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PP146-SUN PREDICTION OF RESTING ENERGY EXPENDITURE WITH CURRENT TOOLS IN HOSPITALIZED MALNOURISHED ELDERLY PATIENTS FAILS
Nutritional assessment II
79
ND groups, patients in our three hospitals were older, but fewer of them had lost weight before hospital admission; more of our patients received protein supplement and their care episodes were shorter. Internal medicine (10 wards)
Presence of a nutrition team national guidelines local guidelines Presence of a routine for control of body weight at admission Median age, years Mean length of stay, days Share of patients (%) who had lost weight the last 3 months had lost >8 kilograms received protein supplementation
Surgery (5 wards)
Oncology (2 wards)
ND total (n = 163) ND total (n = 1257) (n = 933)
(n = 82)
ND total (n = 393
(n = 19)
58% 45% 52% 63%
0 1/10 7/10 9/10
60% 27% 46% 60%
0 1/5 3/5 4/5
46% 57% 49% 74%
0 0 1/2 2/2
69 13
69 85 03 20
62 13
64 80 06 14
64 16
64 65 05
46 13 06
11 50 0 33 0 35
47 13 05
15 68 0 22 0 91
57 19 12
18 63 0 13 10 88
Conclusion: Our experience of participation in ND is twofold; interesting comparisons with other wards has provided a ground for further quality development, but participation is time consuming and questionnaires as well as instructions for data collection were not unequivocal. Disclosure of Interest: None Declared
PP145-SUN VALIDITY OF MUST AND SNAQ FOR UNDERNUTRITION SCREENING IN HOSPITAL OUTPATIENTS E. Leistra1,2 , M. Visser2,3 , M.A. van Bokhorst1,2 , A.M. Evers2 , H.M. Kruizenga1,2,3 . 1 Nutrition & Dietetics, VU University Medical Center, 2 Dutch Malnutrition Steering Group, 3 Health Sciences, Faculty of Earth and Life Sciences, VU University, Amsterdam, Netherlands Rationale: Although undernutrition prevalence among hospital outpatients is relatively low, this adds up to thousands of undernourhished patients per year. The majority of them is unrecognized and thus untreated. We aimed to assess the diagnostic accuracy of MUST and SNAQ to improve recognition in this population. Methods: This study was carried out in nine hospitals. Patients were classified according to the following definition: severely undernourished (BMI < 18.5 (65 y) or <20 (>65 y) and/or unintentional weight loss 5% in the last month or 10% in the last six months), moderately undernourished (BMI 18.5 20 (65 y) or 20 22 (>65 y) and/or 5 10% unintentional weight loss in the last six months) or not undernourished. Diagnostic accuracy of the screening tools was expressed as sensitivity (Se), specificity (Sp), positive predictive value (PPV) and negative predictive value (NPV). Results: Out of 2236 outpatients, 6% were severely, 7% moderately and 87% not undernourished. MUST and SNAQ identified 9% and 3% as severely undernourished respectively. MUST had a low PPV (Se = 75, Sp = 95; PPV = 48; NPV = 98) whereas SNAQ had a low sensitivity (Se = 42, Sp = 99, PPV = 72, NPV = 97). About half of the undernourished patients was identified so based on low BMI. By post-hoc combining SNAQ with BMI, the diagnostic accuracy improved (Se = 95; Sp = 99; PPV = 89; NPV = 100). Conclusion: The validity of both MUST and SNAQ is insufficient for hospital outpatients. While SNAQ identifies too little patients as undernourished, MUST identifies too many patients as undernourished. Combining SNAQ with
BMI results in a valid screening tool, however, it would be preferable to use the definition of BMI and weight loss. Disclosure of Interest: None Declared
PP146-SUN PREDICTION OF RESTING ENERGY EXPENDITURE WITH CURRENT TOOLS IN HOSPITALIZED MALNOURISHED ELDERLY PATIENTS FAILS F. Neelemaat1 , M.A. van Bokhorst1 , P.J. Weijs1 . 1 Nutrition and Dietetics, Internal Medicine and EMGO Institute for Health and Care Research, VU University Medical Center, Amsterdam, Netherlands Rationale: Measuring resting energy expenditure (REE) by indirect calorimetry is often not feasible because of (lack of) equipment, time, and trained personnel. In current practice, predictive equations or kcal/kg are used, which we have evaluated in malnourished hospitalized elderly patients in this study. Methods: Indirect calorimetry measurements were performed in malnourished (BMI < 20 and/or >5% weight loss/ 1 month and/or >10% weight loss/6 months) elderly patients (>60 y) at hospital admission and three months after discharge. From literature we collected 63 REE predictive equations and kcal/kg. Evaluation of predictive equations or kcal/kg was performed by (1) percentage of accurate predictions (within 10% of measured REE), (2) percentage bias, and (3) root mean squared prediction error (RMSE). Results: 195 patients (mean age 74±9.1 year, BMI 20.7 ±4.0 kg/m2 , weight loss/1 month 4.5±6.5 kg, weight loss/ 6 months 9.6±7.4 kg) were measured at hospital admission and 108 of them were re-measured three months after hospital discharge. The predictive value of the most frequently used equations and 21 kcal/kg (Alix et al. 2007, REE = 21 kcal/kg × 1.3 stress/activity factor), are presented in Table 1. Table 1: Predictive equations of resting energy expenditure in malnourished elderly patients Hospital admission (n = 195)
REE measured Harris-Benedict 1919 FAO weight/height 21 kcal/kg
3 months after discharge (n = 108)
REE in kcal/d
% Accurate % Bias predictions
RMSE in kcal/d
REE in kcal/d
% Accurate % Bias predictions
RMSE in kcal/d
1473 1235 1364 1268
29.2 40.5 34.4
339 265 346
1448 1282 1398 1330
42.6 60.2 40.7
246 212 264
13.6 4.3 11.7
7.9 0.8 4.1
Conclusion: Prediction of energy expenditure by equations fails in hospitalized malnourished elderly patients. There is an urgent need to study the factors that explain REE in this specific group in order to improve prediction of REE. Disclosure of Interest: None Declared
PP147-SUN NEW PROGNOSIS INFLAMMATORY AND NUTRITIONAL INDEXES: COMPARISON WITH THE P.I.N.I. AS REFERENCE INDEX F. Ziegler1,2 , L. Codevelle2 , E. Houivet3 , J. B´ enichou3 , 1,2 1 1 A. Lavoinne , P. D´ echelotte . EA4311, IHU, IFR23, 2 Institute for Clinical Biology, 3 Biostatistical Unit, Rouen University Hospital, Rouen, France Rationale: The Prognostic Inflammatory and Nutritional Index (PINI) includes the determination of 4
79
ND groups, patients in our three hospitals were older, but fewer of them had lost weight before hospital admission; more of our patients received protein supplement and their care episodes were shorter. Internal medicine (10 wards)
Presence of a nutrition team national guidelines local guidelines Presence of a routine for control of body weight at admission Median age, years Mean length of stay, days Share of patients (%) who had lost weight the last 3 months had lost >8 kilograms received protein supplementation
Surgery (5 wards)
Oncology (2 wards)
ND total (n = 163) ND total (n = 1257) (n = 933)
(n = 82)
ND total (n = 393
(n = 19)
58% 45% 52% 63%
0 1/10 7/10 9/10
60% 27% 46% 60%
0 1/5 3/5 4/5
46% 57% 49% 74%
0 0 1/2 2/2
69 13
69 85 03 20
62 13
64 80 06 14
64 16
64 65 05
46 13 06
11 50 0 33 0 35
47 13 05
15 68 0 22 0 91
57 19 12
18 63 0 13 10 88
Conclusion: Our experience of participation in ND is twofold; interesting comparisons with other wards has provided a ground for further quality development, but participation is time consuming and questionnaires as well as instructions for data collection were not unequivocal. Disclosure of Interest: None Declared
PP145-SUN VALIDITY OF MUST AND SNAQ FOR UNDERNUTRITION SCREENING IN HOSPITAL OUTPATIENTS E. Leistra1,2 , M. Visser2,3 , M.A. van Bokhorst1,2 , A.M. Evers2 , H.M. Kruizenga1,2,3 . 1 Nutrition & Dietetics, VU University Medical Center, 2 Dutch Malnutrition Steering Group, 3 Health Sciences, Faculty of Earth and Life Sciences, VU University, Amsterdam, Netherlands Rationale: Although undernutrition prevalence among hospital outpatients is relatively low, this adds up to thousands of undernourhished patients per year. The majority of them is unrecognized and thus untreated. We aimed to assess the diagnostic accuracy of MUST and SNAQ to improve recognition in this population. Methods: This study was carried out in nine hospitals. Patients were classified according to the following definition: severely undernourished (BMI < 18.5 (65 y) or <20 (>65 y) and/or unintentional weight loss 5% in the last month or 10% in the last six months), moderately undernourished (BMI 18.5 20 (65 y) or 20 22 (>65 y) and/or 5 10% unintentional weight loss in the last six months) or not undernourished. Diagnostic accuracy of the screening tools was expressed as sensitivity (Se), specificity (Sp), positive predictive value (PPV) and negative predictive value (NPV). Results: Out of 2236 outpatients, 6% were severely, 7% moderately and 87% not undernourished. MUST and SNAQ identified 9% and 3% as severely undernourished respectively. MUST had a low PPV (Se = 75, Sp = 95; PPV = 48; NPV = 98) whereas SNAQ had a low sensitivity (Se = 42, Sp = 99, PPV = 72, NPV = 97). About half of the undernourished patients was identified so based on low BMI. By post-hoc combining SNAQ with BMI, the diagnostic accuracy improved (Se = 95; Sp = 99; PPV = 89; NPV = 100). Conclusion: The validity of both MUST and SNAQ is insufficient for hospital outpatients. While SNAQ identifies too little patients as undernourished, MUST identifies too many patients as undernourished. Combining SNAQ with
BMI results in a valid screening tool, however, it would be preferable to use the definition of BMI and weight loss. Disclosure of Interest: None Declared
PP146-SUN PREDICTION OF RESTING ENERGY EXPENDITURE WITH CURRENT TOOLS IN HOSPITALIZED MALNOURISHED ELDERLY PATIENTS FAILS F. Neelemaat1 , M.A. van Bokhorst1 , P.J. Weijs1 . 1 Nutrition and Dietetics, Internal Medicine and EMGO Institute for Health and Care Research, VU University Medical Center, Amsterdam, Netherlands Rationale: Measuring resting energy expenditure (REE) by indirect calorimetry is often not feasible because of (lack of) equipment, time, and trained personnel. In current practice, predictive equations or kcal/kg are used, which we have evaluated in malnourished hospitalized elderly patients in this study. Methods: Indirect calorimetry measurements were performed in malnourished (BMI < 20 and/or >5% weight loss/ 1 month and/or >10% weight loss/6 months) elderly patients (>60 y) at hospital admission and three months after discharge. From literature we collected 63 REE predictive equations and kcal/kg. Evaluation of predictive equations or kcal/kg was performed by (1) percentage of accurate predictions (within 10% of measured REE), (2) percentage bias, and (3) root mean squared prediction error (RMSE). Results: 195 patients (mean age 74±9.1 year, BMI 20.7 ±4.0 kg/m2 , weight loss/1 month 4.5±6.5 kg, weight loss/ 6 months 9.6±7.4 kg) were measured at hospital admission and 108 of them were re-measured three months after hospital discharge. The predictive value of the most frequently used equations and 21 kcal/kg (Alix et al. 2007, REE = 21 kcal/kg × 1.3 stress/activity factor), are presented in Table 1. Table 1: Predictive equations of resting energy expenditure in malnourished elderly patients Hospital admission (n = 195)
REE measured Harris-Benedict 1919 FAO weight/height 21 kcal/kg
3 months after discharge (n = 108)
REE in kcal/d
% Accurate % Bias predictions
RMSE in kcal/d
REE in kcal/d
% Accurate % Bias predictions
RMSE in kcal/d
1473 1235 1364 1268
29.2 40.5 34.4
339 265 346
1448 1282 1398 1330
42.6 60.2 40.7
246 212 264
13.6 4.3 11.7
7.9 0.8 4.1
Conclusion: Prediction of energy expenditure by equations fails in hospitalized malnourished elderly patients. There is an urgent need to study the factors that explain REE in this specific group in order to improve prediction of REE. Disclosure of Interest: None Declared
PP147-SUN NEW PROGNOSIS INFLAMMATORY AND NUTRITIONAL INDEXES: COMPARISON WITH THE P.I.N.I. AS REFERENCE INDEX F. Ziegler1,2 , L. Codevelle2 , E. Houivet3 , J. B´ enichou3 , 1,2 1 1 A. Lavoinne , P. D´ echelotte . EA4311, IHU, IFR23, 2 Institute for Clinical Biology, 3 Biostatistical Unit, Rouen University Hospital, Rouen, France Rationale: The Prognostic Inflammatory and Nutritional Index (PINI) includes the determination of 4