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

Transcatheter Aortic Valve Implantation Wait-Time Management: Derivation and Validation of the Canadian TAVI Triage Tool (CAN3T)

BACKGROUND

Transcatheter aortic valve implantation (TAVI) has seen indication expansion and thus exponential growth in demand over the past decade. In many jurisdictions, the growing demand has outpaced capacity, increasing wait times and preprocedural adverse events. In this study, we derived prediction models that estimate the risk of adverse events on the waitlist and developed a triage tool to identify patients who should be prioritized for TAVI.

METHODS AND RESULTS

We included adult patients in Ontario, Canada referred for TAVI and followed up until one of the following events first occurred: death, TAVI procedure, removal from waitlist, or end of the observation period. We used subdistribution hazards models to find significant predictors for each of the following outcomes: (1) all-cause death while on the waitlist; (2) all-cause hospitalization while on the waitlist; (3) receipt of urgent TAVI; and (4) a composite outcome. The median predicted risk at 12 weeks was chosen as a threshold for a maximum acceptable risk while on the waitlist and incorporated in the triage tool to recommend individualized wait times. Of 13 128 patients, 586 died while on the waitlist, and 4343 had at least 1 hospitalization. A total of 6854 TAVIs were completed, of which 1135 were urgent procedures. We were able to create parsimonious models for each outcome that included clinically relevant predictors.

CONCLUSIONS

The Canadian TAVI Triage Tool (CAN3T) is a triage tool to assist clinicians in the prioritization of patients who should have timely access to TAVI. We anticipate that the CAN3T will be a valuable tool as it may improve equity in access to care, reduce preventable adverse events, and improve system efficiency.

Post transcatheter aortic valve replacement ejection fraction response is predictor of survival among patients with whole range of systolic dysfunction

AIMS

The objective of this study is to assess the prognostic effects of T ranscatheter aortic valve replacement (TAVR) on the patients with different degrees of left ventricular systolic (LVS) function and severe symptomatic aortic stenosis. Also examines the prognostic association of LV remodelling after TAVR.

METHODS AND RESULTS

Patients stratified into four subgroups with respect to baseline LV ejection fraction (LVEF) (LVEF > 25%, LVEF 25%-40%, LVEF 41%-49% and LVEF ≥ 50%). We compared the baseline characteristics and temporal changes in echocardiographic parameters of the patients after TAVR, and determined all-cause mortality (ACM) in a follow-up period of mean 20.7 ± 15.8 months (up to 84). There were 495 patients at 8 centres. ACM was similar in all groups (28.1%, 29.5%, 22.5% and 24.1% respectively; p = .44). Immediately after TAVR, there was an improvement in LVEF (from 38.7 ± 9.4 to 44.9% ± 10.9%, p < .001). The percent change in LVEF (pDelta-EF) immediately after TAVR was more prominent in the patients with LVEF < 25% (48.1 ± 49.6, 21.9 ± 29.6), (8.4% ± 15.2%) and (2.1 ± 7)) (p < .01). Importantly, a 12% increase in absolute Delta-EF was observed in patients with LVEF< 25% with 100% sensitivity and 42.5% specificity for the prediction of ACM.

CONCLUSION

This study shows that TAVR is beneficial in the whole range of LVS function, irrespective of the baseline EF. Early recovery in LVEF after TAVR is critical for survival, however, it seems to be more eye catching in the patients with advanced heart failure with reduced EF.

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.

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.