Effectiveness of telemedicine in patients with heart failure according to frailty phenotypes: Insights from the iCOR randomised controlled trial

Yun, Sergi; Enjuanes, Cristina; Calero-Molina, E; Hidalgo, E; Jose, N; Calvo, Elena; Verdú-Rotellar, José María; Garcimartín, Paloma; Chivite, David; Formiga, Francesç; Jiménez-Marrero, Santiago; Garay, Alberto; Alcoberro, L; Moliner, Pedro; Corbella, Xavier; Comín‐Colet, Josep

doi:10.1016/j.ejim.2021.09.021

Cited by 16 publications

(7 citation statements)

References 52 publications

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“…The efficacy of telehealth is further demonstrated by Yun et al. 2 In their study, clusters of patients were identified presenting distinct susceptibility to adverse outcomes. Their findings, consistent among all clusters, show a reduction in the occurrence of nonfatal heart failure events in patients receiving telemedicine over usual care.…”

mentioning

confidence: 79%

Telehealth: The Future of Consultations?

Ganeshalingham¹,

Ahmad²

2022

JACC: Heart Failure

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mentioning

confidence: 79%

Telehealth: The Future of Consultations?

Ganeshalingham¹,

Ahmad²

2022

JACC: Heart Failure

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“…[23][24][25] In fact, different studies have demonstrated that within the comprehensive management of patients with HF, the addition of telemedicine may result in better outcomes, including the prevention of further HF events and reduction of costs, regardless frailty status of patients. [26][27][28][29][30][31] Therefore, both, telemedicine and face-to-face visits are useful tools that can be adapted to the need that the patient require at each moment. [23][24][25] Additionally, electronic consultations are an excellent instrument that facilitates the communication between different health-care providers, promoting the comprehensive management of patients with HF.…”

Section: Dovepressmentioning

confidence: 99%

Optimization of Patient Pathway in Heart Failure with Reduced Ejection Fraction and Worsening Heart Failure. Role of Vericiguat

González-Juanàtey¹,

Comín‐Colet²,

Pascual-Figal³

et al. 2023

PPA

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Heart failure (HF) is a progressive condition with periods of apparent stability and repeated worsening HF events. Over time, unless optimization of HF treatment, worsening HF events become more frequent and patients enter into a cycle of recurrent events with high morbidity and mortality. In patients with HF there is an activation of deleterious neurohormonal pathways, such as the renin angiotensin aldosterone system and the sympathetic system, and an inhibition of protective pathways, including natriuretic peptides and guanylate cyclase. Therefore, HF burden can be reduced only through a holistic approach that targets all neurohormonal systems. In this context, vericiguat may play a key role, as it is the only HF drug that activates the nitric oxide-soluble guanylate cyclase-cyclic guanosine monophosphate system. On the other hand, it has been described relevant disparities in the management of HF population. Consequently, it is necessary to homogenize the management of these patients, through an integrated patient-care pathway that should be adapted at the local level. In this context, the development of new technologies (ie, video call, specific platforms, remote control devices, etc.) may be very helpful. In this manuscript, a multidisciplinary group of experts analyzed the current evidence and shared their own experience to provide some recommendations about the therapeutic optimization of patients with recent worsening HF, with a particular focus on vericiguat, and also about how the integrated patient-care pathway should be performed.

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“…Among the 47 included studies, 23(48.9%) studies focused on patients with generalized HF (7, 13, 17-31), while the rest 24 studies focused on patients with specific categories of HF, among which 19 studies (40.4%) focused on patients with HFpEF (32-50), and the other 5 studies (14, 51-54) (10.6%) focused on HFrEF. Of these studies, only one In all the included studies, the corresponding authors were from 13 different countries, including the United States (22, 46.8%) (18-22, 24, 26-28, 33, 36, 37, 40, 41, 44, 48, 53, 54, 56-59), the Netherlands (5, 10.6%) (23, 45, 47, 55, 60), France (5, 10.6%) (13, 17, 32, 43, 51), Spain (3, 6.4%) (25,34,38), China (3, 6.4%) (7,35,49) and Japan (2, 4.3%) (31). Australia (52), Germany (30), Italy (14), Poland (39), Switzerland (46), Canada (50) and the United Kingdom ( 29) each had only one study (1, 2.1%).…”

Section: Characteristics Of the Included Studiesmentioning

confidence: 99%

Identifying novel subgroups in heart failure patients with unsupervised machine learning: A scoping review

Sun

Guo

Wang

et al. 2022

Front. Cardiovasc. Med.

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BackgroundHeart failure is currently divided into three main forms, HFrEF, HFpEF, and HFmrEF, but its etiology is diverse and highly heterogeneous. Many studies reported a variety of novel subgroups in heart failure patients, with unsupervised machine learning methods. The aim of this scoping review is to provide insights into how these techniques can diagnose and manage HF faster and better, thus providing direction for future research and facilitating its routine use in clinical practice.MethodsThe review was performed following PRISMA-SCR guideline. We searched the PubMed database for eligible publications. Studies were included if they defined new subgroups in HF patients using clustering analysis methods, and excluded if they are (1) Reviews, commentary, or editorials, (2) Studies not about defining new sub-types, or (3) Studies not using unsupervised algorithms. All study screening and data extraction were conducted independently by two investigators and narrative integration of data extracted from included studies was performed.ResultsOf the 498 studies identified, 47 were included in the analysis. Most studies (61.7%) were published in 2020 and later. The largest number of studies (46.8%) coming from the United States, and most of the studies were authored and included in the same country. The most commonly used machine learning method was hierarchical cluster analysis (46.8%), the most commonly used cluster variable type was comorbidity (61.7%), and the least used cluster variable type was genomics (12.8%). Most of the studies used data sets of less than 500 patients (48.9%), and the sample size had negative correlation with the number of clustering variables. The majority of studies (85.1%) assessed the association between cluster grouping and at least one outcomes, with death and hospitalization being the most commonly used outcome measures.ConclusionThis scoping review provides an overview of recent studies proposing novel HF subgroups based on clustering analysis. Differences were found in study design, study population, clustering methods and variables, and outcomes of interests, and we provided insights into how these studies were conducted and identify the knowledge gaps to guide future research.

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Effectiveness of telemedicine in patients with heart failure according to frailty phenotypes: Insights from the iCOR randomised controlled trial

Cited by 16 publications

References 52 publications

Telehealth: The Future of Consultations?

Telehealth: The Future of Consultations?

Optimization of Patient Pathway in Heart Failure with Reduced Ejection Fraction and Worsening Heart Failure. Role of Vericiguat

Identifying novel subgroups in heart failure patients with unsupervised machine learning: A scoping review

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