Phenotyping heart failure using model‐based analysis and physiology‐informed machine learning

Jones, Edith; Randall, E. Benjamin; Hummel, Scott L.; Cameron, David M.; Beard, Daniel A.; Carlson, Brian E.

doi:10.1113/jp281845

Cited by 25 publications

(27 citation statements)

References 45 publications

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“…Using the unsupervised machine learning method from Jones et al. (2021), convex hulls of the four disease groups were created, and the subjects were clustered into two groups to account for one hypothetically more healthy and one hypothetically more diseased group. Before the PCA and clustering were applied, the parameters were standardized by centring and dividing each parameter value by the SD of that parameter.…”

Section: Methodsmentioning

confidence: 99%

Haemodynamic effects of hypertension and type 2 diabetes: Insights from a 4D flow MRI‐based personalized cardiovascular mathematical model

Tunedal

Viola

Garcia

et al. 2023

The Journal of Physiology

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Type 2 diabetes (T2D) and hypertension increase the risk of cardiovascular diseases mediated by whole‐body changes to metabolism, cardiovascular structure and haemodynamics. The haemodynamic changes related to hypertension and T2D are complex and subject‐specific, however, and not fully understood. We aimed to investigate the haemodynamic mechanisms in T2D and hypertension by comparing the haemodynamics between healthy controls and subjects with T2D, hypertension, or both. For all subjects, we combined 4D flow magnetic resonance imaging data, brachial blood pressure and a cardiovascular mathematical model to create a comprehensive subject‐specific analysis of central haemodynamics. When comparing the subject‐specific haemodynamic parameters between the four groups, the predominant haemodynamic difference is impaired left ventricular relaxation in subjects with both T2D and hypertension compared to subjects with only T2D, only hypertension and controls. The impaired relaxation indicates that, in this cohort, the long‐term changes in haemodynamic load of co‐existing T2D and hypertension cause diastolic dysfunction demonstrable at rest, whereas either disease on its own does not. However, through subject‐specific predictions of impaired relaxation, we show that altered relaxation alone is not enough to explain the subject‐specific and group‐related differences; instead, a combination of parameters is affected in T2D and hypertension. These results confirm previous studies that reported more adverse effects from the combination of T2D and hypertension compared to either disease on its own. Furthermore, this shows the potential of personalized cardiovascular models in providing haemodynamic mechanistic insights and subject‐specific predictions that could aid in the understanding and treatment planning of patients with T2D and hypertension. imageKey points The combination of 4D flow magnetic resonance imaging data and a cardiovascular mathematical model allows for a comprehensive analysis of subject‐specific haemodynamic parameters that otherwise cannot be derived non‐invasively. Using this combination, we show that diastolic dysfunction in subjects with both type 2 diabetes (T2D) and hypertension is the main group‐level difference between controls, subjects with T2D, subjects with hypertension, and subjects with both T2D and hypertension. These results suggest that, in this relatively healthy population, the additional load of both hypertension and T2D affects the haemodynamic function of the left ventricle, whereas each disease on its own is not enough to cause significant effects under resting conditions. Finally, using the subject‐specific model, we show that the haemodynamic effects of diastolic dysfunction alone are not sufficient to explain all the observed haemodynamic differences. Instead, additional subject‐specific variations in cardiac and vascular function combine to explain the complex haemodynamics of subjects affected by hypertension and/or T2D.

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Section: Methodsmentioning

confidence: 99%

Haemodynamic effects of hypertension and type 2 diabetes: Insights from a 4D flow MRI‐based personalized cardiovascular mathematical model

Tunedal

Viola

Garcia

et al. 2023

The Journal of Physiology

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“…Several studies have identified subgroups of patients with distinct phenotypes of HFpEF based on their profiles and outcomes using unsupervised clustering algorithms (Table 4). 6,[45][46][47][48][49][50][51][52][53][54] Across the studies, types of clustering used primarily are hierarchical, K-means, and density based (Figure 2). Hierarchical agglomerative clustering is a bottom-up approach such that each data point begins in a separate cluster, and pairs of clusters at the bottom are merged going up the hierarchy.…”

Section: Phenomappingmentioning

confidence: 99%

Section: Applications Of Artificial Intelligence In Heart Failurementioning

confidence: 99%

Artificial intelligence and heart failure: A state‐of‐the‐art review

Khan

Arshad

Greene

et al. 2023

European J of Heart Fail

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Heart failure (HF) is a heterogeneous syndrome affecting more than 60 million individuals globally. Despite recent advancements in understanding of the pathophysiology of HF, many issues remain including residual risk despite therapy, understanding the pathophysiology and phenotypes of patients with HF and preserved ejection fraction (HFpEF), and the challenges related to integrating a large amount of disparate information available for risk stratification and management of these patients. Risk prediction algorithms based on artificial intelligence (AI) may have superior predictive ability compared to traditional methods in certain instances. AI algorithms can play a pivotal role in the evolution of HF care by facilitating clinical decision making to overcome various challenges such as allocation of treatment to patients who are at highest risk or are more likely to benefit from therapies, prediction of adverse outcomes, and early identification of patients with subclinical disease or worsening HF. With the ability to integrate and synthesize large amounts of data with multidimensional interactions, AI algorithms can supply information with which physicians can improve their ability to make timely and better decisions. In this review, we provide an overview of the AI algorithms that have been developed for establishing early diagnosis of HF, phenotyping HF with preserved ejection fraction, and stratifying HF disease severity. This review also discusses the challenges in clinical deployment of AI algorithms in HF, and the potential path forward for developing future novel learning‐based algorithms to improve HF care.This article is protected by copyright. All rights reserved.

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“…6 Prior studies have succeeded in stratifying HFpEF into several phenotypes in multiple cohorts that included a small number of Asian but not Japanese patients. [7][8][9][10][11][12] The distribution of patient population characteristics was significantly different between Western and Eastern countries, especially in terms of mean body mass index (BMI) and the incidence of obesity. In the recent HFpEF worldwide registry, the Asian patient population demonstrated lower BMI, a frequent incidence of atrial fibrillation (AF), poor kidney function, and a history of HF admission.…”

Section: Introductionmentioning

confidence: 99%

Heart failure with preserved ejection fraction phenogroup classification using machine learning

et al. 2023

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Aims Heart failure (HF) with preserved ejection fraction (HFpEF) is a complex syndrome with a poor prognosis. Phenotyping is required to identify subtype-dependent treatment strategies. Phenotypes of Japanese HFpEF patients are not fully elucidated, whose obesity is much less than Western patients. This study aimed to reveal model-based phenomapping using unsupervised machine learning (ML) for HFpEF in Japanese patients. Methods and resultsWe studied 365 patients with HFpEF (left ventricular ejection fraction >50%) as a derivation cohort from the Nara Registry and Analyses for Heart Failure (NARA-HF), which registered patients with hospitalization by acute decompensated HF. We used unsupervised ML with a variational Bayesian-Gaussian mixture model (VBGMM) with common clinical variables. We also performed hierarchical clustering on the derivation cohort. We adopted 230 patients in the Japanese Heart Failure Syndrome with Preserved Ejection Fraction Registry as the validation cohort for VBGMM. The primary endpoint was defined as all-cause death and HF readmission within 5 years. Supervised ML was performed on the composite cohort of derivation and validation. The optimal number of clusters was three because of the probable distribution of VBGMM and the minimum Bayesian information criterion, and we stratified HFpEF into three phenogroups. Phenogroup 1 (n = 125) was older (mean age 78.9 ± 9.1 years) and predominantly male (57.6%), with the worst kidney function (mean estimated glomerular filtration rate 28.5 ± 9.7 mL/min/1.73 m 2 ) and a high incidence of atherosclerotic factor. Phenogroup 2 (n = 200) had older individuals (mean age 78.8 ± 9.7 years), the lowest body mass index (BMI; 22.78 ± 3.94), and the highest incidence of women (57.5%) and atrial fibrillation (56.5%). Phenogroup 3 (n = 40) was the youngest (mean age 63.5 ± 11.2) and predominantly male (63.5 ± 11.2), with the highest BMI (27.46 ± 5.85) and a high incidence of left ventricular hypertrophy. We characterized these three phenogroups as atherosclerosis and chronic kidney disease, atrial fibrillation, and younger and left ventricular hypertrophy groups, respectively. At the primary endpoint, Phenogroup 1 demonstrated the worst prognosis (Phenogroups 1-3: 72.0% vs. 58.5% vs. 45%, P = 0.0036). We also successfully classified a derivation cohort into three similar phenogroups using VBGMM. Hierarchical and supervised clustering successfully showed the reproducibility of the three phenogroups. Conclusions ML could successfully stratify Japanese HFpEF patients into three phenogroups (atherosclerosis and chronic kidney disease, atrial fibrillation, and younger and left ventricular hypertrophy groups).

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Phenotyping heart failure using model‐based analysis and physiology‐informed machine learning

Abstract: support-information-section).

Cited by 25 publications

References 45 publications

Haemodynamic effects of hypertension and type 2 diabetes: Insights from a 4D flow MRI‐based personalized cardiovascular mathematical model

Haemodynamic effects of hypertension and type 2 diabetes: Insights from a 4D flow MRI‐based personalized cardiovascular mathematical model

Artificial intelligence and heart failure: A state‐of‐the‐art review

Heart failure with preserved ejection fraction phenogroup classification using machine learning

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