Development of a new risk model for a prognostic prediction after transcatheter aortic valve replacement

Five-Year Transcatheter Aortic Valve Replacement Outcomes in Chronic Hemodialysis vs. Non-Hemodialysis Patients Using Balloon-Expandable Devices

BACKGROUND

Based on the results of a clinical trial in Japan, transcatheter aortic valve replacement (TAVR) for hemodialysis (HD) patients gained approval; however, mid-term TAVR outcomes and transcatheter aortic valve (TAV) durability in HD patients remain unexplored.

METHODS AND RESULTS

We analyzed background, procedural, in-hospital outcome, and follow-up data for 101 HD patients and 494 non-HD patients who underwent TAVR using balloon-expandable valves (SAPIEN XT or SAPIEN 3) retrieved from Osaka University Hospital TAVR database. Periprocedural mortality and TAVR-related complications were comparable between HD and non-HD patients. However, Kaplan-Meier analysis revealed that HD patients had significantly lower survival rates (log-rank test, P<0.001). In addition, HD patients had significantly higher rates of severe structural valve deterioration (SVD) than non-HD patients (Gray test, P=0.038).

CONCLUSIONS

TAVR in HD patients had comparable periprocedural mortality but inferior mid-term survival and TAV durability than in non-HD patients. Indications for TAVR in younger HD patients should be carefully determined, considering the possibility of a TAV-in-TAV procedure when early SVD occurs.

Direct deep learning-based survival prediction from pre-interventional CT prior to transcatheter aortic valve replacement

PURPOSE

To investigate survival prediction in patients undergoing transcatheter aortic valve replacement (TAVR) using deep learning (DL) methods applied directly to pre-interventional CT images and to compare performance with survival models based on scalar markers of body composition.

METHOD

This retrospective single-center study included 760 patients undergoing TAVR (mean age 81 ± 6 years; 389 female). As a baseline, a Cox proportional hazards model (CPHM) was trained to predict survival on sex, age, and the CT body composition markers fatty muscle fraction (FMF), skeletal muscle radiodensity (SMRD), and skeletal muscle area (SMA) derived from paraspinal muscle segmentation of a single slice at L3/L4 level. The convolutional neural network (CNN) encoder of the DL model for survival prediction was pre-trained in an autoencoder setting with and without a focus on paraspinal muscles. Finally, a combination of DL and CPHM was evaluated. Performance was assessed by C-index and area under the receiver operating curve (AUC) for 1-year and 2-year survival. All methods were trained with five-fold cross-validation and were evaluated on 152 hold-out test cases.

RESULTS

The CNN for direct image-based survival prediction, pre-trained in a focussed autoencoder scenario, outperformed the baseline CPHM (CPHM: C-index = 0.608, 1Y-AUC = 0.606, 2Y-AUC = 0.594 vs. DL: C-index = 0.645, 1Y-AUC = 0.687, 2Y-AUC = 0.692). Combining DL and CPHM led to further improvement (C-index = 0.668, 1Y-AUC = 0.713, 2Y-AUC = 0.696).

CONCLUSIONS

Direct DL-based survival prediction shows potential to improve image feature extraction compared to segmentation-based scalar markers of body composition for risk assessment in TAVR patients.

A Novel Frailty Score Based on Laboratory Parameters (FIMS Score) for the Management of Older Patients with Severe Aortic Stenosis

This study aimed to develop a novel score based on common laboratory parameters able to identify frail and sarcopenic patients as well as predict mortality in elderly patients with severe aortic stenosis (AS) for tailored clinical decision-making. A total of 109 patients (83 ± 5 years; females, 68%) with AS underwent a multidisciplinary pre-operative assessment and finalized a "frailty-based management" for the AS interventional treatment. Laboratory parameters of statistically significant differences between sarcopenic and non-sarcopenic individuals were tested in the structural equation model (SEM) to build a Frailty Inflammation Malnutrition and Sarcopenia score (FIMS score). Mortality at 20 months of follow-up was considered an outcome. FIMS score, in particular, the cut-off value ≥ 1.28 was able to identify "frail" and "early frail" patients and predict mortality with a sensitivity of 83.3% and 82.6%, respectively (p = 0.001) and was an independent determinant associated with a higher risk of mortality (HR 5.382; p-value = 0.002). The FIMS score, easily achievable and usable in clinical practice, was able to identify frail and sarcopenic patients as well as predict their adverse clinical outcomes. This score could provide appropriate guidance during decision-making regarding elderly patients with severe AS.

A Risk Model for 1-Year Mortality After Transcatheter Aortic Valve Replacement From the J-TVT Registry

Background

Although transcatheter aortic valve replacement (TAVR) has demonstrated favorable outcomes in randomized studies, there remains a sizable group of patients in whom TAVR may be futile. Characterizing the survival rate in a wide array of patients undergoing TAVR can help develop effective strategies for improving the allocation of medial resources.

Objectives

The aim of this study was to develop a risk model to estimate 1-year mortality after TAVR from a representative nationwide registry in Japan.

Methods

The J-TVT (Japan Transcatheter Valve Therapies) registry contains complete data, including 1-year outcomes, on patients undergoing TAVR in Japan. A total of 17,655 patients underwent TAVR between 2013 and 2018. They were randomly divided into 2 groups in a 7:3 ratio to form a derivation cohort of 12,316 patients and a validation cohort of 5,339 patients. A risk model was constructed for 1-year mortality in the derivation cohort, and its discrimination and calibration were assessed in the validation cohort.

Results

The mean age of all registered patients was 84.4 years, and 68.8% were women. The mean body size area was 1.43 m2, and the mean Society of Thoracic Surgeons Predicted Risk of Mortality score was 7.3%. The estimated 1-year survival was 91.8%; 202 and 1,316 deaths were observed at 30 days and 1 year, respectively; The estimated C index for the developed model was 0.733 (95% CI: 0.709-0.757) in the validation cohort, with good calibration.

Conclusions

A prediction model for 1-year survival following TAVR derived from a national clinical database performed well and should aid physicians managing TAVR patients.

Machine learning applications in cardiac computed tomography: a composite systematic review

Artificial intelligence and machine learning (ML) models are rapidly being applied to the analysis of cardiac computed tomography (CT). We sought to provide an overview of the contemporary advances brought about by the combination of ML and cardiac CT. Six searches were performed in Medline, Embase, and the Cochrane Library up to November 2021 for (i) CT-fractional flow reserve (CT-FFR), (ii) atrial fibrillation (AF), (iii) aortic stenosis, (iv) plaque characterization, (v) fat quantification, and (vi) coronary artery calcium score. We included 57 studies pertaining to the aforementioned topics. Non-invasive CT-FFR can accurately be estimated using ML algorithms and has the potential to reduce the requirement for invasive angiography. Coronary artery calcification and non-calcified coronary lesions can now be automatically and accurately calculated. Epicardial adipose tissue can also be automatically, accurately, and rapidly quantified. Effective ML algorithms have been developed to streamline and optimize the safety of aortic annular measurements to facilitate pre-transcatheter aortic valve replacement valve selection. Within electrophysiology, the left atrium (LA) can be segmented and resultant LA volumes have contributed to accurate predictions of post-ablation recurrence of AF. In this review, we discuss the latest studies and evolving techniques of ML and cardiac CT.

Deoxyribonuclease is prognostic in patients undergoing transcatheter aortic valve replacement

Degenerative aortic valve stenosis is an inflammatory process that resembles atherosclerosis. Neutrophils release their DNA upon activation and form neutrophil extracellular traps (NETs), which are present on degenerated aortic valves. NETs correlate with pressure gradients in severe aortic stenosis. Transcatheter aortic valve replacement (TAVR) is an established treatment option for aortic valve stenosis. Bioprosthetic valve deterioration promoted by inflammatory, fibrotic and thrombotic processes limits outcome. Deoxyribonuclease is a natural counter mechanism to degrade DNA in circulation. In the present observational study, we investigated plasma levels of double-stranded DNA, deoxyribonuclease activity and outcome after TAVR. 345 consecutive patients undergoing TAVR and 100 healthy reference controls were studied. Double-stranded DNA was measured by fluorescence assays in plasma obtained at baseline and after TAVR. Deoxyribonuclease activity was measured at baseline using single radial enzyme diffusion assays. Follow-up was performed at 12 months, and mean aortic pressure gradient and survival were evaluated. Receiver operating characteristic, Kaplan-Meier curves and Cox regression models were calculated. Baseline double-stranded DNA in plasma was significantly higher compared to healthy controls, was increased at 3 and 7 days after TAVR, and declined thereafter. Baseline deoxyribonuclease activity was decreased compared to healthy controls. Interestingly, low deoxyribonuclease activity correlated with higher C-reactive protein and higher mean transaortic gradient after 12 months. Finally, deoxyribonuclease activity was a strong independent predictor of outcome 12 months after TAVR. Deoxyribonuclease activity is a potential biomarker for risk stratification after TAVR. Pathomechanisms of bioprosthetic valve deterioration involving extracellular DNA and deoxyribonuclease merit investigation.