CT determined psoas muscle area predicts mortality in women undergoing transcatheter aortic valve implantation.

OBJECTIVES: The aim of this study was to assess the predictive value of PMA measurement for mortality. BACKGROUND: Current surgical risk stratification have limited predictive value in the transcatheter aortic valve implantation (TAVI) population. In TAVI workup, a CT scan is routinely performed but body composition is not analyzed. Psoas muscle area (PMA) reflects a patient's global muscle mass and accordingly PMA might serve as a quantifiable frailty measure. METHODS: Multi-slice computed tomography scans (between 2010 and 2016) of 583 consecutive TAVI patients were reviewed. Patients were divided into equal sex-specific tertiles (low, mid, and high) according to an indexed PMA. Hazard ratios (HR) and their confidence intervals (CI) were determined for cardiac and all-cause mortality after TAVI. RESULTS: Low iPMA was associated with cardiac and all-cause mortality in females. One-year adjusted cardiac mortality HR in females for mid-iPMA and high-iPMA were 0.14 [95%CI, 0.05-0.45] and 0.40 [95%CI, 0.15-0.97], respectively. Similar effects were observed for 30-day and 2-years cardiac and all-cause mortality. In females, adding iPMA to surgical risk scores improved the predictive value for 1-year mortality. C-statistics changed from 0.63 [CI = 0.54-0.73] to 0.67 [CI: 0.58-0.75] for EuroSCORE II and from 0.67 [CI: 0.59-0.77] to 0.72 [CI: 0.63-0.80] for STS-PROM. CONCLUSIONS: Particularly in females, low iPMA is independently associated with an higher all-cause and cardiac mortality. Prospective studies should confirm whether PMA or other body composition parameters should be extracted automatically from CT-scans to include in clinical decision making and outcome prediction for TAVI.

Comprehensive geriatric assessment in patients undergoing transcatheter aortic valve implantation - results from the CGA-TAVI multicentre registry.

BACKGROUND: In older patients with aortic stenosis (AS) undergoing TAVI, the potential role of prior CGA is not well established. To explore the value of comprehensive geriatric assessment (CGA) for predicting mortality and/or hospitalisation within the first 3 months after transcatheter aortic valve implantation (TAVI). METHODS: An international, multi-centre, prospective registry (CGA-TAVI) was established to gather data on CGA results and medium-term outcomes in geriatric patients undergoing TAVI. Logistic regression was used to evaluate the predictive value of a multidimensional prognostic index (MPI); a short physical performance battery (SPPB); and the Silver Code, which was based on administrative data, for predicting death and/or hospitalisation in the first 3 months after TAVI (primary endpoint). RESULTS: A total of 71 TAVI patients (mean age 85.4 years; mean log EuroSCORE I 22.5%) were enrolled. Device success according to VARC criteria was 100%. After adjustment for selected baseline characteristics, a higher (poorer) MPI score (OR: 3.34; 95% CI: 1.39-8.02; p = 0.0068) and a lower (poorer) SPPB score (OR: 1.15; 95% CI: 1.01-1.54; p = 0.0380) were found to be associated with an increased likelihood of the primary endpoint. The Silver Code did not show any predictive ability in this population. CONCLUSIONS: Several aspects of the CGA have shown promise for being of use to physicians when predicting TAVI outcomes. While the MPI may be useful in clinical practice, the SPPB may be of particular value, being simple and quick to perform. Validation of these findings in a larger sample is warranted. TRIAL REGISTRATION: The trial was registered in ClinicalTrials.gov on November 7, 2013 ( NCT01991444 ).

Feasibility and safety of early discharge after transfemoral transcatheter aortic valve implantation - rationale and design of the FAST-TAVI registry.

BACKGROUND: There is an increasing trend towards shorter hospital stays after transcatheter aortic valve implantation (TAVI), in particular for patients undergoing the procedure via transfemoral (TF) access. Preliminary data suggest that there exists a population of patients that can be discharged safely very early after TF-TAVI. However, current evidence is limited to few retrospective studies, encompassing relatively small sample sizes. METHODS: The Feasibility And Safety of early discharge after Transfemoral TAVI (FAST-TAVI) registry is a prospective observational registry that will be conducted at 10 sites across Italy, the Netherlands and the UK. Patients will be included if they have been scheduled to undergo TF-TAVI with the balloon-expandable SAPIEN 3 transcatheter heart valve (THV; Edwards Lifesciences, Irvine, CA). The primary endpoint is a composite of all-cause mortality, vascular-access-related complications, permanent pacemaker implantation, stroke, re-hospitalisation due to cardiac reasons, kidney failure and major bleeding, occurring during the first 30 days after hospital discharge. Patients will be stratified according to whether they were high or low risk for early discharge (≤3 days) (following pre-specified criteria), and according to whether or not they were discharged early. Secondary endpoints will include time-to-event (Kaplan-Meier) analysis for the primary outcome and its individual components, analysis of the relative costs of early and late discharge, and changes in short- and long-term quality of life. Multivariate logistic regression will be used to identify factors that indicate that a patient may be suitable for early discharge. DISCUSSION: The data gathered in the FAST-TAVI registry should help to clarify the safety of early discharge after TF-TAVI and to identify patient and procedural characteristics that make early discharge from hospital a safe and cost-effective strategy. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02404467 (registration first received March 23rd 2015).

Dynamic Coronary Roadmap versus standard angiography for percutaneous coronary intervention: the randomised, multicentre DCR4Contrast trial.

BACKGROUND: Decreasing the amount of iodinated contrast is an important safety aspect of percutaneous coronary interventions (PCI), particularly in patients with a high risk of contrast-induced acute kidney injury (CI-AKI). Dynamic Coronary Roadmap (DCR) is a PCI navigation support tool projecting a motion-compensated virtual coronary roadmap overlay on fluoroscopy, potentially limiting the need for contrast during PCI. AIMS: This study investigates the contrast-sparing potential of DCR in PCI, compared to standard angiographic guidance. METHODS: The Dynamic Coronary Roadmap for Contrast Reduction (DCR4Contrast) trial is a multicentre, international, prospective, unblinded, stratified 1:1 randomised controlled trial. Patients were randomised to either DCR-guided PCI or to conventional angiography-guided PCI. The primary endpoint was the total volume of iodinated contrast administered, and the secondary endpoint was the number of cineangiography runs during PCI. RESULTS: The study population included 356 randomised patients (179 in DCR and 177 in control groups, respectively). There were no differences in patient demographics, angiographic characteristics or estimated glomerular filtration rate (eGFR) between the two groups. The total contrast volume used during PCI was significantly lower with DCR guidance compared with conventional angiographic guidance (64.6±44.4 ml vs 90.8±55.4 ml, respectively; p<0.001). The total number of cineangiography runs was also significantly reduced in the DCR group (8.7±4.7 vs 11.7±7.6 in the control group; p<0.001). CONCLUSIONS: Compared to conventional angiography-guided PCI, DCR guidance was associated with a significant reduction in both contrast volume and the number of cineangiography runs during PCI. (ClinicalTrials.gov: NCT04085614).

Detecting aortic valve stenosis based on the non-invasive blood pressure waveform-a proof of concept study.

The incidence of aortic valve stenosis (AoS) increases with age, and once diagnosed, symptomatic severe AoS has a yearly mortality rate of 25%. AoS is diagnosed with transthoracic echocardiography (TTE), however, this gold standard is time consuming and operator and acoustic window dependent. As AoS affects the arterial blood pressure waveform, AoS-specific waveform features might serve as a diagnostic tool. Aim of the present study was to develop a novel, non-invasive, AoS detection model based on blood pressures waveforms. This cross-sectional study included patients with AoS undergoing elective transcatheter or surgical aortic valve replacement. AoS was determined using TTE, and patients with no or mild AoS were labelled as patients without AoS, while patients with moderate or severe AoS were labelled as patients with AoS. Non-invasive blood pressure measurements were performed in awake patients. Ten minutes of consecutive data was collected. Several blood pressure-based features were derived, and the median, interquartile range, variance, and the 1st and 9th decile of the change of these features were calculated. The primary outcome was the development of a machine-learning model for AoS detection, investigating multiple classifiers and training on the area under the receiver-operating curve (AUROC). In total, 101 patients with AoS and 48 patients without AoS were included. Patients with AoS showed an increase in left ventricular ejection time (0.02 s, p = 0.001), a delayed maximum upstroke in the systolic phase (0.015 s, p < 0.001), and a delayed maximal systolic pressure (0.03 s, p < 0.001) compared to patients without AoS. With the logistic regression model, a sensitivity of 0.81, specificity of 0.67, and AUROC of 0.79 were found. The majority of the population without AoS was male (85%), whereas in the population with AoS this was evenly distributed (54% males). Age was significantly (5 years, p < 0.001) higher in the population with AoS. In the present study, we developed a novel model able to distinguish no to mild AoS from moderate to severe AoS, based on blood pressure features with high accuracy. Clinical registration number: The study entailing patients with TAVR treatment was registered at ClinicalTrials.gov (NCT03088787, https://clinicaltrials.gov/ct2/show/NCT03088787 ). The study with elective cardiac surgery patients was registered with the Netherland Trial Register (NL7810, https://trialsearch.who.int/Trial2.aspx?TrialID=NL7810 ).

Low muscle quality on a procedural computed tomography scan assessed with deep learning as a practical useful predictor of mortality in patients with severe aortic valve stenosis.

BACKGROUND & AIMS: Accurate diagnosis of sarcopenia requires evaluation of muscle quality, which refers to the amount of fat infiltration in muscle tissue. In this study, we aim to investigate whether we can independently predict mortality risk in transcatheter aortic valve implantation (TAVI) patients, using automatic deep learning algorithms to assess muscle quality on procedural computed tomography (CT) scans. METHODS: This study included 1199 patients with severe aortic stenosis who underwent transcatheter aortic valve implantation (TAVI) between January 2010 and January 2020. A procedural CT scan was performed as part of the preprocedural-TAVI evaluation, and the scans were analyzed using deep-learning-based software to automatically determine skeletal muscle density (SMD) and intermuscular adipose tissue (IMAT). The association of SMD and IMAT with all-cause mortality was analyzed using a Cox regression model, adjusted for other known mortality predictors, including muscle mass. RESULTS: The mean age of the participants was 80 ± 7 years, 53% were female. The median observation time was 1084 days, and the overall mortality rate was 39%. We found that the lowest tertile of muscle quality, as determined by SMD, was associated with an increased risk of mortality (HR 1.40 [95%CI: 1.15-1.70], p < 0.01). Similarly, low muscle quality as defined by high IMAT in the lowest tertile was also associated with increased mortality risk (HR 1.24 [95%CI: 1.01-1.52], p = 0.04). CONCLUSIONS: Our findings suggest that deep learning-assessed low muscle quality, as indicated by fat infiltration in muscle tissue, is a practical, useful and independent predictor of mortality after TAVI.

Quantitative aortography for assessment of aortic regurgitation in the era of percutaneous aortic valve replacement.

Paravalvular leak (PVL) is a shortcoming that can erode the clinical benefits of transcatheter valve replacement (TAVR) and therefore a readily applicable method (aortography) to quantitate PVL objectively and accurately in the interventional suite is appealing to all operators. The ratio between the areas of the time-density curves in the aorta and left ventricular outflow tract (LVOT-AR) defines the regurgitation fraction (RF). This technique has been validated in a mock circulation; a single injection in diastole was further tested in porcine and ovine models. In the clinical setting, LVOT-AR was compared with trans-thoracic and trans-oesophageal echocardiography and cardiac magnetic resonance imaging. LVOT-AR > 17% discriminates mild from moderate aortic regurgitation on echocardiography and confers a poor prognosis in multiple registries, and justifies balloon post-dilatation. The LVOT-AR differentiates the individual performances of many old and novel devices and is being used in ongoing randomized trials and registries.

Assessing the accuracy of a new 3D2D registration algorithm based on a non-invasive skin marker model for navigated spine surgery.

PURPOSE: We assessed the accuracy of a new 3D2D registration algorithm to be used for navigated spine surgery and explored anatomical and radiologic parameters affecting the registration accuracy. Compared to existing 3D2D registration algorithms, the algorithm does not need bone-mounted or table-mounted instruments for registration. Neither does the intraoperative imaging device have to be tracked or calibrated. METHODS: The rigid registration algorithm required imaging data (a pre-existing CT scan (3D) and two angulated fluoroscopic images (2D)) to register positions of vertebrae in 3D and is based on non-invasive skin markers. The algorithm registered five adjacent vertebrae and was tested in the thoracic and lumbar spine from three human cadaveric specimens. The registration accuracy was calculated for each registered vertebra and measured with the target registration error (TRE) in millimeters. We used multivariable analysis to identify parameters independently affecting the algorithm's accuracy such as the angulation between the two fluoroscopic images (between 40° and 90°), the detector-skin distance, the number of skin markers applied, and waist circumference. RESULTS: The algorithm registered 780 vertebrae with a median TRE of 0.51 mm [interquartile range 0.32-0.73 mm] and a maximum TRE of 2.06 mm. The TRE was most affected by the angulation between the two fluoroscopic images obtained (p < 0.001): larger angulations resulted in higher accuracy. The algorithm was more accurate in thoracic vertebrae (p = 0.004) and in the specimen with the smallest waist circumference (p = 0.003). The algorithm registered all five adjacent vertebrae with similar accuracy. CONCLUSION: We studied the accuracy of a new 3D2D registration algorithm based on non-invasive skin markers. The algorithm registered five adjacent vertebrae with similar accuracy in the thoracic and lumbar spine and showed a maximum target registration error of approximately 2 mm. To further evaluate its potential for navigated spine surgery, the algorithm may now be integrated into a complete navigation system.

Evaluation of a Fully Automatic Deep Learning-Based Method for the Measurement of Psoas Muscle Area.

Background: Manual muscle mass assessment based on Computed Tomography (CT) scans is recognized as a good marker for malnutrition, sarcopenia, and adverse outcomes. However, manual muscle mass analysis is cumbersome and time consuming. An accurate fully automated method is needed. In this study, we evaluate if manual psoas annotation can be substituted by a fully automatic deep learning-based method. Methods: This study included a cohort of 583 patients with severe aortic valve stenosis planned to undergo Transcatheter Aortic Valve Replacement (TAVR). Psoas muscle area was annotated manually on the CT scan at the height of lumbar vertebra 3 (L3). The deep learning-based method mimics this approach by first determining the L3 level and subsequently segmenting the psoas at that level. The fully automatic approach was evaluated as well as segmentation and slice selection, using average bias 95% limits of agreement, Intraclass Correlation Coefficient (ICC) and within-subject Coefficient of Variation (CV). To evaluate performance of the slice selection visual inspection was performed. To evaluate segmentation Dice index was computed between the manual and automatic segmentations (0 = no overlap, 1 = perfect overlap). Results: Included patients had a mean age of 81 ± 6 and 45% was female. The fully automatic method showed a bias and limits of agreement of -0.69 [-6.60 to 5.23] cm2, an ICC of 0.78 [95% CI: 0.74-0.82] and a within-subject CV of 11.2% [95% CI: 10.2-12.2]. For slice selection, 84% of the selections were on the same vertebra between methods, bias and limits of agreement was 3.4 [-24.5 to 31.4] mm. The Dice index for segmentation was 0.93 ± 0.04, bias and limits of agreement was -0.55 [1.71-2.80] cm2. Conclusion: Fully automatic assessment of psoas muscle area demonstrates accurate performance at the L3 level in CT images. It is a reliable tool that offers great opportunities for analysis in large scale studies and in clinical applications.

Immediate reduction in left ventricular ejection time following TAVI is associated with improved quality of life.

Background: TAVI has shown to result in immediate and sustained hemodynamic alterations and improvement in health-related quality of life (HRQoL), but previous studies have been suboptimal to predict who might benefit from TAVI. The relationship between immediate hemodynamic changes and outcome has not been studied before. This study sought to assess whether an immediate hemodynamic change, reflecting myocardial contractile reserve, following TAVI is associated with improved HRQoL. Furthermore, it assessed whether pre-procedural cardiac power index (CPI) and left ventricular ejection fraction (LVEF) could predict these changes. Methods: During the TAVI procedure, blood pressure and systemic hemodynamics were prospectively collected with a Nexfin® non-invasive monitor. HRQoL was evaluated pre-procedurally and 12 weeks after the procedure, using the EQ-5D-5L classification tool. Results: Overall, 97/114 (85%) of the included patients were eligible for analyses. Systolic, diastolic and mean arterial pressure, heart rate, and stroke volume increased immediately after TAVI (all p < 0.005), and left ventricular ejection time (LVET) immediately decreased with 10 ms (95%CI = -4 to -16, p < 0.001). Overall HRQoLindex increased from 0.810 [0.662-0.914] before to 0.887 [0.718-0.953] after TAVI (p = 0.016). An immediate decrease in LVET was associated with an increase in HRQoLindex (0.02 index points per 10 ms LVET decrease, p = 0.041). Pre-procedural CPI and LVEF did not predict hemodynamic changes or change in HRQoL. Conclusion: TAVI resulted in an immediate hemodynamic response and increase in HRQoL. Immediate reduction in LVET, suggesting unloading of the ventricle, was associated with an increase in HRQoL, but neither pre-procedural CPI nor LVEF predicted these changes. Clinical trial registration: https://clinicaltrials.gov/ct2/show/NCT03088787.

Machine Learning for Predicting Mortality in Transcatheter Aortic Valve Implantation: An Inter-Center Cross Validation Study.

Current prognostic risk scores for transcatheter aortic valve implantation (TAVI) do not benefit yet from modern machine learning techniques, which can improve risk stratification of one-year mortality of patients before TAVI. Despite the advancement of machine learning in healthcare, data sharing regulations are very strict and typically prevent exchanging patient data, without the involvement of ethical committees. A very robust validation approach, including 1300 and 631 patients per center, was performed to validate a machine learning model of one center at the other external center with their data, in a mutual fashion. This was achieved without any data exchange but solely by exchanging the models and the data processing pipelines. A dedicated exchange protocol was designed to evaluate and quantify the model's robustness on the data of the external center. Models developed with the larger dataset offered similar or higher prediction accuracy on the external validation. Logistic regression, random forest and CatBoost lead to areas under curve of the ROC of 0.65, 0.67 and 0.65 for the internal validation and of 0.62, 0.66, 0.68 for the external validation, respectively. We propose a scalable exchange protocol which can be further extended on other TAVI centers, but more generally to any other clinical scenario, that could benefit from this validation approach.

Local and Distributed Machine Learning for Inter-hospital Data Utilization: An Application for TAVI Outcome Prediction.

Background: Machine learning models have been developed for numerous medical prognostic purposes. These models are commonly developed using data from single centers or regional registries. Including data from multiple centers improves robustness and accuracy of prognostic models. However, data sharing between multiple centers is complex, mainly because of regulations and patient privacy issues. Objective: We aim to overcome data sharing impediments by using distributed ML and local learning followed by model integration. We applied these techniques to develop 1-year TAVI mortality estimation models with data from two centers without sharing any data. Methods: A distributed ML technique and local learning followed by model integration was used to develop models to predict 1-year mortality after TAVI. We included two populations with 1,160 (Center A) and 631 (Center B) patients. Five traditional ML algorithms were implemented. The results were compared to models created individually on each center. Results: The combined learning techniques outperformed the mono-center models. For center A, the combined local XGBoost achieved an AUC of 0.67 (compared to a mono-center AUC of 0.65) and, for center B, a distributed neural network achieved an AUC of 0.68 (compared to a mono-center AUC of 0.64). Conclusion: This study shows that distributed ML and combined local models techniques, can overcome data sharing limitations and result in more accurate models for TAVI mortality estimation. We have shown improved prognostic accuracy for both centers and can also be used as an alternative to overcome the problem of limited amounts of data when creating prognostic models.

Online Quantitative Aortographic Assessment of Aortic Regurgitation After TAVR: Results of the OVAL Study.

OBJECTIVES: The aim of this study was to investigate the online assessment feasibility of aortography using videodensitometry in the catheterization laboratory during transcatheter aortic valve replacement (TAVR). BACKGROUND: Quantitative assessment of regurgitation after TAVR through aortography using videodensitometry is simple, reproducible, and validated in vitro, in vivo, in clinical trials, and in "real-world" patients. However, thus far the assessment has been done offline. METHODS: This was a single center, prospective, proof-of-principle, feasibility study. One hundred consecutive patients with aortic stenosis and indications to undergo TAVR were enrolled. All final aortograms were analyzed immediately after acquisition in the catheterization laboratory and were also sent to an independent core laboratory for blinded offline assessment. The primary endpoint of the study was the feasibility of the online assessment of regurgitation (percentage of analyzable cases). The secondary endpoint was the reproducibility of results between the online assessment and the offline analysis by the core laboratory. RESULTS: Patients' mean age was 81 ± 7 years, and 56% were men. The implanted valves were either SAPIEN 3 (97%) or SAPIEN 3 Ultra (3%). The primary endpoint of online feasibility of analysis was 92% (95% confidence interval [CI]: 86% to 97%) which was the same feasibility encountered by the core laboratory (92%; 95% CI: 86% to 97%). Reproducibility assessment showed a high correlation between online and core laboratory evaluations (R2 = 0.87, p < 0.001), with an intraclass correlation coefficient of 0.962 (95% CI: 0.942 to 0.975; p < 0.001). CONCLUSIONS: This study showed high feasibility of online quantitative assessment of regurgitation and high agreement between the online examiner and core laboratory. These results may pave the way for the application of videodensitometry in the catheterization laboratory after TAVR. (Online Videodensitometric Assessment of Aortic Regurgitation in the Cath-Lab [OVAL]; NCT04047082).

The Impact of Percutaneous Coronary Intervention on Mortality in Patients With Coronary Lesions Who Underwent Transcatheter Aortic Valve Replacement.

OBJECTIVES: To analyze the effect of percutaneous coronary intervention (PCI) before transcatheter aortic valve replacement (TAVR) on all-cause and cardiovascular mortality after TAVR, differentiating between significant proximal lesions and the non-proximal (residual) lesions. METHODS: An institutional TAVR database was complemented with data on the extent of coronary artery disease (CAD), lesion location, lesion severity, and the location of PCI. Survival analysis was performed to investigate the impact on 6-month and 3-year mortality after TAVR in all patients and in subgroups of patients with significant proximal lesions (>70% diameter stenosis [DS], >50% DS in left main), the non-proximal residual lesions, and in a propensity score matched cohort. RESULTS: Among the 577 included patients, mean age was 83 years, 50% were female, and 31% had diabetes mellitus. Preprocedural PCI of unselected lesions was independently associated with increased 6-month mortality (hazard ratio, 2.2; 95% confidence interval, 1.0-4.6; P=.04), but selective PCI of significant proximal lesions did not have an association with higher mortality, nor did we find a significant effect of PCI on mortality in the propensity-matched cohort. CONCLUSION: Routine pre-TAVR PCI is not associated with mortality reduction in TAVR patients with coronary lesions in any segment or in patients with proximal coronary lesions. Despite the lack of a beneficial effect of routine pre-TAVR PCI, we cannot exclude a beneficial effect in a selection of patients with proximal lesions. Therefore, we strongly support the current clinical guidelines to only consider pre-TAVR PCI in proximal coronary lesions, while advocating a restrictive pre-TAVR PCI strategy.

Late onset of new conduction disturbances requiring permanent pacemaker implantation following TAVI.

BACKGROUND: The timing of onset and associated predictors of late new conduction disturbances (CDs) leading to permanent pacemaker implantation (PPI) following transcatheter aortic valve implantation (TAVI) are still unknown, however, essential for an early and safe discharge. This study aimed to investigate the timing of onset and associated predictors of late onset CDs in patients requiring PPI (LCP) following TAVI. METHODS AND RESULTS: We performed retrospective analysis of prospectively collected data from five large volume centres in Europe. Post-TAVI electrocardiograms and telemetry data were evaluated in patients with a PPI post-TAVI to identify the onset of new advanced CDs. Early onset CDs were defined as within 48 hours after procedure, and late onset CDs as after 48 hours. A total of 2804 patients were included for analysis. The PPI rate was 12%, of which 18% was due to late onset CDs (>48 hours). Independent predictors for LCP were pre-existing non-specific intraventricular conduction delay, pre-existing right bundle branch block, self-expandable valves and predilation. At least one of these risk factors was present in 98% of patients with LCP. Patients with a balloon-expandable valve without predilation did not develop CDs requiring PPI after 48 hours. CONCLUSIONS: Safe early discharge might be feasible in patients without CDs in the first 48 hours after TAVI if no risk factors for LCP are present.

Prespecified Risk Criteria Facilitate Adequate Discharge and Long-Term Outcomes After Transfemoral Transcatheter Aortic Valve Implantation.

Background Despite the availability of guidelines for the performance of transcatheter aortic valve implantation (TAVI), current treatment pathways vary between countries and institutions, which impact on the mean duration of postprocedure hospitalization. Methods and Results This was a prospective, multicenter registry of 502 patients to validate the appropriateness of discharge timing after transfemoral TAVI, using prespecified risk criteria from FAST-TAVI (Feasibility and Safety of Early Discharge After Transfemoral [TF] Transcatheter Aortic Valve Implantation), based on hospital events within 1-year after discharge. The end point-a composite of all-cause mortality, vascular access-related complications, permanent pacemaker implantation, stroke, cardiac rehospitalization, kidney failure, and major bleeding-was reached in 27.0% of patients (95% CI, 23.3-31.2) within 1 year after intervention; 7.5% (95% CI, 5.5-10.2) had in-hospital complications before discharge and 19.6% (95% CI, 16.3-23.4) within 1 year after discharge. Overall mortality within 1 year after discharge was 7.3% and rates of cardiac rehospitalization 13.5%, permanent pacemaker implantation 4.2%, any stroke 1.8%, vascular-access-related complications 0.7%, life-threatening bleeding 0.7%, and kidney failure 0.4%. Composite events within 1 year after discharge were observed in 18.8% and 24.3% of patients with low risk of complications/early (≤3 days) discharge and high risk and discharged late (>3 days) (concordant discharge), respectively. Event rate in patients with discordant discharge was 14.3% with low risk but discharged late and increased to 50.0% in patients with high risk but discharged in ≤3 days. Conclusions The FAST-TAVI risk assessment provides a tool for appropriate, risk-based discharge that was validated with the 1-year event rate after transfemoral TAVI. Registration URL: https://www.ClinicalTrials.gov; Unique identifier: NCT02404467.

Quantitative Assessment of Acute Regurgitation Following TAVR: A Multicenter Pooled Analysis of 2,258 Valves.

OBJECTIVES: The aim of this study was to assess acute regurgitation following transcatheter aortic valve replacement, comparing different implanted transcatheter heart valves. BACKGROUND: Regurgitation following transcatheter aortic valve replacement influences all-cause mortality. Thus far, no quantitative comparison of regurgitation among multiple commercially available transcatheter heart valves has been performed. METHODS: Aortograms from a multicenter cohort of consecutive 3,976 transcatheter aortic valve replacements were evaluated in this pooled analysis. A total of 2,258 (58.3%) were considered analyzable by an independent academic core laboratory using video densitometry. Results of quantitative regurgitation are shown as percentages. The valves evaluated were the ACURATE (n = 115), Centera (n = 11), CoreValve (n = 532), Direct Flow Medical (n = 21), Evolut PRO (n = 95), Evolut R (n = 295), Inovare (n = 4), Lotus (n = 546), Lotus Edge (n = 3), SAPIEN XT (n = 239), and SAPIEN 3 (n = 397). For the main analysis, only valves with more than 50 procedures (7 types) were used. RESULTS: The Lotus valve had the lowest mean regurgitation (3.5 ± 4.4%), followed by Evolut PRO (7.4 ± 6.5%), SAPIEN 3 (7.6 ± 7.1%), Evolut R (7.9 ± 7.4%), SAPIEN XT (8.8 ± 7.5%), ACURATE (9.6 ± 9.2%) and CoreValve (13.7 ± 10.7%) (analysis of variance p < 0.001). The only valves that statistically differed from all their counterparts were Lotus (as the lowest regurgitation) and CoreValve (the highest). The proportion of patients presenting with moderate or severe regurgitation followed the same ranking order: Lotus (2.2%), Evolut PRO (5.3%), SAPIEN 3 (8.3%), Evolut R (8.8%), SAPIEN XT (10.9%), ACURATE (11.3%), and CoreValve (30.1%) (chi-square p < 0.001). CONCLUSIONS: In this pooled analysis stemming from daily clinical practice, the Lotus valve was shown to have the best immediate sealing. This analysis reflects the objective evaluation of regurgitation by an academic core laboratory (nonsponsored) in a real-world cohort of patients using a quantitative technique.

[Transcatheter aortic valve implantation: an overview]. / Transkatheter aortaklepvervanging (TAVI).

In recent years, transcatheter aortic valve implantation (TAVI) has evolved from a treatment for patients who are inoperable or at high risk of surgical complications, to an equivalent alternative to surgical valve replacement for almost all patients with symptomatic, severe aortic valve stenosis. In the Netherlands, the number of patients who undergo TAVI gradually increases, while outcomes after TAVI improve and complication rates decrease. Before TAVI can be considered as the preferred treatment for aortic valve stenosis, however, additional insight is needed in valve durability, selection criteria for patients who benefit most from TAVI, and treatment outcomes regarding quality of life.

Value of a comprehensive geriatric assessment for predicting one-year outcomes in patients undergoing transcatheter aortic valve implantation: results from the CGA-TAVI multicentre registry.

BACKGROUND: In a three-month report from the CGA-TAVI registry, we found the Multidimensional Prognostic Index (MPI) and Short Physical Performance Battery (SPPB) to be of value for predicting short-term outcomes in elderly patients undergoing transcatheter aortic valve implantation (TAVI). In the present analysis, we examined the association of these tools with outcomes up to one year post-TAVI. METHODS: CGA-TAVI is an international, observational registry of geriatric patients undergoing TAVI. Patients were assessed using the MPI and SPPB. Efficacy of baseline values and any postoperative change for predicting outcome were established using logistic regression. Kaplan-Meier analysis was carried out for each comprehensive geriatric assessment tool, with survival stratified by risk category. RESULTS: One year after TAVI, 14.1% of patients deceased, while 17.4% met the combined endpoint of death and/or non-fatal stroke, and 37.7% the combined endpoint of death and/or hospitalisation and/or non-fatal stroke. A high-risk MPI score was associated with an increased risk of all-cause mortality (aOR = 36.13, 95% CI: 2.77-470.78, P = 0.006) and death and/or non-fatal stroke (aOR = 10.10, 95% CI: 1.48-68.75, P = 0.018). No significant associations were found between a high-risk SPPB score and mortality or two main combined endpoints. In contrast to a worsening SPPB, an aggravating MPI score at three months post-TAVI was associated with an increased risk of death and/or non-fatal stoke at one year (aOR = 95.16, 95% CI: 3.41-2657.01). CONCLUSIONS: The MPI showed value for predicting the likelihood of death and a combination of death and/or non-fatal stroke by one year after TAVI in elderly patients.

Optimising patient discharge management after transfemoral transcatheter aortic valve implantation: the multicentre European FAST-TAVI trial.

AIMS: Treatment pathway optimisation in TAVI should include timely patient discharge with a minimised risk for out-of-hospital adverse events. The aim of this study was to define a standardised set of risk criteria that allows a safe and timely discharge, to validate their appropriateness prospectively in different centres and multiple European countries, and to assess post-discharge outcomes. METHODS AND RESULTS: We defined and validated the adequacy of a set of discharge criteria and its ability to predict timely and safe discharge properly after the intervention in a prospective, European, multicentre registry. A total of 502 unselected patients were enrolled at 10 sites in three countries. The primary endpoint, defined as a composite of all-cause mortality, vascular access-related complications, permanent pacemaker implantation, stroke, re-hospitalisation due to cardiac reasons, kidney failure and major bleeding at 30 days, was reached in 12.9% of patients (95% CI: 11.3-16.5). The overall 30-day mortality was 1.1% (95% CI: 0.2-2.0), and the rates of stroke/TIA 1.7% (95% CI: -0.6 to 4.0), PPI 7.3% (95% CI: 5.8-8.9), major vascular complications 1.9% (95% CI: 0.7-3.1), major/life-threatening bleeding 2.4% (95% CI: 1.0-3.8) and cardiac re-hospitalisation 3.7% (95% CI: 1.4-6.0). Patients appropriately discharged early had a significantly lower risk of the primary endpoint (7.0 vs. 26.4%; p<0.001) which was reflected in some of its relevant components: stroke (0.0 vs. 2.8%; p=0.015), PPI (4.3 vs. 15.9%; p<0.001), major vascular complications (0.3 vs. 4.7%; p=0.004) and major/life-threatening bleeding (0.3 vs. 6.5%; p<0.001). CONCLUSIONS: We validated the appropriateness of a pre-specified set of risk criteria that allows a safe and timely discharge. The rate of 30-day complications did not reveal any risk increase with this strategy compared with the reported outcomes in major TAVI trials and registries. ClinicalTrials.gov Identifier: NCT02404467.