Cargando…

Nutritional Risk Indicators for Predicting a Change in Quadriceps Muscle Thickness in Acute Patients with Stroke

INTRODUCTION: To date, no studies have assessed the prognostic ability of nutritional indicators to predict changes in quadriceps muscle thickness (QMT). Hence, this study aimed to identify the optimal nutritional indicators for predicting the change in QMT during the acute phase in patients with st...

Descripción completa

Detalles Bibliográficos
Autores principales: Kokura, Yoji, Nishioka, Shinta
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Japan Medical Association 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8826440/
https://www.ncbi.nlm.nih.gov/pubmed/35224261
http://dx.doi.org/10.31662/jmaj.2021-0107
Descripción
Sumario:INTRODUCTION: To date, no studies have assessed the prognostic ability of nutritional indicators to predict changes in quadriceps muscle thickness (QMT). Hence, this study aimed to identify the optimal nutritional indicators for predicting the change in QMT during the acute phase in patients with stroke. METHODS: This retrospective cohort study was a post-hoc analysis of a prospective study in a single hospital. The Geriatric Nutritional Risk Index (GNRI), Controlling Nutritional Status (CONUT), and Mini Nutritional Assessment - Short Form (MNA-SF) were assessed. The primary outcome was the 2-week change in QMT from the time of admission in the paralytic and non-paralytic sides. QMT was evaluated at the rectus femoris and the vastus intermedius in both lower limbs using B-mode ultrasound imaging. The sum of both measurements was defined as QMT. Univariate and multivariate analyses were performed to confirm the effects of nutritional risks assessed by each nutritional indicator on QMT change. RESULTS: We analyzed 118 patients (mean age, 80.2 ± 8.8 years). No significant difference was found in QMT change in the non-paralytic limbs between the groups stratified based on GNRI and CONUT. However, the difference was significant between the malnourished and normal nutritional status in patients categorized by MNA-SF. After adjusting for potential confounders, a significant association was found between MNA-SF and change in QMT (malnourished vs. normal nutritional status; B = −0.143; 95% confidence interval [CI], −0.254 to −0.031) in the non-paralytic limbs. MNA-SF was not independently associated with change in QMT in the paralytic limb. Furthermore, GNRI and CONUT were not independently associated with change in QMT in both paralytic and non-paralytic limbs. CONCLUSIONS: Although MNA-SF might be useful for predicting the QMT change in non-paralytic limbs, GNRI and CONUT cannot predict the QMT change in either the paralytic or non-paralytic limb.