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

Treatment: Nutritional

supplementation muscle strength,

bone loss (10)

LOS (10)

Weight loss prevention (11)

Malnutrition Complications:

admission rates (9)

rates of morbidities (8)

death rates (5)

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 1st – December 31st, 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

“Non-Acutely Ill” subgroupthose who did not present with

these conditions

“Acutely Ill” subgroupthose who presented with kidney,

liver, inflammatory disease

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 (X2) (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 characteristicsAverage Total Group

(n=306)

Acutely Ill

(n=94)

Non-Acutely Ill

(n=212)

Age (years) 82.3 81.3 82.7

Sex (M:F) 89:217 27:67 62:150

Length of Stay (d) 42.7 42.4 42.8

Height (cm) 162.6 163.0 162.4

Weight (kg) 65.6 67.9 64.5

BMI (kg/m2) 24.7 25.5 24.4

Albumin (g/L) 34.9 34.6 35.1

Results

PrevalenceTotal Group Acutely Ill Non-Acutely Ill

Total Number 306 94 212

Pro Malnourished

Prevalence

74

24.2%

29

30.9%

45

21.2%

Pro-E Malnourished

Prevalence

76

24.8%

22

23.4%

54

25.8%

Either

Prevalence

150

49.0%*

51

54.3%**

99

46.7%***

*p=0.755; **p=0.470; ***p=0.372

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

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intervention studies in elderly people. J Gerontol 2004:59:724-729.2. American Dietetic Association and Dietitians of Canada. Manual of clinical

dietetics 6th edition. Nutrition assessment of adults. Illinois: Library of Congress, 2000.

3. Mahan LK, Escott-Strump S. Krause’s food, nutrition & diet therapy 11th edition. Philadelphia: Elsevier, 2004:440.

4. Novartis Nutrition Corporation. Resource manual for long term care. Mississauga, 2006.

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References12. 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.

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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.

Questions?

Thank you!