Factors influencing the attrition rate of a 10-week multimodal rehabilitation program in patients after lung transplant: a neural network analysis
Mostra el registre complet Registre parcial de l'ítem
- dc.contributor.author Dávalos Yerovi, Vanesa
- dc.contributor.author Sánchez-Rodríguez, María Dolores
- dc.contributor.author Gómez-Garrido, Alba
- dc.contributor.author Launois, Patricia
- dc.contributor.author Tejero Sánchez, Marta
- dc.contributor.author Pujol-Blaya, Vicenta
- dc.contributor.author Curbelo Peña, Yulibeth
- dc.contributor.author Donohoe, Owen
- dc.contributor.author Marco Navarro, Ester
- dc.date.accessioned 2025-11-18T15:43:26Z
- dc.date.available 2025-11-18T15:43:26Z
- dc.date.issued 2024
- dc.date.updated 2025-11-18T15:43:26Z
- dc.description.abstract Background/Objectives: Despite the effectiveness of exercise and nutritional interventions to improve aerobic capacity and quality of life in lung transplant (LT) recipients, their compliance is low. Strategies to reduce the high attrition rate (participants lost over time) is a major challenge. Artificial neural networks (ANN) may assist in the early identification of patients with high risk of attrition. The main objective of this study is to evaluate the usefulness of ANNs to identify prognostic factors for high attrition rate of a 10-week rehabilitation program after a LT. Methods: This prospective observational study included first-time LT recipients over 18 years of age. The main outcome for each patient was the attrition rate, which was estimated by the amount of missing data accumulated during the study. Clinical variables including malnutrition, sarcopenia, and their individual components were assessed at baseline. An ANN and regression analysis were used to identify the factors determining a high attrition rate. Results: Of the 41 participants, 17 (41.4%) had a high rate of attrition in the rehabilitation program. Only 23 baseline variables had no missing data and were included in the analysis, from which a low age-dependent body mass index (BMI) was the most important conditioning factor for a high attrition rate (p = 7.08 × 10), followed by end-stage respiratory disease requiring PT (p = 0.000111), low health-related quality-of-life (HRQoL) (p = 0.0009078), and low handgrip strength (p = 0.023). Conclusions: The prevalence of high attrition rate in LT recipients is high. The profile of patients with a high probability of attrition includes those with chronic obstructive pulmonary disease, low BMI and handgrip strength, and reduced HRQoL.
- dc.format.mimetype application/pdf
- dc.identifier.citation Davalos-Yerovi V, Sanchez-Rodriguez D, Gomez-Garrido A, Launois P, Tejero-Sanchez M, Pujol-Blaya V, Curbelo YG, Donohoe O, Marco E. Factors influencing the attrition rate of a 10-week multimodal rehabilitation program in patients after lung transplant: a neural network analysis. Healthcare. 2024;12(22):2239. DOI: 10.3390/healthcare12222239
- dc.identifier.doi http://dx.doi.org/10.3390/healthcare12222239
- dc.identifier.issn 2227-9032
- dc.identifier.uri http://hdl.handle.net/10230/71927
- dc.language.iso eng
- dc.publisher MDPI
- dc.relation.ispartof Healthcare. 2024 Nov 10;12(22):2239
- dc.rights © 2024 by the authors. 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/).
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.rights.uri http://creativecommons.org/licenses/by/4.0/
- dc.subject.keyword Artificial neural network
- dc.subject.keyword Attrition rate
- dc.subject.keyword Compliance
- dc.subject.keyword Lung transplant
- dc.subject.keyword Multimodal rehabilitation interventions
- dc.title Factors influencing the attrition rate of a 10-week multimodal rehabilitation program in patients after lung transplant: a neural network analysis
- dc.type info:eu-repo/semantics/article
- dc.type.version info:eu-repo/semantics/publishedVersion