Inter-Examiner and Intra-Examiner Reliability of Quantitative and Qualitative Ultrasonography Assessment of Peripheral and Respiratory Muscles in Critically Ill Patients.
Felipe Douglas Silva BarbosaJosé Lucas Dos SantosMaria Emilia Dantas AlvesJuliana de Ávila Barreto AlvesTelma Cristina Fontes CerqueiraValter Joviniano De Santana FilhoPublished in: International journal of environmental research and public health (2023)
ICU patients are exposed to several factors that can lead to muscle structural and functional changes, and ultrasonography can identify them. Although several studies have analyzed the reliability of muscle ultrasonography assessment, a protocol with more muscle assessments becomes a challenge. The aim of this study was to analyze the inter and intra-examiner reliability of peripheral and respiratory muscle ultrasonography assessment in critically ill patients. The sample size was 10 individuals aged ≥ 18 years who were admitted to the ICU. Practical training of four health professionals from different backgrounds was performed. After training, each examiner acquired three images to assess the thickness and echogenicity of the muscle groups: biceps brachii, forearm flexor group, quadriceps femoris, tibialis anterior and diaphragm. For the reliability analysis, an intraclass correlation coefficient was performed. Six hundred US images were analyzed for muscle thickness and 150 for echogenicity. Excellent intra-examiner reliability for echogenicity (ICC: 0.867-0.973) and inter-examiner reliability for thickness were found in all muscle groups (ICC: 0.778-0.942). For muscle thickness intra-examiner reliability, excellent results were found (ICC: 0.798-0.988), with a "good" correlation in one diaphragm assessment (ICC: 0.718). Excellent inter- and intra-examiner reliability of the thickness assessment and intra-examiner echogenicity of all muscles analyzed were found.
Keyphrases
- skeletal muscle
- optical coherence tomography
- magnetic resonance imaging
- contrast enhanced
- mechanical ventilation
- intensive care unit
- deep learning
- end stage renal disease
- chronic kidney disease
- acute respiratory distress syndrome
- convolutional neural network
- computed tomography
- prognostic factors
- machine learning
- extracorporeal membrane oxygenation
- newly diagnosed
- peritoneal dialysis
- high resolution