Temporal trends and outcomes in utilisation of transcatheter and surgical aortic valve therapies in aortic valve stenosis patients with heart failure

Al‐Khadra, Yasser; Sattar, Yasar; Ullah, Waqas; Pacha, Homam Moussa; Baibars, Motaz; Darmoch, Fahed; Abu-Mahfouz, Mohammed; Afonso, Luís; Glazier, James J.; Devireddy, Chandan; Anwaruddin, Saif; Sorajja, Paul; Ajmal, Rasikh; Kwok, Chun Shing; Asfour, Abedelrahim I; Zehr, Kenton J.; Mamas, Mamas A.; Alraies, M. Chadi

doi:10.1111/ijcp.13711

Cited by 3 publications

(2 citation statements)

References 28 publications

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“…The negative impact of heart failure on the patient outcomes is amplified in the setting of acute decompensated heart failure, with worse survival and overall outcomes for both TAVR and iSAVR [38]. In recent years, the use of TAVR for treating heart failure patients with aortic stenosis has been on the rise, while the mortality following the intervention has experienced a steeper decline compared to iSAVR [39].…”

Section: Discussionmentioning

confidence: 99%

A Sneak-Peek into the Physician’s Brain: A Retrospective Machine Learning-Driven Investigation of Decision-Making in TAVR versus SAVR for Young High-Risk Patients with Severe Symptomatic Aortic Stenosis

Hašimbegović

Papp

Grahovac

et al. 2021

JPM

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Transcatheter aortic valve replacement (TAVR) has rapidly become a viable alternative to the conventional isolated surgical aortic valve replacement (iSAVR) for treating severe symptomatic aortic stenosis. However, data on younger patients is scarce and a gap exists between data-based recommendations and the clinical use of TAVR. In our study, we utilized a machine learning (ML) driven approach to model the complex decision-making process of Heart Teams when treating young patients with severe symptomatic aortic stenosis with either TAVR or iSAVR and to identify the relevant considerations. Out of the considered factors, the variables most prominently featured in our ML model were congestive heart failure, established risk assessment scores, previous cardiac surgeries, a reduced left ventricular ejection fraction and peripheral vascular disease. Our study demonstrates a viable application of ML-based approaches for studying and understanding complex clinical decision-making processes.

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Section: Discussionmentioning

confidence: 99%

A Sneak-Peek into the Physician’s Brain: A Retrospective Machine Learning-Driven Investigation of Decision-Making in TAVR versus SAVR for Young High-Risk Patients with Severe Symptomatic Aortic Stenosis

Hašimbegović

Papp

Grahovac

et al. 2021

JPM

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“…Transcatheter aortic valve implantation (TAVI) is a minimally invasive approach to treat symptomatic severe aortic stenosis, with similar or even lower mortality rates compared with conventional surgical procedure, 1,2 thus representing the reference intervention in case of very high or prohibitive surgical risk 3 with expanding indications to intermediate‐ and low‐risk patients 4,5 …”

Section: Introductionmentioning

confidence: 99%

Red blood cell distribution width in patients undergoing transcatheter aortic valve implantation: Implications for outcomes

Valenti

Vitolo

Manicardi

et al. 2021

Int J Clinical Practice

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Background Red cell distribution width (RDW) is recently emerging as a prognostic indicator in many cardiovascular diseases. However, less is known about its predictive role in patients undergoing transcatheter aortic valve implantation (TAVI). Methods We retrospectively included very high‐risk patients with severe aortic valve stenosis undergoing TAVI between February 2012 and December 2019. Patients were classified according to RDW tertiles. Our primary endpoint was long‐term all‐cause mortality. The secondary endpoint was a composite of in‐hospital major adverse events as defined by the Valve Academic Research Consortium 2 criteria and/or long‐term all‐cause mortality. Results A total of 424 patients [median age 83.5 years, 52.6% females] were analysed. After a median follow‐up of 1.55 years, all‐cause mortality was 25.5%. At the multivariate‐adjusted Cox regression analysis, patients in the highest RDW tertile were associated with a higher risk for all‐cause mortality [hazard ratio [HR] 1.73, 95%confidence interval [CI] 1.02‐2.95] compared with the lowest tertile. When considering RDW as a continuous variable, we found an 11% increased risk in overall mortality [HR 1.11, 95% CI 1.00‐1.24] for each increased point in RDW. The highest RDW tertile was also independently associated with the occurrence of the composite endpoint [odds ratio [OR] 2.10, 95% CI 1.17‐3.76] compared with lower tertiles. Conclusions In our cohort, elevated basal RDW values were independent predictors of increased long‐term mortality and higher rate of in‐hospital adverse events. The inclusion of a routinely available biomarker as RDW, may help the pre‐operative risk assessment in potential TAVI candidates and optimise their management.

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Device therapy for patients with atrial fibrillation and heart failure with preserved ejection fraction

Zhang,

Xiao,

Dai

et al. 2023

Heart Fail Rev

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Device therapy is a nonpharmacological approach that presents a crucial advancement for managing patients with atrial fibrillation (AF) and heart failure with preserved ejection fraction (HFpEF). This review investigated the impact of device-based interventions and emphasized their potential for optimizing treatment for this complex patient demographic. Cardiac resynchronization therapy, augmented by atrioventricular node ablation with His-bundle pacing or left bundle-branch pacing, is effective for enhancing cardiac function and establishing atrioventricular synchrony. Cardiac contractility modulation and vagus nerve stimulation represent novel strategies for increasing myocardial contractility and adjusting the autonomic balance. Left ventricular expanders have demonstrated short-term benefits in HFpEF patients but require more investigation for long-term effectiveness and safety, especially in patients with AF. Research gaps regarding complications arising from left ventricular expander implantation need to be addressed. Device-based therapies for heart valve diseases, such as transcatheter aortic valve replacement and transcatheter edge-to-edge repair, show promise for patients with AF and HFpEF, particularly those with mitral or tricuspid regurgitation. Clinical evaluations show that these device therapies lessen AF occurrence, improve exercise tolerance, and boost left ventricular diastolic function. However, additional studies are required to perfect patient selection criteria and ascertain the long-term effectiveness and safety of these interventions. Our review underscores the significant potential of device therapy for improving the outcomes and quality of life for patients with AF and HFpEF.

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Temporal trends and outcomes in utilisation of transcatheter and surgical aortic valve therapies in aortic valve stenosis patients with heart failure

Cited by 3 publications

References 28 publications

A Sneak-Peek into the Physician’s Brain: A Retrospective Machine Learning-Driven Investigation of Decision-Making in TAVR versus SAVR for Young High-Risk Patients with Severe Symptomatic Aortic Stenosis

A Sneak-Peek into the Physician’s Brain: A Retrospective Machine Learning-Driven Investigation of Decision-Making in TAVR versus SAVR for Young High-Risk Patients with Severe Symptomatic Aortic Stenosis

Red blood cell distribution width in patients undergoing transcatheter aortic valve implantation: Implications for outcomes

Device therapy for patients with atrial fibrillation and heart failure with preserved ejection fraction

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