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Research Report

Feasibility of the Glittre-ADL test in the preoperative period of thoracic surgery

, MSc, PTORCID Icon, , PTORCID Icon, , SPTORCID Icon & , PhD, MDORCID Icon
Received 28 Mar 2023, Accepted 03 Jul 2023, Published online: 10 Jul 2023
 

ABSTRACT

Background

The Glittre-ADL test (TGlittre) broadly meets the need for an objective evaluation of physical function, using similar activities to those of daily living.

Objective

To assess whether TGlittre in the preoperative assessment of patients who are candidates for thoracic surgery is associated with measures of pulmonary function, body balance, and quality of life and, secondarily, whether TGlittre may be related to postoperative complications.

Methods

This study evaluated 34 patients in the preoperative period of thoracic surgery. Participants underwent the following assessments: TGlittre; pulmonary function tests; St. George’s Respiratory Questionnaire; and Berg Balance Scale. For participants who underwent thoracic surgery (n = 18), the following variables were taken: surgical time; time in intensive care unit; chest drain duration; and hospital stay after surgery.

Results

The median time to perform TGlittre tasks compared to the predicted was 137 (116–179) % predicted. There was significant correlation between TGlittre time and the diffusing capacity for carbon monoxide (rs = -0.334, p = .042). TGlittre time correlated significantly with the Berg Balance Scale (rs = -0.359, p = .036). We observed a significant correlation between TGlittre time and chest drain duration in the postoperative period (rs = 0.651, p = .003).

Conclusion

Patients in the preoperative period of thoracic surgery have a reduced functional capacity to exertion, which can be explained at least in part by worse pulmonary gas exchange and body imbalance. Furthermore, TGlittre is possibly a prognostic test for postoperative complications, especially with respect to chest tube duration.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Conselho Nacional de Desenvolvimento Científico e Tecnólogico (CNPq) under Grant number #301967/2022-9, Brazil, the Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ) under Grant numbers #E-26/010.002124/2019, #E-26/211.187/2021, #E-26/211.104/2021, and #E-26/200.929/2022, Brazil, and the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) under Grant numbers FinanceCode 001, 88881.708719/2022-01, and 88887.708718/2022-00, Brazil.

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