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Personalized Preoperative Prediction of the Length of Hospital Stay after TAVI Using a Dedicated Decision Tree Algorithm

Background: The aim of this study was to identify pre-operative parameters able to predict length of stay (LoS) based on clinical data and patient-reported outcome measures (PROMs) from a scorecard database in patients with significant aortic stenosis who underwent TAVI (transfemoral aortic valve im...

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Autores principales: Zisiopoulou, Maria, Berkowitsch, Alexander, Neuber, Ralf, Gouveris, Haralampos, Fichtlscherer, Stephan, Walther, Thomas, Vasa-Nicotera, Mariuca, Seppelt, Philipp
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8950279/
https://www.ncbi.nlm.nih.gov/pubmed/35330346
http://dx.doi.org/10.3390/jpm12030346
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author Zisiopoulou, Maria
Berkowitsch, Alexander
Neuber, Ralf
Gouveris, Haralampos
Fichtlscherer, Stephan
Walther, Thomas
Vasa-Nicotera, Mariuca
Seppelt, Philipp
author_facet Zisiopoulou, Maria
Berkowitsch, Alexander
Neuber, Ralf
Gouveris, Haralampos
Fichtlscherer, Stephan
Walther, Thomas
Vasa-Nicotera, Mariuca
Seppelt, Philipp
author_sort Zisiopoulou, Maria
collection PubMed
description Background: The aim of this study was to identify pre-operative parameters able to predict length of stay (LoS) based on clinical data and patient-reported outcome measures (PROMs) from a scorecard database in patients with significant aortic stenosis who underwent TAVI (transfemoral aortic valve implantation). Methods: 302 participants (51.7% males, age range 78.2–84.2 years.) were prospectively recruited. After computing the median LoS value (=6 days, range = 5–8 days), we implemented a decision tree algorithm by setting dichotomized values at median LoS as the dependent variable and assessed baseline clinical variables and PROMs (Clinical Frailty Scale (CFS), EuroQol-5 Dimension-5 Levels (EQ-5D) and Kansas City Cardiomyopathy Questionnaire (KCCQ)) as potential predictors. Results: Among clinical parameters, only peripheral arterial disease (p = 0.029, HR = 1.826) and glomerular filtration rate (GFR, cut-off < 33 mL/min/1.73 m(2), p = 0.003, HR = 2.252) were predictive of LoS. Additionally, two PROMs (CFS; cut-off = 3, p < 0.001, HR = 1.324 and KCCQ; cut-off = 30, p = 0.003, HR = 2.274) were strong predictors. Further, a risk score for LoS (RS_LoS) was calculated based on these predictors. Patients with RS_LoS = 0 had a median LoS of 5 days; patients RS_LoS ≥ 3 had a median LoS of 8 days. Conclusions: based on the pre-operative values of the above four predictors, a personalized prediction of LoS after TAVI can be achieved.
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spelling pubmed-89502792022-03-26 Personalized Preoperative Prediction of the Length of Hospital Stay after TAVI Using a Dedicated Decision Tree Algorithm Zisiopoulou, Maria Berkowitsch, Alexander Neuber, Ralf Gouveris, Haralampos Fichtlscherer, Stephan Walther, Thomas Vasa-Nicotera, Mariuca Seppelt, Philipp J Pers Med Article Background: The aim of this study was to identify pre-operative parameters able to predict length of stay (LoS) based on clinical data and patient-reported outcome measures (PROMs) from a scorecard database in patients with significant aortic stenosis who underwent TAVI (transfemoral aortic valve implantation). Methods: 302 participants (51.7% males, age range 78.2–84.2 years.) were prospectively recruited. After computing the median LoS value (=6 days, range = 5–8 days), we implemented a decision tree algorithm by setting dichotomized values at median LoS as the dependent variable and assessed baseline clinical variables and PROMs (Clinical Frailty Scale (CFS), EuroQol-5 Dimension-5 Levels (EQ-5D) and Kansas City Cardiomyopathy Questionnaire (KCCQ)) as potential predictors. Results: Among clinical parameters, only peripheral arterial disease (p = 0.029, HR = 1.826) and glomerular filtration rate (GFR, cut-off < 33 mL/min/1.73 m(2), p = 0.003, HR = 2.252) were predictive of LoS. Additionally, two PROMs (CFS; cut-off = 3, p < 0.001, HR = 1.324 and KCCQ; cut-off = 30, p = 0.003, HR = 2.274) were strong predictors. Further, a risk score for LoS (RS_LoS) was calculated based on these predictors. Patients with RS_LoS = 0 had a median LoS of 5 days; patients RS_LoS ≥ 3 had a median LoS of 8 days. Conclusions: based on the pre-operative values of the above four predictors, a personalized prediction of LoS after TAVI can be achieved. MDPI 2022-02-24 /pmc/articles/PMC8950279/ /pubmed/35330346 http://dx.doi.org/10.3390/jpm12030346 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zisiopoulou, Maria
Berkowitsch, Alexander
Neuber, Ralf
Gouveris, Haralampos
Fichtlscherer, Stephan
Walther, Thomas
Vasa-Nicotera, Mariuca
Seppelt, Philipp
Personalized Preoperative Prediction of the Length of Hospital Stay after TAVI Using a Dedicated Decision Tree Algorithm
title Personalized Preoperative Prediction of the Length of Hospital Stay after TAVI Using a Dedicated Decision Tree Algorithm
title_full Personalized Preoperative Prediction of the Length of Hospital Stay after TAVI Using a Dedicated Decision Tree Algorithm
title_fullStr Personalized Preoperative Prediction of the Length of Hospital Stay after TAVI Using a Dedicated Decision Tree Algorithm
title_full_unstemmed Personalized Preoperative Prediction of the Length of Hospital Stay after TAVI Using a Dedicated Decision Tree Algorithm
title_short Personalized Preoperative Prediction of the Length of Hospital Stay after TAVI Using a Dedicated Decision Tree Algorithm
title_sort personalized preoperative prediction of the length of hospital stay after tavi using a dedicated decision tree algorithm
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8950279/
https://www.ncbi.nlm.nih.gov/pubmed/35330346
http://dx.doi.org/10.3390/jpm12030346
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