Differences were observed in the proportion of patients classified as medium or high risk between diagnosis categories (Fig. 2). We found a higher proportion of high-risk patients among those affected with a gastrointestinal noninfectious disease using both NSTs (27.6%–34.5% depending of the examiner expertise and the NST used). In the case of patients who have undergone surgery and those admitted because of an infectious disease, notable differences were observed between STRONGkids and STAMP classification (Fig. 2).
Focusing on high-risk patients, the mean age of STRONGkids high-risk patients was higher than that of the low- and medium-risk patients (P < 0.05); this was due to a lower proportion of infants and a higher number of children 10 years or older included in the high-risk category with STRONGkids (Table 1). No difference in age was observed between STAMP risk categories. Regarding the diagnosis, by comparison with the other patients, STRONGkids high-risk patients presented a significantly higher proportion of noninfectious gastrointestinal diseases, this being the most frequent cause of admission in this group. Similar differences were not found in STAMP high-risk patients.
High-risk patients, independent of the NST used and the expertise of the examiner, showed a higher prevalence of mild and severe malnutrition when compared to low- and medium-risk patients (Table 1). Likewise, high-risk scores on admission using either STRONGkids or STAMP were associated with longer LOS when compared with low- and medium-risk patients, whether assessed by expert or nonexpert staff (Table 1). After adjustment for age in the lineal regression, the relation between high-risk scores and longer LOS—both by experts and nonexperts—remained significant, although higher differences were observed for STRONGkids high-risk patients (Table 3).
The ideal NST should be reproducible and reliable in the identification of individuals at risk of malnutrition (12) and should be suitable for use by nurses or other health care professionals without specialized training in nutritional assessment (ie, nonmembers of the nutritional support team) during admission (9). Although with a similar goal, to detect those patients at high nutritional risk who would need further nutritional assessment, STRONGkids and STAMP differ in several aspects.
STRONGkids was designed to be applied by a physician rather than by nursing staff (11), and it has also been validated in other populations by physicians who are experts in paediatric nutrition (18). This tool, however, includes subjective items (such as subjective clinical assessment) that, a priori, could entail difficulties for staff without previous experience in paediatric nutrition. A study conducted in Belgium evaluated the inter-rater reliability of STRONGkids, although in only 29 patients out of the 368 enrolled in the study, and showed moderate concordance (κ 0.61) (16). More recently, Moeeni et al (10) studied the inter-rater reliability of a simplified version of the STRONGkids questionnaire in 162 children in New Zealand, also finding moderate agreement (κ 0.65). These results were slightly worse than those of the present study but, if we analyse the individual agreement shown by Moeeni et al, of the 15 nurses who participated, the vast majority had an individual agreement above κ 0.65.
On the contrary, STAMP was developed to be used by nurses, although it has also been externally validated by experts in nutrition (19). In the original validation study, the questionnaire completed by nurses was compared with a full nutritional assessment performed by a registered dietician. Concordance was fair to moderate (κ 0.54 [0.38–0.69]) (12), but in a convenience subsample of participants (20%), which was independently reviewed by a second registered dietician to assess the reliability of the classification of nutrition risk as determined by the full nutrition assessment, inter-rater reliability increased to 0.921. Our study is the largest studying reliability for STRONGkids and STAMP and the first to provide separate data to compare it. Global inter-rater agreements between assessors of the 3 NSTs (Paediatric Yorkhill Malnutrition Score, STAMP, and STRONGkids) analysed by Moeeni et al (20) in 15 patients were high for the 3 tools (κ 0.89–0.93), but separate data were not provided.
One important difference between STAMP and STRONGkids is the rate of children classified as high risk: STAMP figures double those of STRONGkids, both when used by expert and nonexpert staff. These results agree with studies that have analysed each NST separately on admitted patients (11,12,16,19,20). Only 2 studies, however, compare STRONGkids and STAMP and in both cases the NSTs were applied by skilled paediatricians. Ling et al (18) compared these NSTs in a sample of 43 patients in which the high-risk patient rate was much higher with STAMP. Moreover, Moeeni et al (20), in a sample of 162 children, obtained 27% of high-risk patients with STAMP and 4% using STRONGkids. In our study, these marked differences between both tools are in part explained by a higher proportion of surgical patients and patients with infectious diseases classified as high risk by STAMP (Fig. 2, Table 1). These results are in agreement with those previously observed by Ling et al (18). It is still to be answered whether STAMP detects a large number of false positive or not: data are needed about the outcome of those patients in terms of a worse nutritional and/or clinical evolution. We also can compare our figures in admitted patients with data available in the primary care using STAMP applied by nurses; in this setting the high-risk rate was 6.6%, as might be expected, lower than that in admitted patients (21). Finally, although we did not record data about time spent by different observers to apply NST, this could be another difference between both tools; an estimation made in the study by Ling et al suggests that STAMP is considerably more cumbersome to apply than STRONGkids when it is applied by skilled paediatricians, in part because the latter do not included weight and height data, but further work is needed to verify this (18).
In the present study, prevalence of acute malnutrition was lower than that in recently reported European figures from a large multicentre study (7%) (2). In that study, which did not include Spanish patient data, malnutrition prevalence varied greatly between countries (range 4.0%–9.3% across countries). Seasonal differences (our study was conducted in the Spring) and patient characteristics given the complexity of each hospital could partly explain these differences. Even so, a recent multicentre study in Spain has shown a reduction in malnutrition figures with respect to previous data while establishing a close relation with diagnosis at admission (22).
Focusing on high-risk patients, malnutrition rates were significantly higher than that in low- and medium-risk patients both in experts and nonexperts hands. Similar results have been reported previously (11,12,16,20,23). Another difference observed in the STRONGkids high-risk category was a mean age above the overall average. This is partly due to the significantly higher proportion of underlying chronic diseases in our sample among children older than 10 years, but it is also explained by the small number of infants classified as high risk by STRONGkids. The latter has been previously described in other studies (11,23) and could reflect a worse sensitivity inside this age group. Once again, information about nutritional and clinical evolution would be needed to assess this point, but it is necessary to keep this fact in mind when STRONGkids is applied.
In relation to diagnosis on admission, we observed a higher proportion of noninfectious gastrointestinal disease among STRONGkids high-risk patients when compared with the rest of the sample. One other study has shown this tendency in a small group of patients both for STRONGkids and STAMP (18). Moreover, in a sample of 46 paediatric patients experiencing inflammatory bowel disease, 40% were classified as high risk by both STAMP and STRONGkids (24). Taking into account the number of high-risk patients, and the greater rate of chronic underlying diseases in the >10 years age group in our sample, no firm conclusions can be reached at this point and further studies should corroborate this observation.
Finally, STRONGkids has previously shown a good correlation with patient LOS both when used by skilled paediatricians (11,18,20) and in the Belgian study, in which it was applied by nurses, a paediatric resident, and a dietician in training (16). In contrast, the STAMP high-risk category was not related to longer LOS according to Moeeni et al (20). In our sample, high-risk patients had longer LOS for both NSTs, independent of the expertise of the examiner who applied the test, but the differences were greater for STRONGkids. This association remained significant even after adjusting for age in the multiple regression analysis. At this point, we must consider the fact that patient LOS depends on many factors, not only nutritional risk and age. It, however, remains important to stress the ability of the NST to select a small group of patients with a remarkably longer LOS (eg, 6.7% STRONGkids high-risk patients identified by nonexpert staff showed a 5.79 days longer stay than low- and medium-risk patients). These data could reflect the ability of the NST to predict adverse outcomes.
Firstly, we have not contemplated corrections for conditions, such as fluid overload, that would affect patient weight at admission. This fact could have affected the STAMP score of some patients, although our sample size contributes to minimize the effect of this possible bias. Secondly, the main objective of the present study was to analyse the reproducibility and reliability of 2 NSTs, once they had been validated by experts. Our findings alone, however, do not sufficiently distinguish one from the other, although, in our opinion, they do provide data relevant to making such a choice. We have shown that NST in nonexpert hands can identify patients with acute malnutrition and patients with longer LOS but NST still need to demonstrate their usefulness in contributing to better patient outcomes after screening and subsequent nutritional intervention, and their cost-effectiveness.
We conclude that STRONGkids and STAMP are reliable and reproducible, independent of the expertise of the examiner. Agreement between expert and nonexpert staff was good, producing a similar high-risk patient profile, although with different proportion of high-risk patients. Our results demonstrate that these NSTs are useful in nutritional screening in settings in which users have no previous experience in the field. Although high-risk patients of both NSTs were characterized by a higher prevalence of malnutrition and a longer LOS, the figures were higher with STRONGkids. Nevertheless, which NST offers the highest utility for each condition (surgery, critical care, infectious disease, etc) remains undefined. Further studies are required to determine the precise influence of nutritional characteristics on LOS, taking into account other variables such us underlying conditions, disease epidemiology, food delivery in the hospital setting, or existence of nutrition teams.
The authors would like to thank all the participant children and their parents, and the nursing staff and paediatric residents for their cooperation.
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Keywords:© 2017 by European Society for Pediatric Gastroenterology, Hepatology, and Nutrition and North American Society for Pediatric Gastroenterology,
children; clinical outcome; hospitalized; malnutrition; nutritional screening