Diagnostic Performance of Muscle Echo Intensity and Fractal Dimension for the Detection of Frailty Phenotype

Correlation of ultrasound measurement of limb muscle thickness and echo intensity with frailty assessment in elderly patients undergoing malignancies surgery

Introduction

It remains controversial which frailty score correlates with adverse outcomes. Instead of these subjective and time consuming scores, we studied whether ultrasound guided lower extremity thickness measurements more closely are associated with adverse outcomes.

Method

Patients undergoing gastrointestinal malignancy surgery were included as study subjects. Frailty was identified using the FRAIL scale assessment. Ultrasound measured the muscle thickness and echo intensity of the patient's upper and lower limbs. ANOVA was used to analyze the relationship between muscle data and frailty assessment. Significant indicators from the one-way analysis were included in the multivariate logistic regression equation.

Results

A total of 160 study subjects were included, 52 in the normal group, 78 in the prefrailty group, and 30 in the frailty group. The ANOVA showed that there were significant differences in age, hemoglobin, albumin, history of hypertension, history of coronary artery disease, the history of cerebrovascular disease, rate of postoperative complications, rate of transferring to intensive care unit, time out of bed, length of hospitalization, thickness and echo intensity of quadriceps femoris the vastus lateralis and the tibialis anterior, echo intensity of the medial gastrocnemius among the three groups (P < 0.05). Pearson's correlation analysis showed FRAIL score was correlated with muscle thickness and echo intensity of the lower limbs. Multifactor logistic regression analysis showed that the prefrailty group was positively correlated with age（β = 0.146, P = 0.004）, echo intensity of the medial gastrocnemius (β = 0.055, P = 0.031), and rate of postoperative complications（β = 1.439, P = 0.021）, also negatively correlated with muscle thickness of the tibialis anterior（β = -2.124, P = 0.007）. The frailty group was positively correlated with age（β = 0.22, P = 0.005）, tibialis anterior echo intensity (β = 0.082, P = 0.015), medial gastrocnemius echo intensity (β = 0.089, P = 0.026), cerebrovascular disease history (β = 2.311, P = 0.04), and postoperative complication rate (β = 2.684, P = 0.003). It was negatively correlated with albumin (β = -0.26, P = 0.017), quadriceps muscle thickness (β = -2.257, P = 0.017), and tibialis anterior muscle thickness (β = -5.408, P = 0.001).

Conclusion

Ultrasound measurement of lower (not upper) extremity muscle thickness and echo intensity was significantly associated with discriminating severity of frailty and postoperative outcomes than frailty scores in elderly patients.

Body Composition Evaluation and Clinical Markers of Cardiometabolic Risk in Patients with Phenylketonuria

Cardiovascular diseases are the main cause of mortality worldwide. Patients with phenylketonuria (PKU) may be at increased cardiovascular risk. This review provides an overview of clinical and metabolic cardiovascular risk factors, explores the connections between body composition (including fat mass and ectopic fat) and cardiovascular risk, and examines various methods for evaluating body composition. It particularly focuses on nutritional ultrasound, given its emerging availability and practical utility in clinical settings. Possible causes of increased cardiometabolic risk in PKU are also explored, including an increased intake of carbohydrates, chronic exposure to amino acids, and characteristics of microbiota. It is important to evaluate cardiovascular risk factors and body composition in patients with PKU. We suggest systematic monitoring of body composition to develop nutritional management and hydration strategies to optimize performance within the limits of nutritional therapy.

Assessment and risk prediction of frailty using texture-based muscle ultrasound image analysis and machine learning techniques

The purpose of this study was to evaluate texture-based muscle ultrasound image analysis for the assessment and risk prediction of frailty phenotype. This retrospective study of prospectively acquired data included 101 participants who underwent ultrasound scanning of the anterior thigh. Participants were subdivided according to frailty phenotype and were followed up for two years. Primary and secondary outcome measures were death and comorbidity, respectively. Forty-three texture features were computed from the rectus femoris and the vastus intermedius muscles using statistical methods. Model performance was evaluated by computing the area under the receiver operating characteristic curve (AUC) while outcome prediction was evaluated using regression analysis. Models developed achieved a moderate to good AUC (0.67 ≤ AUC ≤ 0.79) for categorizing frailty. The stepwise multiple logistic regression analysis demonstrated that they correctly classified 70-87% of the cases. The models were associated with increased comorbidity (0.01 ≤ p ≤ 0.18) and were predictive of death for pre-frail and frail participants (0.001 ≤ p ≤ 0.016). In conclusion, texture analysis can be useful to identify frailty and assess risk prediction (i.e. mortality) using texture features extracted from muscle ultrasound images in combination with a machine learning approach.

The Usefulness of Radiomics Methodology for Developing Descriptive and Prognostic Image-Based Phenotyping in the Aging Population: Results From a Small Feasibility Study

Background: Radiomics is an emerging field that translates medical images into quantitative data to enable phenotypic profiling of human disease. In this retrospective study, we asked whether it is possible to use image-based phenotyping to describe and determine prognostic factors in the aging population.

Methods: A radiomic frailty cohort with 101 patients was included in the analysis (65 ± 15 years, 55 men). A total of 44 texture features were extracted from the segmented muscle area of the ultrasound images of the anterior thigh. Univariate and multivariate analyses were performed to assess the image data sets and clinical data.

Results: Our results showed that the heterogeneity of muscle was associated with an increased incidence of hearing impairment, stroke, myocardial infarction, dementia/memory loss, and falls in the following two years. Regression analysis revealed a muscle radiomic model with 87.1% correct predictive value with good sensitivity and moderate specificity (p = 0.001).

Conclusion: It is possible to develop and identify image-based phenotypes in the elderly population. The muscle radiomic model needs to further be validated. Future studies correlated with biological data (genomics, transcriptomics, metabolomics, etc.) will give further insights into the biological basis and molecular processes of the developed radiomic model.