scholarly journals Outcome prediction following transcatheter aortic valve implantation: Multiple risk scores comparison

2016 ◽  
Vol 23 (2) ◽  
pp. 169-177 ◽  
Author(s):  
Karol Zbroński ◽  
Zenon Huczek ◽  
Dominika Puchta ◽  
Katarzyna Paczwa ◽  
Janusz Kochman ◽  
...  
Keyword(s):  
Aortic Valve ◽  
Transcatheter Aortic Valve Implantation ◽  
Outcome Prediction ◽  
Risk Scores ◽  
Transcatheter Aortic Valve ◽  
Aortic Valve Implantation ◽  
Multiple Risk ◽  
Valve Implantation

Rescue Transcatheter Aortic Valve Implantation and Simultaneous Percutaneous Coronary Intervention on Cardiopulmonary Bypass in a Patient with an Extreme Risk Profile

2012 ◽  
Vol 15 (3) ◽  
pp. 164 ◽  
Author(s):  
Miralem Pasic ◽  
Stephan Dreysse ◽  
Evgenij Potapov ◽  
Axel Unbehaun ◽  
Semih Buz ◽  
...  
Keyword(s):  
Aortic Valve ◽  
Transcatheter Aortic Valve Implantation ◽  
Coronary Revascularization ◽  
Multiorgan Failure ◽  
Coronary Intervention ◽  
Risk Scores ◽  
Transcatheter Aortic Valve ◽  
Percutaneous Coronary ◽  
Aortic Valve Implantation ◽  
Valve Implantation

We report on successful emergency transcatheter aortic valve implantation combined with percutaneous coronary revascularization in a polymorbid and preterminal patient in profound cardiogenic shock and with multiorgan failure. The risk scores were almost unbelievably high (Society of Thoracic Surgeons mortality score, 83.9%; Society of Thoracic Surgeons morbidity and mortality score, 96.8%; logistic EuroSCORE, 96.7%). Two and a half years after the procedure, the patient is doing very well.

Transcatheter Aortic Valve Implantation in Nonagenarians

2016 ◽  
Vol 11 (6) ◽  
pp. 390-395 ◽  
Author(s):  
Adam Penkalla ◽  
Joerg Kempfert ◽  
Axel Unbehaun ◽  
Semih Buz ◽  
Thorsten Drews ◽  
...  
Keyword(s):  
Aortic Valve ◽  
Transcatheter Aortic Valve Implantation ◽  
Nyha Class ◽  
Pacemaker Implantation ◽  
Risk Scores ◽  
Syntax Score ◽  
Aortic Valve Area ◽  
Transcatheter Aortic Valve ◽  
Aortic Valve Implantation ◽  
Valve Implantation

Objective In this report, we assess the outcome of transcatheter aortic valve implantation (TAVI) in nonagenarians at our institution during a 6-year period. Methods Between April 2008 and July 2014, 40 patients with a mean ± SD age of 91.8 ± 2.3 years (range, 90–98 years) underwent TAVI. Thirty-three patients (82.5%) received transapical TAVI, and seven patients (17.5%) received transfemoral TAVI. Baseline characteristics were as follows: mean ± SD EuroSCORE II, 23.9 ± 14.21; mean ± SD Society of Thoracic Surgeons mortality score, 24.2 ± 11.4; mean ± SD SYNTAX score, 7.6 ± 9.3; mean ± SD NYHA class, 3.5 ± 0.5; mean ± SD transvalvular gradient, 46.8 ± 17.8 mm Hg; mean ± SD aortic valve area, 0.7 ± 0.2 cm2. Results Intraoperative mortality was 2.5% and 30-day all-cause mortality was 10%. The actuarial survival rates at 1 and 5 years were 58.6% and 30.4%, respectively. Seven patients (17.5%) underwent simultaneous elective TAVI and percutaneous coronary intervention. Three patients (7.5%) were operated on with the use of cardiopulmonary bypass. No conversion to open surgery occurred. In transesophageal echocardiography assessment, no moderate or severe prosthetic aortic valve regurgitation was observed. Four patients (10%) had postoperative acute renal failure stage 3 and needed new dialysis (P = 0.125). Three patients (7.5%) had a disabling stroke. Periprocedural myocardial infarction occurred in one patient (2.5%). Seven patients (17.5%) needed postoperative pacemaker implantation. Male sex and renal insufficiency were found to be predictors of mortality in univariable analysis. Conclusions Transcatheter aortic valve implantation can be performed in nonagenarians despite very high preoperative risk scores and substantial multimorbidity, with acceptable outcomes.

P3859Stroke after transcatheter aortic valve implantation: an unpredictable risk?

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
O Maier ◽  
G Bosbach ◽  
K Hellhammer ◽  
T Zeus ◽  
M Kelm ◽  
...  
Keyword(s):  
Aortic Valve ◽  
Hospital Stay ◽  
Thoracic Aorta ◽  
Transcatheter Aortic Valve Implantation ◽  
Risk Scores ◽  
Agatston Score ◽  
Procedure Time ◽  
Transcatheter Aortic Valve ◽  
Aortic Valve Implantation ◽  
Valve Implantation

Abstract Background Transcatheter aortic valve implantation (TAVI) has become the preferred alternative to surgical valve replacement in high risk patients with severe symptomatic aortic stenosis. Stroke is still a feared complication following TAVI, associated with increased mortality and severe impact on patients' daily living. Despite technological development and knowledge improvement, cerebrovascular events (CVE) are still not predictable so far and simple risk scores are lacking. The expansion of TAVI therapy towards younger and lower risk populations will force us to discover the mechanisms determining stroke after TAVI. Purpose This study aimed to evaluate different patient and procedure related factors for prediction of stroke after TAVI, especially regarding severity of aortic calcification. Methods From May 2011 to January 2018 a total of 1365 patients underwent TAVI with a balloon-expandable (32.4%) or self-expandable (67.6%) valve at our institution. 60 patients (4.4%) suffered from new neurological impairment in terms of CVE after TAVI during hospital stay (mean 11.2±6.7 days). We performed propensity score matching (1:10) to balance baseline characteristics between patients with and without stroke following TAVI, resulting in 56 patients with in-hospital stroke and 521 patients without neurological disorder. Stroke was defined according to the Valve Academic Research Consortium recommendations (VARC-2). Results Body surface area (stroke vs. control: 2.73±0.27 vs. 2.81±0.29 m2; p=0.0451) and prior stroke (stroke vs. control: 17.86% vs. 8.64%; p=0.0256) were patient related predictors of stroke after TAVI during in-hospital stay. While aortic valve Agatston score (stroke vs. control: 2475±1593 vs. 2060±1344 AU; p=0.0383) and ascending thoracic aorta Agatston score (stroke vs. control: 986.5±1989 vs. 505.2±1018 AU; p=0.0045) showed to be good predictors, peripheral vascular diseases were not associated with stroke (stroke vs. control: 35.7% vs. 31.3%; p=0.4986). A procedural predictor of acute CVE was extended procedure time (stroke vs. control: 101.8±39.6 vs. 90.0±31.3 hours; p=0.0105). Finally, stroke after TAVI resulted in clearly prolonged hospital stay (stroke vs. control: 16.1±9.0 vs. 10.7±6.2 days; p<0.0001). Conclusion The severity of aortic valve and ascending thoracic aorta calcification predicts stroke after TAVI as well as extended procedure time, possibly due to increased mechanical intravascular manipulation by prolonged catheterisation. These correlations could guide us in identifying those patients who are most likely to benefit from transcatheter cerebral embolic protection devices.

scholarly journals Machine Learning for Predicting Mortality in Transcatheter Aortic Valve Implantation: An Inter-Center Cross Validation Study

2021 ◽  
Vol 8 (6) ◽  
pp. 65
Author(s):  
Marco Mamprin ◽  
Ricardo R. Lopes ◽  
Jo M. Zelis ◽  
Pim A. L. Tonino ◽  
Martijn S. van Mourik ◽  
...  
Keyword(s):  
Machine Learning ◽  
Aortic Valve ◽  
Transcatheter Aortic Valve Implantation ◽  
Data Exchange ◽  
External Validation ◽  
Machine Learning Techniques ◽  
Risk Scores ◽  
Transcatheter Aortic Valve ◽  
Aortic Valve Implantation ◽  
Valve Implantation

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.

scholarly journals Performance of Surgical Risk Scores to Predict Mortality after Transcatheter Aortic Valve Implantation

2015 ◽  
Author(s):  
Leonardo Sinnott Silva ◽  
Paulo Ricardo Avancini Caramori ◽  
Antonio Carlos Bacelar Nunes Filho ◽  
Marcelo Katz ◽  
João Carlos Vieira da Costa Guaragna ◽  
...  
Keyword(s):  
Aortic Valve ◽  
Transcatheter Aortic Valve Implantation ◽  
Risk Scores ◽  
Surgical Risk ◽  
Transcatheter Aortic Valve ◽  
Aortic Valve Implantation ◽  
Valve Implantation

scholarly journals Decision Trees for Predicting Mortality in Transcatheter Aortic Valve Implantation

2021 ◽  
Vol 8 (2) ◽  
pp. 22
Author(s):  
Marco Mamprin ◽  
Jo M. Zelis ◽  
Pim A. L. Tonino ◽  
Sveta Zinger ◽  
Peter H. N. de With
Keyword(s):  
Machine Learning ◽  
Aortic Valve ◽  
Decision Trees ◽  
Transcatheter Aortic Valve Implantation ◽  
Feature Analysis ◽  
Risk Scores ◽  
Heart Team ◽  
Transcatheter Aortic Valve ◽  
Aortic Valve Implantation ◽  
Valve Implantation

Current prognostic risk scores in cardiac surgery do not benefit yet from machine learning (ML). This research aims to create a machine learning model to predict one-year mortality of a patient after transcatheter aortic valve implantation (TAVI). We adopt a modern gradient boosting on decision trees classifier (GBDTs), specifically designed for categorical features. In combination with a recent technique for model interpretations, we developed a feature analysis and selection stage, enabling the identification of the most important features for the prediction. We base our prediction model on the most relevant features, after interpreting and discussing the feature analysis results with clinical experts. We validated our model on 270 consecutive TAVI cases, reaching a C-statistic of 0.83 with CI [0.82, 0.84]. The model has achieved a positive predictive value ranging from 57% to 64%, suggesting that the patient selection made by the heart team of professionals can be further improved by taking into consideration the clinical data we identified as important and by exploiting ML approaches in the development of clinical risk scores. Our approach has shown promising predictive potential also with respect to widespread prognostic risk scores, such as logistic European system for cardiac operative risk evaluation (EuroSCORE II) and the society of thoracic surgeons (STS) risk score, which are broadly adopted by cardiologists worldwide.

scholarly journals Early and Late Mortality in Patients Undergoing Transcatheter Aortic Valve Implantation: Comparison of the Novel EuroScore II with Established Risk Scores

Cardiology ◽  
2013 ◽  
Vol 126 (1) ◽  
pp. 15-23 ◽  
Author(s):  
Barbara E. Stähli ◽  
Hanna Tasnady ◽  
Thomas F. Lüscher ◽  
Cathérine Gebhard ◽  
Fran Mikulicic ◽  
...  
Keyword(s):  
Aortic Valve ◽  
Transcatheter Aortic Valve Implantation ◽  
Risk Scores ◽  
The Novel ◽  
Late Mortality ◽  
Transcatheter Aortic Valve ◽  
Aortic Valve Implantation ◽  
Valve Implantation
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