Using novel machine learning tools to predict optimal discharge following transcatheter aortic valve replacement - 23/01/25
Bienvenue sur EM-consulte, la référence des professionnels de santé.
Article gratuit.
Connectez-vous pour en bénéficier!
Graphical abstract |
Highlights |
• | TAVR requires extensive healthcare resources. |
• | ANN was more accurate than LR and XGBoost in predicting optimal discharge. |
• | ANN and XGBoost can be augmented into institution-specific TAVR algorithms. |
Abstract |
Background |
Although transcatheter aortic valve replacement has emerged as an alternative to surgical aortic valve replacement, it requires extensive healthcare resources, and optimal length of hospital stay has become increasingly important. This study was conducted to assess the potential of novel machine learning models (artificial neural network and eXtreme Gradient Boost) in predicting optimal hospital discharge following transcatheter aortic valve replacement.
Aim |
To determine whether artificial neural network and eXtreme Gradient Boost models can be used to accurately predict optimal discharge following transcatheter aortic valve replacement.
Methods |
Data were collected from the 2016–2018 National Inpatient Sample database using International Classification of Diseases, Tenth Revision codes. Patients were divided into two cohorts based on length of hospital stay: optimal discharge (length of hospital stay 0–3 days); and late discharge (length of hospital stay 4–9 days). χ2 and t tests were performed to compare patient characteristics with optimal discharge and prolonged discharge. Logistic regression, artificial neural network and eXtreme Gradient Boost models were used to predict optimal discharge. Model performance was determined using area under the curve and F1 score. An area under the curve≥0.80 and an F1 score≥0.70 were considered strong predictive accuracy.
Results |
Twenty-five thousand and eight hundred and seventy-four patients who underwent transcatheter aortic valve replacement were analysed. Predictability of optimal discharge was similar amongst the models (area under the curve 0.80 in all models). In all models, patient disposition and elective procedure were the most important predictive factors. Coagulation disorder was the strongest co-morbidity predictor of whether a patient had an optimal discharge.
Conclusions |
Artificial neural network and eXtreme Gradient Boost models had satisfactory performances, demonstrating similar accuracy to binary logistic regression in predicting optimal discharge following transcatheter aortic valve replacement. Further validation and refinement of these models may lead to broader clinical adoption.
Le texte complet de cet article est disponible en PDF.Keywords : TAVR, Artificial intelligence, Neural network, XGBoost
Plan
| ☆ | X post (Tweet): Machine-learning models (ANN, XGBoost) were used to predict optimal discharge (0–3 days) post-TAVR. ANN, XGBoost, and logistic regression showed similar accuracy (AUC≥0.80). Patient disposition and elective status were key predictors. |
Vol 118 - N° 1
P. 26-34 - janvier 2025 Retour au numéro
Article précédent
- Automatized quantitative electrocardiography from digitized paper electrocardiograms: A new avenue for risk stratification in patients with Brugada syndrome
- Pierre-Léo Laporte, Martino Vaglio, Isabelle Denjoy, Pierre Maison-Blanche, Charlène Coquard, Nathan El Bèze, Philippe Maury, Alexis Hermida, Didier Klug, Alice Maltret, Fabio Badilini, Antoine Leenhardt, Fabrice Extramiana
- Incidence of ventricular arrhythmias after implantable cardioverter-defibrillator implantation or replacement, and driving restriction consequences
- Thomas Marc, Karim Benali, Pierre Groussin, Redwane Rakza, Joana Brito, Nathalie Behar, Philippe Mabo, Dominique Pavin, Christophe Leclercq, Vincent Galand, Raphaël P. Martins
Bienvenue sur EM-consulte, la référence des professionnels de santé.