Bioelectrical impedance vector analysis is different according to the comorbidity burden in post–acute myocardial infarction

• Autor(a) principal: Fayh, Ana Paula Trussardi
• Data de Publicação: 2023
• Outros Autores: Lopes, Márcia Marília Gomes Dantas, Sousa, Iasmin Matias, Queiroz, Sandra Azevedo, Bezerra, Mara Rubia de Oliveira, Gonzalez, Maria Cristina
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UFRN
• Texto Completo: https://repositorio.ufrn.br/handle/123456789/55767; http://dx.doi.org/10.1002/ncp.11074
• topic: Bioelectrical impedance; body composition; charlson comorbidity index; myocardial infarction

Background: The prevalence of a high comorbidity burden in patients who suffered an acute myocardial infarction (AMI) is increasing with the aging population, and the nutrition status also may be a predictor of clinical outcomes for these patients. This study aimed to investigate the relationship between the comorbidity burden and the characteristics of the bioelectrical impedance vector analysis (BIVA) in patients post‐AMI. Methods: This prospective observational cohort study was conducted with adult patients who were hospitalized with AMI. Pre‐existing comorbidities were assessed by the Charlson comorbidity index (CCI) adjusted by age, and anthropometric and BIVA characteristics were evaluated after the hemodynamic stabilization. All patients were followed‐up until hospital discharge, and their length of stay was observed. Results: A total of 184 patients (75% were males; mean age, 60.2 ± 12.3 years) were included. The most common comorbidities were dyslipidemia (73.9%), hypertension (62%), and type 2 diabetes (34.2%). A higher CCI (≥3) was associated with sex (P = 0.008) and age (P < 0.001). Regarding BIVA, statistically significant differences were detected between sex (P < 0.001), age (P < 0.001), and CCI (P = 0.003), with longer vectors in female, older adults, and those with CCI ≥ 3. Conclusion: Finding a relationship between BIVA and CCI suggests the first identified coherent differences, potentially correlated with diseases, representing a first contribution to support this type of assessment. Therefore, with BIVA, healthcare professionals may monitor abnormalities and adopt preventive nutrition care measures on patients post‐AMI to improve their clinical status