Sie befinden Sich nicht im Netzwerk der Universität Paderborn. Der Zugriff auf elektronische Ressourcen ist gegebenenfalls nur via VPN oder Shibboleth (DFN-AAI) möglich. mehr Informationen...
The fallacy of indexed effective orifice area charts to predict prosthesis–patient mismatch after prosthesis implantation
Ist Teil von
European heart journal cardiovascular imaging, 2020-10, Vol.21 (10), p.1116-1122
Ort / Verlag
England: Oxford University Press
Erscheinungsjahr
2020
Quelle
Oxford Journals 2020 Medicine
Beschreibungen/Notizen
Abstract
Aims
Indexed effective orifice area (EOAi) charts are used to determine the likelihood of prosthesis–patient mismatch (PPM) after aortic valve replacement (AVR). The aim of this study is to validate whether these EOAi charts, based on echocardiographic normal reference values, can accurately predict PPM.
Methods and results
In the PERIcardial SurGical AOrtic Valve ReplacemeNt (PERIGON) Pivotal Trial, 986 patients with aortic valve stenosis/regurgitation underwent AVR with an Avalus valve. Patients were randomly split (50:50) into training and test sets. The mean measured EOAs for each valve size from the training set were used to create an Avalus EOAi chart. This chart was subsequently used to predict PPM in the test set and measures of diagnostic accuracy (sensitivity, specificity, and negative and positive predictive value) were assessed. PPM was defined by an EOAi ≤0.85 cm2/m2, and severe PPM was defined as EOAi ≤0.65 cm2/m2. The reference values obtained from the training set ranged from 1.27 cm2 for size 19 mm up to 1.81 cm2 for size 27 mm. The test set had an incidence of 66% of PPM and 24% of severe PPM. The EOAi chart inaccurately predicted PPM in 30% of patients and severe PPM in 22% of patients. For the prediction of PPM, the sensitivity was 87% and the specificity 37%. For the prediction of severe PPM, the sensitivity was 13% and the specificity 98%.
Conclusion
The use of echocardiographic normal reference values for EOAi charts to predict PPM is unreliable due to the large proportion of misclassifications.