Multi-modality artificial intelligence-based transthyretin amyloid cardiomyopathy detection in patients with severe aortic stenosis.

PURPOSE: Transthyretin amyloid cardiomyopathy (ATTR-CM) is a frequent concomitant condition in patients with severe aortic stenosis (AS), yet it often remains undetected. This study aims to comprehensively evaluate artificial intelligence-based models developed based on preprocedural and routinely collected data to detect ATTR-CM in patients with severe AS planned for transcatheter aortic valve implantation (TAVI). METHODS: In this prospective, single-center study, consecutive patients with AS were screened with [99mTc]-3,3-diphosphono-1,2-propanodicarboxylic acid ([99mTc]-DPD) for the presence of ATTR-CM. Clinical, laboratory, electrocardiogram, echocardiography, invasive measurements, 4-dimensional cardiac CT (4D-CCT) strain data, and CT-radiomic features were used for machine learning modeling of ATTR-CM detection and for outcome prediction. Feature selection and classifier algorithms were applied in single- and multi-modality classification scenarios. We split the dataset into training (70%) and testing (30%) samples. Performance was assessed using various metrics across 100 random seeds. RESULTS: Out of 263 patients with severe AS (57% males, age 83 ± 4.6years) enrolled, ATTR-CM was confirmed in 27 (10.3%). The lowest performances for detection of concomitant ATTR-CM were observed in invasive measurements and ECG data with area under the curve (AUC) < 0.68. Individual clinical, laboratory, interventional imaging, and CT-radiomics-based features showed moderate performances (AUC 0.70-0.76, sensitivity 0.79-0.82, specificity 0.63-0.72), echocardiography demonstrated good performance (AUC 0.79, sensitivity 0.80, specificity 0.78), and 4D-CT-strain showed the highest performance (AUC 0.85, sensitivity 0.90, specificity 0.74). The multi-modality model (AUC 0.84, sensitivity 0.87, specificity 0.76) did not outperform the model performance based on 4D-CT-strain only data (p-value > 0.05). The multi-modality model adequately discriminated low and high-risk individuals for all-cause mortality at a mean follow-up of 13 months. CONCLUSION: Artificial intelligence-based models using collected pre-TAVI evaluation data can effectively detect ATTR-CM in patients with severe AS, offering an alternative diagnostic strategy to scintigraphy and myocardial biopsy.

Combined Significant Aortic Stenosis and Mitral Regurgitation: Challenges in Timing and Type of Intervention.

In this narrative review, we aim to summarize the literature surrounding the assessment and management of the common, yet understudied combination of aortic stenosis (AS) and mitral regurgitation (MR), the components of which are complexly inter-related and interdependent from diagnostic, prognostic, and therapeutic perspectives. The hemodynamic interdependency of AS and MR confounds the assessment of the severity of each valve disease, thus underscoring the importance of a multimodal approach integrating valvular and extravalvular indicators of severity. A large body of literature suggests that baseline MR is associated with reduced survival post aortic valve (AV) intervention and that regression of MR post-AV intervention confers a mortality benefit. Functional MR is more likely to regress after AV intervention than primary MR. The respective natural courses of the 2 valve diseases are not synchronized; therefore, significant AS and MR at or above the respective threshold for intervention might not coincide. Surgery is primarily a 1-stop-shop procedure because of a considerable perioperative risk of repeat interventions, whereas transcatheter treatment modalities allow for a more tailored timing of intervention with reassessment of concomitant MR after AV replacement and a potential staged intervention in the absence of MR regression. In summary, AS and MR, when combined, are interlaced into a complex hemodynamic, diagnostic, and prognostic synergy, with important therapeutic implications. Contemporary approaches should consider stepwise intervention by exploiting the advantage of transcatheter options. However, evidence is needed to demonstrate the efficacy of different timing and therapeutic options.

Association of atrial fibrillation with survival in patients with low-flow low-gradient aortic stenosis with preserved ejection fraction undergoing TAVI.

AIMS: There is limited evidence on the prognostic significance of atrial fibrillation (AF) in patients with low flow, low gradient aortic stenosis with preserved ejection fraction (LFLG-pEF AS). We aimed to evaluate the recovery of stroke volume after transcatheter aortic valve implantation (TAVI) and clinical outcomes in patients with LFLG-pEF AS stratified by presence or absence of AF. METHODS AND RESULTS: In a prospective TAVI registry, patients with preserved left ventricular ejection fraction (LVEF ≥ 50%) were stratified according to flow-gradient status and presence of AF. Among 2 259 TAVI patients with preserved LVEF between August 2007 and June 2021, 765 had high-gradient AS (HG AS) and 444 had LFLG-pEF AS. AF was observed in 199 patients with HG AS (26.0%) and 190 patients with LFLG-pEF AS (42.8%). At 1 year, SVi was significantly improved in LFLG-pEF AS patients without AF, while SVi remained low in patients with AF (from 25.9 ± 8.5 mL/m2 to 37.2 ± 9.9 mL/m2 and from 26.8 ± 5.1 mL/m2 to 26.1 ± 9.1 mL/m2, respectively). LFLG-pEF AS patients with AF had an increased risk of 1-year all-cause mortality compared with those without AF (HRadjusted 2.57; 95% CI 1.44-4.59). LFLG-pEF AS patients without AF had similar mortality compared with HG AS patients without AF (HRadjusted 0.85; 95% CI 0.49-1.46). CONCLUSIONS: Patients with LFLG-pEF AS and AF experienced no relevant recovery of stroke volume after TAVI, but a more than two-fold increased risk of death compared to patients with HG AS or LFLG-pEF AS without AF. Clinical Trial Registration: https://www.clinicaltrials.gov. NCT01368250.

Diuretic and renal effects of angiotensin receptor-neprilysin inhibitor in patients hospitalized for acute heart failure.

In patients hospitalized for acute decompensation of heart failure (HF), the impact of angiotensin receptor-neprilysin inhibitor (ARNI) on diuresis and renal function has not been fully investigated. Patients with HF and reduced ejection fraction who were hospitalized for acute decompensation and newly initiated ARNI after hemodynamic stabilization were enrolled. Changes in urine volume (UV), body weight, estimated glomerular filtration rate (eGFR), and urine N-acetyl-beta-d-glucosaminidase (uNAG) levels before and after ARNI initiation were investigated. Changes in the diuretic response [DR, calculated as urine volume/(intravenous furosemide volume/40 mg)], N-terminal pro-brain natriuretic peptide (NT-proBNP), hematocrit, and plasma volume (PV) were also evaluated. A total of 60 patients were enrolled. ARNI was initiated at a median of 6 [5, 7] days after hospitalization. After initiation of ARNI, body weight, NT-proBNP, and PV decreased. UV and DR increased only on the day of ARNI initiation (delta UV 400 ± 957 ml and delta DR 1100 ± 3107 ml/40 mg furosemide) and then decreased to baseline levels. In the multivariable linear regression analysis, younger age, higher BMI, and higher NT-proBNP levels were significantly associated with greater UV after ARNI initiation. eGFR and uNAG did not significantly change after the initiation of ARNI [delta eGFR -1.7 ± 12.0 mL/min/1.73 m2 and delta uNAG 2.0 (-5.6, 6.9) IU/L]. In patients hospitalized for HF, the initiation of ARNI was associated with a small and transient increase in UV and DR, and was not associated with worsening of renal function or tubular injury.