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Abstract

Study design

Scoping review.

Objective

To identify which markers are used as surrogates for malnutrition in metastatic spine disease and which are the most studied outcomes associated with it.

Methods

A scoping review was performed by searching the PubMed/Medline, EMBASE, and Web of Science databases up to July 2022. We searched for articles exploring markers of malnutrition in spine oncology patients including but not limited to albumin, body weight, weight loss, and nutrition indices. A narrative synthesis was performed.

Results

A total of 61 articles reporting on 31,385 patients met inclusion criteria. There were 13 different surrogate markers of nutrition, with the most common being albumin in 67% of studies (n = 41), body weight/BMI in 34% (n = 21), and muscle mass in 28% (n = 17). The most common studied outcomes were survival in 82% (n = 50), complications in 28% (n = 17), and length of stay in 10% (n = 6) of studies. Quality of life and functional outcomes were assessed in 2% (n = 1) and 3% (n = 2) of studies, respectively. Out of 61 studies, 18% (n = 11) found no association between the examined markers and outcome.

Conclusion

Assessment of nutritional status in patients with spinal metastases is fundamental. However, there is lack of a comprehensive and consistent way of assessing malnutrition in oncologic spine patients and therefore inconsistency in its relationship with outcomes. A consensus agreement on the assessment and definition of malnutrition in spine tumor patients is needed.

Keywords

spine oncology malnutrition cancer nutrition disorder

Introduction

Malnutrition affects 30 – 85% of cancer patients.^1,2 Inadequate nutrition is known to impact the tolerability of chemotherapy,³ radiation,⁴ and surgery,⁵ and it is also associated with shorter survival.^6,7 Nevertheless, malnutrition is a multi-dimensional condition and diagnosing it can be challenging. Surrogate markers that are oftentimes used include hypoalbuminemia, weight loss, low body mass index (BMI), low muscle mass, and others.⁸ To overcome this heterogeneity in assessment and diagnosis, leading nutrition societies in the United States and Europe have developed tools to standardize the definition of malnutrition and include items such as insufficient energy intake, weight loss, and loss of muscle mass.⁹ However, implementation and adoption can be challenging given the multi-dimensional nature of these questionnaires/forms and the time-consuming factor.

The spine is the third most common site for metastatic spread after the lung and liver. By the year 2040, the World Health Organization estimates an incidence of 29 million cancer cases, out of which an estimated 16% (4.6 million patients) will develop symptomatic metastatic spine disease.¹⁰ Patients with metastatic spine disease represent a unique and challenging sub-population of cancer patients given the predisposition to back pain, limited mobility, neurological deficits, and overall lower activity levels. The real prevalence of malnutrition in this population is unknown, but has been reported to be as high as 90%.¹¹ While studies have found significant associations between malnutrition and higher risk of complications and shorter survival,^11,12 there remains a need for a consensus agreement on how best to define it and how it uniquely affects metastatic spine tumor patients.

Thus, the purpose of this scoping review was to: (1) Identify which markers are most used as surrogates for malnutrition in metastatic spine disease, and (2) describe the most studied outcomes associated with malnutrition in metastatic spinal tumor patients.

Materials and Methods

The current study was performed in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines.¹³ This study did not require institutional review board approval since it is a review of the literature and not considered human subjects research. No informed consent was needed either.

Literature Search

An online search was performed by author RDLGR on July 26, 2022 in the PubMed/Medline, EMBASE, and Web of Science databases. No limits on publication dates or languages were applied. Initial searches were performed using a combination of keywords derived from the National Cancer Institute’s Nutrition in Cancer Care website – “malnutrition”, “weight loss”, “albumin”, “sarcopenia”, “muscle mass”, “cachexia”, “anorexia”, “visceral fat”, “subcutaneous fat” and “spinal metastasis”.⁸ Specific search algorithms are shown in Supplementary Table 1.

Inclusion and Exclusion Criteria

Eligible studies included patients with metastatic spine disease. Studies investigating nutritional status and its association with clinical outcomes were included. Markers of nutritional status included nutritional screening tools or other health markers such as body weight, body mass index (BMI), weight loss, albumin, prealbumin, sarcopenia, muscle mass, and visceral fat.⁸ The following type of studies were excluded: performance assessments, external validations, and comparisons of previously developed scoring systems; abstracts; case reports; editorials or commentaries; systematic reviews; and narrative reviews.

Study Selection

The study algorithm is shown in Figure 1. All retrieved studies were downloaded to an Endnote X9 (Clarivate, London, United Kingdom) libraries and duplicates were removed. Studies were first screened based on their title and abstract for potential eligibility based on our inclusion criteria. All potentially relevant studies were downloaded, and full texts reviewed. Any potential disagreements were resolved by consensus among authors RDLGR, JHS, CRG, RCM, and MHW.

Figure 1.

Study selection algorithm.

Primary and Secondary Questions

The primary question addressed which markers are most used as surrogates of malnutrition in patients with metastatic spine disease. The secondary question addressed which were the most common outcomes associated with malnutrition in patients with metastatic spine disease (such as survival, complications, length of hospital stay, and others).

Analysis

Explorative data analysis with descriptive statistics and a narrative review were performed. Levels of evidence were assigned based on the North American Spine Society guidelines.

Results

A total of 61 studies met inclusion criteria and were analyzed.^11,12,14-48 Table 1 provides a summary description of included studies.^49-72 Among all studies, 89% were retrospective (n = 54),^{12,14,16-25,27-51,54-61,63-67,69-72} 8% were prospective (n = 5),^11,15,26,52 and 3% were both (n = 2).^17,62 Publication years ranged from 1998 to 2022. The total number of patients reported was 31,385 with sample sizes ranging from 36³⁸ to 4583 patients.¹² Among studies that reported sex, the male prevalence was 60% (16,781 of 29,269) and female prevalence 40% (12,488 of 29,269). Ten studies reported on single cancer primary sites^{30,38,43,46,59,63,65,66,68,69} and 51 studies investigated mixed cancer diagnoses or did not specify the type of cancer. The most common intervention was surgery in 87% of studies (n = 53), followed by radiation in 54% (n = 33) and systemic therapy in 41% (n = 25). Among all studies, 89% (n = 54) were Level III studies and 11% (n = 7) were Level II studies.

Table 1.

Characteristics of Included Studies.

Author and year	Study Design	Age, years	Sample Size, Gender	Primary Cancer type(s) and treatment(s)	Malnutrition Proxy marker(s)	Patient outcome(s)	Study Results	Level of Evidence
McPhee et al., 1998	Retrospective cohort	Mean 54	n = 53	Lung, breast, prostate, kidney, colon, melanoma, hematologic, others Surgery, radiation	Mean albumin, serum albumin level <3.5 g/dL, serum protein <60 g/dL	Wound complications	Univariate analysis: Mean albumin was lower in patients with infection (3.5 g/dL vs 3.9 g/dL, P = .032), higher proportion of patients had low protein count in the infected group (35.7% vs 3.5%, P = .002)	III
			M = 30
			F = 23
Guo et al., 2003	Retrospective cohort	35 patients ≤60 years	n = 60	Lung, breast, gastrointestinal, genitourinary, hematologic, others	Serum albumin level ≤3.5 g/dL	Survival	Univariate analysis: no association between proxy markers and outcome	III
Guo et al., 2003	Retrospective cohort	25 patients ≥60 years	n = 60	No intervention	Serum albumin level ≤3.5 g/dL	Survival		III
Ogihara et al., 2006	Retrospective cohort	Mean 65	n = 114	Lung	Serum albumin level <3.0 g/dL	Survival	Multivariate analysis: albumin <3.0 g/dL associated with shorter mean survival (3.1 months vs7.4 months) for non-small cell lung cancer	III
			M = 69	Surgery			Univariate analysis: albumin <3.0 g/dL associated with survival for small cell lung cancer
			F = 45	Surgery
Oh et al., 2012	Retrospective cohort	Mean 61	n = 52	Lung, colorectal, cervix, thyroid, others	Serum albumin level ≤3.5 g/dL	Survival	Multivariate analysis: albumin <3.5 g/dL associated with survival (HR 1.93 [1.1 – 3.5] P = .028)	III
			M = 20	Surgery
			F = 32	Surgery
Boehling et al., 2012	Prospective cohort	Mean 56	n = 93	Lung, breast, kidney, thyroid, sarcoma, others	Obesity	Fracture progression	Multivariate analysis: Obesity was protective for fracture progression (HR .02 [0 – .2])	II
			M = 44	Surgery, radiation
			F = 49	Surgery, radiation
Ghori et al., 2015	Retrospective cohort	Mean 60	n = 318	Lung, breast, prostate, kidney, thyroid, gastrointestinal, hematologic, others	Serum albumin level >3.5 g/dL	Survival	Multivariate analysis: albumin >3.5 g/dL associated with 1-year survival (OR 2.8 [1.7 – 4.7] P < .001)	III
			M = 185	Surgery, radiation, chemotherapy
			F = 133	Surgery, radiation, chemotherapy
Tabouret et al., 2015	Retrospective cohort	Mean 61	n = 148	Lung, breast, prostate, kidney, colorectal, hematologic	Weight loss	Survival	Univariate analysis: weight loss was present in 5% of patients 5 who survived ≥2 years vs 27% of patients who survived <2 years (P = .003)	III
			M = 77	Surgery, radiation, chemotherapy
			F = 71	Surgery, radiation, chemotherapy
Gakhar et al., 2015	Prospective cohort	Alive at 1 yr mean 62	n = 86	Lung, breast, prostate, kidney, hematologic, gastrointestinal, others	Skeletal muscle mass (TPA/VBA)	Survival	Univariate analysis: Mortality rates at 1 year were significantly high among patients in the lowest quartile of muscle mass, compared with those in the highest quartile (57.1 vs 23.8%, P = .02)	II
		Dead at 1 yr mean 68	M = 44	Surgery
		Dead at 1 yr mean 68	F = 42	Surgery
Switlyk et al., 2015	Retrospective cohort	Median 65	n = 173	Lung, breast, prostate, kidney, gastrointestinal, liver, others	Serum albumin level <3.0 g/dL	Survival	Multivariate analysis: albumin <3.0 g/dL associated with survival (HR 2.9 [1.5 – 5.6] P = .001)	III
			M = 97	Radiation, chemotherapy
			F = 76	Radiation, chemotherapy
Kumar et al., 2015	Retrospective cohort	Mean 60	n = 98	Not specified	Serum albumin level <3.5 g/dL, Malnutrition Universal screening tool (MUST) score	Wound complications	Multivariate analysis: no association between proxy markers and outcome	III
			M = 51	Surgery
			F = 44	Surgery
Schoenfeld et al., 2016	Retrospective cohort	Mean 60	n = 318	Breast, lung, and hematologic	BMI, serum albumin level ≥3.5 g/dL	Survival	Multivariate analysis: albumin ≥3.5 g/dL associated with 30-day survival (OR 9.0 [ 3.1 – 26.6] P < .001), albumin ≥3.5 g/dL associated with 90-day survival (OR 3.9 [ 2.2 – 6.8] P < .001)	III
			M = 185	Surgery, radiation, chemotherapy
			F = 133	Surgery, radiation, chemotherapy
Zakaria et al., 2016	Retrospective cohort	Mean 65	n = 168	Lung	Skeletal muscle mass (psoas size)	Survival	Patients whose total psoas size was below the median size had significantly shorter survival compared with those greater than the median size: 146 days vs 253.5 days, respectively, (HR 1.4 [1.1 – 1.9] P = .025)	III
			M = 90	Radiation
			F = 78	Radiation
De la Garza Ramos et al., 2016	Retrospective cohort	Median 62	n = 4583	Lung, breast, prostate, kidney, thyroid	Malnutrition (including nutritional marasmus and severe protein-calorie malnutrition),	Inpatient mortality, complications, length of stay	Multivariate analysis: malnutrition associated with complications (OR 2.1 [1.5 – 3.0] P < .001]	III
			M = 2650	Surgery
			F = 1931	Surgery
Han et al., 2016	Retrospective cohort	Mean 46	n = 37	Lymphoma	Serum albumin level <3.5 g/dL	Survival	Univariate analysis: no association between proxy markers and outcome	III
			M = 14	Surgery, radiation, chemotherapy
			F = 23	Surgery, radiation, chemotherapy
Kato et al., 2016	Retrospective cohort	Mean 59	n = 36	Kidney	Serum albumin level <4.0 g/dL	Survival	Univariate analysis: no association between proxy markers and outcome	III
			M = 26	Surgery, radiation, chemotherapy
			F = 10	Surgery, radiation, chemotherapy
Karhade et al., 2016	Retrospective cohort	25% of patients <46	n = 2207	Not specified	BMI, weight loss, serum albumin level <3.5 g/dL	Complications, unplanned reoperation, and unplanned readmission	Multivariate analysis: no association between proxy markers and outcome	III
		25% of patients 46-58	M = 1203	Surgery
		25% of patients 58-67	F = 1004
		25% of patients >67	F = 1004
Hersh et al., 2018	Retrospective cohort	Age <51 = 27%	n = 616	Not specified	BMI, weight loss, serum albumin level <3.5 g/dL	Survival, complications	Multivariate analysis: albumin <3.5 g/dL associated with 30-day complications (75% vs 39%, P < .001) and 30-day mortality (OR 3.6 [1.8 – 7.0] P < .001)	III
		Age 51 - 60 = 25%	M = 384	Not specified
		Age 61 - 70 = 27%	M = 384	Surgery
		Age 71 - 80 = 16%	F = 232
		Age >80 = 6%	F = 232
Sarkiss et al., 2018	Retrospective cohort	Age <51 = 22%	n = 300	Not specified	BMI, weight loss, serum albumin level <3.5 g/dL	Survival, complications	Univariate analysis: albumin <3.5 g/dL associated with 30-day complications (38.5% vs 18.1%, P = .002)	III
		Age 51 - 60 = 25%	M = 182	Not specified
		Age 61 - 70 = 32%	M = 182	Surgery
		Age 71 - 80 = 14%	F = 118
		Age >80 = 6%	F = 118
Zakaria et al., 2018	Retrospective cohort	Mean 64	n = 118	Breast	Skeletal muscle mass (psoas size)	Survival	Multivariate analysis: patients in the lowest tertile for average psoas size had significantly shorter survival compared with the highest tertile (HR 1.8 [1.1 – 2.9] P = .31), the shorter survival was also true for the lowest tertile vs the middle tertile,(HR 2.0 [1.2 – 3.3] P = .008)	III
			M = 0	Radiation
			F = 118	Radiation
Zakaria et al., 2018	Retrospective cohort	Mean 73	n = 92	Prostate	Skeletal muscle mass (psoas size)	Survival	Multivariate analysis: patients in the smallest third for average psoas size had significantly shorter survival compared with patients in the largest third (HR 2.4 [1.3 – 4.4] P = .004), patients in the middle third for average psoas size also had shorter survival compared with patients in the largest third (HR 2.3 [1.3 – 4.3] P = .007)	III
			M = 92	Radiation
			F = 0	Radiation
Liu et al., 2018	Retrospective cohort	≥60	n = 54	Lung, breast, prostate, colorectal, pancreatic, esophageal, others	Serum albumin level ≤3.5 g/dL	Survival, functional outcome (postop Frankel grade)	Univariate analysis: no association between proxy markers and outcome	III
			M = 29	Surgery, radiation, chemotherapy
			F = 25	Surgery, radiation, chemotherapy
Dohzono et al., 2019	Retrospective cohort	Mean 68	n = 78	Gastrointestinal	Serum albumin level <3.7 g/dL, skeletal muscle mass, skeletal muscle density	Survival	Multivariate analysis: Lower paravertebral muscle density associated with survival (HR 2.2 [1.2 – 4.0] P = .007), presence of abnormal laboratory data (including albumin <3.7 g/dL) associated with survival (HR 4.8 [1.6 – 14.4] P = .005)	III
			M = 44	Surgery, radiation, chemotherapy
			F = 34	Surgery, radiation, chemotherapy
Schoenfeld et al., 2019	Retrospective cohort	Mean 58	n = 571	Lung, breast, others	Serum albumin level ≤3.5 g/dL	Survival, complications, readmissions, ambulatory outcome	Multivariate analysis: albumin ≤3.5 g/dL associated with 6-month mortality (OR 3.6 [2.7 – 4.6]), 1-year mortality (OR 3.2 [2.4 – 4.3]), non-ambulatory at 6 months (OR 4.9 [2.7 – 8.1]), complications (OR 1.4 [1.0 – 1.8]), and readmissions (OR 1.4 [1.2 – 1.7])	III
			M = 272	Surgery, radiation, chemotherapy
			F = 299	Surgery, radiation, chemotherapy
Hussain et al., 2019	Retrospective cohort	400 patients ≥65	n = 1498	Not specified	Serum albumin level <3.5 g/dL, BMI, weight loss	Survival, complications, readmission, length of stay, discharge disposition	Multivariate analysis: albumin <3.5 g/dL associated with 30-day mortality (OR 5.2 [3.4 – 8.0] P < .001), any complication (OR 3.2 [2.4 – 4.1] P < .001), sepsis (OR 3.1 [1.9 – 4.9] P < .001), any transfusion (OR 1.4 [1.1 – 1.8] P = .005), prolonged length of stay (OR 4.3 [3.3 – 5.6] P < .001), non-home discharge (OR 2.9 [2.3 – 3.7] P < .001), readmission (OR .2 (.01 – .6) P = .007)	III
		1098 patients ≤65	M = 644	Surgery
		1098 patients ≤65	F = 340	Surgery
Versteeg et al., 2019	Prospective cohort	Mean 62	n = 39	Lung, breast, prostate, kidney, others	Patient-generated subjective Global Assessment (PG-SGA)	Spinal oncology study group outcomes questionnaire 2.0, survival, length of stay	Univariate analysis: Malnourishment was associated with lower baseline SOSGOQ2.0 total scores, SOSGOQ2.0 physical function, mental health and social functioning scores, EQ-5D total scores and EQ-5D mobility scores. No association between malnutrition and survival could be determined	II
			M = 20	Surgery, radiation
			F = 19	Surgery, radiation
Karhade et al., 2019	Retrospective cohort	Mean 61	n = 732	Solid tumors, hematologic	BMI, serum albumin level ≤3.8 g/dL	Survival	Machine learning models: albumin was one of the three most important predictors of 90-day mortality	III
			M = 426	Surgery
			F = 306	Surgery
Karhade et al., 2019	Retrospective cohort	1017 patients <65	n = 1790 M = 1093 F = 695	Not specified	Serum albumin level <3.5 g/dL	Survival	Machine learning models: albumin <3.5 g/dL was significantly more common in patients who died within 30 days, albumin was a significant predictor of 30-day mortality	III
		682 patients 65-79		Surgery
		91 patients ≥80		Surgery
Lun et al., 2019	Retrospective cohort	Mean 60	n = 169	Lung, breast, prostate, kidney, liver, gastrointestinal, others	Serum albumin level <3.5 g/dL	Survival	Multivariate analysis: albumin <3.5 g/dL was significantly associated with survival (HR 6.9 [2.5 – 19.1] P < .001)	III
			M = 102	Surgery
			F = 67	Surgery
Paulino Pereira et al., 2019	Retrospective cohort	Mean 60	n = 647	Lung, breast, prostate, kidney, skin, colorectal, sarcomatous, liver, esophageal, others	Serum albumin level (continuous)	Complications, revision surgery	Multivariate analysis: Lower albumin levels significantly associated with 30-day complications (OR .7 [.5 – .9] P = .021)	III
			M = 375	Surgery
			F = 272	Surgery
Dohzono et al., 2019	Retrospective cohort	Mean 66	n = 97	Lung, breast, prostate, colorectal, gastric, pancreatic, other	Serum albumin level <3.7 g/dL, skeletal muscle mass, skeletal muscle density	Survival	Multivariate analysis: The lowest percentage quartile (0%–25%) of the psoas muscle mass was associated with poor overall survival (HR 1.9 [1.1 – 3.3] P = .017)
			M = 51	Surgery, radiation, chemotherapy
			F = 46	Surgery, radiation, chemotherapy
Zang et al., 2019	Both retrospective and prospective	≤60	n = 239	Lung	Serum albumin level >3.5/dL	Survival	Multivariate analysis: no association between proxy markers and outcome	II
		>60	M = 121	Surgery, chemotherapy
		>60	F = 118	Surgery, chemotherapy
Pennington et al., 2019	Retrospective cohort	Mean 57	n = 75	Lung, breast, prostate, kidney, colorectal, hematologic, others	BMI, skeletal muscle mass, visceral fat	Survival	Multivariate analysis: Visceral fat associated with 3-month survival (OR 1.1 [1.0 – 1.2] P = .020), BMI associated with 6-month survival (1.2 [1.0 – 1.4] P = .009), BMI associated with 12-month survival (1.2 [1.0 – 1.4] P = .020)	III
			M = 43	Surgery, chemotherapy
			F = 32	Surgery, chemotherapy
Boaro et al., 2020	Retrospective cohort	Mean 62	n = 1176	Not specified	Weight loss, serum albumin level <3.5 g/dL	Complications	Multivariate analysis: no association between proxy markers and outcome	III
			M = 776	Surgery
			F = 400	Surgery
Schoenfeld et al., 2020	Retrospective cohort	Mean 57	n = 834	Lung, breast, prostate, kidney, colon, thyroid, head and neck, other	BMI, serum albumin level <3.5 g/dL	Revision surgery	Univariate analysis: no association between proxy markers and outcome	III
			M = 384	Surgery, radiation, chemotherapy
			F = 450	Surgery, radiation, chemotherapy
Schoenfeld et al., 2020	Retrospective cohort	Mean 58	n = 1216	Lung, breast, others	BMI, serum albumin level >3.5 g/dL	Survival	Multivariate analysis: albumin >3.5 g/dL associated with 6-month mortality (OR .3 [.2 – .4]), 1-year mortality (OR .4 [ .3 – .5]), and treatment failure (OR .3 [.2 – .5])	III
			M = 608	Surgery, radiation, chemotherapy
			F = 608	Surgery, radiation, chemotherapy
Pielkenrood et al., 2020	Prospective cohort	Mean 67	n = 310	Lung, breast, prostate, other	Skeletal muscle mass, muscle density, visceral fat, subcutaneous fat	Survival	Multivariate analysis: Only skeletal muscle density associated with 90-day survival (HR .6 [.7 – 2.4)] and 1-year survival (HR .70 [.5 – .9])	II
			M = 194	Radiation
			F = 116	Radiation
Zhao et al., 2020	Retrospective cohort	Mean 53	n = 144	Breast	Albumin-globulin ratio >1.5	Survival	Multivariate analysis: Albumin-globulin ratio associated with survival (HR .5 [.3 – .9] P = .017)	III
			M = 0	Surgery, radiation, chemotherapy, targeted therapy, bisphosphonate therapy
			F = 144
Bourassa-Moreau et al., 2020	Retrospective cohort	Mean 63	n = 108	Lung, breast, prostate, kidney, other	Skeletal muscle mass	Survival, complications	Multivariate analysis: L3-TPA/VB associated with complications (OR .3 [.1 – .9] P = .037), 30-day mortality (P = .008), and 90-day mortality (P < .001)	III
			M = 57	Surgery, radiation
			F = 51	Surgery, radiation
Zakaria et al., 2020	Retrospective cohort	Tertile 1: 61	n = 271	Lung, breast, prostate, kidney, gastrointestinal, nasopharynx, thyroid, liver, skin, other, hematologic	Skeletal muscle mass	Survival	Multivariate analysis: Psoas size tertile 2 vs 1 associated with 90-day survival (OR .2 [.1 – .6]), tertile 3 vs 1 associated with 90-day survival (OR .2 [.1 – .4], tertile 2 vs 1 associated with overall survival (HR .5 [.4 – .7]), tertile 3 vs 1 associated with overall survival (HR .5 [.3 – .7])	III
		Tertile 2: 61	M = 157	Surgery, radiation, chemotherapy
		Tertile 3: 57	F = 114	Surgery, radiation, chemotherapy
Zakaria et al., 2020	Retrospective cohort	Mean 65	n = 417	Lung, breast, prostate, kidney, gastrointestinal, nasopharynx, thyroid, liver, skin, other, hematologic	Skeletal muscle mass	Survival	Multivariate analysis: patients in the lowest psoas tertile had shorter survival (222 d, 95% CI = 185-323 d) as compared to the largest tertile (579 d, 95% CI = 405-815 d), (HR 1.5, P = .005). Median psoas size as a cutoff value was also strongly predictive for survival (HR 1.5, P = .002). The psoas/vertebral body ratio was associated with overall (HR 1.5, P = .010)	III
			M = 182	Radiation, chemotherapy
			F = 187	Radiation, chemotherapy
Uei et al., 2020	Retrospective cohort	37 patients ≤69	n = 62	Liver	Serum albumin level ≥3.5 g/dL	Survival	Multivariate analysis: albumin ≥3.5 g/dL associated with survival (HR .4 [.2 – .8], P = .010)	III
		25 patients ≥70	M = 56	Surgery, radiation, chemotherapy
		25 patients ≥70	F = 6	Surgery, radiation, chemotherapy
Ehresman et al., 2020	Retrospective cohort	Nutrition consult Mean 63	n = 95	Lung, breast, prostate, kidney, liver, colorectal, skin, bladder, thyroid, pancreaticSurgery	BMI, preoperative nutrition consult, nutritional risk index score, serum albumin level (continuous)	Complications, length of stay	Multivariate analysis: Preoperative nutrition consult associated with lower risk of complications (parameter estimate – .6 [-1.2 – -.1] P = .040), NRI score <97.5 associated with higher risk of complications (parameter estimate .7 [.2 – 1.3] P = .010), preoperative nutrition consult associated with shorter length of stay (parameter estimate -2.7 [5.2 – -.7] P = .012), NRI score <97.5 associated with longer length of stay (parameter estimate 2.7 [.9 – 4.5] P = .004)	III
		No nutrition consult Mean 60	M = 57
		No nutrition consult Mean 60	F = 38
Xu et al., 2020	Retrospective cohort	Mean 54	n = 95	Kidney	Albumin-globulin ratio <1.5	Survival	Multivariate analysis: no association between proxy markers and outcome	III
			M = 84	Surgery, radiation, chemotherapy
			F = 11	Surgery, radiation, chemotherapy
He et al., 2020	Retrospective cohort	Mean 59	n = 1665	Lung, breast, prostate, kidney, liver	Weight loss	Spinal Metastasis	Multivariate analysis: weight loss associated with occurrence of spinal metastases (P < .001)	III
			M = 1088	No intervention
			F = 577	No intervention
Prost et al., 2020	Prospective cohort	Mean 64	n = 264	Lung, breast, kidney, prostate, colorectal, head and neck, thyroid, liver, skin, gynecologic, bladder, other	Serum albumin level <3.5 g/dL	Survival, complications	Univariate analysis: albumin <3.5 g/dL associated with mortality (OR .5)	II
			M = 149	Surgery
			F = 115	Surgery
Cook et al., 2020	Retrospective cohort	Mean 65	n = 106	Lung, breast, prostate, kidney, others	Serum albumin level >3.5 g/dL	Survival	Multivariate analysis: albumin >3.5 g/dL associated with survival (HR .5 [.3 – .7] P < .001)	III
			M = 66	Surgery, radiation
			F = 40	Surgery, radiation
De Meue et al., 2021	Retrospective cohort	NR	n = 62	Lung, kidney, breast, thyroid, esophagus, prostate, soft tissue sarcoma	Weight loss, serum albumin level (continuous)	Survival	Multivariate analysis: albumin associated with survival (HR .8 [.7 – .9] P = .002)	III
De Meue et al., 2021	Retrospective cohort	NR	n = 62	Surgery	Weight loss, serum albumin level (continuous)	Survival		III
Massad et al., 2021	Retrospective cohort	Mean 63	n = 88	Kidney	Prognostic nutritional index, skeletal muscle mass	Survival	Multivariate analysis: no association between proxy markers and outcome	III
			M = 65	Surgery, radiation, chemotherapy
			F = 23	Surgery, radiation, chemotherapy
Ehresman et al., 2021	Retrospective cohort	Mean 57	n = 346	Not specified	BMI, serum albumin level (continuous)	Non-routine discharge; length of stay	Multivariate analysis: albumin associated with length of stay (OR .4 per g/dL; P < .001)	III
			M = 186	Surgery
			F = 160	Surgery
DiSilvestro et al., 2021	Retrospective cohort	Mean 59	n = 2094	Not specified	BMI, weight loss, serum albumin level <3.5 g/dL	Survival	Multivariate analysis: albumin <3.5 g/dL associated with 30-day mortality (OR 1.9 [1.2 – 3.0] P = .004)	III
			M = 1275	Surgery
			F = 819	Surgery
Gelfand et al., 2021	Retrospective cohort	Mean 61	n = 700	Not specified	BMI, serum albumin level >3.5 g/dL, 2.6 – 3.4 g/dL, and ≤2.5 g/dL	Survival	Multivariate analysis: mild hypoalbuminemia (OR 1.7 [1.0 – 3.0] P = .050) and severe hypoalbuminemia (OR 6.2 [2.8 – 13.5] P < .001) associated with 30-day mortality	III
			M = 451	Surgery
			F = 249	Surgery
Pennington et al., 2021	Retrospective cohort	Mean 57	n = 350	Not specified	BMI, serum albumin level (continuous)	Complications, blood loss	Multivariate analysis: albumin associated with venous thromboembolism (OR .4 [.2 – .8] P = .007) and pulmonary embolism (OR .3 [.2 – .7] P = .003)	III
			M = 186	Surgery
			F = 164	Surgery
Pennington et al., 2021	Retrospective cohort	Mean 57	n = 274	Not specified	BMI, serum albumin level (continuous)	Complications, blood loss	Multivariate analysis: albumin associated with intraoperative blood loss (β = -244.9 per g/dL, P = .011)	III
			M = 145	Surgery
			F = 129	Surgery
Karhade et al., 2022	Retrospective cohort	Mean 64	n = 3001	Not specified	Serum albumin level (continuous)	Survival	Elastic-net penalized logistic regression: albumin was the most important predictor of survival	III
			M = 1665	Surgery, radiation
			F = 1336	Surgery, radiation
Ushiku et al., 2022	Retrospective cohort	Mean 69	n = 78	Lung, prostate, kidney, hematologic, uterus, breast, unknown, colon others	BMI, skeletal muscle mass	Performance status	Multivariate analysis: The area of the psoas major muscle at the L3 level normalized by the vertebral area not in the first tertile was associated with 30-day performance status (OR .2 [.01 – .8], P < .050)	III
			M = 57	Surgery
			F = 21	Surgery
Massad et al., 2022	Retrospective cohort	Mean 63	n = 484	Genitourinary, lung, gastrointestinal, breast, hematologic, skin, bone sarcoma, head & neck, thyroid, soft-tissue sarcoma, other	BMI, serum albumin level, skeletal muscle mass, skeletal muscle density, subcutaneous fat area, visceral fat area	Survival, complications, length of stay	Multivariate analysis: Patients in cluster 2 (high-risk cluster) had lower muscle mass index (mean ± SD 41.2 ± 8.0 vs 50.1 ± 10.5 cm²/m²), lower subcutaneous fat area (147.62 ± 57.80 vs 289.83 ± 109.31 cm²), lower visceral fat area (82.28 ± 48.96 vs 239.26 ± 98.40 cm²), higher muscle radiodensity (35.67 ± 9.94 vs 31.13 ± 9.07 Hounsfield units [HU]), and significantly higher risk of 1-year mortality (HR 1.5 [1.1 – 2.0] P = .020) than individuals in cluster 1 (low-risk cluster)	. III
			M = 175	Surgery
			F = 128	Surgery
Bongers et al., 2022	Retrospective cohort	Mean 62	n = 196	Lung, breast, kidney, hematologic, others	BMI, serum albumin level, skeletal muscle mass, skeletal muscle density, sarcopenia, subcutaneous fat area/density, visceral fat area/density	Survival	Multivariate analysis: Sarcopenia associated with 1-year mortality (HR 1.7 [1.1 – 2.6] P = .020)	III
			M = 123	Surgery
			F = 73	Surgery
Hu et al., 2022	Retrospective cohort	Mean 58	n = 180	Lung, breast, kidney, hematologic, liver, others	BMI, skeletal muscle area	Survival	Multivariate analysis: Larger psoas muscle area is associated with better 90-day, but not 1-year survival	III
			M = 110	Surgery
			F = 70	Surgery
Molho et al., 2022	Retrospective cohort	Mean 65	n = 198	Lung, kidney, breast, prostate, thyroid, colon, skin, others	Serum albumin ≤3.5 g/dL	Survival, complications	Multivariate analysis: albumin ≤3.5 g/dL associated with wound complications (OR 2.3 [1.0 – 5.2] P = .044)	III
			M = 121	Surgery
			F = 77	Surgery
Dohzono et al., 2022	Retrospective cohort	Mean 68	n = 539	Lung, breast, prostate, colorectal, gastric, liver, hematologic, pancreatic, kidney, bladder, biliary, thyroid, esophageal, uterine, unknown, other	Skeletal muscle mass and density	Survival	Univariate analysis: A significant trend toward a low muscle density with the increase in age was found for both sexes. Patients who survived less than 3 months had significantly smaller trunk muscle area than those who survived for more than 3 months in both sexes	III
			M = 273	Surgery, radiation, chemotherapy
			F = 242	Surgery, radiation, chemotherapy
Yang et al., 2022	Both retrospective and prospective	Mean 55	n = 373	Unknown primary	BMI, weight loss, serum albumin level (continuous)	Survival	Multivariate analysis: weight loss associated with survival (HR .7 [.5 – .9] P = .005)	III
			M = 248	Surgery, radiation, chemotherapy
			F = 125	Surgery, radiation, chemotherapy

Utilized Nutritional Markers

Data extraction revealed 13 different surrogate markers of nutrition (Figure 2); the most common was serum albumin in 67% of studies (n = 41),^{12,14,18-22,24,25,27-30,33-41,43-49,53-57,59,62,68,70-73} followed by body weight/BMI in 34% (n = 21),^{15,16,19,20,24,25,31-33,35,37,42,49,51,54,56,57,60,62,71,72} and muscle mass in 28% (n = 17). Among the 13 surrogate markers, these were subdivided into six categories (Figure 3). There were two biochemical markers (albumin and serum protein), two anthropometric measurements (body weight/BMI and weight loss), two measures of body composition assessed by imaging (muscle mass and fat), five nutrition indices (Patient-Generated Subjective Global Assessment, Malnutrition Universal Screening Tool Score, Nutritional Risk Index, albumin-globulin ratio, and Prognostic Nutritional Index), one study examining preoperative nutrition consults, and one study examining “malnutrition” (including nutritional marasmus and severe protein-calorie malnutrition) (Figure 3).

Figure 2.

Markers of nutrition and frequency.

Figure 3.

Markers of malnutrition and common methods of assessment.

Outcomes

The examined outcomes were survival in 82% of studies (n = 50), complications (including wound complications) in 28% (n = 17), length of stay in 10% (n = 6), revision surgery in 5% (n = 3), functional outcome (one study examining postoperative Frankel grade and one study examining postoperative Eastern Cooperative Oncology Group performance status) in 3% (n = 2),^40,60 discharge disposition in 3% (n = 2),^25,33 postoperative ambulation in 2% (n = 1),⁵⁵ health-related quality of life outcome measures in 2% (n = 1),¹¹ and vertebral fracture progression in 2% (n = 1).¹⁵ Out of 61 studies, 18% (n = 11) found no association between the examined markers and outcome on definitive (e.g. multivariate) analyses.^{14,29,30,37-40,43,57,61,68}

Biochemical Markers

Serum albumin was the most studied biochemical marker.^{12,14,18-22,24,25,27-30,33-41,43-49,53-57,59,62,68,70-73} However, there was wide variability in how this was assessed, with studies examining albumin as a continuous variable and others defining hypoalbuminemia as a level <3.0 g/dL, ≤3.5 g/dL, or ≤3.7 g/dL. Most studies examined the association between albumin and survival, but few also examined the association between albumin and wound complications,^39,44 and albumin and ambulatory outcome,⁵⁵ for example. Out of 47 studies examining albumin, 79% (37 of 47) found a significant association between albumin and the studied outcome (predominantly survival); the remaining 21% of studies found no association. Serum protein levels were assessed in one study that found that patients who had a wound infection after oncologic surgery had lower serum protein levels compared to controls.⁴⁴

Anthropometric Measures

Body weight and BMI were the second most studied markers in our review.^{15,16,19,20,24,25,31-33,35,37,42,49,51,54,56,57,60,62,71,72} Like albumin, there was variability on assessment methods, with studies examining BMI as a continuous variable and others at specific thresholds such as BMI >30 kg/m². Weight loss was also assessed in 15% of studies (n = 9).^{14,19,31,33,37,58,62,71,72} Definitions of “weight loss” included loss of ≥10% body weight within 6 months preceding surgery, weight loss of >10% within 3 months preceding surgery,¹⁹ and weight loss >6%.⁶² Out of 21 studies examining BMI, only 10% (2 of 21) found an association between BMI and pathologic fracture progression¹⁵ or survival.⁵¹ Out of 9 studies examining weight loss, 33% (3 of 9) found an association between weight loss and outcome.^31,58,62

Body Composition Measures

Body composition measures included muscle mass and body fat assessed on perioperative computed tomography scans or MRI. Many different methods were used to assess muscle mass, including total psoas area (TPA) at the L3 or L4 level by itself or as a ratio in relationship to the vertebral body (TPA/VB),^{17,21,22,26,32,60,63-67} total muscle area (TMA) including psoas, erector spinae, quadratus lumborum, transversus abdominus, external and internal oblique, and rectus abdominus muscles,^42,43,51,52 and paraspinal/psoas muscle area.^16,23 All studies examining muscle mass studied the association with survival; one also studied the association with complications¹⁷ and another with length of stay.⁴² Out of 17 studies examining muscle mass, 88% (15 of 17) found an association with outcome, mainly postoperative survival. Four studies looked at body fat composition.^16,42,51,52 Body fat was assessed via measurement of visceral and subcutaneous fat areas, and 50% of these studies (2 of 4) found an association with outcome.

Nutrition Indices

Six studies examined five different nutrition indices.^{11,24,39,43,61,69} These included the Patient-Generated Subjective Global Assessment (PG-SGA), Malnutrition Universal Screening Tool (MUST) score, Nutritional Risk Index (NRI), albumin-globulin ratio (AGR), and Prognostic Nutritional Index (PNI) (Table 2). The PG-SGA was evaluated in “short” form in only one study, finding that 92% of patients undergoing surgery for metastatic spine disease had suspected malnutrition or malnutrition.¹¹ A univariate analysis revealed an association between higher PG-SGA scores (i.e. higher risk of malnutrition) and lower baseline SOSGOQ2.0 total scores, SOSGOQ2.0 physical function, mental health and social functioning scores, EQ-5D total scores and EQ-5D mobility scores. No association between malnutrition and survival was found in the study by Versteeg et al.¹¹ The MUST score was evaluated in one study finding that 17% of patients were at moderate or high risk of malnutrition, but found no association between malnutrition and wound complications.³⁹ The NRI was evaluated in a study of 95 patients.²⁴ Following multivariable analysis, a NRI <97.5 was significantly associated with 30-day complication rates and hospital length of stay.²⁴ The AGR is a nutritional-inflammatory biomarker⁷⁴ and was assessed in two studies with conflicting results.^61,69 Zhao et al. found that an AGR >1.5 was associated with improved survival;⁶⁹ Xu et al. found no association between AGR and survival.⁶¹ Lastly, the PNI was evaluated in one study examining outcomes of patients with metastatic renal cell carcinoma to the spine. Although the PNI was associated with survival on univariate analysis, the definitive multivariate analysis failed to show a significant association with survival.⁴³

Table 2.

Nutrition Indices.

Index	Components and Calculation
PG-SGA (short form)	Completed by the patient and includes assessment of body weight, weight change, nutritional intake, physical symptoms, and performance status
	A score of 9 or above on the PG-SGA denotes the need for nutritional intervention
	Additionally, using the total score patients are stratified into well-nourished (I), suspected malnutrition (II), and malnutrition (III)
MUST	Consists of three parts: Evaluation of BMI and obtaining a BMI score (based on provided charts), noting the percentage weight loss and obtaining a weight loss score (based on provided charts), and establishing acute disease effect and obtaining a score
MUST	Total score 0 = low risk (continue routine clinical care), total score 1 = medium risk (observe), total score 2 = high risk (treat)
NRI	NRI = [1.519 × serum albumin (g/dl)] + [41.7 × current weight (kg)/ideal body weight (kg)]
AGR	AGR = albumin (g/dl)/(total protein (g/dl) – albumin (g/dl))
PNI	PNI = [10 × albumin (g/dl)] + [.005 × total lymphocyte count (thousand per mm3)]

PG-SGA Patient-Generated Subjective Global Assessment, MUST Malnutrition Universal Screening Tool, NRI Nutritional Risk Index, AGR albumin-globulin ratio, PNI Prognostic Nutritional Index.

Preoperative Nutrition Consult

The utility of obtaining a preoperative nutrition consult in metastatic spine disease was evaluated in one study.²⁴ Authors compared outcomes of 17 patients who received a preoperative nutrition consult to 78 who did not, finding that patients in the nutrition consult group had had lower rates of 30-day morbidity and shorter hospital stays. However, no information regarding the indication for the nutrition consult or the intervention/optimization performed after patients received the consult was disclosed.

Malnutrition

One study of a national inpatient database in the United States examined “malnutrition” as a broad term and its association with complications, in-hospital mortality, and length of stay.¹² Malnutrition was defined based on ICD-9 codes and included a composite measure of patients with diagnoses of protein malnutrition and marasmus. Malnutrition was significantly and independently associated with the examined outcomes and was then incorporated into a “frailty index”.¹²

Sub-Analysis Based on Cancer Type

As mentioned earlier, 10 studies focused on a single cancer type (Table 1). Among these, three studies examined outcomes in lung cancer,^46,63,68 one on lymphoma,³⁰ two on renal cell carcinoma,^38,43 two on breast cancer,^65,69 and one on hepatocellular carcinoma.⁵⁹ For patients with lung cancer undergoing surgery, Ogihara et al. found an association between hypoalbuminemia and survival,⁴⁶ while Zang et al. found none.⁶⁸ On the other hand, Zakaria et al. found an association between low muscle mass and survival after radiation therapy.⁶³ For patients with renal cell carcinoma, one study found no association between albumin <4.0 g/dL with survival after surgery,³⁸ and another study found no association between the PNI or low muscle mass with outcome after multivariate analysis.⁴³ Lastly, for patients with breast cancer one study found an association between the albumin-globulin ratio and survival after multimodal treatment⁶⁹ and another between low muscle mass and survival after radiation therapy.⁶⁵

Discussion

Malnutrition is highly prevalent in cancer patients. For patients with metastatic spine disease, a malnourished or undernourished state can result in decreased tolerability to surgery, a higher risk of perioperative complications, and limited survival, among others. In this scoping review, we identified 61 manuscripts examining 13 different surrogate markers for malnutrition, finding that most studies found a significant association between malnutrition and the examined outcomes, which in most cases was survival.

The first step in evaluating the prevalence and risk of malnutrition in patients with metastatic spine disease is to critically appraise the tools and criteria for assessment. In general terms, malnutrition in cancer is considered a form of disease-related malnutrition with inflammation,⁷⁵ defined as “a state resulting from lack of intake or uptake of nutrition that leads to altered body composition (decreased fat free mass) and body cell mass leading to diminished physical and mental function and impaired clinical outcome from disease.”⁷⁶ The European Society for Clinical Nutrition and Metabolism (ESPEN) requires that prior to a diagnosis of malnutrition, patients need to be considered at risk according to any validated nutritional risk screening tools. Patients need to have a BMI <18.5 kg/m2 or unintentional weight loss (>10% over an indefinite amount of time or >5% over last 3 months) in addition to reduced BMI (using age-dependent cut-offs) or reduced gender-dependent fat free mass index.⁷⁵ On the other hand, the American Society of Parenteral and Enteral Nutrition (ASPEN) and the Academy of Nutrition and Dietetics (Academy) classify patients as “malnourished” when two of the following criteria are met: low energy intake, weight loss, loss of muscle mass, loss of subcutaneous fat, fluid accumulation, and/or hand grip strength.⁷⁵ In our review, however, none of the examined studies used any of these aforementioned criteria.

Malnutrition in Spine Oncology

In patients with metastatic spine disease, there has been limited research on the impact of malnutrition (as defined by ESPEN, ASPEN, or Academy) on outcomes. Rather, multiple different surrogate markers have been studied, some as single prognostic factors and others as part of nutrition indices. Our scoping review found that 67% of studies used albumin as a biomarker of nutritional status, followed by body weight/BMI in 34%, and muscle mass in 28%.

Albumin is the most abundant serum protein in humans and has been used for decades as an indicator of malnutrition.⁷⁷ In cancer patients, it is also widely accepted that pretreatment hypoalbuminemia (i.e. albumin levels before chemotherapy, radiotherapy, or surgery are undertaken) is associated with decreased survival.⁷ Our review showed that out of 47 studies examining albumin, 79% (37 of 47) found a significant association between albumin levels and survival, complications, postoperative ambulation, readmissions, and others. The remaining 21% of studies found no association between albumin levels and the studied outcomes. Nonetheless, one of the main criticisms of using albumin as a proxy marker for malnutrition is that its concentration is also affected by age, inflammation, kidney disease, and GI disease, among others.⁷⁸ While hypoalbuminemia can be seen in patients who are malnourished, low albumin levels can also be seen in the context of physiologic stress as a result of disease or inflammation.⁷⁹ As such, albumin should be considered a nutritional-inflammatory marker and using it alone as a surrogate marker for nutritional status may not always be appropriate.

Body weight/BMI and a history of weight loss (i.e., anthropometric measures) were the second most common markers found in our review. These are both components of the ESPEN/ASPEN/Academy criteria, but there was wide variability on how studies assessed Body weight/BMI and weight loss. Furthermore, only 10% of studies examining BMI and 33% of studies examining weight loss found a significant association with the studied outcome, suggesting that using these parameters as single prognostic tools may not be appropriate. Measures of body composition such as muscle mass and body fat were described in 17 and 4 studies, respectively. Assessment was typically performed on perioperative imaging studies and many different methods were used. Muscle mass was most consistently associated with survival, with 88% (15 of 17) of studies reporting a significant association between low muscle mass and outcome. While muscle mass seems promising, its use in a clinical setting remains challenging. No clear threshold for low muscle mass has been defined. Additionally, while some of these measures can be performed easily (for example TPA/VB) others requires advanced software analysis.

Nutrition indices and questionnaires were among the least examined markers of malnutrition, despite them being considered more “objective” measures. Indices are calculated based on common laboratory markers such as albumin level or lymphocyte count. On the other hand, the PG-SGA short form and the MUST score are more traditional questionnaires/forms that can be time-consuming. Interestingly, malnutrition as defined by the PG-SGA score was only associated with worse quality of life but not survival in one study.¹¹ Similarly, malnutrition defined by the MUST score was not associated with wound complications.³⁹ Incorporation of indices and questionnaires can be understandably hard to obtain given the emergent nature of spinal cord compression. However, surgeons and mid-level providers can consider obtaining these measures as an inpatient, with serial follow-up in clinic postoperatively.

Implications for Clinical Practice

Other than malnutrition, cancer patients are often afflicted or at risk for frailty and sarcopenia. These three conditions frequently overlap and as seen in this review, many markers such as weight loss and decreased muscle mass are common to more than one of these conditions. Frailty corresponds to a state of abnormal homeostasis leading to increased vulnerability to stressors that can occur independent of advanced age. It is characterized by an accumulation of health deficits as seen in indices such as the Metastatic Spinal Tumor Frailty Index.¹² Sarcopenia refers to a syndrome characterized by both loss of muscle mass and loss of strength/function. While many studies refer to low muscle mass as “sarcopenia”, many fail to objectively assess muscle strength and function. It is a manifestation of frailty and is also referred to as cancer cachexia.

It is evident that malnutrition is also seen in frail and sarcopenic patients and further work into achieving a consensus definition in spine oncology is needed. Our review showed that albumin, body weight, and BMI were the most employed proxies for malnutrition. These variables are generally collected in most institutional databases, which explains their popularity. However, we believe that these are not reflecting the true nutritional status and therefore, should not be used in isolation to define malnutrition. Further research into the utilization of ESPEN/ASPEN/Academy criteria is needed, as well on the possible correlation between these and other markers of malnutrition. A study performed at an urban underserved cancer center found correlations between the Prognostic Nutritional Index and Nutritional Risk Index, Nutritional Risk Index and BMI, and Nutritional Risk Index and body weight, among others.⁸⁰ Given that some of the objective measures such as the PG-SGA may be time-consuming and difficult to implement in an emergency setting, research into the correlation of these measures and common laboratory markers/indices may reveal that they are perhaps correlated with one another and thus easier to utilize for prediction. Furthermore, the appropriateness of using some of these parameters such as albumin as markers of malnutrition needs to be reassessed, given they can also overlap with inflammation, older age, and illness.

One of the most interesting findings in our review is the very limited data on the impact of these markers/malnutrition on other important outcomes such as quality of life or ambulatory status. While most studies focused on overall survival, the impact of malnutrition on wound complications, tolerability of physical therapy, on the likelihood of postoperative ambulation, on the levels of postoperative pain, and on quality of life, among others, is something that remains unanswered.

Lastly, perioperative optimization, when feasible, may also have a role in improving outcome of patients undergoing treatment for spinal metastases that are considered as being at risk of or already suffering from malnutrition. A nutritional surveillance program could identify and treat symptoms such as nausea, vomiting, anorexia, and dysphagia in a timely manner. Depending on the degree of malnutrition, a stepwise approach could include optimizing regular food intake, food fortification with micro- and macronutrients, protein powder, oral nutritional supplements, parenteral nutrition, enteral nutrition, and others.⁸¹ Nonetheless, patients presenting with symptomatic cord compression may not have the time for optimization, and thus malnutrition may not be a modifiable risk factor. In these urgent instances, identification of preoperative malnutrition may aid in counseling, expectations, risk stratification, and perhaps clinical decision making. On the other hand, nutritional counseling with inpatient dietitians for the immediate postoperative period can be considered and may also have the potential to improve outcomes, though this is an area that certainly requires more research.

Limitations

There are several limitations to this study. Given the heterogeneity of markers, thresholds, cancer diagnoses, and treatments, a meta-analysis was not possible. Even when attempting to focus on a single tumor type on our sub-analyses, there was variability in the surrogate markers used to identify nutritional status, as well as the type of intervention and outcomes evaluated. The quality of studies was also a limiting factor, given that the vast majority (89%) were retrospective cohort studies. The studied outcomes were also varied, but most focused on survival and complications; very few studies examined other outcomes including functional status or quality of life. Few studies have assessed the impact of malnutrition in spine oncology in a prospective fashion and it is certainly an area of opportunity for future research.

Conclusion

While the association between malnutrition and poor outcomes in the spine metastatic population is intuitive, our review of the literature has demonstrated the lack of a comprehensive and consistent way of assessing malnutrition and therefore the inconsistency in its relationship with outcomes. The most employed proxies for malnutrition were albumin and body weight/BMI. We believe these measures in isolation should not be used to define malnutrition as they do not capture the complexity of nutritional status. Given the absence of strong evidence towards the utilization of any single biomarker as a surrogate for malnutrition, our recommendation based on the current available evidence would be to employ and research objective evaluations such as those outlined by the ESPEN/ASPEN/Academy when feasible, followed by nutrition indices such as the PG-SGA or PNI. However, a consensus agreement on the assessment and definition of malnutrition in spine tumor patients has not been reached and warrants further investigation.

Supplemental Material

Supplemental Material - Malnutrition in Spine Oncology: Where Are We and What Are We Measuring?

Supplemental Material for Malnutrition in Spine Oncology: Where Are We and What Are We Measuring? by Rafael De la Garza Ramos, Raphaële Charest-Morin, C Rory Goodwin, Scott L Zuckerman, Ilya Laufer, Nicolas Dea, Arjun Sahgal, Laurence D Rhines, Ziya L Gokaslan, Chetan Bettegowda, Anne L. Versteeg, Hanbo Chen, Cordula Netzer, Daniel M Sciubba, John E O’Toole, Michael G Fehlings, Naresh Kumar, Alexander C Disch, Byron Stephens, Tony Goldschlager, Michael H Weber, John H. Shin in Global Spine Journal.

Footnotes

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) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was organized and funded by AO Spine through the AO Spine Knowledge Forum Tumor, a focused group of international Tumor experts. AO Spine is a clinical division of the AO Foundation, which is an independent medically-guided not-for-profit organization.

ORCID iDs

Rafael De la Garza Ramos

Raphaele Charest-Morin

C. Rory Goodwin

Anne L. Versteeg

Hanbo Chen

Michael G. Fehlings

Tony Goldschlager

Supplemental Material

Supplemental material for this article is available online.

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