Evidence mapPaperPMID 39595437Full record

ArticleHealthcare (Basel, Switzerland)2024

Factors Influencing the Attrition Rate of a 10-Week Multimodal Rehabilitation Program in Patients After Lung Transplant: A Neural Network Analysis.

Vanesa Dávalos-Yerovi, Dolores Sánchez-Rodríguez, Alba Gómez-Garrido, Patricia Launois, Marta Tejero-Sánchez, Vicenta Pujol-Blaya, Yulibeth G Curbelo, Owen Donohoe, Ester Marco

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Article in Healthcare (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Vanesa Dávalos-YeroviRehabilitation Research Group, Hospital del Mar Medical Research Institute, 08003 Barcelona, Spain.ORCID 0000-0003-2676-5389
Dolores Sánchez-RodríguezRehabilitation Research Group, Hospital del Mar Medical Research Institute, 08003 Barcelona, Spain.ORCID 0000-0001-8662-5172
Alba Gómez-GarridoPhysical Medicine and Rehabilitation Department, Hospital Vall d'Hebron, 08035 Barcelona, Spain.ORCID 0000-0002-3884-5899
Patricia LaunoisPhysical Medicine and Rehabilitation Department, Hospital Vall d'Hebron, 08035 Barcelona, Spain.ORCID 0000-0001-8115-2564
Marta Tejero-SánchezPhysical Medicine and Rehabilitation Department, Hospital del Mar, 08003 Barcelona, Spain.ORCID 0000-0002-1904-456X
Vicenta Pujol-BlayaPhysical Medicine and Rehabilitation Department, Hospital Vall d'Hebron, 08035 Barcelona, Spain.ORCID 0000-0002-8086-0531
Yulibeth G CurbeloPhysical Medicine and Rehabilitation Department, Hospital del Mar, 08003 Barcelona, Spain.
Owen DonohoeDepartment of Parasitic and Infectious Diseases, Faculty of Veterinary Medicine, University of Liège, 4000 Liège, Belgium.ORCID 0000-0002-1068-5248
Ester MarcoRehabilitation Research Group, Hospital del Mar Medical Research Institute, 08003 Barcelona, Spain.ORCID 0000-0002-3412-0356

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND/

objectivesDespite 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.

methodsThis 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.

resultsOf 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 (

conclusionsThe 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.

Indexed as

artificial neural networkattrition ratecompliancelung transplantmultimodal rehabilitation interventions

Identifiers

PMID39595437
PMCPMC11593418

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