Underrecognition of Malnutrition in Advanced Cancer: The Role of the Dietitian and Clinical Practice Variations

Abstract

Introduction:

Malnutrition (MN) often goes unrecognized due to ineffective screening techniques. Published standards for multidisciplinary care exist but no consensus on best nutritional assessment for hospitalized patients. Malnutrition is common in cancer and adversely affects clinical outcomes. The Cleveland Clinic Nutrition Therapy Department used in-house criteria to classify MN in hospitalized patients. This study aimed to evaluate the registered dietitian (RD)’s role, the use of these criteria in the acute care palliative medicine unit (ACPMU), and investigate MN prevalence and severity among admitted patients with cancer.

Methods:

Electronic medical records were reviewed for newly admitted patients with cancer to the ACPMU with a first time RD consult and completed nutritional therapy assessment. Physician (MD) assessments were derived from admission notes. Cox regression model assessed the association of MN prevalence and severity with survival. McNemar’s test determined whether a prevalence difference existed between RD and MD.

Results:

Variations existed in criteria used to identify MN. Seventy percent had MN, with the majority (61%) classed as moderate to severe. Prevalence (hazard ratio [HR]: 1.88; P = .002) and severity (HR: 1.22; P = .006) were associated with significantly increased mortality. Evaluations by RD and MD were highly congruent, but MDs underrecorded nutritional status.

Conclusion:

Malnutrition was prevalent and clinically important, even in those on nutritional support. Variations in MN identification were common. Physicians underrecorded MN but were accurate for prevalence and severity when recorded. The data confirm the RD’s important role in MN assessment. Comparable clinical practice and better communication between physicians and dietitians should improve cancer care and optimize quality of life.

Keywords

cancer dietitian malnutrition prognosis symptoms weight loss

Introduction

The prevalence of malnutrition (MN) varies from about 10% in the community to 50% in hospital.^1,2 There is no clear consensus definition.^3,4 It often goes unrecognized⁵ due to ineffective screening⁶; only 50% who are malnourished are identified by medical and nursing staff.⁷ Nutritional screening is required for hospital accreditation in the United States.⁸ Early screening allows for quicker treatment⁷ which may prevent severe MN and improve outcomes.⁹

There are published standards for multidisciplinary care,^10,11 but no consensus on the best nutritional assessment for hospitalized patients.¹² At the time of this study, the Cleveland Clinic Nutrition Therapy Department had criteria to classify MN in hospitalized patients. An International Consensus Guideline Committee has since recommended an etiology-based approach to standardize diagnosis and documentation of MN.¹³

In 2015, nearly 600,000 people in the United States are expected to die from cancer.¹⁴ Malnutrition is common and adversely affects clinical outcomes including hospital length of stay, quality of life, treatment toxicity, and survival.^15
-17 Progressive weight loss (WL) is common.

Percentage of WL defines severity and helps classify MN status, that is, mild, moderate, or severe.¹⁸ The differences between mild, moderate, and severe MN (except as defined by % WL) are unclear.¹³ Current approaches to diagnosis of MN vary widely, specifically with regard to criteria used.¹³ Prevalence of MN is inaccurate in many cancer centers.¹⁹ Furthermore, available studies have focused on single tumor sites and/or disease stages.²⁰

At the Cleveland Clinic, all inpatients are screened by nursing staff within 24 hours of admission. Subsequent registered dietitian (RD) consultations are based upon identified risk per the nursing screen assessment with a consult request placed by any multidisciplinary team member. Within 48 hours of the consult, the RD completes a full assessment. Their role is to develop a nutritional care plan through individualized interventions with interdisciplinary collaboration. This plan is revised as indicated by the clinical situation. Reassessment (by Departmental Policy) varies from 1 to 7 days later based on clinical complexity. The Harry R. Horvitz Center for Palliative Medicine is a 23-bed acute care palliative medicine unit (ACPMU) within the main hospital,²¹ 1 of 3 inpatient cancer units. Most admissions are cancer related. This study was undertaken because the role of the RD on the ACPMU was ill-defined.²² We also aimed to establish prevalence and severity of MN in an ACPMU as determined by an RD with a standard nutritional therapy assessment (NTA).

Methods

Data Collection

Ethical approval and a written informed consent waiver were provided by the Cleveland Clinic Institutional Review Board. Electronic medical records (EMRs) were reviewed for all newly admitted patients with cancer to the ACPMU with a first time RD consult and a complete NTA. Exclusion criteria were noncancer diagnoses; in-hospital transfers to the ACPMU; attending an outpatient visit; reassessment by the ACPMU RD; secondary assessments when length of stay exceeds >7 days; and RD consults for education, food preferences, or food allergies. Physician assessments were derived from EMR admission notes. Two research interns and 1 clinical dietetic intern extracted EMR data and entered it into the Research Electronic Data Capture (REDCap, Vanderbilt University, Nashville, Tennessee).²³ Entries were then independently verified by trained research staff. Information collated included age (years), gender, race, primary cancer site, extent of disease, specified comorbidities, height (cm), weight (kg), body mass index (BMI; kg/m²), calculated resting metabolic rate (RMR; kcal/24 h by the Mifflin Sr Jeor equation²⁴), estimated minimum and maximum caloric needs (kcal/d), serum creatinine (mg/dL), albumin (g/dL), prealbumin (mg/dL), transferrin (mg/dL), and potential nutrition-related gastrointestinal (GI) symptoms. Performance status was not available in the EMR.

Cleveland Clinic Nutrition Assessment Protocol

The Cleveland Clinic developed in-house MN criteria for hospitalized adults in 2008 (Appendix A). They were not cancer specific. The presence of ≥2 of 6 criteria was recommended for a diagnosis of MN. These were chosen at the discretion of the RD and were as follows:

unintentional WL (UWL; % and trajectory);

low BMI (<19 kg/m²);

visible muscle wasting on clinical examination;

low nutrient intake (% and trajectory);

any wound (stages 1-4); and

specific abnormal laboratory values (serum prealbumin, albumin, or transferrin).

Statistical Analysis

Descriptive statistics

Categorical variables were summarized as frequency counts and percentages; continuous variables were summarized as median and range.

Predictors of prevalence

Study variables were examined to identify clinical characteristics associated with MN prevalence. Some were excluded because of insufficient EMR data, for example, skin metastasis. Associations were assessed by the Cochran-Armitage trend test (ordinal variables) or χ2 test (categorical variables). Results are presented as the number of patients in each category and the number and percentage with MN.

Predictors of severity

Associations of clinical characteristics and MN severity were assessed using Spearman correlation (ordinal or continuous variables), Wilcoxon rank-sum test (categorical variables with 2 categories), or Kruskal-Wallis test (categorical variables with 3 or more categories).

Prognosis

Kaplan-Meier method estimated survival by both MN prevalence and severity. Ninety-five percent confidence intervals (CI) were calculated for survival estimates and for median survival. Cox proportional hazards analysis assessed association with survival; Cox results are shown as the hazard ratio (HR) and 95% CI for HR.

RD and MD (physician) malnutrition assessment

Prevalence data for both RD and MD were available for 77 people and severity for 71. McNemar’s test was used to compare prevalence between RD and MD; Spearman correlation was used to compare severity.

All statistical tests were 2-sided; P < .05 indicated statistical significance. Analyses were performed with SAS software (SAS Institute Inc, Cary, North Carolina). Percentages were rounded to the nearest whole number.

Results

Electronic medical record data for 182 consecutive patients with cancer newly admitted to the ACPMU with an NTA were evaluated. Median age was 61 years (range 28-96). Fifty-one percent were female and most (81%) were caucasian (Table 1). Median height was 168 cm (range 48-196) and weight 67 kg (range 31-155). The most common cancers were lung (21%), GI (16%), and genitourinary (GU) (15%). The majority had metastatic disease; the most common metastatic sites were bone (41%) and lymph node (34%). Two of the most common comorbidities were hypertension (19%) and diabetes mellitus (10%). Age, gender, and cancer primary site distribution closely resembled 2013 US cancer mortality data.¹⁴ More than two-thirds (70%) had MN, with 61% classed as moderate to severe and 9% as mild. Physicians did not routinely document nutritional status in the EMR so only the NTA by the RD was analyzed.

Table 1.

Demographic Characteristics.

Variable	# Patients	N	%
Gender	182
Female		92	51
Male		90	49
Age, years, median (range)	182	61 (28-96)
Race	180
Caucasian		145	81
African American		27	15
Hispanic		4	2
Other		4	2
Primary cancer site	182
Lung		38	21
GI		29	16
GU		28	15
Other		87	48
Metastatic sites^a	182
Bone		74	41
Lymph node		62	34
Other		49	27
Liver		47	26
Lung		40	22
Brain		36	20
Other		20	11
Number of metastatic sites^b (of 8)	182
0		33	18
1		52	29
2		49	27
3		27	15
>3		21	11

Abbreviations: GI, gastrointestinal; GU, genitourinary.

^a>1 Possible, percentages do not add to 100%.

^b“Other” counts as 1 site.

Of the 6 Cleveland Clinic malnutrition criteria, BMI data were available in the EMR for 162 patients (Table 2). The majority (86%) had normal BMI values; median 24 kg/m² (range 14-43). Unintentional WL was available for 132; nearly three-quarters (n = 95) had UWL. In 64% (n = 84) it was severe. Nutrition intake was assessed in 117; 64% had reduced food intake. Muscle wasting was assessed in 62 patients and was present in 53%. Wounds were assessed in 70 patients, with 93% having none. Of 156 with a recorded albumin level, 69% were abnormal. Median albumin was 3.1 g/dL (range 1.6-4.7). Prealbumin was recorded in 2 patients; transferrin was not measured at all.

Table 2.

EMR Documentation of Cleveland Clinic Malnutrition Criteria.

Variable	N_total	%	N	%
BMI, kg/m²	162	89
Normal (>19)			140	86
Low
Mild (17-19)			11	7
Moderate (16-16.99)			7	4
Severe (<16)			4	2
Albumin, g/dL	156	86
Normal (>3.4)			49	31
Abnormal (low)
Minor (3.0-3.4)			46	30
Intermediate (2.5-2.9)			36	23
Major (<2.5)			25	16
Unintentional weight loss	132	73
No			37	28
Yes
Moderate			11	8
Severe			84	64
Nutrition intake	117	64
Normal			42	36
Abnormal (low)
Mild/moderate			48	41
Severe			27	23
Wounds	70	39
None			65	93
Stage 3-4^a			5	7
Visible muscle wasting	62	34
No			29	47
Yes			33	53
Prealbumin, mg/dL	2	11
Moderate (5-9)			1	50
Severe (<5)			1	50

Abbreviations: BMI, body mass index; EMR, electronic medical record.

^aIncludes unstageable, multiple stages of lesser degree, nonhealing wounds.

Median creatinine level was 0.7 mg/dL (range 0.3-13). Resting metabolic rate (RMR) median was 1305 kcal/24 h (range 715-1979). Median estimated minimum energy needs was 1805 kcal (range 1084-2620) and maximum was 2133 kcal (range 1265-3150). Of 148 patients with data on nutritional intervention on admission, 48% (n = 71) had none, 46% (n = 68) were on oral supplements, 5% (n = 8) enteral, and 1% (n = 1) parenteral nutrition.

There was considerable variation among which of the 6 MN criteria were chosen by the RD to identify MN (Table 3). Two or 3 of the possible 6 criteria were used in more than half the time (53%).

Table 3.

RD’s Utilization of Cleveland Clinic Malnutrition Criteria.

Variable	# Patients	N	%
Criteria (>1 used)^a	182
Unintentional weight loss (any)		85	47
Nutrition intake		71	39
Serum albumin		52	29
Visible muscle wasting		34	19
BMI		15	8
Wounds		5	3
Serum prealbumin		1	1
Serum transferrin		0	0
Number of criteria	182
0		75	41
1		4	2
2		57	31
3		40	22
4		5	3
5		1	1
Severity of malnutrition	145
None		43	30
Mild		13	9
Moderate		45	31
Severe		44	30

Abbreviation: BMI, body mass index; RD: registered dietitian.

^a>1 Used, percentages do not add to 100%.

Physicians recorded nutritional status in half (n = 91) of the EMR. The majority (77%) of those had MN, with 44% moderate to severe; 9% were noted as malnourished but of unspecified severity. “Cachexia,” “anorexia-cachexia syndrome,” and “cachectic” were also noted separately. Of 18 individual GI symptoms recorded, 1 to 3 per patient were documented in 73%, 4 to 7 in 14%, and none in 14%. Anorexia (53%) was more common than nausea (42%) or vomiting (24%).

Malnutrition Prevalence

Malnutrition was less common in hematologic malignancies compared to solid tumor primary sites (43% vs 73%, P = .018) and in breast cancer compared to other cancers (46% vs 73%, P = .045). It was more common in head and neck (H&N) primary sites (100% vs 68%, P = .025) and with lymph node (83% vs 63%, P = .008) or liver metastases (83% vs 66%, P = .049). Prevalence increased with total number of metastatic sites (P = .035) and total GI symptom number (P = .006). Malnutrition was noted more often when physicians noted vomiting (83% vs 66%, P = .049), abdominal pain (90% vs 67%, P = .038), and cachexia (95% vs 67%, P = .012) but less so with constipation (54% vs 74%, P = .042). Malnutrition severity was associated with abdominal pain (P =.024), cachexia (P = .011), constipation (P = .004), and vomiting (P = .012), and the total number of GI symptoms recorded (Spearman correlation = .25; P = .002).

Multiple additional factors were associated with higher MN prevalence (P < .05; Table 4). It was lower with increased RMR (P < .05). Prevalence was not associated with age, gender, race, comorbidities (hypertension and diabetes mellitus), estimated minimum and maximum caloric needs, anorexia, difficulty swallowing, early satiety, nausea, or serum creatinine.

Table 4.

Malnutrition Prevalence: Predictors.

	Malnutrition
Variable	N_total	#	%	P Value
Nutritional intervention on admission
No	59	32	54	<.001
Yes	64	53	83
Unintentional weight loss (any)
No	30	4	13	<.001
Yes	86	79	92
Unintentional weight loss (severity)
None	30	4	13	<.001
Moderate	11	7	64
Severe	75	72	96
BMI, kg/m²
Normal (>19)	111	69	62	.004
Mild (17-19)	10	9	90
Moderate/severe (<17)	11	11	100
Visible muscle wasting
No	22	6	27	<.001
Yes	33	33	100
Wounds
None	52	29	56	.05
Stage 3 to 4	5	5	100
Nutrition intake
Normal	38	9	24	<.001
Mild/moderate	46	46	100
Severe	25	24	96
Albumin, g/dL
Normal (>3.4)	39	20	51	<.001
Abnormal (low)
Minor (3-3.4)	39	28	72
Intermediate (2.5-2.9)	32	29	91
Major (<2.5)	18	17	94
Number of malnutrition criteria selected
0	39	0	0	<.001
1	3	1	33
2	57	56	98
3	40	39	98
4-5	6	6	100
RMR
<1200	43	33	77	.04
1200-1499	43	31	72
≥1500	37	23	62
Physician-noted vomiting
No	109	72	66	.049
Yes	36	30	83
Physician-noted constipation
No	119	88	74	.042
Yes	26	14	54
Physician-noted abdominal pain
No	125	84	67	.038
Yes	20	18	90
Physician-noted cachexia
No	126	84	67	.012
Yes	19	18	95
Number of GI symptoms in physician notes
0	19	10	53	.006
1	29	17	59
2	46	35	76
3	30	21	70
≥4	21	19	91

Abbreviations: BMI, body mass index; GI, gastrointestinal; RMR, resting metabolic rate.

Malnutrition Severity

Severity was associated with H&N primary tumor sites (P = .043), with bone (P = .016), lymph node (P = .046), brain (P = .038), and peritoneal metastases (P = .043), specific nutrition intervention on admission (P = .016), and diabetes mellitus (P = .02). Severity was inversely related to BMI score (P < .001), serum albumin level (P < .001), and RMR (Spearman correlation = −.18; P = .046). However, it was directly related to increased WL prevalence (P <. 001), muscle wasting (P < .001), and wounds (P = .045).

Malnutrition and Prognosis

Prevalence (HR: 1.88; P = .002) and severity (HR: 1.22; P = .006) were associated with significant increases in mortality (Table 5; Figures 1 and 2).

Table 5.

Survival Estimates by Malnutrition Prevalence and Severity.

Malnutrition	N	3	6	9	12	15	18	Median, months	# Deaths (%)
Prevalence
None	43	53 (39-68)	42 (27-57)	37 (23-52)	25 (12-38)	20 (7-32)	20 (7-32)	3.2 (1.7-9.0)	34 (79%)
Any	102	29 (20-38)	17 (9-24)	11 (5-18)	9 (4-15)	8 (3-14)	8 (3-14)	1.5 (1.1-2.1)	92 (90%)
Severity
Mild	13	50 (22-79)	17 (0-38)	17 (0-38)	8 (0-24)	8 (0-24)	8 (0-24)	4.4 (1.1-5.0)	12 (92%)
Moderate	45	23 (11-36)	14 (4-24)	7 (0-15)	7 (0-15)	5 (0-11)	5 (0-11)	1.4 (0.7-1.9)	43 (96%)
Severe	44	29 (15-43)	19 (7-31)	14 (4-25)	12 (2-22)	12 (2-22)	12 (2-22)	1.5 (0.9-2.5)	37 (84%)

The RD and MD Assessment Congruence

The RD and MD assessments were compared based on prevalence (n = 77) and severity (n = 71). Prevalence rates were similar between RD and MD (77% vs 75%; P = .78). Severity assessments were also moderately well correlated (r = .65; P < .001).

Discussion

Malnutrition was highly prevalent, affecting 70% of patients seen by the RD. It was usually identified by the following 3 criteria:

unintentional WL (47%),

low nutrient intake (39%), and

low serum albumin (29%).

Unintentional WL is a well-validated marker for MN in high-risk populations. Involuntary WL >10% suggests severe MN and worse outcomes.^13,25 However, it may be difficult to determine true WL by history; reported accuracy is 33%.²⁶ Timing of interventions for WL is controversial. One definition of clinically significant WL is >5% body weight over a 6 to 12-month period, especially when progressive.²⁷ Weight loss >10% normal body weight represents protein–energy MN.²⁷

Malnutrition is widespread among hospital inpatients,^28

-31 and inpatients with cancer have the highest prevalence.^28,32 Head and neck cancers often have moderate to severe MN.^33

-36 Findings here support these observations.

Weight loss was common and severe; 72% of those assessed for UWL had either moderate or severe WL. Half were either on oral or on enteral nutrition on admission. Body mass index did not suggest MN. Malnourished people may have a BMI within the healthy or overweight range due to obesity masking loss of lean body mass. Overweight or obese, severely ill adults are at risk of MN and often need intervention.^37,38 A systematic review of the influence of cachexia and sarcopenia on pancreatic cancer reported that in patients with normal weight (BMI: 18.5-24.9 kg/m²), the prevalence of sarcopenia ranged from 29.7% to 65%; in overweight or obese patients (BMI >25 kg/m²) it was 16.2% to 67%.³⁹ Body mass index alone is not a sensitive parameter to analyze nutritional status because it may still yield high or normal results in patients with ascites or edema.⁴⁰ Serum albumin levels were low in most (69%). Serum albumin can vary with hydration, infection, and renal function,⁴¹ so it may not consistently or predictably change with WL.¹³

Malnutrition prevalence was not associated with age, gender, race, comorbidities (hypertension and diabetes mellitus), serum creatinine, or estimated caloric needs. Low BMI was present in 14%, but only 8% had MN. Low BMI has been associated with MN even if WL and BMI correlated poorly.⁴² Body mass index can detect MN without WL or identify the obese at higher risk of MN.⁴³ Resting metabolic rate can support the presence of systemic inflammatory response. Paradoxically, MN was less frequent with higher RMR. Controversy exists about RMR in cancer MN; an elevated RMR may depend on individual patient and disease characteristics.⁴⁴

Prevalence was associated with significantly increased mortality which supports previous findings.⁴³ Increased severity was inversely related to shorter survival. Early identification of MN may improve cancer prognosis. Adequate nutrition may also help reduce cancer-related fatigue and promote quality of life.⁴⁵

In acute care settings, nurses often complete nutrition screening as part of an admission assessment.⁴⁶ Patients identified through screening as malnourished or “at risk” are referred for further nutritional assessment.²⁹ Physicians also have a role in identifying the need for and supporting effective nutrition treatment. This analysis showed physicians identified an extensive range of GI symptoms; these can cause weight changes in cancer^47,48 and negatively impact nutrient intake.⁴⁹ Specifically, abdominal pain, constipation, and vomiting were associated with greater prevalence of MN. Most patients with cancer had WL on admission to ACPMU; this demonstrates the importance of early physician assessment to coordinate care with the RD.

This is the first study to report the combined RD and MD evaluation of cancer MN. Prevalence rates and severity rankings were similar between RD and MD, despite the fact that physicians did not use a formal EMR nutritional assessment tool. They did however underrecord and underassess nutritional status. An earlier study found that only 18% of patients experiencing a recent loss of weight were identified based on nutritional documentation.⁵⁰ In addition, their assessments focused primarily on issues in general clinical care. There was physician variation in how and what was documented in the EMR. Use of standardized language is essential to comprehensively document and communicate information. Greater physician knowledge on the importance of assessment and documentation of nutritional status would assist in early diagnosis of MN. Time pressures, inadequate nutrition training for physicians, and lack of RD resources may hinder screening. A simple, rapidly administered screening tool such as the Malnutrition Screening Tool (MST) should be considered; it was designed for use by busy health-care professionals with no particular training in nutrition. It is straightforward, consisting of 2 simple questions evaluating weight change and appetite and takes <5 minutes to perform.⁵¹ Tools such as this would allow nutrition screening to become an integral part of routine clinical practice without being viewed as burdensome to patients or imposing a significant workload on staff.

This study had several limitations. There was a selection bias as the study population included only those referred to the RD. Performance status was not available from the EMR. The Cleveland Clinic Nutrition Assessment Protocol was empiric and applied at a single time point. A validated assessment tool was not used. Inaccurate weight values due to edema or ascites could have underestimated nutritional risk. Similarly, loss of lean muscle mass could have been masked by obesity causing them to remain in the normal or overweight range (BMI). Prior cancer treatments or medications for other comorbidities were not recorded. The EMR had some inherent defects due to variable record keeping.

Malnutrition prevalence and severity were explored in a consecutive unselected cohort of patients with cancer-associated MN in the ACPMU at a tertiary academic care center. The population was representative of a hospitalized advanced cancer population by age, gender, and primary tumor site. The data confirm the important role of the RD in assessment. The development and use of validated screening and assessment tools are essential for effective nutritional intervention and appropriate cancer care. It is noteworthy that the guidelines from the Academy of Nutrition and Dietetics (Academy) and the American Society for Parenteral and Enteral Nutrition (ASPEN)¹³ have been adopted by the Cleveland Clinic since completion of this study.

In clinical practice, there are barriers to accurate MN assessment and management. Clinical care of patients with cancer has often focused on the tumor with less attention to disease-related MN. Communication between physicians and dietitians in the recognition of MN will benefit patient care and optimize quality of life.

Conclusion

Malnutrition was highly prevalent in this study population and predicted poor prognosis. Most had a normal BMI which may be misleading and accounted for by preexisting obesity with subsequent WL. Although the majority had a low serum albumin, it is unclear if this is a reflection of disease severity or MN. The MDs identified a range of nutrition impact symptoms some of which predicted MN prevalence.

Physician and RD assessments were highly congruent for prevalence and severity of MN despite the fact that MDs did not use a formal nutritional assessment tool. The Cleveland Clinic Nutrition Assessment Protocol allowed up to 6 different variables to be used in the identification of MN, leading to variation in choice of parameter used between RDs. While an assessment tool was available to RDs, MDs did not use one; this resulted in underrecognition of MN. Improved communication between the RD and the MD could improve recognition and diagnosis of MN. Further areas of research could include multidisciplinary strategies to identify those at nutritional risk and provide early interventions.

Footnotes

Appendix A

Table A1.

Cleveland Clinic Nutrition Assessment Protocol.^a

Criteria	Mild	Moderate	Severe
Unintentional weight loss (%)
		1% to 2%, 1 week	>2%, 1 week
		5%, 1 month	>5%, 1 month
		7.5%, 3 months	>7.5%, 3 months
		10%, 6 months	>10%, 6 months
BMI, kg/m²	17.0-19.0	16.0-16.9	<16.0
Visible muscle wasting	No	Yes	Yes
Wounds	Stage 1	Stage 2	Stage 3, 4, or unstageable, multiple stages of lesser degree and/or nonhealing wounds
Nutrient intake	Inadequate intake <50% for >7days	Inadequate intake <50% for >7days	Inadequate intake <25% for >7days
Laboratory values
Prealbumin, mg/dL	10-16	5-9	<5
Albumin, g/dL	3.0-3.4	2.4-2.9	<2.5
Transferrin, mg/dL	150-175	100-149	<100
Number of criteria recommended to diagnose malnutrition	two bolded criteria	two bolded criteria	two bolded criteria

Abbreviation: BMI, body mass index.

^aAt least 2 criteria must be present to diagnose malnutrition

Authors’ Note

The Harry R. Horvitz Center for Palliative Medicine is a World Health Organization Demonstration Project in Palliative Medicine and is an ESMO Designated Integrated Center of Supportive Oncology and Palliative Care. This study was presented in part at the 7th European Association for Palliative Care Research Congress, June 7-9, 2012, Trondheim, Norway.

Acknowledgments

The authors would like to thank Chanell Upshaw and Vanna Shute for their administrative support.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

References

Morley

. Undernutrition in older adults. Fam Pract. 2012;29(suppl 1):i89–i93.

Correia

Hegazi

Higashiguchi

. Evidence-based recommendations for addressing malnutrition in health care: an updated strategy from the feed M.E. Global Study Group. J Am Med Dir Assoc. 2014;15(8):544–550.

Rojer

Kruizenga

Trappenburg

. The prevalence of malnutrition according to the new ESPEN definition in four diverse populations. Clin Nutr. 2016;35(3):758–762. doi:10.1016/j.clnu.2015.06.005.

Cederholm

Bosaeus

Barazzoni

. Diagnostic criteria for malnutrition—an ESPEN Consensus Statement. Clin Nutr. 2015;34(3):335–340.

Kirkland

Kashiwagi

Brantley

Scheurer

Varkey

. Nutrition in the hospitalized patient. J Hosp Med. 2013;8(1):52–58.

Singh

Watt

Veitch

Cantor

Duerksen

. Malnutrition is prevalent in hospitalized medical patients: are housestaff identifying the malnourished patient? Nutrition. 2006;22(4):350–354.

Holyday

Daniells

Bare

Caplan

Petocz

Bolin

. Malnutrition screening and early nutrition intervention in hospitalised patients in acute aged care: a randomised controlled trial. J Nutr Health Aging. 2012;16(6):562–568.

The Joint Commission. Comprehensive Accreditation Manual for Hospitals. The Official Handbook. Oakbrook Terrace, IL: The Joint Commission on Accreditation of Healthcare Organizations; 2008. Web site. http://www.x32healthcare.com/documents/JcahoManual2008.pdf. Accessed February 23, 2016.

Hamirudin

Charlton

Walton

. Outcomes related to nutrition screening in community living older adults: a systematic literature review. Arch Gerontol Geriatr. 2016;62:9–25. doi:10.1016/j.archger.2015.09.007.

10.

Mueller

Compher

Ellen

. A.S.P.E.N. clinical guidelines: nutrition screening, assessment, and intervention in adults. JPEN J Parenter Enteral Nutr. 2011;35(1):16–24.

11.

American Society for Parenteral and Enteral Nutrition (A.S.P.E.N.) Board of Directors. Clinical Guidelines for the Use of Parenteral and Enteral Nutrition in Adult and Pediatric Patients, 2009. JPEN J Parenter Enteral Nutr. 2009;33(3):255–259.

12.

Patel

Romano

Corkins

. Nutrition screening and assessment in hospitalized patients: a survey of current practice in the United States. Nutr Clin Pract. 2014;29(4):483–490.

13.

White

Guenter

Jensen

Malone

Schofield

; Academy of Nutrition and Dietetics Malnutrition Work Group; A.S.P.E.N. Malnutrition Task Force; A.S.P.E.N. Board of Directors. Consensus statement of the Academy of Nutrition and Dietetics/American Society for Parenteral and Enteral Nutrition: characteristics recommended for the identification and documentation of adult malnutrition (undernutrition). J Acad Nutr Diet. 2012;112(5):730–738.

14.

American Cancer Society. Cancer Facts & Figures 2013. Atlanta, GA: American Cancer Society; 2013. Web site. http://www.cancer.org/research/cancerfactsfigures/cancerfactsfigures/cancer-facts-figures-2013. Accessed February 23, 2016.

15.

Felder

Lechtenboehmer

Bally

. Association of nutritional risk and adverse medical outcomes across different medical inpatient populations. Nutrition. 2015;31(11-12):1385–1393.

16.

Lim

Ong

Chan

Loke

Ferguson

Daniels

. Malnutrition and its impact on cost of hospitalization, length of stay, readmission and 3-year mortality. Clin Nutr. 2012;31(3):345–350.

17.

Ordoñez

Madalozzo Schieferdecker

Cestonaro

Cardoso Neto

Ligocki Campos

. Nutritional status influences the length of stay and clinical outcomes in patients hospitalized in internal medicine wards. Nutr Hosp. 2013;28(4):1313–1320.

18.

Cunningham

Huhmann

. Nutritional disturbances. In: Yarbro

Wujcik

Gobel

, eds. Cancer Nursing: Principles and Practice. 7th ed. Sudbury, MA: Jones and Bartlett; 2011:830.

19.

Platek

Popp

Possinger

Denysschen

Horvath

Brown

. Comparison of the prevalence of malnutrition diagnosis in head and neck, gastrointestinal, and lung cancer patients by 3 classification methods. Cancer Nurs. 2011;34(5):410–416.

20.

Ehrsson

Langius-Eklöf

Laurell

. Nutritional surveillance and weight loss in head and neck cancer patients. Support Care Cancer. 2012;20(4):757–765.

21.

Lagman

Rivera

Walsh

LeGrand

Davis

. Acute inpatient palliative medicine in a cancer center: clinical problems and medical interventions—a prospective study. Am J Hosp Palliat Care. 2007;24(1):20–28.

22.

Maillet

Potter

Heller

. Position of the American Dietetic Association: ethical and legal issues in nutrition, hydration, and feeding. J Am Diet Assoc. 2002;102(5):716–726.

23.

Harris

Taylor

Thielke

Payne

Gonzalez

Conde

. Research electronic data capture (REDCap)—a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377–381.

24.

Mifflin

St Jeor

Hill

Scott

Daugherty

Koh

. A new predictive equation for resting energy expenditure in healthy individuals. Am J Clin Nutr. 1990;51(2):241–247.

25.

Jensen

Hsiao

Wheeler

. Adult nutrition assessment tutorial. JPEN J Parenter Enteral Nutr. 2012;36(3):267–274.

26.

Kahn

Oman

Rudkin

Anderson

Sultani

. Can ED staff accurately estimate the weight of adult patients? Am J Emerg Med. 2007;25(3):307–312.

27.

Collins

. Protein-energy malnutrition and involuntary weight loss: nutritional and pharmacological strategies to enhance wound healing. Expert Opin Pharmacother. 2003;4(7):1121–1140.

28.

Silva

de Oliveira

Souza

Figueroa

Santos

. Factors associated with malnutrition in hospitalized cancer patients: a cross-sectional study. Nutr J. 2015;14:123. doi:10.1186/s12937-015-0113-1.

29.

Barker

Gout

Crowe

. Hospital malnutrition: prevalence, identification and impact on patients and the healthcare system. Int Environ Res Public Health. 2011;8(2):514–527.

30.

Konturek

Herrmann

Schink

Neurath

Zopf

. Malnutrition in hospitals: it was, is now, and must not remain a problem! Med Sci Monit. 2015;21:2969–2975. doi:10.12659/MSM.894238.

31.

Giryes

Leibovitz

Matas

. Measuring nutrition risk in hospitalized patients: MENU, a hospital-based prevalence survey. Isr Med Assoc J. 2012;14(7):405–409.

32.

Planas

Álvarez-Hernández

León-Sanz

. Prevalence of hospital malnutrition in cancer patients: a sub-analysis of the PREDyCES((R)) study. Support Care Cancer. 2016;24(1):429–435.

33.

Alshadwi

Nadershah

Carlson

Young

Burke

Daley

. Nutritional considerations for head and neck cancer patients: a review of the literature. J Oral Maxillofac Surg. 2013;71(11):1853–1860.

34.

Platek

Myrick

McCloskey

. Pretreatment weight status and weight loss among head and neck cancer patients receiving definitive concurrent chemoradiation therapy: implications for nutrition integrated treatment pathways. Support Care Cancer. 2013;21(10):2825–2833.

35.

Bossola

. Nutritional interventions in head and neck cancer patients undergoing chemoradiotherapy: a narrative review. Nutrients. 2015;7(1):265–276.

36.

Langius

Twisk

Kampman

. Prediction model to predict critical weight loss in patients with head and neck cancer during (chemo)radiotherapy. Oral Oncol. 2016;52:91–96. doi:10.1016/j.oraloncology.2015.10.021.

37.

Cruz-Jentoft

Baeyens

Bauer

. Sarcopenia: European consensus on definition and diagnosis: Report of the European Working Group on Sarcopenia in Older People. Age Ageing. 2010;39(4):412–423.

38.

Han

Tajar

Lean

. Obesity and weight management in the elderly. Br Med Bull. 2011;97:169–196. doi:10.1093/bmb/ldr002.

39.

Ozola Zalite

Zykus

Francisco Gonzalez

. Influence of cachexia and sarcopenia on survival in pancreatic ductal adenocarcinoma: a systematic review. Pancreatology. 2015;15(1):19–24.

40.

Santarpia

Contaldo

Pasanisi

. Nutritional screening and early treatment of malnutrition in cancer patients. J Cachexia Sarcopenia Muscle. 2011;2(1):27–35.

41.

Read

Crockett

Volker

. Nutritional assessment in cancer: comparing the Mini-Nutritional Assessment (MNA) with the scored Patient-Generated Subjective Global Assessment (PGSGA). Nutr Cancer. 2005;53(1):51–56.

42.

Kruizenga

Wierdsma

van Bokhorst

. Screening of nutritional status in The Netherlands. Clin Nutr. 2003;22(2):147–152.

43.

Pressoir

Desné

Berchery

. Prevalence, risk factors and clinical implications of malnutrition in French Comprehensive Cancer Centres. Br J Cancer. 2010;102(6):966–971.

44.

Bosaeus

Daneryd

Svanberg

Lundholm

. Dietary intake and resting energy expenditure in relation to weight loss in unselected cancer patients. Int J Cancer. 2001;93(3):380–383.

45.

Gagnon

Murphy

Eades

. A prospective evaluation of an interdisciplinary nutrition-rehabilitation program for patients with advanced cancer. Curr Oncol. 2013;20(6):310–318.

46.

Davies

. Nutritional screening and assessment in cancer-associated malnutrition. Eur J Oncol Nurs. 2005;9(suppl 2):S64–S73.

47.

MacDonald

. Is there evidence for earlier intervention in cancer-associated weight loss? J Support Oncol. 2003;1(4):279–286.

48.

MacDonald

Easson

Mazurak

Dunn

Baracos

. Understanding and managing cancer cachexia. J Am Coll Surg. 2003;197(1):143–161.

49.

Bovio

Fonte

Baiardi

. Prevalence of upper gastrointestinal symptoms and their influence on nutritional state and performance status in patients with different primary tumors receiving palliative care. Am J Hosp Palliat Care. 2014;31(1):20–26.

50.

Adams

Bowie

Simmance

Murray

Crowe

. Recognition by medical and nursing professionals of malnutrition and risk of malnutrition in elderly hospitalised patients. Nutr Diet. 2008;65(2):144–150. doi:10.1111/j.1747-0080.2008.00226.x.

51.

Ferguson

Capra

Bauer

Banks

. Development of a valid and reliable malnutrition screening tool for adult acute hospital patients. Nutrition. 1999;15(6):458–464.