Positive and Negative Deviance: Keys to. Elucidating Thresholds of Nutritional Care Mary Pat Kelly, MS, RD,” Mary Ann Kight, PhD, RD,t Vince Migliore, BA,* and SDC-RD/RA Scientific Supports I Objective: To ascertain biochemical and clinical variables associated with positive deviance (low risk) and negative deviance (high risk for hospitalization with infection) as predicted by the Hemodialysis Prognostic Nutrition Index (HD-PNI). I Design: Secondary, descriptive. I Setting: Seven nonprofit, freestanding dialysis clinics. I Patients: 402 hemodialysis patients (219 female, 154 with diabetic history). I Intervention: Nonintervention study; routinely collected data were analyzed. I Main outcome measure: Positive deviance indicators were those statistically different (P < .05, Student’s t tests/chi squared analysis) between the 25% in the low-risk group (HD-PNI) and those in the middle-risk group. Variables associated with negative deviance showed statistical difference between the 25% in the high risk group and those in the middle-risk group. I Results: Diabetic patients showed negative deviance, with 16% classified as low risk and 30% as high risk (P < .Ol). Only history of hospitalization (days, times), albumin (ALB) level, and white blood cell (WBC) count played roles in both positive and negative deviance. Negative deviance was characterized by an ALB level of 36 g/L (3.6 g/dL) or lower, a WBC count of 9.1 log/L (K/pL) or higher, a cholesterol level of 4.1 mmol/L (157 mg/dL) or lower, 88% usual weight or less, hospitalized 2.9 times, and hospitalized 18.9 days. Positive deviance indicators included absence of hospitalization and infection linked with a serum ALB level of 41 g/L (4.1 g/dL) or higher, a blood urea nitrogen level of 29.5 mmol/L (83 mg/dL) or higher, and acreatinine level of 1,123 pmol/L (12.7 mg/dL) or higher. I Conclusion: Negative deviance indicators characterized a probable high-risk nutritional unwellness syndrome related to inadequate nutritional replacement. Likewise, variables associated with positive deviance enabled further conceptualization of nutritional wellness defined as minimal risk for hospitaliza- tion with infection. o 1995 by the National Kidney Foundation, Inc. *Renal Nutrition Specialist and Principal Investigator, University of California Renal Center, San Francisco, CA. T HE HISTORICAL APPROACH to renal nutrition research frequently has in- fProfessor of Nutrition and Coinvestigator, Univer- volved separating a subset sample on the sity of Arizona, Tucson, AZ. #President, Accu-Stat, and Project Statistician, San Jose, CA. $Satellite Dialysis Centers’ Renal DietitianslRe- search Associates: Sidney Belanger, RD, Menlo Park; Elaine Emery, RD, Turlock; Faith Tootell Bianchessi, MS, RD, San Jose; Karen Hansen, RD, Watsonville; Marty Torres, RD, Modesto; Susan Weakley, RD, San Jose; and Sally Brooks-Schulke, MS, RD, San Jose, CA. Suooolted bv an Allied Health Care Grant from the basis of an undesirable clinical variable or outcome so that the characteristics of this subset sample could be compared with the rest of the study population. Although this statistical method has enabled renal nutri- tion specialists to isolate vulnerable groups, it has identified variables primarily associ- ated with poor outcome. In the absence of I, I National Kidney Foundation of Northern California. data describing patients with more optimal Address reprint requests to Mary Pat Kelly, MS, RD, outcome. renal nutrition researchers have c/o University of California Renal Center, San Fran- cisco General Hospital, Bldg 100, Room 34OA, San Francisco, CA 94100. o 1995 bv the National Kidnev Foundation, Inc. been left to assume that correction or improvement of variables associated with poor outcome would result in a more favor- 1051-22j6/95/0503-0004$~33.00/0 able prognosis. 124 Journal of Renal Nutrition, Vol 5, No 3 (July), 1995: pp 124-l 32
Transcript
Positive and Negative Deviance: Keys to. Elucidating Thresholds of Nutritional Care Mary Pat Kelly, MS, RD,” Mary Ann Kight, PhD, RD,t Vince Migliore, BA,* and SDC-RD/RA Scientific Supports
I Objective: To ascertain biochemical and clinical variables associated with positive deviance (low risk) and negative deviance (high risk for hospitalization with infection) as predicted by the Hemodialysis Prognostic Nutrition Index (HD-PNI). I Design: Secondary, descriptive. I Setting: Seven nonprofit, freestanding dialysis clinics. I Patients: 402 hemodialysis patients (219 female, 154 with diabetic history). I Intervention: Nonintervention study; routinely collected data were analyzed. I Main outcome measure: Positive deviance indicators were those statistically different (P < .05, Student’s t tests/chi squared analysis) between the 25% in the low-risk group (HD-PNI) and those in the middle-risk group. Variables associated with negative deviance showed statistical difference between the 25% in the high risk group and those in the middle-risk group. I Results: Diabetic patients showed negative deviance, with 16% classified as low risk and 30% as high risk (P < .Ol). Only history of hospitalization (days, times), albumin (ALB) level, and white blood cell (WBC) count played roles in both positive and negative deviance. Negative deviance was characterized by an ALB level of 36 g/L (3.6 g/dL) or lower, a WBC count of 9.1 log/L (K/pL) or higher, a cholesterol level of 4.1 mmol/L (157 mg/dL) or lower, 88% usual weight or less, hospitalized 2.9 times, and hospitalized 18.9 days. Positive deviance indicators included absence of hospitalization and infection linked with a serum ALB level of 41 g/L (4.1 g/dL) or higher, a blood urea nitrogen level of 29.5 mmol/L (83 mg/dL) or higher, and acreatinine level of 1,123 pmol/L (12.7 mg/dL) or higher. I Conclusion: Negative deviance indicators characterized a probable high-risk nutritional unwellness syndrome related to inadequate nutritional replacement. Likewise, variables associated with positive deviance enabled further conceptualization of nutritional wellness defined as minimal risk for hospitaliza- tion with infection. o 1995 by the National Kidney Foundation, Inc.
*Renal Nutrition Specialist and Principal Investigator, University of California Renal Center, San Francisco, CA. T HE HISTORICAL APPROACH to renal
nutrition research frequently has in- fProfessor of Nutrition and Coinvestigator, Univer- volved separating a subset sample on the
sity of Arizona, Tucson, AZ. #President, Accu-Stat, and Project Statistician, San
Jose, CA. $Satellite Dialysis Centers’ Renal DietitianslRe-
search Associates: Sidney Belanger, RD, Menlo Park; Elaine Emery, RD, Turlock; Faith Tootell Bianchessi, MS, RD, San Jose; Karen Hansen, RD, Watsonville; Marty Torres, RD, Modesto; Susan Weakley, RD, San Jose; and Sally Brooks-Schulke, MS, RD, San Jose, CA.
Suooolted bv an Allied Health Care Grant from the
basis of an undesirable clinical variable or outcome so that the characteristics of this subset sample could be compared with the rest of the study population. Although this statistical method has enabled renal nutri- tion specialists to isolate vulnerable groups, it has identified variables primarily associ- ated with poor outcome. In the absence of
I , I
National Kidney Foundation of Northern California. data describing patients with more optimal Address reprint requests to Mary Pat Kelly, MS, RD, outcome. renal nutrition researchers have
c/o University of California Renal Center, San Fran- cisco General Hospital, Bldg 100, Room 34OA, San Francisco, CA 94100.
o 1995 bv the National Kidnev Foundation, Inc.
been left to assume that correction or improvement of variables associated with poor outcome would result in a more favor-
1051-22j6/95/0503-0004$~33.00/0 able prognosis.
124 Journal of Renal Nutrition, Vol 5, No 3 (July), 1995: pp 124-l 32
POSITIVE AND NEGATIVE DEVIANCE 125
Research in the child development area has established that characteristics associ- ated with nutritional risk are not always those identified with nutritional protection.i Through an innovative statistical design,2 study population samples have been di- vided into three groups: the 25% doing poorly, the 25% doing well, and the remain- ing middle 50% used as a reference group. Nutritional variables significantly different by Student’s t tests (P < .05) between the poor-outcome group and the middle-out- come group identify study subjects at risk and characterize negative deviance. Those variables found to have significance be- tween the good-outcome and the middle- groups identify subjects nutritionally pro- tected and are associated with positive deviance. In this way, children identified using characteristics associated with nega- tive deviance are nutritionally supplemented to achieve the nutritional status docu- mented in the positive deviance group.
Renal dietitians are responsible for identi- fying end-stage renal disease (ESRD) pa- tients at high risk and for overseeing nutri- tional replacement therapies. Although the initial goal of intervention is guiding the patient out of nutritional risk, the long-term objective involves moving the patient along a wellness continuum to achieve a level of nutritional replacement that enables protec- tion. Although recent publications have ad- dressed variables associated with risk,3-5 few have considered nutritional characteris- tics of those ESRD patients who are least likely to suffer poor outcomes.
This study applied the trimodal statistical technique tested in the child development literature to the Hemodialysis Prognostic Nutrition index (HD-PNI)” study population to answer the following questions:
1. Do the same biochemical and clinical variables characterize both positive and negative deviance in assessing risk for hospitalization with infection as defined by the HD-PNI?
2. What nutritional thresholds are identi- fied by the arithmetic means of posi- tive and negative deviance to signal
nutritional protection or vulnerability, respectively?
3. Would mean variables associated with positive deviance provide the neces- sary scientific basis for a functional definition of nutritional replacement adequacy?
SUBJECTS AND METHODS
All 402 patients involved in the HD-PNI were separated into one of three risk catego- ries based on the calculated discriminate function from the HD-PNI formula and sub- sequent grid plotting to predict probability of risk for being hospitalized with infection:
HD-PNI = 3.33 - 1.06 (mean albumin/g/dl)
+ 0.514 (times hospitalized)
+ 0.180 (mean creatinine change/mg/dL)
+ 0.04 (days hospitalized).
The 25% of the population least likely to be hospitalized (probability < 10%) were se- lected as positive deviants (N = 100); the 25% most likely to be hospitalized (N = 101) were classified as negative deviants (prob- ability > 45%); and the 50% remaining (N = 201) were identified as the middle-risk group (probability between 11% and 44%) as shown in Fig I.
Clinical variables compared in the three different groups included gender, presence of a diabetic history, age, history of hospital- ization (days, times), months with ESRD, and percentage of usual weight (calculated by dividing the patient’s current weight by the body weight before ESRD). Biochemi- cal variables included blood urea nitrogen (BUN), creatinine (CRE), albumin (ALB), cholesterol (CHOL), and white blood count (WBC). All biochemical values were deter- mined by Satellite Laboratory Services (SDC) according to methods previously described.6
Statistical analysis was performed on a Macintosh SE (Apple, Cupertino, CA) using the Statistical Package for the Social Sci- ences7 (SPSS, Inc, Chicago, IL) Version 4.0. Before statistical tests were conducted, biochemical variables were corrected for gender difference. The Student’s t test
126 KELLY ET AL
0
00”: 0000
00000 000000
;0 DO0 0000 OOOOQ 000000
-___ N = 101 N = 201 N = 100
Assessed Probability of Hospitalization with Infection >45% t l-44% c 10%
High Middle Low Negative Deviants Risk Group Positive Deviants
FIGURE 1. The application of a trimodal statistical de- sign to the HD-PNI population for differentiat- ing positive and negative deviance, ie, char- acteristics pivotal to elucidating nutritional replacement.
negative deviant, and middle-risk groups. Chi-squared testing showed less than a 2% variation in gender makeup of each risk group when compared to the percentage of men and women in the entire study. On the other hand, only 16% of persons with a diabetic history were found in the low-risk group, whereas 54% fell into the middle risk and 30% into the high-risk groups (Table 1). These differences were significant using chi-squared analysis (P < .Ol). A radar graph in Fig 2 shows expected versus actual distribution of diabetic patients into the three risk categories.
(P < .05) was used to determine statistical difference between negative deviants and the middle-risk Group and between the middle-risk group and the positive deviants. Chi-squared testing (P < .05) was used to determine statistical difference in the distri- bution of men, women, and those with and without a diabetic history into the three risk categories for hospitalization with infection.
RESULTS
Clinical variables that were significantly different between the positive deviants and the middle-risk group (Table 2) included age and the absence of hospitalization (both days and times). Positive deviants were on average 6 years younger and had experienced no hospitalizations in the 6-month study period. No significant differ- ence was found in months with ESRD and percentage of usual weight between posi- tive deviants and the middle-risk group. When compared with the middle-risk group, biochemistries associated with positive de- viants included higher BUN, CRE, and ALB levels but a lower WBC count (Table 3). There was no significant difference in CHOL levels.
No gender difference was found in the Negative deviants were identified by in- distribution of patients into positive deviant, creased hospitalization (both days and
TABLE 1. Distribution Into Low-, Middle-, and High-Risk Categories for Hospitalization With Infection by Gender and Diabetic History
Low Risk (Positive
Categories Deviants)
Women Count (%) 5.5 (25) % of 3 risk groups 55”
Men Count (%) 45 (25) % of 3 risk groups 45t
Positive diabetic history Count (%) 24 (16) % of 3 risk groups 24
Negative diabetic history Count (%) 76 (31) % of 3 risk groups 760
*/tNot significantly different using chi-square test. */§P < .Ol using chi-square test.
Middle
111 (51) 55*
90 (49) 43
84 (54) 42*
117 (47) 588
High (Negative Deviants)
53 (24) 53*
48 (26) 47t
46 (30) 45*
55 (22) 550
Totals
219 (55)*
183 (45)t
154 (38)*
248 (62)§
POSITIVE AND NEGATIVE DEVIANCE 127
Low Risk 25%
High Risk 30%
H Expected
- Actual
Y 54%
Middle Risk FIGURE 2. Actual distribution of hemodialysis patients with a diabetic history into low-, middle-, and high-risk categories for hospitalization with infection.
times), a shorter length of time on dialysis, and a lower percentage of usual weight when compared with the middle-risk group. There was no significant difference in the age of negative deviants and those in the middle-risk group. Negative deviants also had lower serum ALB and CHOL levels but higher WBC counts. No significant differ- ence was found between BUN and CRE levels when comparing the middle-risk group with the negative deviants.
When plotted with SDC laboratory norms, the HD-PNI patient subsets were found consistently either skewed or completely outside of the established ranges. ALB
values were significantly skewed to the left of the normal range, with the negative deviant group mean (36 -f- 3 g/L [3.59 & 0.34 g/dL]) completely outside of laboratory normal range (Fig 3). Subset variation in this group measured by comparisons of the standard deviation (SD) as a percent of the respec- tive mean suggested less variability in the positive deviant group when compared with the middle-risk group and negative deviant subsets. Mean serum CHOL and percent- age of usual weight values were found drifting to the left of norm. WBC count means drifted to the right of laboratory norms, confirming the presence of infection (Fig 4) with mean BUN and CRE values 3 to 10 times normal values and thus confirming protein end-product toxicity.
DISCUSSION
As discovered in the child development literature, characteristics associated with increased risk for hospitalization in HD pa- tients were not the same as those identified in the subset with reduced risk. In the HD-PNI population, only a history of previ- ous hospitalization (days and times) and ALB and WBC values played statistically significant roles in both positive and nega- tive deviance.
When compared with the middle-risk sample, negative deviance was character- ized by the presence of a diabetic history, six times the number of hospitalizations, eight times the number of days hospital- ized, and a dry weight decreasing to less
TABLE 2. Means, SDS, and Statistical Significance of Selected Clinical Charalcteristics in Low-, Middle-, and High-Risk Hospitalization Groups
Hospitalization Months of % Usual Risk Age W Times Days ESRD Weight
deviants) 63.2 r 14.6* 2.88 k 1.60* 18.9 ‘- 18.3* 31.1 -r- 28.2* 87.8 k 13.0t
Abbreviation: NS, not significant. *P < .Ol between asterisked groups in same column. tP < .05.
128 KELLY ET AL
TABLE 3. Means, SDS, and Statistical Significance of Selected Nutritional Biochemistries (Study Months 4 Through 9) in Low-, Middle-, and High-Risk Groups for Hospitalization With Infection
Risk
BUN CRE ALB WBC CHOL (mmol/L * SD) (pmol/L f SD) (g/L 2 SD) (109/L) (mmol/L 2 SD)
Abbreviation: NS, not significant. *P < .Ol between asterisked groups in same column tP < .05.
than 88% of the usual weight before illness. Those in the negative deviant group also were newer to dialysis, averaging 12 fewer months with ESRD. In addition to reduced serum ALB levels and elevated WBC counts, negative deviants were found to have se- rum CHOL 0.26 mmol/L (10 mg/dL) less than those in the middle-risk group, under- lining the reduction in total CHOL noted with infection.8
Although the collection of variables asso- ciated with poor outcome will not surprise most renal dietitians, mean thresholds for increased risk were remarkably high. A mean ALB of 36 & 3 g/L (3.59 2 0.34 g/dL) generally is not greeted with grave concern, but a second look at Fig 3 shows that it lies
completely outside of a range defined by the mean minus 2 SDS of the positive deviance group. In addition, the arbitrary figure of 3.5 g/L (3.5 g/dL) used commonly in the criteria to discontinue intravenous nutritional therapy actually is beneath the mean of the negative deviant group.
Loss of somatic stores also were reveal- ing; even the positive deviance group had experienced a mean 8% weight loss from their earlier predialysis weights. The nega- tive deviance group displayed a further 4% loss, confirming the clinical significance of an earlier finding that a 5% weight loss in a 3-month period resulted in twice the risk for hospitalization9 Thus, erosion of somatic stores to approximately 88% of usual weight
36+3 (3.59 + 0.34)
Tzi2Mean
Negative Deviants
(3.79 lO.27) SD = 7.9% of Mean ~~~S~~- 1 - .xF-
Middle Risk
41 +2 (4.10 f 0.20) SD = 4.8% of Mean
Positive Deviants
37 - 52 13.7 - 5.2)
I SDC Clinical Lab Norms I
I I I I I / I I 1 I I I I 1 I I
& (2.:) (440 (2, g, Mean Serum Albumin in g/L, (gldl)
FIGURE 3. Means and SDS for se- rum albumin levels in low-risk (positive devi- ant); middle-risk, and high-risk (negative de- viant) subsets as re- lated to laboratory norms for serum albu- min values.
Negative Deviants
Mean White Blood Cells (WBC) of Risk Category
D Standard Deviation
:w+~ Second Standard Deviation
7.52 f 2.05
~$~*&w$w+;?- B%&W$$f9;%~$
Positive Deviants
FIGURE 4. Means and SDS for se- rum WBC counts in I
4.8 - 10.8
SDC 1 low-risk (positive devi- Clinical Lab Norms ant), middle-risk, and , , , , , I , , , / , I I , I high-risk (negative de- 0 5 10 15 20
viant) subsets. Mean Serum White Cells log/L (K/pL)
129
defined the mean limit for those at highest nutritional risk for infection.
Positive deviance was associated with younger age, the complete absence of hospitalization, lower WBC count, and higher BUN, CRE, and ALB levels. The mean serum ALB level for those with nutritional protection was exceptionally high at 41 g/L (4.1 g/dL). In the presence of adequate dialysis therapy, the higher BUN and CRE values associated with the positive devi- ance group may reflect more appropriate nutritional replacement.
Based on these data, moving HD pa- tients from high- to middle-risk groups for hospitalization with infection will require mini- mizing infection as well as hospital times
and days and maintaining at least 91% of usual weight. Biochemical goals defined by the mean of the middle-risk group include achieving a serum ALB level of 38 g/L (3.8 g/dL) or higher and a serum CHOL level of 4.3 mmol/L (167 mg/dL). These nutritional practice goals are described spatially in a radar graph (Fig 5).
To achieve nutritional protection against hospitalization with infection, clinical efforts must be directed at avoiding all hospitaliza- tion and minimizing infection. Biochemical goals would include achieving a serum ALB level of 41 g/L (4.1 gm/dL) and high normal values for protein end-products in HD pa- tients evidenced by the mean BUN level of 29.5 mmol/L (83 mg/dL) and CRE level of
Times Hospitalized Days Hospitalized 2.9 to 0.5 18.9to 2.3
\ / \ / \ \ /I \ \ /I 1’ \
White Blood Cells
b--- % Usual Weight
9.1t08.1109/L(K/pL)------- --
88to91% / \
FIGURE 5. \
/I \ Radar graph showing
\ necessary corrections \ of selected biochemi-
/I \ - Middle Risk \ cal and clinical vari-
/ +---+ High Risk ables to move hemodi- /I \ alysis patients from
Cholesterol Albumin high- to middle risk 4.1 to 4.3 mmol/L 36to 38 g/L groups for hospitaliza-
(157to 167 mg/dL) (3.6to 3.8 g/dL) tion with infection.
130
Creatinine 1008 to 1123 vol/L (11.4to 12.7mgldL)
Albumin 38 to 41 g/L
(3.8 to 4.1 g/dL)
KELLY ET AL
Blood Urea Nitrogen 27.8 to 29.5 mmol/L
White Blood Cells _--_-- 8.1 to 7.5 109/L (K/FL) FIGURE 6. Radar graph showing
(78 to 83 mg/dL) /\ \
/I \ / \
/I \
\
/I \
\
/I \
\ Days Hospitalized limes Hospitalized
‘2.3 to None 0.5 to None
necessary corrections of selected biochemi-
- Middle Risk cal and clinical vari- ables to move hemodi-
- Low Risk alysis patients from middle- to low-risk for hospitalization with in- fection.
1,123 pmol/L (12.7 mg/dL) documented after the longest interdialytic space. Nutri- tional replacement capable of moving indi- cators from middle to low risk is shown in the radar graph in Fig 6.
It is the belief of the researchers that the mean values for clinical and biochemical variables in the three risk groups do provide the variable-specific criteria necessary to accurately assess nutritional risk for hospi- talization with infection. Because of the scientific derivation from a data base of 402 HD patients, statistical means associated with positive and negative deviance may be used to describe nutritional status. These thresholds of nutritional risk are shown in Table 4 along with the investigators’ diag- nostic impression.
These relationships have profound impli- cations for clinical practice. With the unmis-
takable role of hospitalization in the erosion of nutritional status, lo they suggest that all medical/nursing/nutritional therapies be tar- geted to prevent hospitalization. Although RDs generally do not see themselves as preventing hospitalization, an earlier pro- spective multicenter study9 of 613 HD pa- tients showed hospitalization rates of 44%, 36%, and 19% in patients receiving less than 20 RD minutes, between 20 and 30 RD minutes, and more than 30 RD minutes/ patient/wk, respectively (&i-squared test, P < .Ol).
Once adequate RD staffing is assessed” and achieved, all nutritional therapies must be aimed at maintaining the patient’s so- matic and visceral stores. Careful attention to weight loss and monthly protein cata- bolic rates will enable assessment of pro- tein and caloric adequacy. Nutrition physi-
TABLE 4. Mean Clinical and Biochemical Thresholds Defining Middle- and High-Risk Groups for Hospitalization With Infection in 402 Hemodialysis Patients and Dietetic-Specific Nutritional Diagnostic Impression
0.5 2.9 ------------ Probable high risk nutritional unwellness 2.3 18.9 ___--.-.__-- syndrome (D-24.001) could lead to hos-
38 36 pitalization related to inadequate nutri- f3.81 L3.61 tional replacement: characterized by P3.11 i-9.11 _--_.._____- negative deviance indicators
91 88 ___“.-.___--
4.3 4.1 ___________-
[I671 D571
POSITIVE AND NEGATIVE DEVIANCE 131
cal examination of patients mandated in recent recommendations by the Joint Com- mission on Accreditation of Health Care Organizations’* for signs of vitamin and mineral deficiency or toxicity will help RDs further assess the nutrient-based lesion stage of nutritional loss.
In hospitalized patients, use of liberalized meal plans may be explored in an effort to improve oral intake. Complete oral and intravenous nutritional therapies should be implemented during hospitalization if food intake fails. Posthospitalization nutritional goals must possibly be implemented to adequately replace any negative deviance experienced, aiming ultimately for the level of nutritional status that confers positive deviance protection.
SUMMARY AND CONCLUSIONS
Commitment to nutritional wellness on the part of the medical director and/or chief executive officer will require (1) prevention of high-risk nutritional unwellness as de- scribed in this article and (2) adequate RD staffing to encourage increased food in- take, provision of oral nutritional supple- ments, and expeditious use of intravenous nutritional therapies when the oral route fails, hence fostering nutritional wellness. It also will demand the dialysis staff to rou- tinely assess patients for adequately pre- scribed and delivered dialysis treatment time, especially during hospitalization or the use of a temporary vascular access.
Nutritional unwellness D24.001 (D = di- etetic specific) has been defined as the presence of signs, symptoms, and signifi- cances of questionable nutritional status not otherwise specified.13 Negative devi- ance indicators found in this study can be used to give deeper characterization to nutritional unwellness in HD patients. Likewise, nutritional wellness can be charac- terized by this study’s positive deviance indicators, specifically the absence of hos- pitalization and infection linked with a se- rum ALB level of 41 g/L (4.1 g/dL) or higher, a BUN level of 29.5 mmol/L (83 mg/dL) or higher, and a CRE level of 1,123 wmol/L (12.7 mg/dL) or higher. Based on
these definitions, ideal nutritional replace- ment goals would achieve the nutritional status defined by the above positive devi- ance indicators.
ACKNOWLEDGMENT
We thank Norman Coplon, MD, president of Satellite Dialysis Centers, for his support of nutritional research within the corporation; Linda McCann, RD, nutritional services coordinator at Satellite Dialysis Centers; the Satellite Dialysis Centers registered dietitians for the accuracy of their data collection and thoughtful insights; the head nurse/assistant administrators; and, most importantly, the patients of Satellite Dialysis Centers for their cooperation in the data collec- tion. Graphic art in Fig 2 through 6 was de- signed by Mary Ann Anning, Anning and Associ- ates, Sunnyvale, CA.
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