Development and external validation of a preprocedural prediction model for 1-year mortality after transcatheter aortic valve implantation

Q: What is the clinical evidence from this study?

Study design: Cohort. Population: Severe aortic stenosis undergoing transcatheter aortic valve implantation (TAVI) (n=2553). Intervention: 12-parameter preprocedural TAVI mortality prediction model vs. EuroSCORE II and STS-PROM surgical risk scores. Primary outcome: All-cause mortality within 1 year after TAVI (AUC 0.72 (internal validation); AUC 0.64 (external validation), 95% CI 0.63-0.81).

Q: What does this research mean for the field?

A novel 12-variable preprocedural prediction model achieves acceptable discrimination for 1-year mortality after TAVI with an AUC of 0.72 in internal validation and 0.64 in external validation. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

Qi Chen; Thierry G. Donati; Monika Hebeisen; Florian A. Wenzl; Neria E. Winkler; Panagiotis Savvoulidis; Chantal Wohlwend; Jonathan Michel; Barbara Elisabeth Stähli; Albert Markus Kasel; Thomas Felix Lüscher; Felix Christoph Tanner

doi:10.1136/openhrt-2026-004304

October 1, 2026Open HeartOpen Access

Development and external validation of a preprocedural prediction model for 1-year mortality after transcatheter aortic valve implantation

View Full Paper

Ask AI

Bookmark

Share

Key result

Novel 12-variable TAVI model predicts 1-year mortality with moderate accuracy, achieving an external AUC of ~0.64.

AUC 0.72 (internal validation); AUC 0.64 (external validation)
95% CI 0.63-0.81
n=2,553

Figures

Why the study?

TAVI is not recommended for severe aortic stenosis patients with a life-expectancy below 1 year, yet 1-year mortality after TAVI remains around 15%, creating a need for accurate prediction models to prevent futile interventions.

Does a 12-variable TAVI-specific preprocedural prediction model improve the prediction of 1-year all-cause mortality compared to EuroSCORE II and STS-PROM in patients with severe aortic stenosis undergoing TAVI?

Key Points

To develop and externally validate a preprocedural prediction model specifically designed to assess 1-year all-cause mortality following transcatheter aortic valve implantation.
Prospectively recruited 1702 patients undergoing TAVI between 2008 and 2021 from a single-centre registry in Zurich, Switzerland, allocating 1278 to model derivation and 424 to internal validation.
Developed a multivariable logistic regression model using 12 preprocedural variables selected by expert cardiologists after multiple imputation of missing data.
Evaluated discrimination and calibration externally in an independent cohort of 851 patients undergoing TAVI between 2008 and 2018 in London, UK.
In internal validation, the 12-variable model achieved an AUC of 0.72 (95% CI 0.63 to 0.81) and a Brier score of 0.08, compared to AUCs of 0.66 (95% CI 0.57 to 0.76; p=0.157) for EuroSCORE II and 0.67 (95% CI 0.58 to 0.76; p=0.191) for STS-PROM.
At a 20% predicted probability threshold, the model correctly identified 21 of 43 patients (49%) who died within 1 year, compared with 16 (39%) by EuroSCORE II and 11 (27.5%) by STS-PROM.
In external validation (851 patients, 109 deaths [12.8%]), the model demonstrated an AUC of 0.64 (95% CI 0.58 to 0.70), a calibration slope of 0.79 (95% CI 0.42 to 1.09), and a Brier score of 0.11.

Study Design

Type

Cohort (n=2,553)

Multicenter

Yes

Structured PICO

Does a 12-variable TAVI-specific preprocedural prediction model improve the prediction of 1-year all-cause mortality compared to EuroSCORE II and STS-PROM in patients with severe aortic stenosis undergoing TAVI?

P

Population

2,553 older adults with severe aortic stenosis undergoing TAVI across Swiss development/internal validation (n=1,702, mean age 80.9 years, 47.1% female) and UK external validation (n=851, mean age 80.4 years, 45.9% female) cohorts, followed for 1 year.

E

Exposure

Preprocedural 12-variable multivariable logistic regression prediction model (incorporating age, male sex, coronary artery stenosis ≥50%, atrial fibrillation, COPD, haemoglobin, creatinine, NT-proBNP, troponin, LVEF, mean transaortic pressure gradient, and severe mitral regurgitation) for 1-year mortality risk stratification after TAVI.

C

Comparator

European System for Cardiac Operative Risk Evaluation (EuroSCORE) II and Society of Thoracic Surgeons Predicted Risk of Mortality (STS-PROM) score.

O

Outcome

All-cause mortality within 1 year after TAVI, recorded through clinical visits and telephone calls in Switzerland and linkage to the UK Office of National Statistics in the external validation cohort.hard clinical

Main Result

Effect estimate: AUC 0.72 (internal validation); AUC 0.64 (external validation) (95% CI 0.63-0.81)

Absolute Event Rate: 13.3% vs 12.8%

A novel 12-variable preprocedural risk model provides acceptable discrimination and calibration for predicting 1-year mortality after TAVI across two independent cohorts, outperforming traditional surgical risk scores (EuroSCORE II and STS-PROM) in identifying high-risk patients to potentially avoid futile procedures.

Limitations

Patients were recruited over an extended time period (2008-2021), during which advances in device technology and vascular access methods may have impacted outcomes.
Absence of frailty-related variables may limit the consideration of patient vulnerability.
In the external validation cohort, all N-terminal pro-B-type natriuretic peptide (NT-proBNP) values were missing and had to be imputed.
Patients were recruited over an extended time period (2008-2021), and advances in device technology and vascular access methods may have impacted outcomes.
Absence of frailty-related variables in the model may limit the consideration of patient vulnerability.
All NT-proBNP values in the external validation cohort were missing and had to be imputed.

Ask about this paper

Answers are grounded in this paper and cite their sources.

Implication

May refine TAVI candidate selection to reduce futile procedures; leaves open prospective validation and outcome impact.

Abstract

Background Transcatheter aortic valve implantation (TAVI) is a well-established treatment for severe aortic stenosis but not recommended for patients with a life-expectancy below 1 year. Nevertheless, 1 year mortality after TAVI is around 15% in current reports. Accurate prediction models are thus needed to prevent futile interventions.Methods Patients undergoing TAVI were prospectively recruited between 2008 and 2021 from a single-centre registry in Zurich, Switzerland and data split into derivation and validation datasets. Four expert cardiologists defined relevant candidate predictors. A prediction model was developed using multiple logistic regression after imputation of missing data. An independent cohort undergoing TAVI from 2008 to 2018 in London, UK served as an external validation dataset. Performance of the prediction model was assessed through internal and external validation. Receiver operating characteristic (ROC) curves and calibration plots were used for discrimination and calibration.Results A total of 1702 patients were included from the Swiss cohort. Among these 1278 were used for model development and 424 for validation. 12 predictors were incorporated including age, male sex, coronary artery disease, atrial fibrillation, chronic obstructive pulmonary disease, haemoglobin, creatinine, N-terminal pro-B-type natriuretic peptide, troponin, left ventricular ejection fraction, mean transaortic pressure gradient and severe mitral regurgitation. The prediction model achieved an area under the ROC curve (AUC) of 0.72 (95% CI 0.63 to 0.81) in internal validation and showed optimal sensitivity and specificity for 1-year mortality prediction at a probability threshold of 20%, outperforming European System for Cardiac Operative Risk Evaluation (EuroSCORE) II and Society of Thoracic Surgeons Predicted Risk of Mortality (STS-PROM) score. In the external validation dataset consisting of 851 patients from the UK cohort, the AUC was 0.64 (95% CI 0.58 to 0.70).Conclusion The TAVI-specific risk model demonstrated acceptable performance for prediction of 1-year mortality after TAVI across two contemporary real-world cohorts, highlighting its potential for clinical application instead of EuroSCORE II or STS-PROM.

Discussion

No takes yet. Share an insight, caveat, or question.

Cite This Study

Chen et al. (2026) conducted a cohort in Severe aortic stenosis undergoing transcatheter aortic valve implantation (TAVI) (n=2,553). 12-parameter preprocedural TAVI mortality prediction model vs. EuroSCORE II and STS-PROM surgical risk scores was evaluated on All-cause mortality within 1 year after TAVI (AUC 0.72 (internal validation); AUC 0.64 (external validation), 95% CI 0.63-0.81). The 12-variable TAVI prediction model achieved an AUC of 0.72 (95% CI 0.63-0.81) in internal validation and 0.64 (95% CI 0.58-0.70) in external validation for predicting 1-year mortality.

synapsesocial.com/papers/6af0dba03c845ca7837ceddc https://doi.org/10.1136/openhrt-2026-004304

View Full Paper

Ask AI

Bookmark

Share

Development and external validation of a preprocedural prediction model for 1-year mortality after transcatheter aortic valve implantation

Figures

Key Points

Study Design

Structured PICO

Main Result

Limitations

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

Development and external validation of a preprocedural prediction model for 1-year mortality after transcatheter aortic valve implantation

Figures

Key Points

Study Design

Structured PICO

Main Result

Limitations

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

Keep reading

Keep reading