Development and Validation of a Practical Model to Identify Patients at Risk of Bleeding After TAVR

Eliano Pio Navarese; Zhongheng Zhang; Jacek Kubica; Felicita Andreotti; Antonella Farinaccio; Antonio L Bartorelli; Francesco Bedogni; Manali Rupji; Fabrizio Tomai; Arturo Giordano; Bernard Reimers; Carmen Spaccarotella; Krzysztof Wilczek; Janina Stepinska; Adam Witkowski; Marek Grygier; Tomasz Kukulski; Wojciech Wanha; Wojciech Wojakowski; Maciej Lesiak; Dariusz Dudek; Michal O Zembala; Sergio Berti; a Joint Effort of the Italian and Polish Cardiac Interventional Societies

doi:10.1016/j.jcin.2021.03.024

Development and Validation of a Practical Model to Identify Patients at Risk of Bleeding After TAVR

JACC Cardiovasc Interv. 2021 Jun 14;14(11):1196-1206. doi: 10.1016/j.jcin.2021.03.024.

Authors

Eliano Pio Navarese¹, Zhongheng Zhang², Jacek Kubica³, Felicita Andreotti⁴, Antonella Farinaccio⁵, Antonio L Bartorelli⁶, Francesco Bedogni⁷, Manali Rupji⁸, Fabrizio Tomai⁹, Arturo Giordano¹⁰, Bernard Reimers¹¹, Carmen Spaccarotella¹², Krzysztof Wilczek¹³, Janina Stepinska¹⁴, Adam Witkowski¹⁴, Marek Grygier¹⁵, Tomasz Kukulski¹³, Wojciech Wanha¹⁶, Wojciech Wojakowski¹⁶, Maciej Lesiak¹⁷, Dariusz Dudek¹⁸, Michal O Zembala¹³, Sergio Berti¹⁹; a Joint Effort of the Italian and Polish Cardiac Interventional Societies

Affiliations

¹ Interventional Cardiology and Cardiovascular Medicine Research, Department of Cardiology and Internal Medicine, Nicolaus Copernicus University, Bydgoszcz, Poland; Faculty of Medicine, University of Alberta, Edmonton, Alberta, Canada; SIRIO MEDICINE Research Network, Bydgoszcz, Poland. Electronic address: elianonavarese@gmail.com.
² Department of Emergency Medicine, Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
³ Interventional Cardiology and Cardiovascular Medicine Research, Department of Cardiology and Internal Medicine, Nicolaus Copernicus University, Bydgoszcz, Poland; SIRIO MEDICINE Research Network, Bydgoszcz, Poland.
⁴ Department of Cardiovascular and Thoracic Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.
⁵ Department of Biotechnology and Biosciences, University of Milano-Bicocca, Italy.
⁶ Centro Monzino, IRCCS and Department of Biomedical and Clinical Sciences "Luigi Sacco," University of Milan, Milan, Italy.
⁷ Department of Clinical and Interventional Cardiology, IRCCS Policlinico San Donato, Milan, Italy.
⁸ Winship Cancer Institute of Emory University, Atlanta, Georgia, USA.
⁹ Division of Cardiology, European Hospital, Rome, Italy.
¹⁰ Unità Operativa di Interventistica Cardiovascolare, Pineta Grande Hospital, Castel Volturno, Italy.
¹¹ Division of Cardiology, CCU and Interventional, Cardiology, Cardio Center, Humanitas Research Hospital IRCCS, Rozzano-Milan, Italy.
¹² Cardiovascular Research Center, University Magna Graecia, Catanzaro, Italy.
¹³ Cardiac and Lung Transplantation Mechanical Circulatory Support, Silesian Center for Heart Diseases, Pomeranian Medical University, Szczecin, Poland.
¹⁴ National Institute of Cardiology, Warsaw, Poland.
¹⁵ Medical University of Poznań, Poznań, Poland.
¹⁶ Department of Cardiology and Structural Heart Diseases, Medical University of Silesia, Katowice, Poland.
¹⁷ Department of Cardiology, Poznań University of Medical Sciences, Poznań, Poland.
¹⁸ Institute of Cardiology, Jagiellonian University Medical College, Krakow, Poland.
¹⁹ Department of Diagnostic and Interventional Cardiology, Gabriele Monasterio Tuscany Foundation, G. Pasquinucci Heart Hospital, Massa, Italy.

PMID: 34112454
DOI: 10.1016/j.jcin.2021.03.024

Abstract

Objectives: No standardized algorithm exists to identify patients at risk of bleeding after transcatheter aortic valve replacement (TAVR). The aim of this study was to generate and validate a useful predictive model.

Background: Bleeding events after TAVR influence prognosis and quality of life and may be preventable.

Methods: Using machine learning and multivariate regression, more than 100 clinical variables from 5,185 consecutive patients undergoing TAVR in the prospective multicenter RISPEVA (Registro Italiano GISE sull'Impianto di Valvola Aortica Percutanea; NCT02713932) registry were analyzed in relation to Valve Academic Research Consortium-2 bleeding episodes at 1 month. The model's performance was externally validated in 5,043 TAVR patients from the prospective multicenter POL-TAVI (Polish Registry of Transcatheter Aortic Valve Implantation) database.

Results: Derivation analyses generated a 6-item score (PREDICT-TAVR) comprising blood hemoglobin and serum iron concentrations, oral anticoagulation and dual antiplatelet therapy, common femoral artery diameter, and creatinine clearance. The 30-day area under the receiver-operating characteristic curve (AUC) was 0.80 (95% confidence interval [CI]: 0.75-0.83). Internal validation by optimism bootstrap-corrected AUC was 0.79 (95% CI: 0.75-0.83). Score quartiles were in graded relation to 30-day events (0.8%, 1.1%, 2.5%, and 8.5%; overall p <0.001). External validation produced a 30-day AUC of 0.78 (95% CI: 0.72-0.82). A simple nomogram and a web-based calculator were developed to predict individual patient probabilities. Landmark cumulative event analysis showed greatest bleeding risk differences for top versus lower score quartiles in the first 30 days, when most events occurred. Predictivity was maintained when omitting serum iron values.

Conclusions: PREDICT-TAVR is a practical, validated, 6-item tool to identify patients at risk of bleeding post-TAVR that can assist in decision making and event prevention.

Keywords: TAVR; bleeding risk; risk score.

Publication types

Multicenter Study
Research Support, Non-U.S. Gov't

MeSH terms

Aortic Valve / diagnostic imaging
Aortic Valve / surgery
Aortic Valve Stenosis* / diagnostic imaging
Aortic Valve Stenosis* / surgery
Humans
Prospective Studies
Quality of Life
Registries
Risk Assessment
Risk Factors
Transcatheter Aortic Valve Replacement* / adverse effects
Treatment Outcome

Associated data

ClinicalTrials.gov/NCT02713932