Nakajima, Hiroyuki; Hara, Kazuhiro

doi:10.1016/j.ijcard.2018.03.098

Cited by 60 publications

(47 citation statements)

References 46 publications

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“…These patients were least often treated with neurohormonal therapies (beta blockers, ACE-Inhibitors) and implanted device therapies (ICD, cardiac resynchronization therapy-defibrillator). Horiuchi et al(11) studied a smaller population of 345 consecutively admitted patients with acute heart failure hospitalized in the cardiovascular intensive care unit, with similar findings.In brief, the previous studies (7-11) subclassified their patient populations into smaller phenotypically distinct groups with unique clinical trajectories in terms of outcomes and response to various treatments. Direct comparison with our results is difficult due to the differences in initial patient population, the variables available and used for clustering, the distinguishing variables that define each phenogroup, and the variation in outcome measures used to risk-stratify phenogroups and report survival analysis.…”

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confidence: 64%

“…Other investigators have used machine learning or cluster analysis to identify subgroups of patients with distinct phenotypes that differ in their risk profiles and survival outcomes (7)(8)(9)(10)(11). These researchers have studied heterogeneous populations of patients with primary HTN and the absence of HF (9), HFpEF alone (7,8), and mixed populations of HFrEF and HFpEF combined (10,11). However, to our knowledge, ours is the first study to use hierarchical clustering to identify subgroups of patients with subclinical diastolic dysfunction.…”

Section: Discussionmentioning

confidence: 99%

“…et al(10) and Horiuchi et al(11) both used K-means clustering to analyze a mixed population of HFrEF and HFpEF. Ahmad et al(10) subdivided a larger population of 44886 patients from the Swedish Heart Failure Registry into 4 subgroups that differed significantly in terms of 1-year survival and response to medication class (diuretics, ACE-Inhibitors, beta blockers, and nitrates).…”

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confidence: 99%

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Identifying Phenogroups in Patients with Subclinical Diastolic Dysfunction Using Unsupervised Statistical Learning

Kaptein

Karagodin²,

Zuo

et al. 2020

Preprint

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Background Subclinical dysfunction is a precursor for developing heart failure with preserved ejection fraction (HFpEF); yet not all patients progress to HFpEF. Our objective was to evaluate clinical and echocardiographic variables to identify patients who develop HFpEF.Methods Clinical, laboratory, and echocardiographic data were retrospectively collected for 81 patients without HF and 81 matched patients with HFpEF at the time of first documentation of subclinical diastolic dysfunction. Density-based clustering or hierarchical clustering to group patients based on 65 total variables including 19 categorical and 46 numerical variables. Logistic regression analysis was conducted on the entire study population as well as each individual cluster to identify independent predictors of HFPEF.Results Unsupervised clustering identified 3 subgroups which differed in gender composition, severity of cardiac hypertrophy and aortic stenosis, NT-proBNP, percentage of patients who progressed to HFpEF, and timing of disease progression from diastolic dysfunction to HFpEF to death. Clusters that had higher percentages of women had progressively milder cardiac hypertrophy, less severe aortic stenosis, lower NT-proBNP, were diagnosed at an older age with HFpEF, and survived to an older age.Independent predictors of HFpEF for the entire cohort included diabetes, chronic kidney disease, atrial fibrillation, and diuretic use, with additional predictive variables found for each cluster.Conclusions Cluster analysis can identify phenotypically distinct subgroups of patients with diastolic dysfunction. Clusters differ in HFpEF and mortality outcome. In addition, the variables that correlate with and predict HFpEF outcome differ among clusters.

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confidence: 64%

Section: Discussionmentioning

confidence: 99%

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confidence: 99%

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Identifying Phenogroups in Patients with Subclinical Diastolic Dysfunction Using Unsupervised Statistical Learning

Kaptein

Karagodin²,

Zuo

et al. 2020

Preprint

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show abstract

“…Other investigators have used machine learning or cluster analysis to identify subgroups of patients with distinct phenotypes that differ in their risk profiles and survival outcomes (7-11). These researchers have studied heterogeneous populations of patients with primary HTN and the absence of HF (9), HFpEF alone (7,8), and mixed populations of HFrEF and HFpEF combined (10,11). However, to our knowledge, ours is the first study to use hierarchical clustering to identify subgroups of patients with subclinical diastolic dysfunction.…”

Section: Discussionmentioning

confidence: 99%

“…Due to the complexity of the data and heterogeneity of patients in medicine, intuitively identifying groups with similar phenotypes can be difficult and therefore the ability to identify these groups using machine learning methods may allow for more targeted diagnostics, therapeutic strategies and prognostication. For example, unsupervised machine learning has been previously used in research to divide large heterogeneous populations of patients into smaller unique phenogroups, including patients with HFpEF (7,8), patients with primary hypertension (HTN) without heart failure (9), and mixed patient groups with HFrEF and HFpEF combined (10,11).…”

Section: Introductionmentioning

confidence: 99%

Identifying Phenogroups in Patients with Subclinical Diastolic Dysfunction Using Unsupervised Statistical Learning

Strande

Kaptein

Karagodin³

et al. 2019

Preprint

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Subclinical diastolic dysfunction is a precursor for developing heart failure with preserved ejection fraction (HFpEF); yet not all patients progress to HFpEF. Our objective was to evaluate clinical and echocardiographic variables to identify patients who develop HFpEF.Methods and Results Clinical, laboratory, and echocardiographic data were retrospectively collected for 81 patients without HF and 81 matched patients with HFpEF at the time of first documentation of subclinical diastolic dysfunction. Unsupervised clustering identified 3 subgroups which differed in gender composition, severity of cardiac hypertrophy and aortic stenosis, NT-proBNP, percentage of patients who progressed to HFpEF, and timing of disease progression from diastolic dysfunction to HFpEF to death. Clusters that had higher percentages of women had progressively milder cardiac hypertrophy, less severe aortic stenosis, lower NT-proBNP, were diagnosed at an older age with HFpEF, and survived to an older age. Independent predictors of HFpEF for the entire cohort included diabetes, chronic kidney disease, atrial fibrillation, and diuretic use, with additional predictive variables found for each cluster.Conclusions Cluster analysis can identify phenotypically distinct subgroups of patients with diastolic dysfunction. Clusters differ in HFpEF and mortality outcome. In addition, the variables that correlate with and predict HFpEF outcome differ among clusters.

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Clinical Applications of Artificial Intelligence in Early and Accurate Detection of Low‐ConcentrationCVDBiomarkers

Laad¹,

Sajna²,

Sadasivuni³

et al. 2022

Predicting Heart Failure

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Identifying novel phenotypes of acute heart failure using cluster analysis of clinical variables

Cited by 60 publications

References 46 publications

Identifying Phenogroups in Patients with Subclinical Diastolic Dysfunction Using Unsupervised Statistical Learning

Identifying Phenogroups in Patients with Subclinical Diastolic Dysfunction Using Unsupervised Statistical Learning

Identifying Phenogroups in Patients with Subclinical Diastolic Dysfunction Using Unsupervised Statistical Learning

Clinical Applications of Artificial Intelligence in Early and Accurate Detection of Low‐ConcentrationCVDBiomarkers

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