2026-04-17T00:42:15Zhttps://recercat.cat/oai/request

oai:recercat.cat:11351/124442025-10-24T10:15:56Zcom_2072_378070com_2072_378040col_2072_378092

Factors Influencing the Attrition Rate of a 10-Week Multimodal Rehabilitation Program in Patients After Lung Transplant: A Neural Network Analysis Davalos-Yerovi, Vanesa Sánchez-Rodríguez, Dolores GOMEZ-GARRIDO, ALBA tejero, marta pujol, vicenta Launois Obregón, Patricia Pulmons - Trasplantació Avaluació de resultats (Assistència sanitària) Exercici terapèutic Pacients - Satisfacció NAMED GROUPS::Persons::Transplant Recipients ANALYTICAL, DIAGNOSTIC AND THERAPEUTIC TECHNIQUES, AND EQUIPMENT::Surgical Procedures, Operative::Thoracic Surgical Procedures::Pulmonary Surgical Procedures::Lung Transplantation ANALYTICAL, DIAGNOSTIC AND THERAPEUTIC TECHNIQUES, AND EQUIPMENT::Therapeutics::Patient Care::Continuity of Patient Care::Aftercare::Rehabilitation::Exercise Therapy HEALTH CARE::Environment and Public Health::Public Health::Epidemiologic Measurements::Demography::Health Status::Quality of Life ANALYTICAL, DIAGNOSTIC AND THERAPEUTIC TECHNIQUES, AND EQUIPMENT::Diagnosis::Prognosis::Treatment Outcome DENOMINACIONES DE GRUPOS::personas::receptores de trasplantes TÉCNICAS Y EQUIPOS ANALÍTICOS, DIAGNÓSTICOS Y TERAPÉUTICOS::intervenciones quirúrgicas::procedimientos quirúrgicos torácicos::procedimientos quirúrgicos pulmonares::trasplante de pulmón TÉCNICAS Y EQUIPOS ANALÍTICOS, DIAGNÓSTICOS Y TERAPÉUTICOS::terapéutica::asistencia al paciente::continuidad de la atención al paciente::asistencia del convaleciente::rehabilitación::tratamiento por actividad física ATENCIÓN DE SALUD::ambiente y salud pública::salud pública::medidas epidemiológicas::demografía::estado de salud::calidad de vida TÉCNICAS Y EQUIPOS ANALÍTICOS, DIAGNÓSTICOS Y TERAPÉUTICOS::diagnóstico::pronóstico::resultado del tratamiento Artificial neural network; Attrition rate; Lung transplant Xarxa neuronal artificial; Taxa de desgast; Trasplantament de pulmó Red neuronal artificial; Tasa de desgaste; Trasplante de pulmón 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 × 10−5), 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. 2025-10-24T10:15:56Z 2025-10-24T10:15:56Z 2025-10-24T10:15:56Z 2025-01-21T07:23:27Z 2025-01-21T07:23:27Z 2024-11-10 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://hdl.handle.net/11351/12444 Healthcare;12(22) https://doi.org/10.3390/healthcare12222239 Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess MDPI Scientia