Inadequacy of existing clinical prediction models for predicting mortality after transcatheter aortic valve implantation

Clinical Frailty Scale as a predictor of adverse outcomes following aortic valve replacement: a systematic review and meta-analysis

OBJECTIVES

Assessment of frailty prior to aortic valve intervention is recommended in European and North American valvular heart disease guidelines. However, there is a lack of consensus on how it is best measured. The Clinical Frailty Scale (CFS) is a well-validated measure of frailty that is relatively quick to calculate. This meta-analysis sought to examine whether the CFS predicts mortality and morbidity following either transcatheter aortic valve implantation (TAVI) or surgical aortic valve replacement (SAVR).

METHODS

Nine electronic databases were searched systematically for data on clinical outcomes post-TAVI/SAVR, where patients had undergone preoperative frailty assessment using the CFS. The primary endpoint was 12-month mortality. TAVI and SAVR data were assessed and reported separately. For each individual study, the incidence of adverse outcomes was extracted according to a CFS score of 5-9 (ie, frail) versus 1-4 (ie, non-frail), with meta-analysis performed using a random effects model.

RESULTS

Of 2612 records screened, nine were included in the review (five TAVI, three SAVR and one which included both interventions). Among 4923 TAVI patients, meta-analysis showed 12-month mortality rates of 19.1% for the frail cohort versus 9.8% for the non-frail cohort (RR 2.53 (1.63 to 3.95), p<0.001, I2=83%). For the smaller cohort of SAVR patients (n=454), mortality rates were 20.3% versus 3.9% for the frail and non-frail cohorts, respectively (RR 5.08 (2.31 to 11.15), p<0.001, I2=5%).

CONCLUSIONS

Frailty, as determined by the CFS, was associated with an increased mortality risk in the 12 months following either TAVI or SAVR. These data would support its use in the preoperative assessment of elderly patients undergoing aortic valve interventions.

Predicting 5-Year Clinical Outcomes After Transcatheter or Surgical Aortic Valve Replacement (a Risk Score from the SURTAVI Trial)

Risk prediction scores for long-term outcomes after transcatheter aortic valve implantation (TAVI) or surgical aortic valve replacement (SAVR) are lacking. This study aimed to develop preprocedural risk scores for 5-year clinical outcomes after TAVI or SAVR. This analysis included 1,660 patients at an intermediate surgical risk with severe aortic stenosis randomly assigned to TAVI (n = 864) or SAVR (n = 796) from the SURTAVI (Surgical Replacement and Transcatheter Aortic Valve Implantation) trial. The primary end point was a composite of all-cause mortality or disabling stroke at 5 years. The secondary end point was a composite of cardiovascular mortality or hospitalizations for valve disease or worsening heart failure at 5 years. Preprocedural multivariable predictors of clinical outcomes were used to calculate a simple risk score for both procedures. At 5 years, the primary end point occurred in 31.3% of the patients with TAVI and 30.8% of the patients with SAVR. Preprocedural predictors differed between TAVI and SAVR. Baseline anticoagulant use was a common predictor for events in both procedures, whereas male sex and a left ventricular ejection fraction <60% were significant predictors for events in patients with TAVI and SAVR, respectively. A total of 4 simple scoring systems were created based on these multivariable predictors. The C-statistics of all models were modest but performed better than the contemporary risk scores. In conclusion, preprocedural predictors of events differ between TAVI and SAVR, necessitating separate risk models. Despite the modest predictive value of the SURTAVI risk scores, they appeared superior to other contemporary scores. Further research is needed to strengthen and validate our risk scores, possibly by including biomarker and echocardiographic parameters.

Temporal validation of 30-day mortality prediction models for transcatheter aortic valve implantation using statistical process control - An observational study in a national population

Background

Various mortality prediction models for Transcatheter Aortic Valve Implantation (TAVI) have been developed in the past years. The effect of time on the performance of such models, however, is unclear given the improvements in the procedure and changes in patient selection, potentially jeopardizing the usefulness of the prediction models in clinical practice. We aim to explore how time affects the performance and stability of different types of prediction models of 30-day mortality after TAVI.

Methods

We developed both parametric (Logistic Regression) and non-parametric (XGBoost) models to predict 30-day mortality after TAVI using data from the Netherlands Heart Registration. The models were trained with data from 2013 to the beginning of 2016 and pre-control charts from Statistical Process Control were used to analyse how time affects the models' performance on independent data from the mid of 2016 to the end of 2019. The area under the Receiver Operating Characteristics curve (AUC) was used to evaluate the models in terms of discrimination and the Brier Score (BS), which is related to calibration, in terms of accuracy of the predicted probabilities. To understand the extent to which refitting the models contribute to the models' stability, we also allowed the models to be updated over time.

Results

We included data from 11,291 consecutive TAVI patients from hospitals in the Netherlands. The parametric model without re-training had a median AUC of 0.64 (IQR 0.54-0.73) and BS of 0.028 (IQR 0.021-0.035). For the non-parametric model, the median AUC was 0.63 (IQR 0.48-0.68) and BS was 0.027 (IQR 0.021-0.036). Over time, the developed parametric model was stable in terms of AUC and unstable in terms of BS. The non-parametric model was considered unstable in both AUC and BS. Repeated model refitting resulted in stable models in terms of AUC and decreased the variability of BS, although BS was still unstable. The refitted parametric model had a median AUC of 0.66 (IQR 0.57-0.73) and BS of 0.027 (IQR 0.020-0.035) while the non-parametric model had a median AUC of 0.66 (IQR 0.57-0.74) and BS of 0.027 (IQR 0.023-0.035).

Conclusions

The temporal validation of the TAVI 30-day mortality prediction models showed that the models refitted over time are more stable and accurate when compared to the frozen models. This highlights the importance of repeatedly refitted models over time to improve or at least maintain their performance stability. The non-parametric approach did not show improvement over the parametric approach.

Transcatheter versus surgical aortic valve replacement in lower-risk and higher-risk patients: a meta-analysis of randomized trials

AIMS

Additional randomized clinical trial (RCT) data comparing transcatheter aortic valve implantation (TAVI) with surgical aortic valve replacement (SAVR) is available, including longer term follow-up. A meta-analysis comparing TAVI to SAVR was performed. A pragmatic risk classification was applied, partitioning lower-risk and higher-risk patients.

METHODS AND RESULTS

The main endpoints were death, strokes, and the composite of death or disabling stroke, occurring at 1 year (early) or after 1 year (later). A random-effects meta-analysis was performed. Eight RCTs with 8698 patients were included. In lower-risk patients, at 1 year, the risk of death was lower after TAVI compared with SAVR [relative risk (RR) 0.67; 95% confidence interval (CI) 0.47 to 0.96, P = 0.031], as was death or disabling stroke (RR 0.68; 95% CI 0.50 to 0.92, P = 0.014). There were no differences in strokes. After 1 year, in lower-risk patients, there were no significant differences in all main outcomes. In higher-risk patients, there were no significant differences in main outcomes. New-onset atrial fibrillation, major bleeding, and acute kidney injury occurred less after TAVI; new pacemakers, vascular complications, and paravalvular leak occurred more after TAVI.

CONCLUSION

In lower-risk patients, there was an early mortality reduction with TAVI, but no differences after later follow-up. There was also an early reduction in the composite of death or disabling stroke, with no difference at later follow-up. There were no significant differences for higher-risk patients. Informed therapy decisions may be more dependent on the temporality of events or secondary endpoints than the long-term occurrence of main clinical outcomes.

Targeted validation: validating clinical prediction models in their intended population and setting

Clinical prediction models must be appropriately validated before they can be used. While validation studies are sometimes carefully designed to match an intended population/setting of the model, it is common for validation studies to take place with arbitrary datasets, chosen for convenience rather than relevance. We call estimating how well a model performs within the intended population/setting "targeted validation". Use of this term sharpens the focus on the intended use of a model, which may increase the applicability of developed models, avoid misleading conclusions, and reduce research waste. It also exposes that external validation may not be required when the intended population for the model matches the population used to develop the model; here, a robust internal validation may be sufficient, especially if the development dataset was large.

Development and validation of a prediction model for early mortality after transcatheter aortic valve implantation (TAVI) based on the Netherlands Heart Registration (NHR): The TAVI-NHR risk model

BACKGROUND

The currently available mortality prediction models (MPM) have suboptimal performance when predicting early mortality (30-days) following transcatheter aortic valve implantation (TAVI) on various external populations. We developed and validated a new TAVI-MPM based on a large number of predictors with recent data from a national heart registry.

METHODS

We included all TAVI-patients treated in the Netherlands between 2013 and 2018, from the Netherlands Heart Registration. We used logistic-regression analysis based on the Akaike Information Criterion for variable selection. We multiply imputed missing values, but excluded variables with >30% missing values. For internal validation, we used ten-fold cross-validation. For temporal (prospective) validation, we used the 2018-data set for testing. We assessed discrimination by the c-statistic, predicted probability accuracy by the Brier score, and calibration by calibration graphs, and calibration-intercept and calibration slope. We compared our new model to the updated ACC-TAVI and IRRMA MPMs on our population.

RESULTS

We included 9144 TAVI-patients. The observed early mortality was 4.0%. The final MPM had 10 variables, including: critical-preoperative state, procedure-acuteness, body surface area, serum creatinine, and diabetes-mellitus status. The median c-statistic was 0.69 (interquartile range [IQR] 0.646-0.75). The median Brier score was 0.038 (IQR 0.038-0.040). No signs of miscalibration were observed. The c-statistic's temporal-validation was 0.71 (95% confidence intervals 0.64-0.78). Our model outperformed the updated currently available MPMs ACC-TAVI and IRRMA (p value < 0.05).

CONCLUSION

The new TAVI-model used additional variables and showed fair discrimination and good calibration. It outperformed the updated currently available TAVI-models on our population. The model's good calibration benefits preprocedural risk-assessment and patient counseling.

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.

A Novel Method to Predict Mortality and Length of Stay after Transfemoral Transcatheter Aortic Valve Implantation

Background and Objectives: We tested if a novel combination of predictors could improve the accuracy of outcome prediction after transfemoral transcatheter aortic valve implantation (TAVI). Materials and Methods: This prospective study recruited 169 participants (49% female; median age 81 years). The primary endpoint was midterm mortality; secondary endpoints were acute Valve Academic Research Consortium (VARC)-3 complication rate and post-TAVI in-hospital length of stay (LoS). EuroSCORE II (ESII), comorbidities (e.g., coronary artery disease), eGFR (estimated glomerular filtration rate; based on cystatin C), hemoglobin, creatinine, N-Terminal pro-Brain Natriuretic Peptide (NTproBNP) levels and patient-reported outcome measures (PROMs, namely EuroQol-5-Dimension-5-Levels, EQ5D5L; Kansas City Cardiomyopathy Questionnaire, KCCQ; clinical frailty scale, CFS) at baseline were tested as predictors. Regression (uni- and multi-variate Cox; linear; binary logistic) and receiver operating characteristic (ROC)-curve analysis were applied. Results: Within a median follow-up of 439 (318–585) days, 12 participants died (7.1%). Independent predictors of mortality using multivariate Cox regression were baseline eGFR (p = 0.001) and KCCQ (p = 0.037). Based on these predictors, a Linear Prediction Score (LPS1) was calculated. The LPS1-area under the curve (AUC)-value (0.761) was significantly higher than the ESII-AUC value (0.597; p = 0.035). Independent predictors for LoS > 6 days (the median LoS) were eGFR (p = 0.028), NTproBNP (p = 0.034), and EQ5D5L values (p = 0.002); a respective calculated LPS2 provided an AUC value of 0.677 (p < 0.001). Eighty participants (47.3%) experienced complications. Male sex predicted complications only in the univariate analysis. Conclusions: The combination of KCCQ and eGFR can better predict midterm mortality than ES II alone. Combining eGFR, NTproBNP, and EQ5D5L can reliably predict LoS after TAVI. This novel method improves personalized TAVI risk stratification and hence may help reduce post-TAVI risk.

Identifying Patients without a Survival Benefit following Transfemoral and Transapical Transcatheter Aortic Valve Replacement

Transcatheter aortic valve replacement (TAVR) offers a novel treatment option for patients with severe symptomatic aortic valve stenosis, particularly for patients who are unsuitable candidates for surgical intervention. However, high therapeutical costs, socio-economic considerations, and numerous comorbidities make it necessary to target and allocate available resources efficiently. In the present study, we aimed to identify risk factors associated with futile treatment following transfemoral (TF) and transapical (TA) TAVR. Five hundred and thirty-two consecutive patients (82 ± 9 years, female 63%) who underwent TAVR between June 2009 and December 2016 at the Vienna Heart Center Hietzing were retrospectively analyzed to identify predictors of futility, defined as all-cause mortality at one year following the procedure for the overall patient cohort, as well as the TF and TA cohort. Out of 532 patients, 91 (17%) did not survive the first year after TAVR. A multivariate logistic model identified cerebrovascular disease, home oxygen dependency, wheelchair dependency, periinterventional myocardial infarction, and postinterventional renal replacement therapy as the factors independently associated with an increased one-year mortality. Our findings underscore the significance of a precise preinterventional evaluation, as well as illustrating the subtle differences in baseline characteristics in the TF and TA cohort and their impact on one-year mortality.

Severe aortic stenosis and transcatheter aortic valve replacement in elderly patients: utility vs futility

INTRODUCTION

Recently, transcatheter aortic valve replacement (TAVR) has emerged as established standard treatment for symptomatic severe aortic stenosis, providing an effective, less-invasive alternative to open cardiac surgery for inoperable or high-risk older patients.

EVIDENCE ACQUISITION

In order to assess the anticipated benefit of aortic replacement, considerable interest now lies in better identifying factors likely to predict outcome. In the elderly population frailty and medical comorbidities have been shown to significantly predict mortality, functional recovery and quality of life after transcatheter aortic valve replacement. Scientific literature focused on the three items will be discussed.

EVIDENCE SYNTHESIS

High likelihood of futility is described in patients with severe chronic lung, kidney, liver disease and/or frailty. The addition of frailty components to conventional risk prediction has been shown to result in improved discrimination for death and disability following the procedure and identifies those individuals least likely to derive benefit. Several dedicated risk score have been proposed to provide new insights into predicted "futile" outcome. However, assessment of frailty according to a limited number of variables is not sufficient, while a multi-dimensional geriatric assessment significantly improves risk prediction.

CONCLUSIONS

A multidisciplinary heart team that includes geriatricians can allow the customization of therapeutic interventions in elderly patients to optimise care and avoid futility.

Identifying New Factors Associated With Cognitive Decline and Delirium After Transcatheter Aortic Valve Implantation: A Study Protocol

Background: Transcatheter aortic valve implantation (TAVI) has become the standard-of-care for treatment of severe symptomatic aortic stenosis and is also being increasingly recommended for low-risk patients. While TAVI boasts positive post-procedural outcomes, it is also associated with cognitive complications, namely delirium and cognitive decline. There is a pressing need for accurate risk tools which can identify TAVI patients at risk of delirium and cognitive decline, as risk scores designed for general cardiovascular surgery fall short. The present effect-finding exploratory study will assess the utility of various measures in the context of aging and frailty in predicting who will and who will not develop delirium or cognitive impairment following TAVI. The measures we propose include gait, visual symptoms, voice, swallowing, mood and sleep. Methods: This is an observational prospective cohort study focused on identifying pre-procedural risk factors for the development of delirium and cognitive decline following TAVI. Potential risk factors will be measured prior to TAVI. Primary outcomes will be post-procedure cognitive decline and delirium. Secondary outcomes include activities of daily living, quality of life, and mortality. Delirium presence will be measured on each of the first 2 days following TAVI. All other outcomes will be assessed at 3-, 6-, and 12-months post-operatively. A series of logistic regressions will be run to investigate the relationship between potential predictors and outcomes (presence vs. absence of either delirium or cognitive decline). Discussion: This study will assess the strengths of associations between a range of measures drawn from frailty and aging literature in terms of association with cognitive decline and delirium following TAVI. Identified measures can be used in future development of TAVI risk prediction models, which are essential for the accurate identification of cognitive at-risk patients and successful application of pre-procedural interventions. Clinical Trial Registration: This trial is registered with the Australian New Zealand Clinical Trials Registry. [https://bit.ly/2PAotP5], [ACTRN12618001114235].

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.

Risk scores for prediction of 30-day mortality after transcatheter aortic valve implantation: Results from a two-center study in Norway

OBJECTIVES

Transcatheter aortic valve implantation (TAVI)-specific risk scores have been developed based on large registry studies. Our aim was to evaluate how both surgical and novel TAVI risk scores performed in predicting all cause 30-day mortality. In addition, we wanted to explore the validity of our own previously developed model in a separate and more recent cohort.

METHODS

The derivation cohort included patients not eligible for open surgery treated with TAVI at the University Hospital of North Norway (UNN) and Oslo University Hospital (OUS) from February 2010 through June 2013. From this cohort, a logistic prediction model (UNN/OUS) for all cause 30-day mortality was developed. The validation cohort consisted of patients not included in the derivation cohort and treated with TAVI at UNN between June 2010 and April 2017. EuroSCORE, Logistic EuroSCORE, EurosSCORE 2, STS score, German AV score, OBSERVANT score, IRRMA score, and FRANCE-2 score were calculated for both cohorts. The discriminative accuracy of each score, including our model, was evaluated by receiver operating characteristic (ROC) analysis and compared using DeLong test where P< .05 was considered statistically significant.

RESULTS

The derivation cohort consisted of 218 and the validation cohort of 241 patients. Our model showed statistically significant better accuracy than all other scores in the derivation cohort. In the validation cohort, the FRANCE-2 had a significantly higher predictive accuracy compared to all scores except the IRRMA and STS score. Our model showed similar results.

CONCLUSION

Existing risk scores have shown limited accuracy in predicting early mortality after TAVI. Our results indicate that TAVI-specific risk scores might be useful when evaluating patients for TAVI.

Cardiac Magnetic Resonance Imaging Right Ventricular Longitudinal Strain Predicts Mortality in Patients Undergoing TAVI

Background: Right ventricular (RV) function predicts survival in numerous cardiac conditions, including left heart disease. The reference standard for non-invasive assessment of RV function is cardiac magnetic resonance imaging (CMR). The aim of this study was to investigate the association between pre-procedural CMR-derived RV functional parameters and mortality in patients undergoing transcatheter aortic valve implantation (TAVI).

Methods: Patients scheduled for TAVI were recruited to undergo pre-procedural CMR. Volumetric function and global longitudinal and circumferential strain (GLS and GCS) of the RV and left ventricle (LV) were measured. The association with the primary endpoint (1-year all-cause mortality) was analyzed with Cox regression.

Results: Of 133 patients undergoing CMR, 113 patients were included in the analysis. Mean age was 81.8 ± 5.8 years, and 65% were female. Median follow-up was 3.9 [IQR 2.3–4.7] years. All-cause and cardiovascular mortality was 14 and 12% at 1 year, and 28 and 20% at 3 years, respectively. One-year all-cause mortality was significantly predicted by RV GLS [HR = 1.109 (95% CI: 1.023–1.203); p = 0.012], RV ejection fraction [HR = 0.956 (95% CI: 0.929–0.985); p = 0.003], RV end-diastolic volume index [HR = 1.009 (95% CI: 1.001–1.018); p = 0.025], and RV end-systolic volume index [HR = 1.010 (95% CI: 1.003–1.017); p = 0.005]. In receiver operating characteristic (ROC) analysis for 1-year all-cause mortality, the area under the curve was 0.705 (RV GLS) and 0.673 (RV EF). Associations decreased in strength at longer follow-up. None of the LV parameters was associated with mortality.

Conclusions: RV function predicts intermediate-term mortality in TAVI patients while LV parameters were not associated with outcomes. Inclusion of easily obtainable RV GLS may improve future risk scores.

Long-term Implications of Post-Procedural Left Ventricular End-Diastolic Pressure in Patients Undergoing Transcatheter Aortic Valve Implantation

Current risk models have only limited accuracy in predicting transcatheter aortic valve Implantation (TAVI) outcomes and there is a paucity of clinical variables to guide patient management after the procedure. The prognostic impact of elevated left ventricular end-diastolic pressure (LVEDP) in TAVI patients is unknown. The aim of the present study was to evaluate the prognostic value of after-procedural LVEDP in patients who undewent TAVI. Consecutive patients with severe symptomatic aortic stenosis who undewent TAVI were divided into 2 groups according to after-procedural LVEDP above and below or equal 12 mm Hg. Collected data included baseline clinical, laboratory and echocardiographic variables. We evaluated the impact of elevated vs. normal LVEDP on in-hospital outcomes, short- and long-term mortality. Eight hundred forty-five patients were included in the study with complete in-hospital and late mortality data available for all survivors (median follow-up 29.5 months [IQR 16.5 to 48.0]). The mean age (±SD) was 82.3±6.2 years and mean Society of Thoracic Surgery score was 4.0%±3.0%. Patients with LVEDP>12 mm Hg (n = 591, 70%) and LVEDP≤12 mm Hg (n = 254, 30%) had a 6-months mortality rate of 6.8% and 2%, respectively (P=0.004) and a 1-year mortality rate of 10.1% vs 4.9%, respectively (p = 0.017). By multivariable analysis, after-procedural LVEDP>12 mm Hg was independently associated with all-cause mortality (HR 2.45, 95% CI 1.58 to 3.76, p <0.001) during long-term follow-up. In conclusion, elevated after-procedural LVEDP in patients who undewent TAVI is an independent predictor of mortality following TAVI. Further research regarding the use of LVEDP as a tool for after-procedural medical management is warranted.

Predicting and measuring mortality risk after transcatheter aortic valve replacement

Introduction: Over the last decade, transcatheter aortic valve replacement (TAVR) has emerged as a treatment option for most patients with severe symptomatic aortic stenosis (AS). With growing indications and exponential increase in the number of TAVR procedures, it is important to be able to accurately predict mortality after TAVR.Areas covered: Herein, we review the surgical and TAVR-specific mortality prediction models (MPMs) and their performance in their original derivation and external validation cohorts. We then discuss the role of other important risk assessment tools such as frailty, echocardiographic parameters, and biomarkers in patients, being considered for TAVR.Expert opinion: Conventional surgical MPMs have suboptimal predictive performance and are mis-calibrated when applied to TAVR populations. Although a number of TAVR-specific MPMs have been developed, their utility is also limited by their modest discriminative ability when applied to populations external to their original derivation cohorts. There is an unmet need for robust TAVR MPMs that accurately predict post TAVR mortality. In the interim, heart teams should utilize the currently available TAVR-specific MPMs in conjunction with other prognostic factors, such as frailty, echocardiographic or computed tomography (CT) imaging parameters, and biomarkers for risk assessment of patients, being considered for TAVR.

Update and, internal and temporal-validation of the FRANCE-2 and ACC-TAVI early-mortality prediction models for Transcatheter Aortic Valve Implantation (TAVI) using data from the Netherlands heart registration (NHR)

Background

The predictive performance of the models FRANCE-2 and ACC-TAVI for early-mortality after Transcatheter Aortic Valve Implantation (TAVI) can decline over time and can be enhanced by updating them on new populations. We aim to update and internally and temporally validate these models using a recent TAVI-cohort from the Netherlands Heart Registration (NHR).

Methods

We used data of TAVI-patients treated in 2013-2017. For each original-model, the best update-method (model-intercept, model-recalibration, or model-revision) was selected by a closed-testing procedure. We internally validated both updated models with 1000 bootstrap samples. We also updated the models on the 2013-2016 dataset and temporally validated them on the 2017-dataset. Performance measures were the Area-Under ROC-curve (AU-ROC), Brier-score, and calibration graphs.

Results

We included 6177 TAVI-patients, with 4.5% observed early-mortality. The selected update-method for FRANCE-2 was model-intercept-update. Internal validation showed an AU-ROC of 0.63 (95%CI 0.62-0.66) and Brier-score of 0.04 (0.04-0.05). Calibration graphs show that it overestimates early-mortality. In temporal-validation, the AU-ROC was 0.61 (0.53-0.67).The selected update-method for ACC-TAVI was model-revision. In internal-validation, the AU-ROC was 0.63 (0.63-0.66) and Brier-score was 0.04 (0.04-0.05). The updated ACC-TAVI calibrates well up to a probability of 20%, and subsequently underestimates early-mortality. In temporal-validation the AU-ROC was 0.65 (0.58-0.72).

Conclusion

Internal-validation of the updated models FRANCE-2 and ACC-TAVI with data from the NHR demonstrated improved performance, which was better than in external-validation studies and comparable to the original studies. In temporal-validation, ACC-TAVI outperformed FRANCE-2 because it suffered less from changes over time.

Safety and feasibility of dobutamine stress echocardiography in symptomatic high gradient aortic stenosis patients scheduled for transcatheter aortic valve implantation

PURPOSE

We aimed to study the safety and feasibility of low-dose dobutamine stress echocardiography in a symptomatic high gradient aortic stenosis population scheduled for transfemoral transcatheter aortic valve implantation (TAVI) and to quantify left ventricular (LV) flow reserve.

METHODS

Fifty patients underwent dobutamine stress echocardiography with 5 minutes increments of 5 μg/kg/min up to 20 μg/kg/min until the heart rate increased ≥20 beats/min from baseline or exceeded 100 beats/min. Other criteria for discontinuing the infusion were major adverse events: ventricular arrhythmia, persistent supraventricular arrhythmia, pulmonary edema, chest pain with significant ST-changes, or minor events: ST-changes, drop in systolic blood pressure >30 mmHg, mild chest pain, and/or dyspnea. LV flow reserve was defined as an increase in stroke volume ≥20% during the test.

RESULTS

Of 50 patients, 45 completed the test according to protocol. No patient had major adverse event. Five patients experienced minor side effects: mild chest pain/dyspnea in three, self-terminating atrial flutter in one, and decrease in blood pressure in one. Significant LV flow reserve was observed in 20 patients (40%).

CONCLUSION

Low-dose dobutamine stress test appeared safe and feasible patients with high gradient aortic stenosis, and showed LV flow reserve in a minority of them.

Short- and medium-term survival after TAVI: Clinical predictors and the role of the FRANCE-2 score

Aim

The aim of this study was to explore the value of the FRANCE-2 score in associating with clinical outcome in the medium and short-term after TAVI and to compare its relative merits with other risk score models.

Methods

187 consecutive patients undergoing TAVI in a single UK centre were retrospectively studied. The FRANCE-2, logistic EuroSCORE, EuroSCORE II, German AV and STS/ACC TVT risk scores were calculated retrospectively and c-statistics associating with mortality were applied. Survival outcomes were compared between different risk groups according to the FRANCE-2 scores.

Results

Of the 187 patients, 57.2% were male and their mean age was 80.9 ± 6.9 years. The c-index of FRANCE-2 score for predicting 30-day mortality was 0.793 (p = 0.009), for 1-year mortality 0.679 (p = 0.016) and for 2-year mortality was 0.613 (p = 0.088). The mean survival time for patients with a high FRANCE-2 score (18.6 months) was significantly less than for patients with low and moderate scores (p = 0.0004). The logistic EuroSCORE and EuroSCORE II were poorly associated with 30-day and 1-year mortality. STS/ACC TVT score was best predictive of 1-year mortality and German AV score was moderately predictive of 30-day mortality.

Conclusions

The FRANCE-2 risk score is associated with differential short- and medium-term survival in patients undergoing TAVI. The presence of a high FRANCE-2 score (>5) is associated with poor survival. The FRANCE-2 scoring system could be considered as a useful additional tool by the Heart multidisciplinary team (MDT) in identifying patients who are likely to have limited survival benefit although this requires further prospective evaluation.

Risk modeling in transcatheter aortic valve replacement remains unsolved: an external validation study in 2946 German patients

Background

Surgical risk prediction models are routinely used to guide decision-making for transcatheter aortic valve replacement (TAVR). New and updated TAVR-specific models have been developed to improve risk stratification; however, the best option remains unknown.

Objective

To perform a comparative validation study of six risk models for the prediction of 30-day mortality in TAVR

Methods and results

A total of 2946 patients undergoing transfemoral (TF, n = 2625) or transapical (TA, n = 321) TAVR from 2008 to 2018 from the German Rhine Transregio Aortic Diseases cohort were included. Six surgical and TAVR-specific risk scoring models (LogES I, ES II, STS PROM, FRANCE-2, OBSERVANT, GAVS-II) were evaluated for the prediction of 30-day mortality. Observed 30-day mortality was 3.7% (TF 3.2%; TA 7.5%), mean 30-day mortality risk prediction varied from 5.8 ± 5.0% (OBSERVANT) to 23.4 ± 15.9% (LogES I). Discrimination performance (ROC analysis, c-indices) ranged from 0.60 (OBSERVANT) to 0.67 (STS PROM), without significant differences between models, between TF or TA approach or over time. STS PROM discriminated numerically best in TF TAVR (c-index 0.66; range of c-indices 0.60 to 0.66); performance was very similar in TA TAVR (LogES I, ES II, FRANCE-2 and GAVS-II all with c-index 0.67). Regarding calibration, all risk scoring models—especially LogES I—overestimated mortality risk, especially in high-risk patients.

Conclusions

Surgical as well as TAVR-specific risk scoring models showed mediocre performance in prediction of 30-day mortality risk for TAVR in the German Rhine Transregio Aortic Diseases cohort. Development of new or updated risk models is necessary to improve risk stratification.

Graphic abstract

Electronic supplementary material

The online version of this article (10.1007/s00392-020-01731-9) contains supplementary material, which is available to authorized users.

UK TAVI registry

The UK Transcatheter Aortic Valve Implantation (TAVI) registry has collected data about every TAVI procedure performed in the UK. The latest data are from 2016 when 3250 procedures (49.5 pmp) were performed. There has been no change in the mean age of patients but there has been a shift to lower risk with fall in mean Logistic Euroscore since 2012. The switch from general anaesthetic to conscious sedation has been rapid, and propensity-adjusted analysis has not shown a difference in outcomes. In-hospital mortality has fallen to 1.8% in 2016, and relative survival analysis has shown outcome the same as the matched general population to 3 years. The UK TAVI registry has provided valuable benchmarks, and a risk adjustment model that includes frailty measures has been successfully developed and is available online.

Value of machine learning in predicting TAVI outcomes

BACKGROUND

Transcatheter aortic valve implantation (TAVI) has become a commonly applied procedure for high-risk aortic valve stenosis patients. However, for some patients, this procedure does not result in the expected benefits. Previous studies indicated that it is difficult to predict the beneficial effects for specific patients. We aim to study the accuracy of various traditional machine learning (ML) algorithms in the prediction of TAVI outcomes.

METHODS AND RESULTS

Clinical and laboratory data from 1,478 TAVI patients from a single centre were collected. The outcome measures were improvement of dyspnoea and mortality. Three experiments were performed using (1) screening data, (2) laboratory data, and (3) the combination of both. Five well-established ML techniques were implemented, and the models were evaluated based on the area under the curve (AUC). Random forest classifier achieved the highest AUC (0.70) for predicting mortality. Logistic regression had the highest AUC (0.56) in predicting improvement of dyspnoea.

CONCLUSIONS

In our single-centre TAVI population, the tree-based models were slightly more accurate than others in predicting mortality. However, ML models performed poorly in predicting improvement of dyspnoea.

Transcatheter aortic valve implantation vs. surgical aortic valve replacement for treatment of symptomatic severe aortic stenosis: an updated meta-analysis

Aims 

Owing to new evidence from randomized controlled trials (RCTs) in low-risk patients with severe aortic stenosis, we compared the collective safety and efficacy of transcatheter aortic valve implantation (TAVI) vs. surgical aortic valve replacement (SAVR) across the entire spectrum of surgical risk patients.

Methods and results 

The meta-analysis is registered with PROSPERO (CRD42016037273). We identified RCTs comparing TAVI with SAVR in patients with severe aortic stenosis reporting at different follow-up periods. We extracted trial, patient, intervention, and outcome characteristics following predefined criteria. The primary outcome was all-cause mortality up to 2 years for the main analysis. Seven trials that randomly assigned 8020 participants to TAVI (4014 patients) and SAVR (4006 patients) were included. The combined mean STS score in the TAVI arm was 9.4%, 5.1%, and 2.0% for high-, intermediate-, and low surgical risk trials, respectively. Transcatheter aortic valve implantation was associated with a significant reduction of all-cause mortality compared to SAVR {hazard ratio [HR] 0.88 [95% confidence interval (CI) 0.78–0.99], P = 0.030}; an effect that was consistent across the entire spectrum of surgical risk (P-for-interaction = 0.410) and irrespective of type of transcatheter heart valve (THV) system (P-for-interaction = 0.674). Transcatheter aortic valve implantation resulted in lower risk of strokes [HR 0.81 (95% CI 0.68–0.98), P = 0.028]. Surgical aortic valve replacement was associated with a lower risk of major vascular complications [HR 1.99 (95% CI 1.34–2.93), P = 0.001] and permanent pacemaker implantations [HR 2.27 (95% CI 1.47–3.64), P < 0.001] compared to TAVI.

Conclusion 

Compared with SAVR, TAVI is associated with reduction in all-cause mortality and stroke up to 2 years irrespective of baseline surgical risk and type of THV system.

Pre-procedural risk models for patients undergoing transcatheter aortic valve implantation

Transcatheter aortic valve implantation (TAVI) has emerged as the standard treatment option for patients with symptomatic aortic stenosis who are considered intermediate to high surgical risk. Nonetheless, optimal clinical outcomes following the procedure require careful consideration of procedural risk by the Heart Team. While this decision-making could be supported through the development of TAVI-specific clinical prediction models (CPMs), current models remain suboptimal. In this review paper, we aimed to outline the performance of several recently derived TAVI CPMs that predict mortality and present some future research directions. We discuss how the existing risk models have achieved only moderate discrimination but highlight that some of the models are well calibrated across multiple populations, indicating the feasibility of using them to aid benchmarking analyses. Moreover, we suggest that future work should focus on the development of CPMs in cohorts of patients with aortic stenosis that include multiple treatment modalities. Supported by appropriate modelling of 'what if' scenarios, this would allow the Heart Teams to predict and compare outcomes across surgical aortic valve replacement, medical management and TAVI, thereby allowing one to personalise treatment decisions to the individual patient. Such a goal could be facilitated by considering novel risk factors, shifting the focus to endpoints other than mortality, and through collaborative efforts to combine the evidence base and existing models across wider populations.

Guideline-defined futility or patient-reported outcomes to assess treatment success after TAVI: what to use? Results from a prospective cohort study with long-term follow-up

Objective

Transcatheter aortic valve implantation (TAVI) provides a significant symptom relief and mortality reduction in most patients; however, a substantial group of patients does not experience the same beneficial results according to physician-determined outcomes.

Methods

Single-centre prospective design; the population comprises all consecutive patients undergoing TAVI in 2012-2017. TAVI futility was defined as the combined endpoint of either no symptomatic improvement or mortality at 1 year. We actively gathered telephone follow-up using a predefined questionnaire.

Results

Guideline defined TAVI futility was present in 212/741 patients. Multivariate regression showed lower albumin and non-transfemoral approach to be predictive for futility. In addition to these, chronic obstructive pulmonary disease, lower estimated glomerular filtration rate, atrial fibrillation, low-flow-low-gradient aortic stenosis and lower Body Mass Index were predictive for 1-year mortality. Patients who showed symptomatic benefit estimated the percentage in which their symptoms were remedied higher than patients who did not (80% vs 60%, p<0.001). Guideline-defined TAVI futility occurs frequently, contrasting with patient-reported outcome measures (PROMs). The vast majority in both groups would again choose for TAVI treatment.

Conclusion

Lower albumin and non-transfemoral access route were predictors for guideline-defined TAVI futility, defined as mortality within 1 year or no objective symptomatic improvement in New York Heart Association class. Futility according to this definition occurred frequently in this study, contrasting with much more positive PROMs. The majority of patients would undergo a TAVI again, underlining the patients' experienced value of TAVI and putting the definition of TAVI futility further on debate. In the near future, less-strict criteria for TAVI futility, that is, using a shorter warranted life expectancy and incorporating patients' perceived outcomes, should be used.

Association of comorbid burden with clinical outcomes after transcatheter aortic valve implantation

Objectives

To investigate the association of the CharlsonComorbidity Index (CCI) with clinical outcomes after transcatheter aortic valve implantation (TAVI).

Background

Patients undergoing TAVI have high comorbid burden; however, there is limited evidence of its impact on clinical outcomes.

Methods

Data from 1887 patients from the UK, Canada, Spain, Switzerland and Italy were collected between 2007 and 2016. The association of CCI with 30-day mortality, Valve Academic Research Consortium-2 (VARC-2) composite early safety, long-term survival and length of stay (LoS) was calculated using logistic regression and Cox proportional hazard models, as a whole cohort and at a country level, through a two-stage individual participant data (IPD) random effect meta-analysis.

Results

Most (60%) of patients had a CCI ≥3. A weak correlation was found between the total CCI and four different preoperative risks scores (ρ=0.16 to 0.29), and approximately 50% of patients classed as low risk from four risk prediction models still presented with a CCI ≥3. Per-unit increases in total CCI were not associated with increased odds of 30-day mortality (OR 1.09, 95% CI 0.96 to 1.24) or VARC-2 early safety (OR 1.04, 95% CI 0.96 to 1.14) but were associated with increased hazard of long-term mortality (HR 1.10, 95% CI 1.05 to 1.16). The two-stage IPD meta-analysis indicated that CCI was not associated with LoS (HR 0.97, 95% CI 0.93 to 1.02).

Conclusion

In this multicentre international study, patients undergoing TAVI had significant comorbid burden. We found a weak correlation between the CCI and well-established preoperative risks scores. The CCI had a moderate association with long-term mortality up to 5 years post-TAVI.

Do frailty measures improve prediction of mortality and morbidity following transcatheter aortic valve implantation? An analysis of the UK TAVI registry

OBJECTIVES

Previous studies indicate frailty to be associated with poor outcomes following transcatheter aortic valve implantation (TAVI), but there is limited evidence from multicentre registries. The aim was to investigate the independent association of frailty with TAVI outcomes, and the prognostic utility of adding frailty into existing clinical prediction models (CPMs).

DESIGN

The UK TAVI registry incorporated three frailty measures since 2013: Canadian Study of Health and Ageing, KATZ and poor mobility. We investigated the associations between these frailty measures with short-term and long-term outcomes, using logistic regression to estimate multivariable adjusted ORs, and Cox proportional hazards models to explore long-term survival. We compared the predictive performance of existing TAVI CPMs before and after updating them to include each frailty measure.

SETTING

All patients who underwent a TAVI procedure in England or Wales between 2013 and 2014.

PARTICIPANTS

2624 TAVI procedures were analysed in this study.

PRIMARY AND SECONDARY OUTCOMES

The primary endpoints in this study were 30-day mortality and long-term survival. The Valve Academic Research Consortium (VARC)-2 composite early safety endpoint was considered as a secondary outcome.

RESULTS

KATZ <6 (OR 2.10, 95% CI 1.39 to 3.15) and poor mobility (OR 2.15, 95% CI 1.41 to 3.28) predicted 30-day mortality after multivariable adjustment. All frailty measures were associated with increased odds of the VARC-2 composite early safety endpoint. We observed a significant increase in the area under the receiver operating characteristic curves by approximately 5% after adding KATZ <6 or poor mobility into the TAVI CPMs. Risk stratification agreement was significantly improved by the addition of each frailty measure, with an increase in intraclass correlation coefficient of between 0.15 and 0.31.

CONCLUSION

Frailty was associated with worse outcomes following TAVI, and incorporating frailty metrics significantly improved the predictive performance of existing CPMs. Physician-estimated frailty measures could aid TAVI risk stratification, until more objective scales are routinely collected.

Comparison of Outcomes of Transfemoral Aortic Valve Implantation in Patients <90 With Those >90 Years of Age

In patients who underwent transcatheter aortic valve implantation (TAVI), postoperative mortality risk is commonly assessed with risk scores such as the Society of Thoracic Surgeons-Postoperative Risk of Mortality (STS-PROM) and EuroSCORE II, in which age plays a dominant role. However, we reason that in the naturally selected oldest-old patients (nonagenarians), this may not be completely justified and that therefore age should play a minor role in decision-making. The objective of this study was to compare procedural outcome and mid-term mortality of transfemoral (TF)-TAVI patients aged ≥90 years with patients aged <90 years. In this single-center analysis of 599 prospectively acquired consecutive TF-TAVI patients between 2009 and 2017, we compared patients aged ≥90 (i.e., nonagenarians, n = 47) with patients aged <90 years (n = 552), using Kaplan-Meyer analysis and multivariate logistic regression. In the nonagenarians, we found more aortic regurgitation, moderate to severe paravalvular leakage, strokes and vascular complications, and less device success and bleeding complications compared with patients <90 years. Both groups showed similar symptomatic improvement. The predicted (STS-PROM) and actual procedural mortality were 8.033% and 2.1% (3.8×) and 4.868% and 1.8% (2.7×) for the nonagenarians and controls, respectively. Survival was not statistically different at the 1-, 2-, 3-, 4-, and 5-year mark. In conclusion, nonagenarians had similar symptomatic improvement and acceptable procedural outcome and mid-term survival to TF-TAVI patients aged <90 years. Thus, age is not a risk factor in predicting postoperative outcome and mortality and therefore should not be a reason to deny the oldest-old patient transfemoral TAVI.

Improvement of Risk Prediction After Transcatheter Aortic Valve Replacement by Combining Frailty With Conventional Risk Scores

OBJECTIVES

This study sought to evaluate whether frailty improves mortality prediction in combination with the conventional scores.

BACKGROUND

European System for Cardiac Operative Risk Evaluation (EuroSCORE) or Society of Thoracic Surgeons (STS) score have not been evaluated in combined models with frailty for mortality prediction after transcatheter aortic valve replacement (TAVR).

METHODS

This prospective cohort comprised 330 consecutive TAVR patients ≥70 years of age. Conventional scores and a frailty index (based on assessment of cognition, mobility, nutrition, and activities of daily living) were evaluated to predict 1-year all-cause mortality using Cox proportional hazards regression (providing hazard ratios [HRs] with confidence intervals [CIs]) and measures of test performance (providing likelihood ratio [LR] chi-square test statistic and C-statistic [CS]).

RESULTS

All risk scores were predictive of the outcome (EuroSCORE, HR: 1.90 [95% CI: 1.45 to 2.48], LR chi-square test statistic 19.29, C-statistic 0.67; STS score, HR: 1.51 [95% CI: 1.21 to 1.88], LR chi-square test statistic 11.05, C-statistic 0.64; frailty index, HR: 3.29 [95% CI: 1.98 to 5.47], LR chi-square test statistic 22.28, C-statistic 0.66). A combination of the frailty index with either EuroSCORE (LR chi-square test statistic 38.27, C-statistic 0.72) or STS score (LR chi-square test statistic 28.71, C-statistic 0.68) improved mortality prediction. The frailty index accounted for 58.2% and 77.6% of the predictive information in the combined model with EuroSCORE and STS score, respectively. Net reclassification improvement and integrated discrimination improvement confirmed that the added frailty index improved risk prediction.

CONCLUSIONS

This is the first study showing that the assessment of frailty significantly enhances prediction of 1-year mortality after TAVR in combined risk models with conventional risk scores and relevantly contributes to this improvement.

Novel United Kingdom prognostic model for 30-day mortality following transcatheter aortic valve implantation

OBJECTIVE

Existing clinical prediction models (CPM) for short-term mortality after transcatheter aortic valve implantation (TAVI) have limited applicability in the UK due to moderate predictive performance and inconsistent recording practices across registries. The aim of this study was to derive a UK-TAVI CPM to predict 30-day mortality risk for benchmarking purposes.

METHODS

A two-step modelling strategy was undertaken: first, data from the UK-TAVI Registry between 2009 and 2014 were used to develop a multivariable logistic regression CPM using backwards stepwise regression. Second, model-updating techniques were applied using the 2013-2014 data, thereby leveraging new approaches to include frailty and to ensure the model was reflective of contemporary practice. Internal validation was performed by bootstrapping to estimate in-sample optimism-corrected performance.

RESULTS

Between 2009 and 2014, up to 6339 patients were included across 34 centres in the UK-TAVI Registry (mean age, 81.3; 2927 female (46.2%)). The observed 30-day mortality rate was 5.14%. The final UK-TAVI CPM included 15 risk factors, which included two variables associated with frailty. After correction for in-sample optimism, the model was well calibrated, with a calibration intercept of 0.02 (95% CI -0.17 to 0.20) and calibration slope of 0.79 (95% CI 0.55 to 1.03). The area under the receiver operating characteristic curve, after adjustment for in-sample optimism, was 0.66.

CONCLUSION

The UK-TAVI CPM demonstrated strong calibration and moderate discrimination in UK-TAVI patients. This model shows potential for benchmarking, but even the inclusion of frailty did not overcome the need for more wide-ranging data and other outcomes might usefully be explored.

Nutrition and mobility predict all-cause mortality in patients 12 months after transcatheter aortic valve implantation

Background

The aim of the study was to determine pre-interventional predictors for all-cause mortality in patients after transcatheter aortic valve implantation (TAVI) with a 12-month follow-up.

Methods

From 10/2013 to 07/2015, 344 patients (80.9 ± 5.0 years, 44.5% male) with an elective TAVI were consecutively enrolled prospectively in a multicentre cohort study. Prior to the intervention, sociodemographic parameters, echocardiographic data and comorbidities were documented. All patients performed a 6-min walk test, Short Form 12 and a Frailty Index (score consisting of activities of daily living, cognition, nutrition and mobility). Peri-interventional complications were documented. Vital status was assessed over telephone 12 months after TAVI. Predictors for all-cause mortality were identified using a multivariate regression model.

Results

At discharge, 333 patients were alive (in-hospital mortality 3.2%; n = 11). During a follow-up of 381.0 ± 41.9 days, 46 patients (13.8%) died. The non-survivors were older (82.3 ± 5.0 vs. 80.6 ± 5.1 years; p = 0.035), had a higher number of comorbidities (2.6 ± 1.3 vs. 2.1 ± 1.3; p = 0.026) and a lower left ventricular ejection fraction (51.0 ± 13.6 vs. 54.6 ± 10.6%; p = 0.048). Additionally, more suffered from diabetes mellitus (60.9 vs. 44.6%; p = 0.040). While the global Frailty Index had no predictive power, its individual components, particularly nutrition (OR 0.83 per 1 pt., CI 0.72–0.95; p = 0.006) and mobility (OR 5.12, CI 1.64–16.01; p = 0.005) had a prognostic impact. Likewise, diabetes mellitus (OR 2.18, CI 1.10–4.32; p = 0.026) and EuroSCORE (OR 1.21 per 5%, CI 1.07–1.36; p = 0.002) were associated with a higher risk of all-cause mortality.

Conclusions

Besides EuroSCORE and diabetes mellitus, nutrition status and mobility of patients scheduled for TAVI offer prognostic information for 1-year all-cause mortality and should be advocated in the creation of contemporary TAVI risk scores.