Application of survival classification and regression tree analysis for identification of subgroups of risk in patients with heart failure and reduced left ventricular ejection fraction

Siriopol, Dimitrie; Popa, Radu; Mihaila, Mihaela; Rusu, Florentina; Sascău, Radu Andy; Stătescu, Cristian; Cătălina, Zahariuc; Vasiliu, Vlad; Bucur, Andreea; Neamtu, Andreea; Siriopol, Ianis; Cianga, Petru; Kanbay, Mehmet; Covic, Adrian

doi:10.1007/s10554-021-02159-6

Cited by 5 publications

(3 citation statements)

References 32 publications

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“…Figure 2 presents search results, reasons for exclusion, included studies, and reported outcomes. The final number of included studies was 31, of which 20 studied heart failure populations (40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50)(51)(52)(53)(54)(55)(56)(57)(58)(59), 10 studied CKD populations (60)(61)(62)(63)(64)(65)(66)(67)(68)(69) and one study included patients with type 2 diabetes with and without CKD (70). Methodological quality varied across studies with no studies excluded due to high risk of bias.…”

Section: Search Resultsmentioning

confidence: 99%

Bioimpedance Indices of Fluid Overload and Cardiorenal Outcomes in Heart Failure and Chronic Kidney Disease: a Systematic Review

Mayne

Shemilt

Keane

et al. 2022

Journal of Cardiac Failure

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Section: Search Resultsmentioning

confidence: 99%

Bioimpedance Indices of Fluid Overload and Cardiorenal Outcomes in Heart Failure and Chronic Kidney Disease: a Systematic Review

Mayne

Shemilt

Keane

et al. 2022

Journal of Cardiac Failure

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“…Table 6 outlines studies evaluating the usage of ML algorithms in determining the HF prognosis using ECG parameters, heart rate variability and laboratory testing. [64][65][66][67][68][69][70][71][72][73] Studies have also utilized echocardiographic parameters for determination of prognosis in patients with HF. Greenberg and colleagues developed the MARKER-HF risk score using a boosted decision tree algorithm that could discriminate between patients with high or low mortality risk.…”

Section: Prognosis Determinationmentioning

confidence: 99%

“…Prognostic stratification may impact HF care but utility in practice is limited due to low reliability at the individual patient level risk, the variety of approaches to choose from, and the complexity of statistical methodologies used. Table 6 outlines studies evaluating the usage of ML algorithms in determining the HF prognosis using ECG parameters, heart rate variability and laboratory testing 64–73 . Studies have also utilized echocardiographic parameters for determination of prognosis in patients with HF.…”

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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