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Research Highlights

Research Highlights 1 1 Guido Lewik, MD, and Fadi Issa, DPhil, FRCS without early-onset CAV (no-CAV). Additionally, 51 disease PEYSTER EG, JANOWCZYK A, SWAMIDOSS A, control biopsies from patients with definitive angiographic ET AL. COMPUTATIONAL ANALYSIS OF ROUTINE CAV diagnosis were assessed. A comparison of no-CAV BIOPSIES IMPROVES DIAGNOSIS AND PREDICTION and (definitive CAV) disease controls revealed 11 variable OF CARDIAC ALLOGRAFT VASCULOPATHY. discriminative features, such as perivascular cellular density CIRCULATION. 24;145(21):1563–1577. and collagen proliferation inside and outside of the myo- cardial compartment. This diagnosis model reached 86.7% ong-term success in heart transplantation remains lim- accuracy on the test set. For predictive modeling, no-CAV Lited by cardiac allograft vasculopathy (CAV), a fibro- samples were compared to pre–early-onset CAV samples. proliferative disease characterized by luminal narrowing of 10 variables were included, such as the ratio of microves- the macro- and microvasculature. To predict CAV devel- sels to myocyte staining areas and perivascular cellular opment in heart transplant patients, clinical, functional, density. Accurate prediction of the test set was found to and macroscopic indicators have been assessed with very be 81.6% with this model. Lastly and most crucially, the limited success. More precise individual risk stratification authors incorporated these histological and clinical predic- http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Transplantation Wolters Kluwer Health

Research Highlights

Transplantation , Volume 106 (8) – Aug 22, 2022

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References (8)

Publisher
Wolters Kluwer Health
Copyright
Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.
ISSN
0041-1337
eISSN
1534-6080
DOI
10.1097/tp.0000000000004272
Publisher site
See Article on Publisher Site

Abstract

1 1 Guido Lewik, MD, and Fadi Issa, DPhil, FRCS without early-onset CAV (no-CAV). Additionally, 51 disease PEYSTER EG, JANOWCZYK A, SWAMIDOSS A, control biopsies from patients with definitive angiographic ET AL. COMPUTATIONAL ANALYSIS OF ROUTINE CAV diagnosis were assessed. A comparison of no-CAV BIOPSIES IMPROVES DIAGNOSIS AND PREDICTION and (definitive CAV) disease controls revealed 11 variable OF CARDIAC ALLOGRAFT VASCULOPATHY. discriminative features, such as perivascular cellular density CIRCULATION. 24;145(21):1563–1577. and collagen proliferation inside and outside of the myo- cardial compartment. This diagnosis model reached 86.7% ong-term success in heart transplantation remains lim- accuracy on the test set. For predictive modeling, no-CAV Lited by cardiac allograft vasculopathy (CAV), a fibro- samples were compared to pre–early-onset CAV samples. proliferative disease characterized by luminal narrowing of 10 variables were included, such as the ratio of microves- the macro- and microvasculature. To predict CAV devel- sels to myocyte staining areas and perivascular cellular opment in heart transplant patients, clinical, functional, density. Accurate prediction of the test set was found to and macroscopic indicators have been assessed with very be 81.6% with this model. Lastly and most crucially, the limited success. More precise individual risk stratification authors incorporated these histological and clinical predic-

Journal

TransplantationWolters Kluwer Health

Published: Aug 22, 2022

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