Validated Screening tools for the Assessment of Cachexia, Sarcopenia and Malnutrition

Validated screening tools for the assessment of Cachexia, Sarcopenia and Malnutrition: A systematic review

  

Abstract

 

Introduction

There is a great deal of overlap between the presentation of cachexia, sarcopenia and malnutrition in the clinical setting. It is difficult to distinguish between these three conditions but the ability to do so would allow for better targeted treatment for patients. There are currently over 70 different screening tools described but a definitive, validated tool to assess all three simultaneously is lacking.

Aim

To systematically review validated screening tools for cachexia, sarcopenia and malnutrition in the generalised adult population and, if a combined tool is absent, to make suggestions for the generation of a novel screening tool.

Methods

A systematic search was performed in Ovid MEDLINE, EMBASE, CINAHL (Cumulative index to Nursing and Allied Health Literature) and Web of Science. Relevant articles were identified by title and abstract, and two reviewers performed data extraction independently. Each tool was judged for validity against a reference method, and psychometric evaluation was performed. The ability of the tool to assess the patient against the agreed consensus definition for each condition was also appraised.

Results

Thirty studies described 22 validated screening tools.

The CASCO was the only screening tool for cachexia that had been validated. It performed well against diagnostic criteria (Fearon 2011), but sensitivities and specificities were not recorded.

Only two tools assessed sarcopenia (the SPSM and the SARC-F) and scored well against the agreed definition (Baumgartner 1998). However, the SPSM required a large amount of equipment and the SARC-F had a very low sensitivity.

Nineteen tools screened for malnutrition. The 3 MinNS proved to be the best, scoring well against the consensus definition (ESPEN) as well as having sensitivities and specificities >80%.

All tools, however, showed inconsistent results and no tool contained all the currently accepted components required to screen for all three conditions. Furthermore, none of the tools has been measured against cross-sectional imaging, a clinical tool that is gaining wider interest as a means of body composition analysis.

Conclusion

No one validated screening tool can be implemented for the simultaneous assessment of cachexia, sarcopenia and malnutrition. The development of a tool that encompasses current consensus definition criteria and directs clinicians towards the specific underlying diagnosis would be optimal in order to target treatment and improve outcomes. We propose that such a future tool should incorporate a stepwise assessment of nutritional status; oral intake; disease status; patient age; muscle mass/function; and metabolic derangement.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Introduction

Unintentional weight loss (UWL) as a form of nutritional depletion is commonly seen in ageing, cancer and many chronic diseases. The main subtypes can be categorised into three primary syndromes: cachexia, age-related sarcopenia and malnutrition; however, it is not clear whether existing screening tools are able to distinguish between these three conditions. This is due in part to the complex overlap between them. There are many definitions for each condition, with nutritional depletion playing a part in each, therefore making it difficult to separate them out [1,2,3,4]. These conditions are also often not noticed in their earlier phases but do become apparent following a critical event or development of disability[5].

More than 70 nutritional screening tools for use in hospitals have been developed to facilitate easy screening or assessment of a patient’s nutritional status or to predict poor clinical outcome related to UWL. Despite increasing research, there appears to be a lack of a practical and implementable clinical screening tool to support diagnosis. Some tools have been endorsed by international nutrition societies: for example, the European Society for Clinical Nutrition and Metabolism advises the use of MUST [6,7], NRS-2002 [8] in the general community and hospital settings, and the MNA (-SF) for the elderly [9,10]. Some tools claim to have been developed to screen in specific target groups however, there are currently no disease specific recommendations. There is no international consensus on a single ‘best tool’ to identify all three syndromes across populations. The use of different tools in different studies hinders their comparison and meta-analyses, and makes drawing any conclusions difficult.

Current diagnostic methods for sarcopenia and cachexia include the assessment of body anthropometry using either body mass index (BMI) or estimated weight loss, or by direct assessment of muscle and fat mass using dual-energy X-Ray absorptiometry (DEXA), bioelectrical impedance analysis (BIA), CT or MRI scanning. Whilst the latter two radiographic modalities are accurate, they are impractical, expensive and some expose the patient to radiation. This diagnostic approach to detect the presence of sarcopenia is time consuming, expensive and requires highly specialised equipment [11]. Therefore, a screening strategy that is implementable in a larger population that allows for early detection is important. This approach would highlight the potential for further assessment with early biomarkers, thus allowing prophylactic intervention in malnutrition and driving further research in sarcopenia and cachexia.

We aimed to generate an overview of validated screening tools for the general adult population to enable clinicians to distinguish between the three syndromes. The specific strengths and limitations of each tool were assessed, as was the appropriateness of the validation population. Through psychometric evaluation and assessment of the tools against the agreed consensus definitions, we also investigated if any one single tool could be used for the simultaneous assessment of all three.

 

 

 

 

 

 

Methods

Methods for conducting systematic reviews of the effectiveness of interventions have been well described. In accordance with PRISMA guidelines [12] we applied the principles to systematically reviewing validated screening tools used in the assessment of cachexia, sarcopenia and malnutrition.

Literature review

A systematic search was performed on 26^th September 2017 in Ovid MEDLINE (1946-2017), EMBASE (1974-2017), CINAHL (Cumulative index to Nursing and Allied Health Literature) and Web of Science. Relevant articles were identified by title and abstract. Reference lists of review articles were also hand searched. Double data extraction was performed by two reviewers.

The basic search strategy was “Sarcopenia” OR “Cachexia” OR “Malnutrition” AND “screening” AND “validation study” using MeSH terms and keywords appropriate to each database. No language restriction was imposed. The search was designed to be broad to ensure all validated tools were identified. A full copy of the search used for MEDLINE can be found in appendix I. There were no disease specific limits.

Inclusion and exclusion criteria

Studies were included if they had developed a screening tool which had been validated for the screening of either cachexia, sarcopenia or malnutrition in adults. (Table I) Disease specific tools were included. Papers were excluded if the tools had not been validated or if they assessed malnutrition in children or obesity in adults. Studies which described modified versions of pre-existing tools were also excluded as this was out with the scope of this review. It was intended that studies that included less than 25 patients should be excluded as they were unlikely to yield robust, generalisable psychometric results, however no studies with numbers smaller than this were found.

Assessment of Validity

Studies had to have evaluation of at least two of the following psychometric characteristics:

• Content validity
• Construct validity e.g. including convergent validity, discriminant validity
• Test-retest reliability
• Internal consistency
• Responsiveness
• Factor analysis
• Criterion validity

Primary criteria used to evaluate the tools were construct validity and responsiveness.

Criterion and construct validity, reference method

Studying the validity of a tool usually compares to a gold standard. Although many research groups are now using cross sectional imaging to investigate UWL there is currently the absence of a perfect gold standard. Studies used different reference methods to validate their tools e.g. DEXA and assessment by a health professional.

The term criterion validity was used for these comparisons. The following reference methods were therefore considered to be less valid:

• Other screening tools as they require further assessment by a professional
• Blood tests such as albumin as they can be influenced by other factors such as acute disease and are therefore not a true reflection of long term nutritional state [13]

For these comparisons, we use the term construct validity. Still, many studies have used these less valid methods as a reference. Since an ideal gold standard is missing, and (research) groups may differ in their opinion on the optimal reference method, we included all validated studies.

Predictive validity

Predictive validity was assessed as the ability of the tool to predict the probability of a better or worse clinical outcome due to nutritional risk.

Table 1 Inclusion criteria

Types of participants  Adult hospital patients (>18 years) undergoing routine screening for cachexia, sarcopenia, or malnutrition	Includes patients with advanced cancer, end stage cardiac, renal and liver disease
Types of tools  Validated, quantitative measurements of cachexia, sarcopenia or malnutrition	Tools developed for clinical or research purposes. Completed by health care professionals
Psychometric evaluation  Content Validity Construct validity, including convergent validity, discriminant validity Test-retest reliability Internal consistency Responsiveness Factor analysis Criterion validity	Demonstration of at least 2 criteria:  Breadth of scope of tool; to what extent does it appear to capture the relevant aspects of unintentional weight loss; are there gaps? How well the tool relates to other measures of the same construct; lack of correlation with dissimilar or unrelated constructs or variables How consistent an individual’s scores are over a defined time-period presuming weight stays constant How closely related are the different items in the tool? Ability to detect clinically meaningful change for individuals For a tool comprising several items, a way of grouping them into factors which may tap into a particular construct A shortened version of a scale, concurrent validity with the longer version which has been validated

Diagnostic criteria

Tools were also assessed for their ability to identify the risk of cachexia, sarcopenia or malnutrition by comparison of their components against the components of each set of diagnostic criteria.

Table II Summary of proposed diagnostic criteria for identification of cachexia, sarcopenia and malnutrition

Syndrome	Diagnostic criteria
Cachexia	Weight loss greater than 5%, or weight loss greater than 2% in individuals already showing depletion according to current bodyweight and height (body-mass index [BMI] <20 kg/m2) or skeletal muscle mass (sarcopenia) [3]
Sarcopenia	Loss of function – 6-minute walk < 400m OR gait speed <1.0m/s  Muscle mass – low appendicular lean mass/height [2](2 standard deviations below the mean diagnostic on DXA) [1,2]
Malnutrition	Protein/energy deficiency – risk indicated by low BMI <18.5 kg/m2 OR weight loss >10% (indefinite time)/5% over last 3 months AND BMI <20 (if <70 years)/ <22 (if>70 years) or FFMI < 15 and 17 kg/m2 in men and women respectively [4]

Secondary criteria

Secondary criteria included face validity, development and content validity, factor analysis, test-retest reliability, internal consistency, respondent and administrative burden (the time and effort required to complete the tool). These are also summarised in table I. Data were extracted concerning the study participants, the tool used and psychometric evaluations (Inclusion criteria Table I).

 

Results

Figure I PRISMA flow diagram [12]

Table III Description of tools to measure unintentional weight loss

	Author	Tool	Description	Validation population	Validation reference	Strengths	Limitations
Sarcopenia	Woo et al (14)  Miller et al (15)	SARC-F  SPSM	A questionnaire regarding ability to carry a heavy load, walking, rising from a chair, climbing stairs and falls frequency  Portable measure that combines estimates of muscle quantity and function into a single scale	Community dwelling Chinese (n=4000)  Community dwelling African Americans (n=998)	3 consensus definitions of sarcopenia  DEXA	Not dependent on cut off values  Portable	No assessment of muscle mass, not validated in hospital populations  Time consuming, equipment dependent, muscle mass not measured
Cachexia	Argiles et al (16)	CASCO	Score to classify cachectic patients into three different groups. Includes five components: body weight loss & composition, inflammation/metabolic disturbances/immunosuppression, physical performance, anorexia and quality of life	Cancer patients (n=186)	Assessment by oncologist	Encompasses all diagnostic criteria	Involves many questions and measurements, does not include questions on disease state
Malnutrition	Weekes et al (17)  Mimiram et al (18) Laporte et al (19) Ignacio et al (20) Guerra et al (21) Abd-El-Gawad et al (22) Tammam et al (23) Ferguson et al (24) Isenring et al (25) Neelemaat et al (26) Nursal et al (27) Young et al (28) Wu et al (29) Ji-Yeon et al (30) Boleo-Tome et al (31) Leistra et al (32) Sharma et al (33) Neelemaat et al (26) Kyle et al (34) Young et al (28) Almedia et al (35) Velasco et al (36) Prasad et al (37) Faramarzi et al (38) Neelemaat et al (26) Kyle et al (34) Young et al (28) Almedia et al (35) Bauer et al (39) Velasco et al (36) Soederhamn et al (40) Duerksen et al (41) Cooper et al (42) Moriana et al (43) Kruizenga et al (44) Leistra et al (32) Harada et al (45) Neelemaat et al (26) Young et al (28) Susetyowati et al (46) Wong et al (47) Xia et al (48) Lim et al (49)	BAPEN  BNST CNST CONUT EDC GNRI INSYST MST MSTC MUST NRI NRS-2002 NUFFE SGA SNAQ SNST Spinal NST R-NST 3-MinNS	Tool based on four nutritional parameters (weight, height, recent unintentional weight loss and appetite)  Score based on UWL, unintentional eating loss and being unable to eat for >5 days Tool containing two items: Weight loss and decreased food intake Evaluates nutrition using albumin, cholesterol and lymphocyte count. Automated system Screening tool based on ESPEN criteria for diagnosis malnutrition Modified nutritional risk index for geriatric patients (based on albumin, current and previous weight) Two-tiered tool – first is a simple pre-screen aiming to establish if malnourished, second provides a more detailed evaluation Two questions regarding appetite and unintentional weight loss Tool based on intake change, weight loss, ECOG performance status and BMI Five step tool including BMI, unplanned weight loss and presence of acute disease Derived from serum albumin concentration and ratio of usual to present weight Tool containing nutritional components of the MUST along with disease severity Three-point ordinal scale with 15 items assessing weight loss, dietary history, appetite and general activity Assessment of nutritional status based on history and examination 26 questions related to eating and drinking difficulties, defecation, condition and pain Six questions including weight loss, appetite and health status Tool which assesses eight criteria including appetite, weight loss and level of spinal cord injury Nine questions assessing malnutrition risk/symptoms combined with albumin, CRP and urea Questionnaire based on diagnostic criteria for malnutrition and muscle wastage	Acute medical and elderly care wards (n=100)  Medical and surgical (n=446) Medical and surgical (n=150) Medical and surgical inpatients (n=53) Medical and surgical inpatients (n=632) Acute geriatrics ward (n=131) Medical, surgical and oncological inpatients (n=61) Medical and surgical inpatients (n=408) Oncology outpatients (n=51) Acute hospitalised (n=193) Medical and surgical inpatients (n=2211) Elderly medical inpatients (n=134) Elderly inpatients (n=157) Oncology inpatients (n=1057) Oncology inpatients (n=450) Medical and surgical outpatients (n=2236) Acute medical inpatients (n=132) Elderly inpatients (n=198) Medical and surgical (n=995) Medical inpatients (n=134) Surgical inpatients (n=300) Medical and surgical (n=400) Peritoneal dialysis patients (n=283) Colorectal cancer (n=52) Elderly inpatients (n=198) Medical and surgical (n=995) Elderly medical patients (n=134) Surgical inpatients (n=300) Acute geriatrics ward (n=121) Medical and surgical (n=400) Elderly care rehab ward (n=114) Acute elderly care and elderly rehab (n=95) End stage renal disease (n=76) Medical and surgical inpatients (n=197) Medical, surgical and oncological inpatients (n=291) Medical and surgical outpatients (n=2236) Oncology outpatients undergoing chemotherapy (n=300) Medical and surgical inpatients (n=2211) Elderly medical inpatients (n=134) Medical and surgical inpatients (n=495) Spinal cord injury patients (n=150) Renal inpatients (n=122) Medical and surgical inpatients (n=818)	Dietician  Dietician SGA SGA PG-SGA MNA MUST and MNA SGA PG-SGA Malnutrition definition PG-SGA PG-SGA Malnutrition definition SGA Definition of malnutrition SGA MNA Geriatric and internal medicine resident, Total body Nitrogen, Anthropometric and biochemical data Malnutrition criteria, CONUT SGA Dietetic assessment SGA SGA	Quick and easy  Easily completed by nursing staff Very brief, can be completed by non-trained rater Simple, automated Includes FFM assessment Good prognosticator, does not require capacity Doesn’t require height and BMI, quick and easy Very quick, does not require calculations Cancer specific Quick, easy Assesses dialysis patients at risk Includes disease severity therefore applicable in ITU Simple as lacks anthropometric measurements Current gold standard Corresponds to ESPEN criteria Can be done by non-trained staff Disease specific Renal specific Quick and easy	Percentage weight loss not quantified  Low importance given to amount of weight loss Assessed on admission only. Validity of re-screening unknown Markers vary depending on disease state, only done on patients who have bloods taken Very low sensitivity Diseases associated with high mortality or hypoalbuminaemia excluded Ease of completing dependent on patient’s cognitive state Non-specific Designed to be performed by dieticians, not nurses Does not pick up patients with normal BMI who are malnourished, UWL reported by patients is subjective Relies on previous weight – limited use with changes in fluid status Ease of completing dependent on patient’s cognitive state Many confounding factors in questionnaire Reproducibility less than in non-elderly, unable to predict severe malnutrition in ESRD, requires experienced operator to carry out High NPV, no outcome data No anthropometric assessment, all subjective Requires specialised scales to measure paralysed patients Patients picked up for conditions other than malnutrition e.g. hyperkalaemia Dependent on cognitive state

SPSM (Short Portable Sarcopenia Measure), CASCO (Cachexia Score), BAPEN (British Association for Parenteral and Enteral Nutrition, BNST (British Nutrition Screening Tool), CNST (Canadian Nutrition Screening Tool), CONUT (Controlling Nutritional Status), EDC (ESPEN Diagnostic Criteria for Malnutrition), GNRI (Geriatric Nutrition Risk Index), INSYST (Imperial Nutritional Screening System), MST (Malnutrition Screening Tool), MSTC (Malnutrition Screening Tool for Cancer), NRI (Nutritional Risk Index), NRS-2002 (Nutritional Risk Screening), NUFFE (Nutritional Form For the Elderly), SGA (Subjective Global Assessment), SNAQ (Short Nutritional Assessment Questionnaire), Spinal NST (Spinal Nutritional Screening Tool) R-NST (Renal Nutritional Screening Tool), 3-MinNS (3 Minute Nutrition Screening)

 

 

 

 

 

 

 

 

 

 

 

Table IV Psychometric evaluation of tools to measure unintentional weight loss

Scale

Environment

Context (OP/IP)

Face validity

Content validity

Factor analysis

Construct validity

Discriminant validity

Predictive validity

Test-retest

Internal consistency

Responsiveness

Acceptability

Time to complete

Sarcopenia

SARC-F  SPSM

Community dwelling  Community dwelling

Outpatients  Outpatients

  –

–  –

–  

  X

–  

  

–  

  

–  

  –

–  

Cachexia

CASCO

Oncology

Outpatients











–

–





–

–

Malnutrition

BAPEN  BNST CNST CONUT EDC GNRI INSYST MST MSTC MUST NRI NRS-2002 NUFFE R-NST SGA SNAQ SNST Spinal NST 3-MinNS

Acute medical and elderly care  Spinal cord injuries Medical and surgical Medical and surgical Medical and surgical Acute geriatrics Medical, surgical and oncology Medical, surgical and oncology Oncology Medical, surgical and oncology Peritoneal dialysis and colorectal cancer Elderly, medical and surgical Elderly care rehab Renal Elderly, renal, medical and surgical Medical, surgical and oncology Medical and surgical Spinal cord injuries Medical and surgical

Inpatients  Inpatients Inpatients Inpatients Inpatients Outpatients Inpatients Inpatients Outpatients Inpatients Inpatients Outpatients Inpatients Inpatients Inpatients Outpatients Inpatients Inpatients Inpatients Outpatients Inpatients Inpatients Inpatients

           –        

–  – – – – –   X  –      – – 

–   – X –  –   X  –    –   –

  –   – –     –      – – 

–  – – – – – – – – – – – – – –   – 

–  –  –   –  –     –    – 

    – – –   – – –   –     

–  – – –  – –  – – – –  –  –  – –

  – –  – –  – –  –  – – –   – 

  – –  –    X    – X –    

  – – – – –  –   –  –  –    

 = tool assessed for and found to be valid   X = tool assessed for and found not to be valid – = tool not assessed for/not enough information provided

 

 

 

 

Table V Domains assessed by tools to measure relevant parameters required to identify risks of malnutrition, sarcopenia and cachexia

Patient reported weight loss

BMI & FFM measurements

Nutritional intake

Assessment of muscle mass and function

Disease state

Measures of metabolic derangement

Quality of life

Disease

Screening tool

Weight loss quantified within specified time frame

Weight loss quantified without timeframe

Weight loss unquantified with time frame

Weight loss unquantified, without time frame

UWL specified

Muscle mass

BMI

FFMI

Loss of appetite

Poor dietary intake/ intake decline

Supplemental feeding in use?

Symptoms that would prevent eating e.g. vomiting, ulcers

Physical performance

Muscle strength

Presence of illness

Fatigue

Increased inflammatory markers

Anaemia

Low serum albumin

Other blood  Tests e.g. glucose/urea

QOL

Sarcopenia

SARC-F  SPSM

X  X

X  X

X  X

X  X

X  X

X  X

X  

X  

X  X

X  X

X  X

X  X

  

  

X  X

X  X

X  X

X  X

X  X

X  X

X  X

Cachexia

CASCO

X



X

X



X

X







X

X



X

X













Malnutrition

BAPEN  BNST CNST CONUT EDC GNRI INSYST MST MSTC MUST NRI NRS-2002 NUFFE R-NST SGA SNAQ SNST SpinalNST 3-MinNS

X  X X X X X  X X  X X X    X X 

X  X X X X X X  X X X  X X X X X X X

X  X  X   X X X X  X X X X X  X X

   X X X X X X  X X X  X X X X  X

    X X       x  X     

X  X X X X X X X X X X X X X X X X X X

   X    X X   X   X   X  

X  X X X  X X X X X X X X X  X X X X

X  X X X X X X  X X X X X    X  X

    X  X  X  X X        

X  X X X X X X X X X X X X X  X X X

X  X X X X X X X X X X X X  X X X X

X  X X X X X X X  X X X X X  X X X X

X  X X X X X X X X X X X X X X X X X X

X  X X  X X  X X  X  X  X X X  

X  X X X X X X X  X X X X X X X X X X

X  X X X X X X X X X X X X X X X X X X

X  X X X X X X X X X X X X X X X X X X

X  X X  X  X X X X  X X X X X X X 

X  X X  X X X X X X X X X X X X X X X

X  X X X X X X X X X X X X X X X X X X

 

 

Table VI Sensitivity, specificity, predictive values, and reproducibility of the studies included in the systematic review

Author	Screening tool	Sensitivity	Specificity	PPV	NPV	Agreement
Woo et al  Miller et al	SARC-F  SPSM	3.8-9.9  –	94.2-99.1  –	8.4-54.8  –	78.4-94.9  –	0.78-0.90  –
Argiles et al	CASCO	–	–	–	–	–
Weekes et al  Mirmiram et al Laporte et al Ignacio et al Guerra et al Abd-El-Gawad et al Tammam et al Ji-Yeon et al Ferguson et al Isenring et al Neelemaat et al Nursal et al Young et al Wu et al Ji-Yeon et al Boleotome et al               Leistra et al                    Sharma et al Neelemaat et al Kyle et al Young et al Almedia et al Velasco et al Prasad et al Faramarzi et al Neelemaat et al                             Kyle et al                              Young et al                       Almeida et al                       Bauer et al                       Velasco et al Soederhamn et al Xia et al Duerksen et al Cooper et al Moriana et al Kruizenga et al Leistra et al Harada et al Neelemaat et al Young et al Susetyowati et al Wong et al Li et al	BAPEN  BNST CNST CONUT EDC GNRI INSYST MST MSTC MUST NRI NRS-2002 NUFFE R-NST SGA SNAQ SNST Spinal NST 3-MinNS	–  86.7 72.6 92.3 17.1 83.1 95-100 93 100 67 49 73 73 39 94 80 75 69.7 96 61 87 85 72 92.9 66 92 62 90 80 70 74 71 97.3 – 59-68 – 79 43 43 75 79 97 85.7 86	–  61.7 85.1 85 98.3 51.2 65-83 93 92 86 86 55 70 93 84.2 89 94 75.8 80 79 86 93 90 32.39 60 85 93 83 89 85 87 86 74.4 – 61-65 – 83 99 99 84 90 80 76.1 83	–  79.1 81.2 – 89.1 78.95 – 98.4 80 – – – – – 67.8 100 43 75.4 – – – – – 80.41 64 – – – – – – – 88.0 – 41-42 – 70 78 – – – 78 62 67	–  73.1 77.0 – 58.9 58.33 – 72.7 100 – – – – – 97.6 100 98 70.1 – – – – – 60.53 62 – – – – – – – 93.6 – 70-83 – 89 96 – – – 92 92 94	0.77  0.74 0.88 0.488 0.803 0.713 0.73 0.7 0.83 0.53 0.33 0.28 – 0.21 0.70 – – 0.49 – – – – – 0.63 0.267 – – – – – – – 0.95 – 0.6 – – – – – – – 0.57 –

Discussion

 

Whilst recent systematic reviews have described the results of studies examining malnutrition screening tools, to our knowledge this is the first review to examine tools that have been validated against another to assess cachexia, sarcopenia and malnutrition. There has only been one prior review on tools for cachexia, sarcopenia and malnutrition [50]. This review did not include psychometric evaluation, did not comment on the validity of the tools, nor compare them to the agreed consensus definitions. Existing systematic reviews of malnutrition screening tools have been limited to describing tools that are non-disease specific and ‘quick and easy’ or have been narrative in nature.

Thirty studies describing 22 tools were identified and judged for validity against a reference method. In the absence of a generally recognised gold standard for screening, assessment by a professional, DEXA, CT, MRI, anthropometry or the screening tools SGA and MNA were considered ‘valid’ reference methods by our research group [13,41-43]. Although cross sectional imaging is now used routinely for body composition analysis, no tools identified in the initial search were validated against CT or MRI. The heterogeneity in populations, age groups, tools and reference methods was large, and therefore pooling of results was impossible. Most tools had only been tested in one population making the drawing of any definitive conclusions difficult. There were too few disease-specific tools to conclude which would be superior for different disease processes.

For the generalised adult population, all tools showed inconsistent results regarding their validity. The SGA which is often considered to be the industry standard [51] and against which many tools are validated has not itself been well validated. It performed well against the diagnostic criteria but sensitivities and specificities were either not recorded or poor. Arguably the most well-known tools ‘MUST’ and ‘NRS-2002’ showed a variation in results from poor to good [26,28,31-36,39], and consistency between groups in which the tools were studied was poor. The less well-known NUFFE showed good validity, but it has been described in only a small volume of literature and is not implemented widely [40]. The “quick and easy” screening tools, including SNAQ and MST performed reasonably (sensitivities <80%) in most studies in which they were used [24-29,32,44,45]. Of note because these tools are quick, they require a further detailed assessment by a qualified health professional if screening is positive. They also miss approximately 20% of at risk patients at initial screen and therefore may be more useful in screening high risk patients.

The tool which performed the best for malnutrition was the 3MinNS [49]. It showed high sensitivity and specificity (>80%) and accurately encompassed the correct diagnostic criteria (percentage weight loss over specified time and measurement of BMI) for malnutrition. It was validated in acute medical and surgical patients and proved quick and easy to complete. It has only been validated in one paper and therefore it cannot be assumed that it would perform as well in different patient populations. Both tools which assessed sarcopenia (SPSM, SARC-F) scored well against the agreed definition [14,15]. However, the SPSM required transport of equipment and the SARC-F had a very low sensitivity [13,14]. The CASCO was the only validated screening tool for cachexia [16]. It performed well against diagnostic criteria, but sensitivities and specificities were not recorded. It has also only been validated in the cancer setting; more work would be needed to validate the tool in other cachectic populations or the general adult population.

Most tools were validated in the adult hospital inpatient setting. Tools for sarcopenia have only been validated in the healthy, community dwelling [14,15]. Length of hospital stay is diminishing worldwide and outpatient nutritional screening is advocated to pick up patients at risk. In this review, we identified eight studies in which outpatients were included. More studies focussing on the construct and predictive validity of tools for outpatient screening are warranted, especially since care is shifting to this setting.

The tool which appeared to have the broadest coverage was the CASCO [16]. It is the only tool which screens for cachexia, but also picks up many of the variables required for a diagnosis of malnutrition. However, assessment of muscle mass or function (required for sarcopenia) is not included. One previous review showed that 20 screening tools appeared relevant for starvation, but none contained all the currently accepted components needed to screen for sarcopenia and cachexia risk [50]. Our study supports this.

A screening tool therefore needs to be developed that encompasses the criteria to pick up all three possible syndromes. This concept is supported by the notion that, in the human being, there may be no “pure” phenotype of cachexia, as it is usually associated with reduced food intake (potential for malnutrition) and increasing age (increasing sarcopenia) [52]. There is also currently a lack of agreement as to the diagnostic criteria of each syndrome and the relative importance of body composition analysis and the nature of depleted tissue within each definition We hypothesised that the overlap between syndromes could be illustrated as in figure II along with the identified best performing tools for each aspect.

Figure II. Diagram to show overlap between cachexia, sarcopenia and malnutrition

There are clearly many existing validated screening tools (at least for malnutrition). It is unlikely that any further novel tools will be devised without breakthroughs in biomarker development. We therefore suggest that the ideal composite tool should be an adaptation of existing tools, and incorporate a stepwise assessment of nutritional status; oral intake; disease status; patient age; muscle mass/function; and metabolic derangement. The presence of underlying disease is a key question in order to stratify the syndromes. Suggested components for use in developing existing screening tools or creating a new tool are depicted below in figure III.

Figure III Suggested stepwise components to be included in a screening tool for cachexia, sarcopenia and malnutrition

 

By screening for all three syndromes, it will allow for a more targeted intervention. Screening for cachexia, sarcopenia or malnutrition is not warranted unless it is accompanied by an intervening care plan. It would be expected that an adequate intervention would prevent any further decline in health status and therefore lead to a positive effect on disease outcome. Most studies did not comment on intervention, which depending on the balance of the three syndromes may need to include varying attention to nutrition, exercise and measures to combat inflammation.

One of the strengths of this review is that it provides a complete overview of tools that have been validated for cachexia, sarcopenia and malnutrition. We did not describe reliability, repeatability or other clinical outcome measures in any great detail. The review used the consensus definitions of each syndrome, we are aware however that many other definitions exist. However, there were a number of study limitations. There was a risk of bias when assessing each tool for their predictive validity. Clinical outcome is known to be affected by many factors other than nutritional status, especially disease severity or stage. Studies may have been biased if they did not adjust for these variables. In addition, nutritional intervention is likely to improve outcomes for malnutrition but potentially not for age-related sarcopenia or established cachexia. Only a few studies discussed whether they did this. There is no agreed ‘gold standard’ tool and therefore we chose cross sectional imaging and the SGA and MNA based on the results of previous studies [13]. Tools that were compared to potentially less valid standards were also included to allow a wider analysis. Full nutrition assessments were different in each study ranging from anthropometric to biochemical measures and full assessment by a medical professional. Conclusions from this study were based upon the original papers in which there may have been varying definitions with regards to the subject group, syndrome or assessment undertaken. Not all studies that assessed predictive validity adjusted for other prognostic factors which may influence outcome and therefore could be considered less valid. Another potential limitation is that we excluded modified versions of pre-existing tools. They were excluded as reliability and validity data would only relate to the modified tool and it was therefore difficult to assess improvements from the original. It is possible that these tools were being improved or evaluated more thoroughly.

One of the strengths of this review is that it provides a complete overview of tools which have been validated for cachexia, sarcopenia and malnutrition. We did not describe reliability, repeatability or other clinical outcome measures in any great detail. The review used the consensus definitions of each syndrome, we are aware however that many other definitions exist.

Conclusion

 

There is no one validated screening tool which can be implemented for the assessment of cachexia, sarcopenia and malnutrition. The adaptation of existing screening tools incorporating all relevant criteria described in this review would be optimal for diagnosis and to direct the content of complex interventions.

Ethical statement

 

This manuscript does not contain clinical studies or patient data. The authors certify that they comply with the ethical guidelines for authorship and publishing of the Journal of Cachexia, Sarcopenia and Muscle [53].

Conflict of interest

The authors declare no conflicts of interest

Acknowledgements

 

Yorkshire Cancer Research endowment programme have funded the open access publishing of this article

References

1. RN Baumgartner, KM Koehler, D Gallagher, L Romero, SB Heymsfield, RR Ross. Epidemiology of sarcopenia among the elderly in New Mexico. Am J Epidemiol. 1998;147:755–763
2. Morley JE, Anker SD, Haehling von S. Prevalence, incidence, and clinical impact of sarcopenia: facts, numbers, and epidemiology-update 2014. J Cachexia Sarcopenia Muscle 2014; 5: 253–259
3. Fearon K, Strasser F, Anker SD, Bosaeus I, Bruera E, Fainsinger RL, Jatoi A, Laprinzi C, MacDonald N, Mantovani G, Davis M, Muscaritoli M, Ottery F, Radbruch L, Ravasco P, Walsh D, Wilcock A, Kaasa S, Baracos VE. Definition and classification of cancer cachexia: an international consensus. Lancet Oncol 2011;12(5):489-95
4. Cederholm T, Bosaeus I, Barazzoni R, Bauer J, Van Gossum A, Klek S, Muscaritoli M, Nyulasi I, Ockenga J, Schneider SM, de van der Schueren MAE, Singer P. Diagnostic criteria for malnutrition – An ESPEN Consensus Statement. Clinical Nutrition 2015; 34:335-340
5. Visvanathan R., Chapman I. Preventing sarcopenia in older people. Maturitas. 2010;66(4):383–388.
6. Kondrup J, Allison SP, Elia M, Vellas B, Plauth M. ESPEN guidelines for nutritional screening 2002. Clinical Nutrition 2003; 22(4): 415-421
7. Elia M. The ‘MUST’ report. In: BAPEN, editor. Nutritional screening of adults: a multidisciplinary responsibility 2003.
8. Kondrup J, Rasmussen HH, Hamberg O, Stanga Z. Nutritional risk screening (NRS 2002): a new method based on an analysis of controlled clinical trials. Clin Nutr 2003;22
9. Guigoz Y, Vellas B, Garry PJ. Mini Nutritional Assessment: a practical assessment tool for grading the nutritional state of elderly patients. Facts and Research in Gerontology. Nutrition 1994;Supplement
10. Rubenstein LZ, Harker JO, Salva A, Guigoz Y, Vellas B. Screening for undernutrition in geriatric practice: developing the short-form mini- nutritional assessment (MNA-SF). J Gerontol A Biol Sci Med Sci 2001;56
11. Rubbieri G, Mossello E, Di Bari M. Techniques for the diagnosis of sarcopenia. Clin Cases Miner Bone Metab. 2014 Sep-Dec; 11(3): 181-184
12. PLoS Medicine (OPEN ACCESS) Moher D, Liberati A, Tetzlaff J, Altman DG, The PRISMA Group (2009). Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Med 6(7): e1000097. doi:10.1371/journal.pmed1000097
13. Van Bokhorst-de van der Schueren M, Guaitoli PR, Jansma EP, de Vet HCW. Nutrition screening tools: Does one size fit all? A systematic review of screening tools for the hospital setting
14. Woo J, Leung J, Morley J. Validating the SARC-F: a suitable community screening tool for sarcopenia? Journal of the American Medical Directors Association 2014; 15(9):630-634
15. Miller D, Malmstrom T, Andresen E, Miller J, Herning MM, Schootman M, Wolinsky FD. Development and validation of a short portable sarcopenia measure in the African American health project. The journals of gerontology 2009; 64(3):388-394
16. Argiles JM, Betancourt A, Guardia-Olmos J, Pero-Cebollero M, Lopez-Soriano FJ,Madeddu C, Serpe R, Busquets S. Validation of the Cachexia SCOre (CASCO). Staging cancer patients: The use of miniCASCO as a simplified tool. Frontiers in physiology 2017; 8:92
17. Weekes CE, Elia M, Emery PW. The development, validation and reliability of a nutrition screening tool based on the recommendations of the British Association for Parenteral and Enteral Nutrition. Clinical nutrition 2004; 23(5):1104-1112
18. Mirmiran P, Hosseinpour-Niazi S, Mehrabani HH, Kavian F, Aziz F. Validity and reliability of a nutrition screening tool in hospitalised patients. Nutrition 2011; 27(6): 647-652
19. Laporte M. Validity and reliability of the new Canadian Nutrition Screening Tool in the ‘real-world’ hospital setting. European Journal of Clinical Nutrition 2015; 69(7):865
20. Ignacio De Ulibarri J, Gonzalez-Madrono A, De Villar NGP, Gonzalez P, Gonzalez B, Mancha A, Rodriguez F. CONUT: a tool for controlling nutritional status. First validation in a hospital population. Nutricion hospitalaria 2005; 20(1):38-45
21. Guerra RS, Fonseca I, Sousa AS, Jesus A, Pichel F. ESPEN diagnostic criteria for malnutrition – A validation study in hospitalised patients. Clinical nutrition 2017; 36(5):1326-1332
22. Abd-El-Gawad W, Wafaa M, Abou-Hashem R, El Maraghy MO, Amin GE. The validity of Geriatric Nutrition Risk Index: simple tool for prediction of nutritional-related complication of hospitalised elderly patients. Comparison with Mini Nutritional Assessment. Clinical nutrition 2014; 33(6): 1108-1116
23. Tammam J D, Gardner L, Hickson M. Validity, reliability and acceptability of the Imperial Nutritional Screening System (INSYST): A tool that does not require body mass index. Journal of Human Nutrition and Dietetics 2009; 22(6): 536-544
24. Ferguson, M, Capra, S, Bauer, J, Banks, M. Development of a valid and reliable malnutrition screening tool for adult acute hospital patients. Nutrition 1999; 15(6): 458-464
25. Isenring E, Cross G, Daniels L, Kellett E. Validity of the malnutrition screening tool as an effective predictor of nutritional risk in oncology patients receiving chemotherapy. Supportive care in cancer 2006; 14(11): 1152-1156
26. Neelemaat F, Meijers J, Kruizenga H, van Ballegooijen H, van Bokhortst-de van der Schueren M. Comparison of five malnutrition screening tools in one hospital population. Journal of Clinical Nursing 2011; 20(15-16): 2144-52
27. Nursal TZ, Noyan T, Atalay BG, Koz N, Karakayali H. Simple two part screening tool for malnutrition. Nutrition 2005; 21(6): 659-65
28. Young A, Kidston S, Banks M, Mudge A, Isenring A. Malnutrition screening tools: comparison against two validated nutrition assessment methods in older medical inpatients. Nutrition 2013; 29: 101-106
29. Wu ML, Courtney MD, Shortridge-Baggett LM, Finalyson K, Isenring EA. Validity of the malnutrition screening tool for older adults at risk of hospital readmission. J Gerontol Nurs 2012; 38(6): 38-45
30. Ji-Yeon K, Gyung-Ah W, Yeong-Ah C, Soo-Min So-Young K, Kum-Hee S, Sang-Jae P, Byung-Ho N, Hyojee J. Development and validation of a nutrition screening tool for hospitalised cancer patients. Clinical nutrition 2011; 30(6): 724-729
31. Boleo-Tome C, Monteiro-Grillo I, Camilo M, Ravasco P. Validation of the Malnutrition Universal Screening Tool (MUST) in cancer. British Journal of Nutrition 2012; 108(2): 343-348
32. Leistra, E, Langius, J A E, Evers, A M, van Bokhorst-de van der Schueren, M A E, Visser, M, de Vet, H C W, Kruizenga, H M. Validity of nutritional screening with MUST and SNAQ in hospital outpatients. European Journal of Clinical Nutrition 2013: 67(7);738-742
33. Sharma Y, Thompson C, Kaambwa B, Shahi R, Miller M. Validity of the Malnutrition Universal Screening Tool (MUST) in Australia hospitalised acutely unwell elderly patients. Asia Pacific Journal of clinical nutrition 2017; 26(6): 994-1000
34. Kyle UG, Kossovsky MP, Karsegard VL, Pichard C. Comparison of tools for nutritional assessment and screening at hospital admission: a population study. Clin Nutr 2006; 25(3): 409-17
35. Almeida AL, Correia M, Camilo M, Ravasco P. Nutritional risk screening in surgery: valid, feasible, easy! Clin Nutr 2012; 31(2):206-11
36. Velasco C, Garcia E, Rodriguez V, Frias L, Garriga R, Alvarez J, Garcia-Peris P, Leon M. Comparison of four nutritional screening tools to detect nutritional risk in hospitalized patients: a multicentre study. Eur J Clin Nutr. 2011 Feb;65(2):269-74
37. Prasad N, Gupta A, Sinha A, Saxena A, Sharma A. Study on the validity of nutrition risk index (NRI) as a malnutrition screening tool in peritoneal dialysis (PD) patients. Peritoneal Dialysis International 2012; 32:S48
38. Faramarzi E, Mahdavi R, Mohammad-Zadeh M. Validation of nutritional risk index method against patient-generated subjective global assessment in screening malnutrition in colorectal cancer patients. Chinese journal of cancer research 2013; 25(5):544-548
39. Bauer JM, Vogl T, Wicklein S, Trogner J, Muhlberg W, Sieber CC. Comparison of the Mini Nutritional Assessment, Subjective Global Assessment and Nutritional Risk Screening (NRS 2002) for nutritional screening and assessment in geriatric hospital patients. Z Gerontol Geriatr 2005;38(5):322-7
40. Soderhamn U, Soderhamn O. Reliability and validity of the nutritional form for the elderly (NUFFE). Journal of advanced nursing 2002; 37(1): 28-34
41. Duerksen D R, Yeo TA, Siemens JL, O’Connor MP. The validity and reproducibility of clinical assessment of nutritional status in the elderly. Nutrition 2000; 16(9): 740-744
42. Cooper BA, Barlett LH, Aslani A, Allen B, Ibels LS, Pollock C. Validity of subjective global assessment as a nutrition marker in end stage renal disease. American journal of kidney disease 2002; 40(1):126-132
43. Moriana M, Civera M, Artero A, Real JT, Caro J, Ascaso JF, Martinez-Valls JF. Validity of subjective global assessment as a screening method for malnutrition. Prevalance of malnutrition in a tertiary hospital. Endocrinologia y nutricion 2014; 61(4):184-189
44. Kruizenga, H M, Seidell, J C, de Vet, H C W, Wierdsma, N J, van Bokhorst-de van der Schueren, M A E. Development and validation of a hospital screening tool for malnutrition: the short nutritional assessment questionnaire (SNAQ). Clinical nutrition 2005; 24(1);75-82
45. Harada K, Ochi K, Taguchi T, Nakamura T, Kuwahata Kanazawa M, Yoshida N, Neriya H, Okagaki M, Nishida N, Takishita Y, Yamamoto Y, Wada S, Yokota I, Sekido K. Validity of the short nutritional assessment questionnaire for Japanese patients with cancer undergoing outpatient chemotherapy. Journal of Medical Investigation 2017; 64(1.2):117-121
46. Susetyowati, Hadi H, Hakimi HM. Development, validation and reliability of the simple nutrition screening tool (SNST) for adult hospital patient in Indonesia. Pakistan Journal of Nutrition 2014; 13(3): 157-163
47. Wong S, Derry F, Jamous A, Hirani SP, Grimble G, Forbes A. Validation of the spinal nutrition screening tool (SNST) in patients with spinal cord injury (SCI): results from a multicentre study. European Journal of Clinical Nutrition 2011; 66(3): 382-387
48. Xia Y, Healy A, Kruger R. Developing and validating a renal nutrition tool to effectively identify undernutrition risk among renal inpatients. Journal of renal nutrition 2016; 26(5): 299-307
49. Lim SL, Tong CY, Ang E, Lee EJ, Loke WC, Chen Y. Development and validation of 3 minute nutrition screening (3-MinNS) tool for acute hospital patients in Singapore. Asia Pacific Journal 2009: 18(3);395-403
50. Healy E, Yaxley A, Isenring E, Bannerman E, Miller M. Ability of existing malnutrition screening tools to identify risk of starvation, sarcopenia and cachexia: A systematic review. E-SPEN Journal 2014;9(3): e109-e122
51. Cederholm T, Jensen GL. To create a consensus on malnutrition diagnostic criteria: A report from the Global Leadership Initiative on Malnutrition (GLIM) meeting at the ESPEN congress 2016. Clin Nutr 2017; 36(1):7-10
52. Skipworth RJE, Stewart G, Dejong CHC, Preston T, Fearon KCH. Pathophysiology of cancer cachexia: Much more than host-tumour interaction? Clin Nutr 2007; 26(6):667-76
53. von Haehling S, Morley JE, Coats AJS, Anker SD. Ethical guidelines for authorship and publishing in the Journal of Cachexia, Sarcopenia and Muscle. J Cachexia Sarcopenia Muscle. 2015;4:315-6.

Appendix I: Search criteria used in MEDLINE

1. Cachexia/ or sarcopenia/ or starvation/ or malnutrition/ or protein-energy malnutrition/ or wasting syndrome/ or muscle strength/ or anorexia/ or fatigue
2. (cachexia or cachectic or sarcopen* or sarcopaen* or starvation or malnutrition or malnourish* or undernutrition or undernourish* or wast* or unintentional weight loss or muscle wastage or muscle strength or muscle mass).mp
3. 1 or 2
4. (nutrition* or malnutrition*).mp and (mass screening/ or nutrition assessment/)
5. ((malnutrition* or nutrition*)adj3(screen* or assess*)).mp
6. 4 or 5
7. questionnaires/
8. (tool* or criter* or questionnaire*).mp
9. 7 or 8
10. validation studies/
11. 9 or 10
12. 3 and 6 and 9 and 11 [50]