Nascent to novel methods to evaluate malnutrition and frailty in the surgical patient

Abstract

Preoperative nutrition status is an important determinant of surgical outcomes, yet malnutrition assessment is not integrated into all surgical pathways. Given its importance and the high prevalence of malnutrition in patients undergoing surgical procedures, preoperative nutrition screening, assessment, and intervention are needed to improve postoperative outcomes. This narrative review discusses novel methods to assess malnutrition and frailty in the surgical patient. The Global Leadership Initiative for Malnutrition (GLIM) criteria are increasingly used in surgical settings although further spread and implementation are strongly encouraged to help standardize the diagnosis of malnutrition. The use of body composition (ie, reduced muscle mass) as a phenotypic criterion in GLIM may lead to a greater number of patients identified as having malnutrition, which may otherwise be undetected if screened by other diagnostic tools. Skeletal muscle loss is a defining criterion of malnutrition and frailty. Novel direct and indirect approaches to assess muscle mass in clinical settings may facilitate the identification of patients with or at risk for malnutrition. Selected imaging techniques have the additional advantage of identifying myosteatosis (an independent predictor of morbidity and mortality for surgical patients). Feasible pathways for screening and assessing frailty exist and may determine the cost/benefit of surgery, long-term independence and productivity, and the value of undertaking targeted interventions. Finally, the evaluation of nutrition risk and status is essential to predict and mitigate surgical outcomes. Nascent to novel approaches are the future of objectively identifying patients at perioperative nutrition risk and guiding therapy toward optimal perioperative standards of care.

Keywords: GLIM; body composition; frailty; imaging; malnutrition; muscle mass; nutritional assessment; nutritional risk; nutritional screening; perioperative nutrition; sarcopenia; surgery.

Figure 1

Graphic illustration of the effect of preoperative nutrition status on surgical outcomes. Please see supplementary material for an alternative version of the figure with North American spelling.

Figure 2

Graphic representation depicting how a patient's preoperative nutrition status determines how critical the catabolic response is. The catabolic stress response is associated with muscle loss (and likely weight loss). However, patients who already have low muscle mass are at greater risk of depleting already compromised reserves, which increases the risk of unfavorable outcomes. Computerized tomography images were used as an example of a method to evaluate changes in body composition (third lumbar vertebra, patients with normal body weight before surgery).

Figure 3

Summary of selected differences between nutrition screening vs assessment. Nutrition assessment can be done using a variety of approaches, such as anthropometric and body composition measurements, food‐ and nutrition‐related history, clinical signs, biochemical data, medical tests, procedures and diagnosis, and functional assessment. Please see supplementary material for an alternative version of the figure with North American spelling.

Figure 4

Overview of Global Leadership Initiative on Malnutrition (GLIM) criteria. GLIM is an effort to adopt a global consensus on criteria for malnutrition diagnosis. It does not exclude the use of other nutrition assessment tools to guide individualized care and treatment. As such, GLIM is to be used alongside nutrition screening and assessment. Phenotypic and etiologic criteria were derived from commonly used nutrition screening and assessment tools. Malnutrition diagnosis is based on the identification of one phenotypic and one etiologic criterion. When present, severity of malnutrition is then determined., BMI, body mass index; CNST, Canadian Nutrition Screening Tool; MNA, Mini Nutritional Assessment; MNA‐SF, Mini Nutritional Assessment—Short‐Form; MST, Malnutrition Screening Tool; MUST, Malnutrition Universal Screening Tool; NRS‐2002, Nutritional Risk Screening‐2002; PG‐SGA, Patient‐Generated Subjective Global Assessment; SGA, Subjective Global Assessment. Please see supplementary material for an alternative version of the figure with North American spelling.

Figure 5

Summary of pros and cons of commonly used anthropometric and body composition approaches to estimate/measure muscle mass. BMI, body mass index.

Figure 6

Graphic representation depicting hidden abnormalities in body composition. Low muscle mass may be hidden in individuals with normal body weight and those living with larger body sizes.

Figure 7

(A) Multivariate odds ratio from studies exploring the association of myosteatosis with poor clinical outcomes (excluding survival) in the surgical context. (B) Multivariate hazard ratio for shorter survival related to myosteatosis from various surgical studies. All studies compared patients with myosteatosis against patients without myosteatosis (reference; odds ratio or hazard ratio: 1). All values presented are significant (P < 0.05). Conduit complications defined in the cited study as clinically silent fistulae, clinically important leaks that required interventions, and frank gastric necroses.

Figure 8

Three‐dimensional, fully automated body composition imaging analysis from computerized tomography (CT) using DAFS—the data analysis facilitation suite by Voronoi Health Analytics Inc, Canada. DAFS can segment any field of view of CT images from head to toe from contrast and noncontrast images and low‐dose and conventional‐dose images in both adults and children. The software is fully automated and provides detailed quality assessment interfaces. AI, artificial intelligence; Adapted with permission from Voronoi Health Analytics Inc, Canada.

Figure 9

Magnetic resonance imaging (MRI) analysis from AMRA Medical. (A) Whole‐body images separated by water (left) and adipose tissue (right), based on MRI processed and analyzed using AMRA Medical machine learning and automation methods; available at selected diagnostic imaging facilities. (B) AMRA BCP Scan (Body Composition Profile) sample report with body composition measurements quantified and compared with reference values and a personalized control group (blue field in the bar plots). (C) Sample adipose tissue– and water‐separated MR images for research from AMRA Medical. Adapted with permission from AMRA Medical.