Management of disease-related malnutrition: a real-world experience with a novel concentrated high-protein energy-dense oral nutritional supplement

López-Gómez, Juan J.; Bachiller, Beatriz Ramos; de Luis Roman, Daniel

doi:10.1080/00325481.2024.2307869

Cited by 2 publications

(1 citation statement)

References 54 publications

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“…Disease-related malnutrition (DRM) [ 1 ] is a prevalent health issue that poses a significant challenge in our healthcare system, affecting 20% to 50% of hospitalized patients [ 2 , 3 ]. Its presence can lead to increased complications and mortality risk.…”

Section: Introductionmentioning

confidence: 99%

Validation of an Artificial Intelligence-Based Ultrasound Imaging System for Quantifying Muscle Architecture Parameters of the Rectus Femoris in Disease-Related Malnutrition (DRM)

García-Herreros,

López Gómez,

Cebria

et al. 2024

Nutrients

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(1) Background: The aim was to validate an AI-based system compared to the classic method of reading ultrasound images of the rectus femur (RF) muscle in a real cohort of patients with disease-related malnutrition. (2) Methods: One hundred adult patients with DRM aged 18 to 85 years were enrolled. The risk of DRM was assessed by the Global Leadership Initiative on Malnutrition (GLIM). The variation, reproducibility, and reliability of measurements for the RF subcutaneous fat thickness (SFT), muscle thickness (MT), and cross-sectional area (CSA), were measured conventionally with the incorporated tools of a portable ultrasound imaging device (method A) and compared with the automated quantification of the ultrasound imaging system (method B). (3) Results: Measurements obtained using method A (i.e., conventionally) and method B (i.e., raw images analyzed by AI), showed similar values with no significant differences in absolute values and coefficients of variation, 58.39–57.68% for SFT, 30.50–28.36% for MT, and 36.50–36.91% for CSA, respectively. The Intraclass Correlation Coefficient (ICC) for reliability and consistency analysis between methods A and B showed correlations of 0.912 and 95% CI [0.872–0.940] for SFT, 0.960 and 95% CI [0.941–0.973] for MT, and 0.995 and 95% CI [0.993–0.997] for CSA; the Bland–Altman Analysis shows that the spread of points is quite uniform around the bias lines with no evidence of strong bias for any variable. (4) Conclusions: The study demonstrated the consistency and reliability of this new automatic system based on machine learning and AI for the quantification of ultrasound imaging of the muscle architecture parameters of the rectus femoris muscle compared with the conventional method of measurement.

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Section: Introductionmentioning

confidence: 99%

Validation of an Artificial Intelligence-Based Ultrasound Imaging System for Quantifying Muscle Architecture Parameters of the Rectus Femoris in Disease-Related Malnutrition (DRM)

García-Herreros,

López Gómez,

Cebria

et al. 2024

Nutrients

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Effectiveness of High-Protein Energy-Dense Oral Supplements on Patients with Malnutrition Using Morphofunctional Assessment with AI-Assisted Muscle Ultrasonography: A Real-World One-Arm Study

López-Gómez,

Primo-Martín,

Cebria

et al. 2024

Nutrients

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Background: User-friendly tools for assessing nutrition status and interventions in malnourished patients are crucial. This study evaluated the effectiveness of a personalised nutrition intervention using a novel oral nutritional supplement and AI-supported morphofunctional assessment to monitor clinical outcomes in patients with disease-related malnutrition (DRM). Methods: This prospective observational study involved patients receiving concentrated high-protein, high-calorie ONS (cHPHC-ONS), per usual clinical practice. Comprehensive assessments were performed at baseline (B0) and three months (M3) post-intervention. Results: 65 patients participated in the study. Significant decreases were observed in the percentage weight loss from B0 (−6.75 ± 7.5%) to M3 (0.5 ± 3.48%) (p < 0.01), in the prevalence of malnutrition (B0: 93.4%; M3: 78.9%; p < 0.01), severe malnutrition (B0: 60.7%; M3: 40.3%; p < 0.01), and sarcopenia (B0: 19.4%; M3: 15.5%; p < 0.04). Muscle area increased (p = 0.03), and there were changes in the echogenicity of the rectus femoris muscle (p = 0.03) from B0 to M3. In patients aged ≥60, an increase in muscle thickness (p = 0.04), pennation angle (p = 0.02), and handgrip strength (p = 0.04) was observed. There was a significant reduction in the prevalence of malnutrition (B0: 93.4%; M3: 78.9%; p < 0.01) and severe malnutrition (B0: 60.7%; M3: 40.3%; p < 0.01). Conclusions: In patients with DRM, a personalised intervention with cHPHC-ONS significantly reduces the prevalence of malnutrition, severe malnutrition, and sarcopenia and improves muscle mass and function.

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Management of disease-related malnutrition: a real-world experience with a novel concentrated high-protein energy-dense oral nutritional supplement

Cited by 2 publications

References 54 publications

Validation of an Artificial Intelligence-Based Ultrasound Imaging System for Quantifying Muscle Architecture Parameters of the Rectus Femoris in Disease-Related Malnutrition (DRM)

Validation of an Artificial Intelligence-Based Ultrasound Imaging System for Quantifying Muscle Architecture Parameters of the Rectus Femoris in Disease-Related Malnutrition (DRM)

Effectiveness of High-Protein Energy-Dense Oral Supplements on Patients with Malnutrition Using Morphofunctional Assessment with AI-Assisted Muscle Ultrasonography: A Real-World One-Arm Study

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