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

A risk model of mortality rate in dialysis patients following transcatheter aortic valve replacement

BACKGROUND

Dialysis patients undergoing transcatheter aortic valve replacement (TAVR) generally have poor prognosis compared with non-dialysis patients. Furthermore, there are few reliable risk models in this clinical setting. Therefore, we aimed to establish a risk model in dialysis patients undergoing TAVR that would be informative for their prognosis and the decision-making process of TAVR.

METHODS

A total 118 dialysis patients (full cohort) with severe aortic stenosis underwent TAVR in our institute between 2012 and 2022. The patients of the full cohort were randomly assigned to two groups in a 2:1 ratio to form derivation and validation cohorts. Risk factors contributing to deaths were analyzed from the preoperative variables and a risk model was established from Cox proportional hazard model.

RESULTS

There were 69 deaths following TAVR derived from infectious disease (43.5 %), cardiovascular-related disease (11.6 %), cerebral stroke or hemorrhage (2.9 %), cancer (1.4 %), unknown origin (18.8 %), and others (21.7 %) during the observational period (811 ± 719 days). The cumulative overall survival rates using the Kaplan-Meier method at 1 year, 3 years, and 5 years in the full cohort were 82.8 %, 41.9 %, and 24.2 %, respectively. An optimal risk model composed of five contributors: peripheral vascular disease, serum albumin, left ventricular ejection fraction < 40 %, operative age, and hemoglobin level, was established. The estimated C index for the developed models were 0.748 (95 % CI: 0.672-0.824) in derivation cohort and 0.705 (95 % CI: 0.578-0.832) in validation cohort. The prediction model showed good calibration [intraclass correlation coefficient = 0.937 (95%CI: 0.806-0.981)] between actual and predicted survival.

CONCLUSIONS

The risk model was a good indicator to estimate the prognosis in dialysis patients undergoing TAVR.

A Neuronal Network-Based Score Predicting Survival in Patients Undergoing Aortic Valve Intervention: The ABC-AS Score

Background: Despite being the most commonly performed valvular intervention, risk prediction for aortic valve replacement in patients with severe aortic stenosis by currently used risk scores remains challenging. The study aim was to develop a biomarker-based risk score by means of a neuronal network. Methods: In this multicenter study, 3595 patients were divided into test and validation cohorts (70% to 30%) by random allocation. Input variables to develop the ABC-AS score were age, the cardiac biomarker high-sensitivity troponin T, and a patient history of cardiac decompensation. The validation cohort was used to verify the scores' value and for comparison with the Society of Thoracic Surgery Predictive Risk of Operative Mortality score. Results: Receiver operating curves demonstrated an improvement in prediction by using the ABC-AS score compared to the Society of Thoracic Surgery Predictive Risk of Operative Mortality (STS prom) score. Although the difference in predicting cardiovascular mortality was most notable at 30-day follow-up (area under the curve of 0.922 versus 0.678), ABC-AS also performed better in overall follow-up (0.839 versus 0.699). Furthermore, univariate analysis of ABC-AS tertiles yielded highly significant differences for all-cause (p < 0.0001) and cardiovascular mortality (p < 0.0001). Head-to-head comparison between both risk scores in a multivariable cox regression model underlined the potential of the ABC-AS score (HR per z-unit 2.633 (95% CI 2.156-3.216), p < 0.0001), while the STS prom score failed to reach statistical significance (p = 0.226). Conclusions: The newly developed ABC-AS score is an improved risk stratification tool to predict cardiovascular outcomes for patients undergoing aortic valve intervention.

The role of Matrix Metalloproteinase-2 and Galectin-3 as predictive biomarkers for all-cause mortality in patients undergoing transfemoral transcatheter aortic valve implantation

BACKGROUND

Currently available risk scores fail to accurately predict morbidity and mortality in patients with severe symptomatic aortic stenosis who undergo transcatheter aortic valve implantation (TAVI). In this context, biomarkers like matrix metalloproteinase-2 (MMP-2) and Galectin-3 (Gal-3) may provide additional prognostic information.

METHODS

Patients with severe aortic stenosis undergoing consecutive, elective, transfemoral TAVI were included. Baseline demographic data, functional status, echocardiographic findings, clinical outcomes and biomarker levels were collected and analysed.

RESULTS

The study cohort consisted of 89 patients (age 80.4 ± 5.1 years, EuroScore II 7.1 ± 5.8%). During a median follow-up period of 526 d, 28 patients (31.4%) died. Among those who died, median baseline MMP-2 (alive: 221.6 [170.4; 263] pg/mL vs. deceased: 272.1 [225; 308.8] pg/mL, p < 0.001) and Gal-3 levels (alive: 19.1 [13.5; 24.6] pg/mL vs. deceased: 25 [17.6; 29.5] pg/mL, p = 0.006) were higher than in survivors. In ROC analysis, MMP-2 reached an acceptable level of discrimination to predict mortality (AUC 0.733, 95% CI [0.62; 0.83], p < 0.001), but the predictive value of Gal-3 was poor (AUC 0.677, 95% CI [0.56; 0.79], p = 0.002). Kaplan-Meier and Cox regression analyses showed that patients with MMP-2 and Gal-3 concentrations above the median at baseline had significantly impaired long-term survival (p = 0.004 and p = 0.02, respectively).

CONCLUSIONS

In patients with severe aortic stenosis undergoing transfemoral TAVI, MMP-2 and to a lesser extent Gal-3, seem to have additive value in optimizing risk prediction and streamlining decision-making.

Epicardial adipose tissue as an independent predictor of long-term outcome in patients with severe aortic stenosis undergoing transcatheter aortic valve replacement

BACKGROUND

Accurate risk stratification is important to improve patient selection and outcome of patients with severe aortic stenosis (AS) undergoing transcatheter aortic valve replacement (TAVR). As epicardial adipose tissue (EAT) is discussed to be involved in cardiovascular disease, it could be useful as a marker of poor prognosis in patients with severe AS undergoing TAVR.

METHODS

A total of 416 patients diagnosed with severe AS by transthoracic echocardiography were assigned for TAVR and enrolled for systematic assessment. Patients underwent clinical surveys and 5-year long-term follow-up, with all-cause mortality as the primary endpoint. EAT volume was quantified on pre-TAVR planning CTs. Patients were retrospectively dichotomized at the median of 74 cm3 of EAT into groups with low EAT and high EAT volumes. Mortality rates were compared using Kaplan-Meyer plots and uni- and multivariable cox regression analyses.

RESULTS

A total number of 341 of 416 patients (median age 80.9 years, 45% female) were included in the final analysis. Patients with high EAT volumes had similar short-term outcome (p = 0.794) but significantly worse long-term prognosis (p = 0.023) compared to patients with low EAT volumes. Increased EAT volumes were associated with worse long-term outcome (HR1.59; p = 0.031) independently from concomitant cardiovascular risk factors, general type of AS, and functional echocardiography parameters of AS severity (HR1.69; p = 0.013).

CONCLUSION

Increased EAT volume is an independent predictor of all-cause mortality in patients with severe AS undergoing TAVR. It can be easily obtained from pre-TAVR planning CTs and may thus qualify as a novel marker to improve prognostication and management of patient with severe AS.

TRIAL REGISTRATION

DRKS, DRKS00024479.

OLD-TAVR score to predict 2-year mortality in patients aged 75 years and more undergoing transcatheter aortic valve replacement

PURPOSE

Decision-making on transcatheter aortic valve replacement (TAVR) in patients aged 75 years and older is complex. It could be facilitated by the identification of predictors of long-term mortality. This study aimed to identify predictors of 2-year mortality to develop a 2-year mortality risk score.

METHODS

Cohort study of consecutive patients aged ≥ 75 years who underwent TAVR after a comprehensive geriatric assessment (CGA) at our university hospital between 2012 and 2019. Predictors of 2-year mortality were determined using multivariable Cox regression. A point-based predictive model was developed based on risk factors and subsequently internally validated by fivefold cross-validation.

RESULTS

The 345 patients (median age 87 years, 54% women) were fit/vulnerable (32%), mildly frail (37%), or moderately/severely frail (31%). The overall 2-year mortality rate was 26%, predicted by atrial fibrillation, hemoglobin ≤ 10 g/dL, age ≥ 87 years, BMI ≤ 24, eGFR ≤ 50 ml/min, and moderate/severe frailty. The risk score (range 0-12), named OLD-TAVR score, for 2-year mortality showed good discriminative power (AUC 0.70) and remained consistent after fivefold cross-validation (cvAUC 0.69). A risk score ≥ 8 (prevalence 20%) predicted a 45% (95%CI: 34-58%) two-year mortality, with high specificity (86%) and good positive predictive power (+ LR 2.43).

CONCLUSION

A 2-year mortality risk score (OLD-TAVR score) for very old patients undergoing TAVR was developed based on six bio-clinical items. A score ≥ 8 identified patients in whom 2-year mortality was very high and thereby the TAVR futile.

TRIAL REGISTRATION NUMBER AND DATE OF REGISTRATION

Study protocol B403, 26/09/2022, retrospectively registered.

Machine learning-based predictive risk models for 30-day and 1-year mortality in severe aortic stenosis patients undergoing transcatheter aortic valve implantation

BACKGROUND

Predictive risk score for mortality plays an important role in the decision-making in patient selection and risk stratification for TAVI. Existing established predictive risk scores had poor discrimination performance in the prediction of mortality after the TAVI.

OBJECTIVES

The present study aimed to develop machine learning-based predictive models for 30-day and 1-year mortality in severe aortic stenosis patients undergoing TAVI.

METHODS

A total of 186 patients in a retrospective cohort study were analyzed. The models were fitted by a decision tree. Each model was tested in 100 iterations of 80:20 stratified random splitting into training/testing samples and 10-fold cross-validation.

RESULTS

Variables that predict 30-day mortality are a set of factors driven mainly by height, chronic lung disease, STS score, preoperative LVEF, age, and preoperative LVOT VTI. Variables that predict 1-year mortality are a set of factors consisting of preoperative LVEF, STS score, heart rate, systolic blood pressure, home oxygen use, serum creatinine level, and preoperative LVOT Vmax. This decision tree-generated predictive models for 30-day and 1- year mortality provided the most precise accuracy of 0.97 and 0.90 with the AUC-ROC curves of 0.83 and 0.71 on 30-day and 1-year mortality on testing data and had better discrimination performance compared to the existing established TAVI predictive risk scores.

CONCLUSIONS

These machine learning models show excellent accuracy and have a better prediction for 30-day and 1-year mortality than the existing established TAVI predictive risk scores. A customized predictive model deems to be properly developed for better risk discrimination among cohorts.

Clinical Impact of Heart Team Decisions for Patients With Complex Valvular Heart Disease: A Large, Single-Center Experience

Background

A multidisciplinary approach might be pivotal for the management of patients with valvular heart disease (VHD), but clinical outcome data are lacking.

Methods and Results

At our institution, since 2014, internal guidelines recommended heart team consultations for patients with VHD. The clinical/echocardiographic characteristics, treatment recommendations, performed treatment, and early clinical outcomes of consecutive, hospitalized patients with VHD undergoing heart team evaluation were collected. Surgical risk was prospectively assessed by the EuroSCORE II and STS‐PROM. The primary end point of the study was early mortality. A total of 1004 patients with VHD with high clinical complexity (mean age, 75 years; mean EuroSCORE II, 9.4%; mean STS‐PROM, 5.6%; 48% ischemic heart disease; 29% chronic kidney disease, 9% oncologic/hematologic diseases) were enrolled. The heart team recommended an interventional treatment for 807 (80%) patients and conservative management for 197 (20%) patients. Management crossovers occurred in only 5% of patients. The recommended intervention was cardiac surgery for 230 (23%) patients, percutaneous treatment in 516 (51%) patients, and hybrid treatment in 61 (6%) patients. Early mortality occurred in 24 patients (2.4%) and was independently predicted by aortic stenosis, left ventricular ejection fraction, pulmonary artery systolic pressure, and conservative management recommendation. In patients referred to treatment, observed early mortality (1.7%) was significantly lower (P<0.001) than expected on the bases of both the STS‐PROM (5.2%) and EuroSCORE II (9.7%).

Conclusions

Within the limitations of its single‐center and observational design, the present study suggests that heart team–based management of patients with complex VHD is feasible and allows referral to a wide spectrum of interventions with promising early clinical results.

Circulating Monocyte Subsets and Transcatheter Aortic Valve Replacement

Transcatheter aortic valve replacement (TAVR), as an alternative to open heart surgery, has revolutionized the treatment of severe aortic valve stenosis (AVS), the most common valvular disorder in the elderly. AVS is now considered a form of atherosclerosis and, like the latter, partly of inflammatory origin. Patients with high-grade AVS have a highly disturbed blood flow associated with high levels of shear stress. The immediate reopening of the valve during TAVR leads to a sudden restoration of a normal blood flow hemodynamic. Despite its good prognosis for patients, TAVR remains associated with bleeding or thrombotic postprocedural complications, involving mechanisms that are still poorly understood. Many studies report the close link between blood coagulation and inflammation, termed thromboinflammation, including monocytes as a major actor. The TAVR procedure represents a unique opportunity to study the influence of shear stress on human monocytes, key mediators of inflammation and hemostasis processes. The purpose of this study was to conduct a review of the literature to provide a comprehensive overview of the impact of TAVR on monocyte phenotype and subset repartition and the association of these parameters with the clinical outcomes of patients with severe AVS who underwent TAVR.

Anatomy of a Transcatheter Mitral Valve Service

Transcatheter mitral therapies offer treatment options to selected patients who are unable to undergo open procedures due to prohibitive surgical risk. Data detailing the design and structure of transcatheter mitral services to ensure appropriate patient selection and tailored management strategies is lacking. We report our initial experience of developing and running a purpose-built transcatheter mitral service. The nature and number of referral sources, the multi-disciplinary make-up of the dedicated Mitral Heart Team and the use of integrative imaging assessment with incorporation of computational solutions are discussed. In addition, a summary of the clinical decision-making process is presented. This report sets out a framework from which future clinics can evolve to improve and streamline the delivery of transcatheter mitral therapies.

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.

National Cardiovascular Data Registry-Acute Kidney Injury (NCDR) vs. Mehran risk models for prediction of contrast-induced nephropathy and need for dialysis after coronary angiography in a German patient cohort

BACKGROUND

Contrast-induced nephropathy (CIN) is a major adverse event in patients undergoing coronary angiography. The Mehran risk model is the gold-standard for CIN risk prediction. However, its performance in comparison to more contemporary National Cardiovascular Data Registry-Acute Kidney Injury (NCDR-AKI) risk models remains unknown. We aimed to compare both in this study.

METHODS AND RESULTS

Predictions of Mehran and NCDR-AKI risk models and clinical events of CIN and need for dialysis were assessed in a total of 2067 patients undergoing coronary angiography with or without percutaneous coronary intervention. Risk models were compared regarding discrimination (receiver operating characteristic analysis), net reclassification improvement (NRI) and calibration (graphical and statistical analysis). The NCDR risk model showed superior risk discrimination for predicting CIN (NCDR c-index 0.75, 95% CI 0.72-0.78; vs. Mehran c-index 0.69, 95% CI 0.66-0.72, p < 0.01), and continuous NRI (0.22; 95% CI 0.12-0.32; p < 0.01) compared to the Mehran model. The NCDR risk model tended to underestimate the risk of CIN, while the Mehran model was more evenly calibrated. For the prediction of need for dialysis, NCDR-AKI-D also discriminated risk better (c-index 0.85, 95% CI 0.79-0.91; vs. Mehran c-index 0.75, 95% CI 0.66-0.84; p_NCDRvsMehran < 0.01), but continuous NRI showed no benefit and calibration analysis revealed an underestimation of dialysis risk.

CONCLUSION

In German patients undergoing coronary angiography, the modern NCDR risk model for predicting contrast-induced nephropathy showed superior discrimination compared to the GRACE model while showing less accurate calibration. Results for the outcome 'need for dialysis' were equivocal.

Incremental Prognostic Value of Coronary Artery Calcium Score for Predicting All-Cause Mortality after Transcatheter Aortic Valve Replacement

Background Current risk models show limited performances for predicting all-cause mortality after transcatheter aortic valve replacement (TAVR). Purpose To determine the prognostic value of coronary artery calcium (CAC) scoring for predicting 30-day and 1-year mortality in patients undergoing TAVR. Materials and Methods In this single-center institutional review board-approved secondary analysis of prospectively collected data (SwissTAVI Registry), the authors evaluated participants who, before TAVR, underwent CT that included a nonenhanced electrocardiography-gated cardiac scan between May 2008 and September 2019 and who had not undergone previous coronary revascularization. Clinical data, including the European System for Cardiac Operative Risk Evaluation (EuroSCORE II), were recorded. The CAC score was determined, and 30-day and 1-year all-cause mortality were assessed by using Cox regression analyses. Results In total, 309 participants (mean age ± standard deviation, 81 years ± 7; 175 women) were included, with a median CAC score of 334 (interquartile range, 104-987). Seventy-seven of the 309 participants (25%) had a CAC score greater than or equal to 1000. A CAC score of 1000 or greater served as an independent predictor of 30-day (hazard ratio [HR], 4.5 [95% CI: 1.5, 13.6] compared with a CAC score <1000; P = .007) and 1-year (HR, 4.3 [95% CI: 1.5, 12.7] compared with a CAC score of 0-99; P = .008) mortality after TAVR. Similar trends were observed for each point increase of the EuroSCORE II as an independent predictor of 30-day (HR, 1.22 [95% CI: 1.10, 1.36]; P < .001) and 1-year (HR, 1.16 [95% CI: 1.08, 1.25]; P < .001) mortality. Adding the CAC score to the EuroSCORE II provided incremental prognostic value for 1-year mortality after TAVR over the EuroSCORE II alone (concordance index, 0.76 vs 0.69; P = .04). Conclusion In participants without prior coronary revascularization, the coronary artery calcium score represented an independent predictor of 30-day and 1-year mortality after transcatheter aortic valve replacement. ClinicalTrials.gov identifier, NCT01368250 © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Almeida in this issue.

Current Therapeutic Options in Aortic Stenosis

Aortic stenosis is the most common valvular disease requiring valve replacement. Valve replacement therapies have undergone progressive evolution since the 1960s. Over the last 20 years, transcatheter aortic valve replacement has radically transformed the care of aortic stenosis, such that it is now the treatment of choice for many, particularly elderly, patients. This review provides an overview of the pathophysiology, presentation, diagnosis, indications for intervention, and current therapeutic options for aortic stenosis.

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.