Transcript The prevalence of protein and protein
The prevalence of protein and protein-energy malnutrition in a population of geriatric rehabilitation patients at SCO Health Service
Amy Nichols, Dietetic Intern Julie Campagna, RD, Research Advisor SCO Health Service July 17th, 2008
Outline
Introduction Objectives Methodology Results Discussion Conclusion
Introduction
SCO Health Service
4 facilities in Ottawa Élisabeth-Bruyère Health Centre Location of Geriatric Rehabilitation Program GRP: 98 beds largest inpatient rehab site http://www.scohs.on.ca
Introduction
Malnutrition
Inadequate nutrition Determinants of malnutrition Body weight, body fat and protein stores, lab values Definitions vary within literature
Introduction
Malnutrition
Protein malnutrition: Alb <35 g/L ; BMI ≥24.0
Protein-energy malnutrition (PEM): Alb <35 g/L ; BMI <24.0 Salva et al (2004), Manual of Clinical Dietetics, Mahan et al (2004)
Introduction
Malnutrition
Prevalence: 35 – 85% (4) Geriatric unit: 35% – 61% with 93% at risk (5,6) Hospitalized: 23% (7) Rehabilitation: 56.1% (8)
Introduction
Malnutrition
Complications: admission rates (9) rates of morbidities (8) death rates (5) Treatment: Nutritional supplementation muscle strength, bone loss LOS (10) (10) Weight loss prevention (11)
Objectives
To assess the overall nutritional status of the group of patients admitted to the SCO Health Service GRP during 2006 To calculate the prevalence of protein and protein-energy malnutrition within this group
Methodology
Subjects
357 eligible GRP patients Admitted January 1 st – December 31 st , 2006 Inclusion criteria: >65 years of age Stable medical condition Serum albumin concentration, height and weight recorded within 7 days of admission
Methodology
Methods
Design: Retrospective chart review Collection of pertinent information from charts: Age Gender Reason for admission to GRP Length of stay (LOS) Relevant current diagnoses
Methodology
Methods
Kidney, liver, inflammatory disease identified as having negative impact on serum albumin concentration (5,8,12,13) Total group “Acutely Ill” subgroup those who presented with kidney, liver, inflammatory disease “Non-Acutely Ill” subgroup those who did not present with these conditions
Methodology
Methods
Classification of protein or protein-energy malnourished patients using Alb and BMI Calculation of prevalence in total group, “Acutely Ill” and “Non-Acutely Ill” subgroups Prevalence = # of malnourished patients x 100 total # of patients
Methodology
Statistics
SPSS version 16.0
Frequency: Crosstabulations Effect of illness: Chi Square Test of Independence (
X
2 ) (p<0.05) Significance: binomial test (p<0.05)
Results
Participant characteristics
306 eligible patients Most common reasons for admission: 50.7% following fracture(s) (n=155) 20.6% following surgery (n=63) 16.7% for deconditionning (n=51)
Results
Participant characteristics Average Age (years) Sex (M:F) Length of Stay (d) Height (cm) Weight (kg) BMI (kg/m 2 ) Albumin (g/L) Total Group (n=306)
82.3
89:217 42.7
162.6
65.6
24.7
34.9
Acutely Ill (n=94)
81.3
27:67 42.4
163.0
67.9
25.5
34.6
Non-Acutely Ill (n=212)
82.7
62:150 42.8
162.4
64.5
24.4
35.1
Results
Prevalence Total Number Pro Malnourished
Prevalence
Pro-E Malnourished
Prevalence
Either
Prevalence
Total Group 306 74
24.2%
76
24.8%
150
49.0%* *p=0.755; **p=0.470; ***p=0.372
Acutely Ill 94 29
30.9%
22
23.4%
51
54.3%**
Non-Acutely Ill 212 45
21.2%
54
25.8%
99
46.7%***
Discussion
Results
Objectives accomplished Prevalence: 49% vs 56.1% (8) Difference likely due to varying definitions of malnutrition and data used to determine status Effect of Illness: 30.9% vs 21.2% Consistent with expected results, though not significant
Discussion
Limitations
Human error Retrospective design Individual variability; limited to data already in charts Albumin as marker of nutritional status Overlap (12) , morbidities (14,15) , inflammation (16) , negative acute phase reactant (3) BMI as marker of nutritional status Possible to be malnourished and have normal BMI
Discussion
Recommendations
Prospective study Alternative methods of identifying and confirming malnutrition Ex: Mini-Nutritional Assessment (MNA) misdiagnosis, better identification of at risk
Conclusion
Malnutrition in disease/mortality rates Treatment: dietary therapies specific to individual populations Objective of study to assess nutritional status of patients at Élisabeth-Bruyère Health Centre’s GRP Despite limitations and lack of statistically significant results, substantial portion of patients found to be malnourished
Conclusion
Implications
Need for dietary intervention identified Justification for implementation of supplementation or food enrichment trial Benefits able to be quantified and evaluated Improvement of health outcome for future patients
Acknowledgements
Special thanks to the following people for their contribution to the development and evolution of this research project: Julie Campagna – Research Advisor Marisa Leblanc – Research Mentor Carole Ryall and Yvon Rollin – SCO Health Service Louise Gariepy – Statistician Danielle – Peer Reviewer Barbara Khouzam – Research Coordinator
References
1. Salva A, Corman B, Andrieu S et al. Minimum data set for nutritional intervention studies in elderly people. J Gerontol 2004:59:724-729.
2. American Dietetic Association and Dietitians of Canada. Manual of clinical dietetics 6 th edition. Nutrition assessment of adults. Illinois: Library of Congress, 2000. 3. Mahan LK, Escott Strump S. Krause’s food, nutrition & diet therapy 11 th edition. Philadelphia: Elsevier, 2004:440.
4. Novartis Nutrition Corporation. Resource manual for long term care. Mississauga, 2006.
5. Sullivan DH, Walls RC, Bopp MM. Protein-energy undernutrition and the risk of mortality within one year of hospital discharge: a follow-up study. J Am Geriatr Soc 1995:43:507-512. 6. Rypkema G, Adang E, Dicke H et al. Cost-effectiveness of an interdisciplinary intervention in geriatric inpatients to prevent malnutrition. J Nutr Health Aging 2003:8:122-7.
References
7. Guigoz Y. The Mini Nutritional Assessment (MNA®) review of the literature – what does it tell us? J Nutr Health Aging 2006:10:466-487.
8. Donini LM, De Bernardini L, De Felice MR et al. Effect of nutritional status on clinical outcome in a population of geriatric rehabilitation patients. Aging Clin Exp Res 2004:16:132-138.
9. Sullivan DH. Risk factors for early hospital readmission in a select population of geriatric rehabilitation patients: the significance of nutritional status. J Am Geriatr Soc 1992:40:792-798. 10. Schürch M-A, Rizzoli R, Slosman D et al. Protein supplements increase serum insulin-like growth factor-I levels and attenuate proximal femur bone loss in patients with recent hip fracture. A randomized double-blind, placebo-controlled trial. Ann Intern Med 1998:128:801-809. 11. Gazzotti C, Arnaud-Battandier F, Parello M et al. Prevention of malnutrition in older people during and after hospitalization: results from a randomised controlled clinical trial. Age Aging 2003:32:321-325.
References
12. Covinsky KE, Covinsky MH, Palmer RM et al. Serum albumin concentration and clinical assessments of nutritional status in hospitalized older people: different sides of different coins? J Am Geriatr Soc 2002:50:631-637.
13. Sergi G, Coin A, Volpato S et al. Role of visceral proteins in detecting malnutrition in the elderly. Eur J Clin Nutr 2006:60:203-209. 14. Sullivan DH, Patch GA, Walls RC et al. Impact of nutritional status on morbidity and mortality in a select population of geriatric patients. Am J Clin Nutr 1990:51:749-758. 15. Sullivan DH, Walls RC. Impact of nutritional status on morbidity in a population of geriatric rehabilitation patients. J Am Geriatr Soc 1994:42:471-477. 16. Sullivan DH, Roberson PK, Johnson LE et al. Association between inflammation-associated cytokines, serum albumins, and mortality in the elderly. J Am Med Dir Assoc 2007:8:458-463.
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