Copyright
©The Author(s) 2019.
World J Gastroenterol. Jul 28, 2019; 25(28): 3823-3837
Published online Jul 28, 2019. doi: 10.3748/wjg.v25.i28.3823
Published online Jul 28, 2019. doi: 10.3748/wjg.v25.i28.3823
Table 1 Demographics of patients with inflammatory bowel disease included in the studies
Study ID | Total (n) (M:F) | CD:UC:ID (n) | Age (yr) | BMI (kg/m2) | Steroid n (%) | Immunomo-dulator n (%) | Biologics n (%) | Previous resection n (%) |
Adams et al[33] | 90 (38:52) | 76:14 | Median: 35 (26-50) | Median: 22.5 | 30 (33) | 40 (44) | 15 (17) | 40 (44) |
Bamba et al[9] | 72 (52:19) | 43:29 | UC Median: 39 (28-55) | Median: 19.5 | - | - | - | 25 (35) |
CD Median: 29 (25-37) | ||||||||
Csontos et al[30] | 173 (92:81) | 126:47 | Mean: 34.8 ± 12.3 | Mean: 23.6 | - | - | - | - |
Cushing et al[34] | 89 (53:29) | 0:89 | Mean: 43 (9 – 86) | Non-sarcopenic: 26 ± 8 | - | 33 (37) | 26 (29) | - |
Sarcopenic: 23 ± 6 | ||||||||
Fujikawa et al[29] | 69 (45:24) | 0:69:0 | Mean: 39.8 ± 14.4 | Mean: 20.40 ± 3.65 | - | - | - | - |
Haskey et al[35] | 110 (47:63) | 75:35 | Mean: 39 ± 15 | Mean BMI: 26.4 ± 5.8 | 5 (4.5) | 17 (15.5) | 17 (15.5) | - |
Holt et al[36] | 44 (20:24) | 44:0 | Mean: 37.8 ± 14.2 | Mean: 23.5 | 20 (45) | 26 (59) | 10 (24) | 44 (100) |
Jansen et al[8] | 55 (19:36) | 55:0 | Mean: 40 ± 11 | Mean: 24.9 | 10 (18) | 31 (56) | 21 (38) | - |
O’Brien et al[31] | 77 (46:31) | 52:21:4 | Median: 42 (20-80) | Median: 24 (16-37) | 42 (55) | - | - | - |
Pedersen et al[12] | 178 (86:92) | 127:51 | Mean: 42.71 (18-86) | - | 86 (48) | 63 (35) | 42 (24) | 178 (100) |
Sumi et al[27] | 16 (12:4) | 16:0 | Responders median: 34 (18-68) | Responders median: 21.7 | 5 (31) | 8 (50) | - | 9 (56) |
Non-responders median: 31 (23-46) | Non-responders Median: 16.8 | |||||||
Takaoka et al[13] | 40 (30:10) | 40:0 | Median: 32.4 (25.3-37.8) | Median:19.2 | 12 (30) | 15 (38) | 30 (75) | 13 (33) |
Thiberge et al[32] | 149 (68:81) | 149:0 | Mean: 41.0 ± 17.5 | Mean: 22.7 ± 6.1 | 108 | 85 | 86 | 85 |
Zhang T et al[10] | 114 (75:39) | 114:0 | Mean: 32 ± 11.47 | Median: 13.66 | - | - | - | 114 (100) |
Zhang T et al[11] | 204 (NR) | 105:99 | NR (min 18; max 65) | Median: 18.41 | 99 (49) | 53 (26) | 25 (12) | 14 (7) |
Zhang W et al[28] | 138 (86:52) | 138:0 | Median: 29 (16-60) | Median: 17.9 | 13 (9) | 50 (36) | - | 37 (27) |
Table 2 Components and interpretation of nutrition screening tools
NST | NRS-2002[9] | MUST[9] | NRI[27] | MIRT[8] | SaskIBD-NR[35] |
NST components | |||||
Initial screening | BMI | Serum albumin | BMI | Symptoms (nausea/vomiting/diarrhea/poor appetite > 2 wk) | |
BMI | Weight loss (last 3-6 mo) | Present weight/usual weight | Weight loss (last 3 mo) | Weight loss (last month) | |
Weight loss (last 6 mo) | Acute disease effect3 | CRP | Anorexia | ||
Dietary intake (last week) | Food restriction | ||||
ICU patient | |||||
Final Screening1 | |||||
Weight loss | |||||
Food intake | |||||
Disease severity2 | |||||
NST score indicating risk of malnutrition | |||||
0 = Low | 0 = Low | > 97.5 = No Risk | Score range = 0-8 | 0-2 = Low risk | |
1 = Mild | 1 = Medium | 83.5-97.5 = Moderate | 0 = Lowest | 3-4 = Medium risk | |
2 = Moderate | ≥ 2 = High | < 83.5 = High | 8 = Highest | ≥ 5 = High risk | |
≥ 3 = High |
Table 3 Components and interpretation of nutrition assessment tools
Nutrition Assessment Tools | |||
SGA[8,9,13] | Comprehensive RD/GI Assessment[35] | BIA[28,30] | CT Scan[9-12,29,31-34,36] |
NAT Components | |||
Nutrient Intake | BMI | SMP | mHUAC |
Weight loss | GI symptoms, oral intake | FFMI | L3 SMI |
Symptoms affecting oral intake | IBD location, severity, concurrent conditions | L4 TPA | |
Functional capacity | Surgical history, medications | ASMI | |
Metabolic requirement | Laboratory parameters (Albumin/Vit D/Iron/Vit B12) | SMA | |
Physical examination | SCAI, HBS | ||
NAT interpretation | |||
A = Well nourished | At risk | Sarcopenia: | Sarcopenia: |
B = Mild/moderately malnourished | Not at risk | FFMI: | mHUAC: Lowest sex quartile at level of L3 vertebrae |
C = Severely malnourished | Men: ≤ 17 kg/m2 | L3 SMI: Lowest sex quartile, variable between studies (Male: < 42-55 cm2/m2; Female: < 35.6-41 cm2/m2) | |
Women: ≤ 15 kg/m2 | L4 TPA: Lowest sex quartile (Male < 56.7 cm2/m2, Female: < 35.6 cm2/m2) | ||
SMP: Continuous variable | ASMI/SMA: Continuous variable |
Table 4 Proportion of nutrition abnormalities via nutrition screening tools
NST | Proportion of low risk patient’s n (%) | Proportion of mild-moderate risk patient’s n (%) | Proportion of high-risk patient’s n (%) | Study ID |
MUST | 12 (16.7) | 27 (37.5) | 49 (68.1) | Bamba et al[9] |
118 (68.2) | 18 (10.4) | 37 (21.4) | Csontos et al[30] | |
93 (84.5) | 12 (10.9) | 5 (4.5) | Haskey et al[35] | |
10 (25.0) | 6 (15) | 24 (60) | Takaoka et al[13] | |
NRI | 5 (31.3) | 11 (68.8) | Sumi et al[27] | |
NRS-2002 | 0 (0) | 24 (33.3) | 48 (66.7) | Bamba et al[9] |
13 (32.5) | 27 (67.5) | Takaoka et al[13] | ||
SaskIBD-NRT | 89 (80.9) | 12 (10.9) | 9 (8.2) | Haskey et al[35] |
Table 5 Proportion of nutrition abnormalities via nutrition assessment tools
NAT measure | ||||
Proportion of non-sarcopenic patients n (%) | Proportion of sarcopenic patients n (%) | Study ID | ||
Sarcopenia | 49 (54.4) | 41 (45.6) | Adams et al[33] | |
42 (58.3) | 30 (41.7) | Bamba et al[9] | ||
125 (72.3) | 48 (27.7) | Csontos et al[30] | ||
25 (30.5) | 57 (69.5) | Cushing et al[34] | ||
51 (73.9) | 18 (26.1) | Fujikawa et al[29] | ||
47 (67.1) | 30 (38.9) | O’Brien et al[31] | ||
134 (75.3) | 44 (24.7) | Pedersen et al[12] | ||
99 (66.4) | 50 (33.6) | Thiberge et al[32] | ||
115 (56.4) | 89 (43.6) | Zhang et al[11] | ||
44 (35.1) | 70 (61.4) | Zhang et al[10] | ||
Comprehensive RD/GI Assessment | Proportion of patients not at risk n (%) | Proportion of patients at risk of malnutrition n (%) | Study ID | |
87 (79.1) | 23 (20.9) | Haskey et al[35] | ||
SGA | Proportion of SGA A | Proportion of SGA B | Proportion of SGA C | Study ID |
8 (11.1) | 37 (51.4) | 27 (37.5%) | Bamba et al[9] | |
8 (20.0) | 17 (42.5) | 15 (37.5%) | Takaoka et al[13] | |
48 (87.3) | 7 (12.7) | Jansen et al[8] |
Table 6 Nutrition screening tools correlating with nutrition assessment tools
NST | Comparative NAT measure | Statistical Variable | Value | Study ID |
MUST | FFMI | Cohen’s Kappa (low/normal FFMI vs low MUST) | κ = 0.53 (95%CI: 0.39-0.67) | Csontos et al[30] |
SMI | Logistic Regression (MUST 0,1 vs ≥ 2) | OR: 0.934, P = 0.014a | Bamba et al[9] | |
RD/GI Assessment | Cohen’s Kappa | κ = 0.15 | Haskey et al[35] | |
MIRT | SGA | Spearman’s Rank Correlation | ρ = 0.394, P = 0.005a | Jansen et al[8] |
NRS-2002 | SMI | Logistic Regression (NRS-2002 1, 2 vs ≥ 3) | OR: 0.928, P = 0.008a | Bamba et al[9] |
SaskIBD-NR | RD/GI Assessment | Cohen’s Kappa | κ = 0.73 | Haskey et al[35] |
Table 7 Significant nutrition screening tool correlations with clinical outcomes
NST | Comparative outcome measure | Statistical variable | Value | Study ID |
MIRT | Hospitalization | Spearman’s rank correlation | ρ = 0.398, P = 0.003a | Jansen et al[8] |
Disease flare | ρ = 0.299, P = 0.030a | |||
Disease complications1 | ρ = 0.333, P = 0.015a | |||
Need for surgery | ρ = 0.371, P = 0.006a | |||
NRI | Response to infliximab | Fischer’s exact test | P = 0.037a | Sumi et al[27] |
NRS-2002 | Length of stay (< 28 vs ≥ 28 d) | Chi-square test | P = 0.032a | Takaoka et al[13] |
Table 8 Significant nutrition assessment tool correlations with clinical outcomes
NAT | Comparative outcome measure | Statistical analysis | Result | Study ID |
SGA | Length of stay in hospital | Chi-square test | P = 0.008 | Takaoka et al[13] |
Sarcopenia | Change in IBD disease activity at 6 mo (HBI) | Paired t-test (baseline vs 6 mo) | Sarcopenic: 0.4 (P = 0.80) | Adams et al[33] |
Non-sarcopenic: -2.3 (P = 0.004) | ||||
Need for operation (operation free survival curve) | Kaplan-Meier Analysis | P = 0.003 | Bamba et al[9] | |
P = 0.003 | Zhang et al[11] | |||
Need for operation | Cox-regression (multivariate) | HR 0.318 (0.126-0.802), P = 0.015 | Bamba et al[9] | |
Need for any rescue therapy (medical/surgical) | Fischers exact test | P = 0.02 | Cushing et al[34] | |
Multivariate logistic regression | OR 3.98 (95%CI 1.12-14.1), P = 0.033 | |||
Post-operative complications (Major)1 | OR 9.24 (95%CI 1.10-77.50). P = 0.04 | Zhang et al[10] | ||
UC disease activity (Mayo Score ≥ 6) | OR 8.49 (95%CI 1.80-40.10), P = 0.007 | Zhang et al[11] | ||
Post-operative surgical site infection | OR 4.91 (95%CI 1.09-23.50), P = 0.03 | Fujikawa et al[29] | ||
Need for red blood cell transfusion | OR 1.31, P = 0.014 | Pedersen et al[12] | ||
ICU admission | OR 1.32, P = 0.016 | |||
Post-operative sepsis | OR 1.325, P = 0.009 | |||
Deep vein thrombosis | OR 1.265, P = 0.0173 | |||
Clavien-Dindo grade 4 complication | OR 1.329, P = 0.0052 | |||
ASMI | Fecal calprotectin | Spearman’s Rank Correlation | ρ = -0.564, P = 0.005 | Holt et al[36] |
L3 SMI | UC disease activity (Mayo Score) | ρ = -0.523, P ≤ 0.01 | Zhang et al[11] | |
SMA | ρ = -0.445, P ≤ 0.01 | |||
SMP | Post-operative complications (Overall)2 | Multivariate logistic regression analysis | OR: 0.487 (95%CI 0.307-0.772) P = 0.002a | Zhang et al[28] |
Post-op complications (Major)1 | OR: 0.588 (95%CI 0.422-0.820) P = 0.002a |
- Citation: Li S, Ney M, Eslamparast T, Vandermeer B, Ismond KP, Kroeker K, Halloran B, Raman M, Tandon P. Systematic review of nutrition screening and assessment in inflammatory bowel disease. World J Gastroenterol 2019; 25(28): 3823-3837
- URL: https://www.wjgnet.com/1007-9327/full/v25/i28/3823.htm
- DOI: https://dx.doi.org/10.3748/wjg.v25.i28.3823