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 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 m2, 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.
Verfasserangaben: | Maria ZisiopoulouORCiDGND, Alexander BerkowitschGND, Ralf Neuber, Haralampos Theodoros GouverisORCiDGND, Stephan Fichtlscherer, Thomas WaltherGND, Mariuca Vasa-NicoteraORCiDGND, Philipp SeppeltORCiDGND |
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URN: | urn:nbn:de:hebis:30:3-827322 |
DOI: | https://doi.org/10.3390/jpm12030346 |
ISSN: | 2075-4426 |
Titel des übergeordneten Werkes (Englisch): | Journal of Personalized Medicine |
Verlag: | MDPI |
Verlagsort: | Basel |
Dokumentart: | Wissenschaftlicher Artikel |
Sprache: | Englisch |
Datum der Veröffentlichung (online): | 24.02.2022 |
Datum der Erstveröffentlichung: | 24.02.2022 |
Veröffentlichende Institution: | Universitätsbibliothek Johann Christian Senckenberg |
Datum der Freischaltung: | 12.03.2024 |
Freies Schlagwort / Tag: | TAVI; algorithm; aortic stenosis; decision tree; hospital length of stay; patient-reported outcomes; prediction |
Jahrgang: | 12 |
Ausgabe / Heft: | 3, art. 346 |
Aufsatznummer: | 346 |
Seitenzahl: | 12 |
Erste Seite: | 1 |
Letzte Seite: | 12 |
Institute: | Medizin |
DDC-Klassifikation: | 6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit |
Sammlungen: | Universitätspublikationen |
Lizenz (Deutsch): | Creative Commons - CC BY - Namensnennung 4.0 International |