Nutritional assessment of the children in pediatric intensive care unit with subjective global nutritional assessment and its relationship with the prognosisABSTRACT
Objective This study aims to evaluate the nutritional status of patients hospitalized in pediatric intensive care unit with Subjective Global Nutritional Assessment and its relationship with prognosis.
Methods A total of 150 patients in pediatric intensive care unit, 6 months to 17 years (mean age: 4.94±4.84, M/F:1.14), that were hospitalized for at least 7 days have been enrolled in this study. Weight, height, mid-upper arm circumference, and triceps skin fold thickness were measured. The anthropometric measurements were recorded. The Nutrition type (enteral, parenteral or both) during hospitalization, PRISM III mortality risk score, and Subjective Global Nutritional Assessment was a determinent part of the study.
Results At hospitalization 18.7% of the patients were moderately malnourished, 32% were severely malnourished and 49.3% were normal. According to PRISM III score 41.3% of the patients were classified as normal, 29.3% as moderate and 29.3% as severe. A strong positive correlation was found between Subjective Global Nutritional Assessment and all of the anthropometric measurements. A significant statistically difference has been observed between malnutrition status at hospitalization concerning weight for age, height for age, weight for height and body mass index (p<0.01). It was also analysed a very relevant statistically connection between malnutrition classification and lenght of hospital stay (p<0.05). The result rate considerably higher in patients with severe malnutrition (p<0.05).
Conclusion A strong correlation was found between Subjective Global Nutritional Assessment and anthropometric measurements. Also significant correlation was found between malnutrition score at admission and prognosis in this study.
Keywords:
Malnutrition; Pediatric intensive care; Prognosis; Subjective Global Nutritional Assessment
RESUMO
Objetivo O objetivo do estudo foi avaliar o estado nutricional de pacientes internados em unidades de terapia intensiva pediátrica por meio da Avaliação Nutricional Global Subjetiva e sua relação com o prognóstico.
Métodos Um total de 150 pacientes em comunidades de terapia intensiva pediátrica entre 6 meses e 17 anos (idade média: 4,94±4,84, M/F:1,14) que foram hospitalizados por menos de 7 dias para todos estão incluídos neste estudo. As medidas antropométricas foram registradas. Foram determinados o tipo de nutrição (enteral, parenteral ou ambas) durante a internação, o score de risco de mortalidade PRISM III e a Avaliação Nutricional Global Subjetiva.
Resultados Na internação 18,7% dos pacientes apresentavam desnutrição moderada, 32% desnutrição grave e 49,3% eram normais. De acordo com o score PRISM III 41,3% dos pacientes foram classificados como normais, 29,3% como moderados e 29,3% como graves. Foi encontrada uma correlação positiva entre a Avaliação Nutricional Global Subjetiva e todas as medidas antropométricas. Observou-se uma diferença estatistica significativa entre estado de desnutrição na internação, peso para idade, altura para idade, peso para altura e índice de massa corporal (p<0,01). Uma relação estatisticamente relevante foi encontrada entre a classificação de desnutrição e a duração da internação hospitalar (p<0,05). A taxa de resultados foi consideravelmente maior em pacientes com desnutrição grave (p<0,05).
Conclusão Foi encontrada forte correlação entre a Avaliação Nutricional Global Subjetiva e as medidas antropométricas neste estudo. Também foi encontrada correlação entre o score de desnutrição na admissão e o prognóstico neste estudo.
Palavras-chave:
Desnutrição; Terapia intensiva pediátrica; Prognóstico; Avaliação Nutricional Global Subjetiva
INTRODUCTION
Malnutrition is defined as a deficiency over energy, protein and micronutrient that can negatively affect growth, development and other relevant outcomes due to an imbalance between nutrient intake and requirement [1]. It is the most important cause of mortality and morbidity in children in developing countries [2], associated with 45% of children’s deaths under the age of five [3].
Malnutrition, including undernutrition and overnutrition, is frequently observed among children admitted to Pediatric İntensive Care Unit (PICU) and associated with mortality and morbidity. The importance of nutrition has come to the fore with the nutritional guideline for the patients in PICU published by American Parenteral Enteral Nutrition Society in 2009 [4], and the studies on this subject have increased in recent years [5-18]. Nutritional status and nutritional pathway has been shown to affect mortality, infectious complications, and Lenght of Hospital Stay (LOS) in PICU [4,5,19].
Subjective Global Nutritional Assessment (SGNA) is a comprehensive screening method which reflects the clinical decision combining the patient’s nutritional history (rating of growth, weight changes, dietary intake, gastrointestinal symptoms, functional capacity, and any metabolic stres) and physical examination findings (loss of subcutaneous fat, muscle wasting, and/or edema) in assessing nutritional status classified as normal/well nourished, moderate malnutrition, or severe malnutrition [20].
This study aims to evaluate the nutritional status and risk scoring of patients hospitalized in the PICU with SGNA, which is a simple, noninvasive, comprehensive and cost-effective screening method, in order to establish the contribution of nutrition management to the prognosis.
METHODS
A total of 150 patients between the ages of 6 months to 17 years who were hospitalized for at least 7 days in PICU between September 2019 and January 2021 were enrolled in this single-center prospective cross-sectional study. The patients who were younger than 6 months, older than 17 years that did not agree to participate in the study or did not give informed consent were excluded from the study. This study was conducted in a PICU of a tertiary hospital.
The nutritional status of the patients were determined by SGNA at admission. A SGNA questionnare form was created (Figure 1). The SGNA was performed by the same physician at the time of admission to PICU. The patient’s medical history as: food and drug intake, changes in weight, presence of gastrointestinal symptoms, functional capacity and presence of acute stress were very determinant to the analysis. A physical examination was performed to look for signs of subcutaneous fat stores, muscle wasting and edema. Were measured weight, height, Mid-Upper Arm Circumference (MUAC) and Triceps Skin Fold Thickness (TSFT). The anthropometric measurements such as Weight For Age (WFA), Height For Age (HFA), Weight For Height (WFH) (in patients <2 years of age), Body Mass İndex (BMI) (in patients >2 years of age) were recorded. Overall, the rating is subjective and not based on a numerical scoring system. When the SGNA questionnare forms were evaluated it appeared that there are more checkmarks on the right side of the form, which indicates that the child is malnourished, whereas if most of the checkmarks are on the left, the child is likely normal (Figure 1) [20]. Other components such as child or caregiver self-reports are also used to support this rating.
According to SGNA, a normal/well-nourished child that has normal growth and weight gain, adequate food intake, no gastrointestinal symptoms, little or no signs of weakness on physical examination, normal functional capacity, and has a normal rating in all categories or is recovering rapidly from a suspected/moderately malnourished condition despite some muscle wasting, decrease in fat sores and weight gain [20]. Moderate malnutrition is defined by having clear signs of decreased growth, weight, and food intake, and also may be having signs of decreased fat stores, muscle mass and functional capacity; a moderate classification in most or all categories have the potential to progress to severe malnutrition [20]. Severely malnourished patients have progressive malnutrition with declining tendency in most or all categories, along with fat loss, muscle wasting, >10% weight loss, decreased food intake, excessive gastrointestinal losses, acute metabolic stress and definite loss of functional capacity [20]. Theses patients have a severe degrading, with no signs of improvement in most or all categories.
The body weight of infants was measured with an infant scales, the bed scales was destined to unconscious and/or immobile patients, and the children that could stand were weighed on their feet. The height of infants was measured with a tape measure, for the unconscious children and/or immobile patients was needed some help from the staff, and the children that could stand were measured on their feet. The MUAC was measured with a tape measure on the left arm, and TSFT was measured three times with a caliper to determinate the mean value.
To assess both acute and chronic malnutrition was used a WFA and the children classified with standard deviation score (SDS) ≤ -2 for WFA were diagnosed as underweight. The HFA is the indicator of linear growth. Children with SDSs ≤ -2 for HFA were considered shorter indicating chronic malnutrition. In the first 2 years of life is used WFH to classify, instead of BMI, and children with SDSs ≤ -2 for WHF were considered as weak and having acute malnutrition. The WFA, HFA, WFH and BMI were calculated according to the child growth standards accepted by WHO [21], and our national growth charts reported by Olcal Neyzi [22]. In this study WFA, HFA, and WFH were used under 2 years of age and WFA, HFA, and BMI were used in patients older than 2 years. Z-score (SDS) >-1 was considered normal, values between -1 ve-2 were considered as moderate malnutrition, and those with >-2 considered as severe malnutrition.
The MUAC is an indicator of muscle mass and subcutaneous fat stores, in which <12.5 cm is considered malnutrition, while <11 cm is severe malnutrition [23]. The TSFT shows triceps muscle thickness and, in terms of malnutrition in children between 1-4 years, <5 mm is considered a significant number.
Pediatric Risk of Mortality score (PRISM III), which is used for evaluation of mortality and morbidity, consists of physical examination (Glasgow coma score, pupillary response), vital signs (heart rate (beat/min), systolic blood pressure (mmHg), temperature (oC), white blood cell count (cell/mm3), platelet count (cell/mm3), potassium (meq/L), glucose (mmol/L), urea (mmol/L), creatinine (mmol/L), pH, PaCO2 (mmHg), PaO2/FiO2 (mmHg), and Prothrombin time (PT)/ Partial thromboplastin time (PTT) (sec); the results of the PRISM’s tests are given back within the first 24 hours of admission to the intensive care unit [24]. Patients with a PRISM III score <4 were considered normal, those with 4-8 were moderate, and those with >8 were considered severe [24].
Hemoglobin, albumin, C-Reactive Protein (CRP) and Absolute Lymphocyte Count (ALC) results were taken at admission, and reexamined weekly, during hospitalization.
During the hospitalization was determined the nutritional status (enteral, parenteral or both). The pediatric nutritional risk score with SGNA was evaluated as normal, moderately malnourished, and severely malnourished. The nutritional status, anthropometric measurements and malnutrition score were re-evaluated weekly, during the hospitalization in PICU.
A written consent was taken from each patient or parents. The study was approved by the Ethics Committee of Sisli Etfal Training and Research Hospital (nº 2524, 09/17/2019).
A statistical analysis was performed using the Number Cruncher Statistical System in 2007 (Kaysville, Utah, U.S.A.). The descriptive statistics were given as mean, standard deviation, median, frequency, minimum and maximum for numerical variables. The conformity of the quantitative and the normal distribution was tested with Shapiro-Wilk test, by and graphical examinations. The Kruskall-Wallis test was used for comparisons of quantitative variables that did not show normal distribution, and the Dunn-Bonferroni test was used for post hoc evaluations. Spearman’s coefficient correlation was used to evaluate the relations between the variables. Pearson Chi-Square test and Fischer-Freeman Halton test were used to compare qualitative data. The p-values of <0.05 were considered statistically significant.
RESULTS
The mean age between the 150 patients was 4.94±4.84, and M/F was 1.14. In addition, 42% (n=63) of the patients were under 2 years of age, 19.3% (n=29) were between the ages of 3-4, 18% (n=27) were between 5-10 and 20.7% (n=31) were older than 10 years. Importantly, most of the patients had respiratory diseases, and in Table 1 is shown more of the the demographic and clinical characteristics of the patients.
While the malnutrition score was normal, with in 49.3% of the patients at admission, it decreased to moderate level in 3 children in the 1st week, and in one child in the 2nd week (Table 2). A severely malnourished child increased to moderate level.
The relationship between anthropometric measurements, biochemical parameters and SGNA at admission is shown in Table 3. Although no significant correlation was observed between biochemical parameters and SGNA at admission (p>0.01), there was found a statistical relation between WFA, HFA, WFH, BMI and SGNA (p=0.001).
When the relation between MUAC, TSFT, and anthropometric measurements was evaluated, it appeared a positive resemblance between WFA, HFA, WFH, BMI and MUAC (r:0.505; p<0.01, r:0.350; p<0.01, r:0,559; p<0,01, and r:0.456; p<0.01, respectively). Moreover, another important aspect is the correlation between WFA, HFA, WFH, BMI and TSFT (r:0.506; p<0.01, r:0.314; p<0.01, r:0.581; p<0.01, and r:0.513 p<0.01, respectively). Nothing significant occured between MUAC, TSFT and biochemical parameters, hemoglobin, albumin, and CRP (p>0.05), but a negative and significant correlation was found between MUAC and ALC (p<0.01), along with a positive correlation between TSFT and ALC (p<0.01).
Although it was found a statistically correlation between malnutrition score at admission and prognosis (p=0.046), nothing significant was observed between PRISM III score and prognosis (Table 4).
There was a major statistical difference in terms of LOS, among patients, according to malnutrition classification (p<0.02). The rate of severe malnutrition was importantly higher in those that were hospitalized for more than one month.
DISCUSSION
Malnutrition is associated with a poor prognosis in hospitalized patients and causes increased risk of infection, difficulty in wound healing, wasting of muscle mass, increased LOS, mortality and morbidity. An early detection of malnutrition by clinicians allows the provision of guidelines to prevent deterioration of patient’s nutritional status and promote their recovery.
Until this date, eight screening methods for determining nutritional status have been used for hospitalized children and adolescents, but there is no consensus on which method is better than the others [25,26]. Screening Tool for Risk on Nutritional Status and Growth (STRONGkids) and SGNA are widely used in clinical practice [10]. In addition, are recommended to be used to determine the nutritional status of children [11-13, 25-33] the: Nutritional Risk Score, Pediatric Nutritional Risk Score, Pediatric Yorkhill Malnutrition Score (PYMS), Pediatric Nutrition Screening Tool (PNST), Screening Tool for the Assessment of Malnutrition in Pediatrics (STAMP).
The SGNA has been reported as the first valid subjective method for evaluating nutritional status in critically ill children [6]. It is a useful method for predicting nutrition-associated morbidities and determining malnutrition in PICU, but was based on a practitioner's clinical judgment rather than objective and quantitative measurements [20].
The SGNA has been used as a screening tool in hospitalized children, children with chronic diseases and malignities [34-36]. Pawaria et al. [36] stated that SGNA, in contrast to anthropometric measures, is a better nutritional assessment tool and it is reliable, comprehensive and predicts poor outcome in childhood chronic liver disease. Ouyang et al. [34] reported that STRONGkids is a simple, predictive and time-saving tool that is strongly recommended to be used in nutritional-risk screening on admission, and also, SGNA is an effective and comprehensive measurement tool to evaluate children's nutritional status. In our country, the SGNA has not been used for malnutrition screening before and this study, that was carried out in our hospital, was the first one.
Ong et al. [16] compared two nutrition screening tool, STAMP and SGNA in their study. Respectively, SGNA and STAMP identified 45% and 79% of the hospitalized children to be at risk of malnutrition. They concluded that, in detecting children who were malnourished, SGNA had a 4-fold higher specificity (70.4%) than STAMP (18.1%).
In this study, the ages at diagnosis were between 6 months and 16.2 years, and the mean age was 4.94±4.84 years, similarly with the studies conducted by Vermilyea et al. [6] and Minocha et al. [37].
Minocha et al. [37] 46.7% of our patients were females, 53.3% were males and M/F were 1.14. In the study of Vermilyea et al. [6] it has been reported as 1.6 and 1.2 in the study of Minocha et al. [37]. In this study, the most common diagnosis of the patients was respiratory diseases (39.3%), whereas Vermilyea et al. [6] has reported cardiovascular diseases (45%) as the most common diagnosis, followed by respiratory illnesses.
When the malnutrition status was evaluated at admission, Vermilyea et al. [6] reported that 78.7% of their patients were normal, 16.7% had moderate malnutrition, and 4.7% had severe malnutrition according to SGNA. Bagri et al. [15] observed that 42.7% of their patients were normal, 17.4% had moderate malnutrition, and 39.7% had severe malnutrition. In this study, 49.3% of our patients were normal, 18.7% moderately malnourished, and 32% severely malnourished.
It has been shown that enteral nutrition is started late in critically ill patients in PICU due to gastrointestinal dysfunction [38]. Baǧci et al. [9] stated that PRISM score was found to be lower in 21.1% of their patients, who started enteral feeding within the first 6 hours of hospitalization compared to the patients who switched to enteral feeding between 7-24 hours. In this study, it was observed that parenteral nutrition was initiated frequently, especially in infected patients. The later enteral nutrition is started, the more the nutritional status of the patients is negatively affected. One of our patients with severe malnutrition that had started on parenteral nutrition switched to enteral nutrition within 1 week, and at the 4th week she was evaluated as moderately malnourished. Two of our patients with normal nutritional status at admission started on parenteral nutrition and switched to enteral nutrition within 2 weeks. These patients were diagnosed as moderately malnourished at the 4th week. Thus, present study also emphasized the importance of early enteral nutrition in PICU.
The PRISM scoring is the most common scoring system used to determine mortality [5]. Vermilyea et al. [6] reported that the mean PRISM III risk score was found to be 3.93 in normal patients and 4.41 in patients with moderate and severe malnutrition (p>0.05). Bagri et al. [15] reported a mortality rate as 36.6% in normal patients, 32.7% in moderately malnourished patients, and 43.9% in patients with severe malnutrition according to Pediatric Index of Mortality 2 scoring. In our study, the mean PRISM III score was 5.66±5.20, and 41.3% of the patients classified as normal, 29.3% moderate, and 29.3% severe. No statistically significant correlation was obtained between PRISM III score and prognosis (p>0.05). Nangalu et al. [5] observed that severe malnutrition is intrinsically associated with higher mortality even with similar PRISM score and suggested an additional score to children with WFA <60% of expected.
Vermilyea et al. [6] has reported no correlation between anthropometric measurements, biochemical parameters, and SGNA. Carniel et al. [18] demonstrated a weak correlation between SGNA and anthropometric measurements (p<0.001). Oliveira et al. [10] found a significant correlation between SGNA HFA and BMI (p<0.001, p=0.022, respectively). There was a significant statistically correlation between WFA, HFA, WFH, BMI and SGNA (p=0.001), whereas no significant correlation was observed between biochemical parameters and SGNA at admission (p>0.01) in this study.
Seremet Kurklu et al. [39] compared three different nutrition screening tools (STRONGkids, PYMS, and PNST) in inpatient children, and stated that PYMS and PNST are suitable for use in malnutrition risk assessment in pediatric inpatients due to these screening tools’ high sensitivity. The PYMS revealed a relatively higher sensitivity of 90.9% and 84.6% for WFA and BMI, respectively, and PNST revealed a relatively higher sensitivity of 88.9% for HFA. Beser et al. [40] applied STRONGkids and PYMS to 1513 inpatient children and stated that no differences were identified in terms of age and sex when they analyzed the patients with increased STRONGkids and PYMS scores during discharge from the hospital. Their patients with increased scores according to both screening tools had more chronic illnesses, and their LOS was longer. In this study, only SGNA and anthropometric measurements were used.
No significant statistical difference was found between malnutrition status and biochemical parameters such as hemoglobin, albumin, CRP, and ALC in this study. It was observed that laboratory examinations did not reflect the nutritional status in critically ill children. Hemoglobin level was affected by the hydration state. Serum albumin is a negative phase reactant, and tends to decrease in admission to PICU due to increased infection process related with variable reasons. The ALC increases in cases of infection, sepsis, trauma, tissue necrosis, physical and emotional stresses [6].
It has been reported that malnutrition-associated diseases and complications are common in hospitalized patients, and LOS is significantly prolonged in these patients [41]. No significant relation has been established between LOS and malnutrition in the study of Vermilyea et al. [6], whereas Bagri et al. [15] and Beser et al. [40] reported prolonged LOS in patients with severe malnutrition. We observed statistically significant correlation between LOS and malnutrition status. The rate of severe malnutrition was significantly higher in those who were hospitalized for more than one month. A statistically significant correlation was found between the malnutrition score at hospitalization and prognosis (p<0.05). In severely malnourished cases, the mortality rate was found to be significantly higher.
Recently, Carter et al. [42] reported an update to SGNA and the study showed updates to the SGNA are not expected to have a significant impact on the validity of the tool and has the potential to improve its applicability to a current day practice. Since our study was conducted before this report, we do not have an oppurtunity to make a comparison.
There are some limitations in this study due to the lack of biochemical tests, limited number of cases, and single-center experience. It was not possible to obtaind the biochemical measurements of all our patients, thus it wasn´t sufficient to evaluate the correlation between SGNA and biochemical parameters.
CONCLUSION
The evaluation of nutritional status in children hospitalized in PICU is important and an early detection of malnutrition is vital for preventing a poor prognosis and prolonged LOS. In addition, SGNA is a valid method for determining malnutrition. Moreover, in this study was found a strong correlation between SGNA and anthropometric measurements, thus the application of SGNA widespread, especially in critically ill patients, can be beneficial in reducing the mortality rate in PICU.
REFERENCES
-
1. Mehta NM, Corkins MR, Lyman B, Malone A, Goday PS, Carney LN, et al American Society for Parenteral and Enteral Nutrition Board of Directors. Defining pediatric malnutrition: A paradigm shift toward etiology-related definitions. JPEN J Parenter Enteral Nutr. 2013;37(4):460-81. https://doi.org/10.1177/0148607113479972
» https://doi.org/10.1177/0148607113479972 - 2. Black R, Bhutta Z, Bryce J, Morris S, Victora C. The Lancet’s Series on Maternal and Child Undernutrition Executive Summary. Lancet. 2008;1-12.
-
3. Unıcef. Joint Child Malnutrition Estimates - 2017 edition. Unıcef Data; 2017 [cited 2024 May 28]. Availalbe form: Availalbe form: https://data.unicef.org/resources/joint-child-malnutrition-estimates-2017-edition/
» https://data.unicef.org/resources/joint-child-malnutrition-estimates-2017-edition/ -
4. Mehta NM, Compher C; A.S.P.E.N. Board of Directors. A.S.P.E.N. Clinical Guidelines: nutrition support of the critically ill child. JPEN J Parenter Enteral Nutr. 2009;33(3):260-76. https://doi.org/10.1177/0148607109333114
» https://doi.org/10.1177/0148607109333114 -
5. Nangalu R, Pooni PA, Bhargav S, Bains HS. Impact of malnutrition on pediatric risk of mortality score and outcome in Pediatric Intensive Care Unit. Indian J Crit Care Med. 2016;20(7):385-90. https://doi.org/10.4103/0972-5229.186218
» https://doi.org/10.4103/0972-5229.186218 -
6. Vermilyea S, Slicker J, El-Chammas K, Sultan M, Dasgupta M, Hoffmann RG, et al Subjective global nutritional assessment in critically ill children. JPEN J Parenter Enteral Nutr. 2013;37(5):659-66. https://doi.org/10.1177/0148607112452000
» https://doi.org/10.1177/0148607112452000 -
7. Srinivasan V, Seiple S, Nagle M, Falk S, Kubis S, Lee HM, et al Improving the Performance of Anthropometry Measurements in the Pediatric Intensive Care Unit. Pediatr Qual Saf. 2017;2(3):e022. https://doi.org/10.1097/pq9.0000000000000022
» https://doi.org/10.1097/pq9.0000000000000022 -
8. Grippa RB, Silva PS, Barbosa E, Bresolin NL, Mehta NM , Moreno YM. Nutritional status as a predictor of duration of mechanical ventilation in critically ill children. Nutrition. 2017;33:91-5. https://doi.org/10.1016/j.nut.2016.05.002
» https://doi.org/10.1016/j.nut.2016.05.002 -
9. Baǧci S, Keleş E, Girgin F, Yıldızdaş DR, Horoz ÖÖ, Yalındağ N, et al Early initiated feeding versus early reached target enteral nutrition in critically ill children: An observational study in paediatric intensive care units in Turkey. J Paediatr Child Health. 2018;54(5):480-6. https://doi.org/10.1111/jpc.13810
» https://doi.org/10.1111/jpc.13810 -
10. Oliveira TC, Albuquerque IZ, Stringhini MLF, Mortoza AS, Morais BA. The nutritional status of hospitalized children and adolescents: A comparison between two nutritional assessment tools with anthropometric parameters. Rev Paul Pediatr. 2017;35(3):273-80. https://doi.org/10.1590/1984-0462/;2017;35;3;00006
» https://doi.org/10.1590/1984-0462/;2017;35;3;00006 -
11. Chourdakis M, Hecht C, Gerasimidis K, Joosten KF, Karagiozoglou-Lampoudi T, Koetse HA, et al Malnutrition risk in hospitalized children: Use of 3 screening tools in a large European population. Am J Clin Nutr. 2016;103(5):1301-10. https://doi.org/10.3945/ajcn.115.110700
» https://doi.org/10.3945/ajcn.115.110700 -
12. Pérez-Solís D, Larrea-Tamayo E, Menéndez-Arias C, Molinos-Norniella C, Bueno-Pardo S, Jiménez-Treviño S, et al Assessment of two nutritional screening tools in hospitalized children. Nutrients. 2020;12(5):1221. https://doi.org/10.3390/nu12051221
» https://doi.org/10.3390/nu12051221 -
13. Hulst JM, Olieman JF, Sibbles BJ, Joosten KF. PP273-SUN: Malnutrition in a tertiary pediatric department: Screening as a performance indicator. Clin Nutr. 2014;33:S121. https://doi.org/10.1016/S0261-5614(14)50314-7
» https://doi.org/10.1016/S0261-5614(14)50314-7 -
14. Bechard LJ, Duggan C, Touger-Decker R, Parrott JS, Rothpletz-Puglia P, Byham-Gray L, et al Nutritional status based on body mass index is associated with morbidity and mortality in mechanically ventilated critically Ill children in the PICU. Crit Care Med. 2016;44(8):1530-7. https://doi.org/10.1097/CCM.0000000000001713
» https://doi.org/10.1097/CCM.0000000000001713 -
15. Bagri NK, Jose B, Shah SK, Bhutia TD, Kabra SK, Lodha R. Impact of Malnutrition on the Outcome of Critically Ill Children. Indian J Pediatr. 2015;82(7):601-5. https://doi.org/10.1007/s12098-015-1738-y
» https://doi.org/10.1007/s12098-015-1738-y -
16. Ong SH, Chee WSS, Lapchmanan LM, Ong SN, Lua ZC, Yeo JX. Validation of the Subjective Global Nutrition Assessment (SGNA) and Screening Tool for the Assessment of Malnutrition in Paediatrics (STAMP) to Identify Malnutrition in Hospitalized Malaysian Children. J Trop Pediatr. 2019;65(1):39-45. https://doi.org/10.1093/tropej/fmy009
» https://doi.org/10.1093/tropej/fmy009 -
17. Liu HY, Zhu DN, Chen GX, Wang YM, Zhao YX, Li QX, et al Clinical effectiveness of Subjective Global Nutritional Assessment in hospitalized children with cerebral palsy. Zhongguo Dang Dai Er Ke Za Zhi. 2020;22(11):1188-92. https://doi.org/10.7499/j.issn.1008-8830.2007153
» https://doi.org/10.7499/j.issn.1008-8830.2007153 -
18. Carniel MP, Santetti D, Andrade JS, Favero BP, Moschen T, Campos PA, et al Validation of a subjective global assessment questionnaire. J Pediatr (Rio J). 2015;91(6):596-602. https://doi.org/10.1016/j.jped.2015.03.005
» https://doi.org/10.1016/j.jped.2015.03.005 -
19. Mehta NM , Skillman HE, Irving SY, Coss-Bu JA, Vermilyea S, Farrington EA, et al Guidelines for the Provision and Assessment of Nutrition Support Therapy in the Pediatric Critically Ill Patient: Society of Critical Care Medicine and American Society for Parenteral and Enteral Nutrition. JPEN J Parenter Enteral Nutr. 2017;41(5):706-42. https://doi.org/10.1177/0148607117711387
» https://doi.org/10.1177/0148607117711387 -
20. Secker DJ, Jeejeebhoy KN. How to perform Subjective Global Nutritional assessment in children. J Acad Nutr Diet. 2012;112(3):424-31.e6. https://doi.org/10.1016/j.jada.2011.08.039
» https://doi.org/10.1016/j.jada.2011.08.039 -
21. WHO Multicentre Growth Reference Study Group. WHO Child Growth Standards based on length/height, weight, and age. Acta Paediatr Suppl. 2006;450:76-85. https://doi.org/10.1111/j.1651-2227.2006.tb02378.x
» https://doi.org/10.1111/j.1651-2227.2006.tb02378.x -
22. Neyzi O, Bundak R, Gökçay G, Günöz H, Furman A, Darendeliler F, et al Reference Values for Weight, Height, Head Circumference, and Body Mass Index in Turkish Children. J Clin Res Pediatr Endocrinol. 2015;7(4):280-93. https://doi.org/10.4274/jcrpe.2183
» https://doi.org/10.4274/jcrpe.2183 -
23. Eaton-Evans J. Nutritional assessment: Anthropometry. Encycl Hum Nutr. 2013;3-4:227-332. https://doi.org/10.1016/B978-0-12-375083-9.00197-5
» https://doi.org/10.1016/B978-0-12-375083-9.00197-5 -
24. Pollack MM, Patel KM, Ruttimann UE. PRISM III: An updated Pediatric Risk of Mortality score. Crit Care Med. 1996;24(5):743-52. https://doi.org/10.1097/00003246-199605000-00004
» https://doi.org/10.1097/00003246-199605000-00004 -
25. Klanjsek P, Pajnkihar M, Marcun Varda N, Povalej Brzan P. Screening and assessment tools for early detection of malnutrition in hospitalized children: A systematic view of validation studies. BMJ Open. 2019;9(5):e025444. https://doi.org/10.1136/bmjopen-2018-025444
» https://doi.org/10.1136/bmjopen-2018-025444 -
26. Hartman C, Shamir R, Hecht C, Koletzko B. Malnutrition screening tools for hospitalized children. Curr Opin Clin Nutr Metab Care. 2012;15(3):303-9. https://doi.org/10.1097/MCO.0b013e328352dcd4
» https://doi.org/10.1097/MCO.0b013e328352dcd4 -
27. Reilly HM, Martineau JK, Moran A, Kennedy H. Nutritional screening--evaluation and implementation of a simple Nutrition Risk Score. Clin Nutr. 1995;14(5):269-73. https://doi.org/10.1016/s0261-5614(95)80063-8
» https://doi.org/10.1016/s0261-5614(95)80063-8 -
28. Sermet-Gaudelus I, Poisson-Salomon AS, Colomb V, Brusset MC, Mosser F, Berrier F, et al Simple pediatric nutritional risk score to identify children at risk of malnutrition. Am J Clin Nutr. 2000;72(1):64-70. https://doi.org/10.1093/ajcn/72.1.64
» https://doi.org/10.1093/ajcn/72.1.64 -
29. Secker DJ, Jeejeebhoy KN. Subjective Global Nutritional Assessment for children. Am J Clin Nutr. 2007;85(4):1083-9. https://doi.org/10.1093/ajcn/85.4.1083
» https://doi.org/10.1093/ajcn/85.4.1083 -
30. McCarthy H, McNulty H, Dixon M, Eaton-Evans MJ. Screening for nutrition risk in children: The validation of a new tool. J Hum Nutr Diet. 2008;21(4):395-6. https://doi.org/10.1111/j.1365-277X.2008.00881_31.x
» https://doi.org/10.1111/j.1365-277X.2008.00881_31.x -
31. Gerasimidis K, Keane O, Macleod I, Flynn DM, Wright CM. A four-stage evaluation of the Paediatric Yorkhill Malnutrition Score in a tertiary paediatric hospital and a district general hospital. Br J Nutr. 2010;104(5):751-6. https://doi.org/10.1017/S0007114510001121
» https://doi.org/10.1017/S0007114510001121 -
32. Hulst JM, Zwart H, Hop WC, Joosten KF. Duch national survey to test the STRONGkids nutritional risk screening tool in hospitalized chidren. Clin Nutr. 2010;29(1):106-11. https://doi.org/10.1016/j.clnu.2009.07.006
» https://doi.org/10.1016/j.clnu.2009.07.006 -
33. White M, Lawson K, Ramsey R, Dennis N, Hutchinson Z, Soh XY, et al A simple nutrition screening tool for pediatric inpatients. JPEN J Parenter Enteral Nutr. 2016;40(3):392-8. https://doi.org/10.1177/0148607114544321
» https://doi.org/10.1177/0148607114544321 -
34. Ouyang N, Lu X, Cai R, Liu M, Liu K. Nutritional screening and assessment, and quality of life in children with cancer: A cross-sectional study in Mainland China. J Pediatr Nurs. 2021;57:99-105. https://doi.org/10.1016/j.pedn.2020.07.013
» https://doi.org/10.1016/j.pedn.2020.07.013 -
35. Bell KL, Benfer KA, Ware RS, Patrao TA, Garvey JJ, Haddow R, et al The pediatric subjective global nutrition assessment classifies more children with cerebral palsy as malnourished compared with anthropometry. J Acad Nutr Diet. 2020;120(11):1893-1901. https://doi.org/10.1016/j.jand.2020.04.012
» https://doi.org/10.1016/j.jand.2020.04.012 -
36. Pawaria A, Khanna R, Sood V, Siloliya M, Benjamin J, Kumar G, et al Subjective global nutritional assessment as a nutritional tool in childhood chronic liver disease. Br J Nutr. 2022;127(6):904-13. https://doi.org/10.1017/S0007114521001604
» https://doi.org/10.1017/S0007114521001604 -
37. Minocha P, Sitaraman S, Choudhary A, Yadav R. Subjective Global Nutritional Assessment: A Reliable Screening Tool for Nutritional Assessment in Cerebral Palsy Children. Indian J Pediatr. 2018;85(1):15-9. https://doi.org/10.1007/s12098-017-2501-3
» https://doi.org/10.1007/s12098-017-2501-3 -
38. Jacobs A, Verlinden I, Vanhorebeek I, Van den Berghe G. Early supplemental parenteral nutrition in critically Ill children: An update. J Clin Med. 2019;8(6):830. https://doi.org/10.3390/jcm8060830
» https://doi.org/10.3390/jcm8060830 -
39. Seremet Kurklu N, Geyin F, Ceylan L, Korkut Genc D, Kamarli Altun H, Karacil Ermumcu MS. Comparison of three different nutrition screening tools for pediatric inpatients. Nutr Clin Pract. 2022;37(3):698-704. https://doi.org/10.1002/ncp.10828
» https://doi.org/10.1002/ncp.10828 -
40. Beser OF, Cokugras FC, Erkan T, Kutlu T, Yagci RV; TUHAMAR Study Group. Evaluation of malnutrition development risk in hospitalized children. Nutrition. 2018;48:40-7. https://doi.org/10.1016/j.nut.2017.10.020
» https://doi.org/10.1016/j.nut.2017.10.020 -
41. Hecht C, Weber M, Grote V, Daskalou E, Dell'Era L, Flynn D, et al Disease associated malnutrition correlates with length of hospital stay in children. Clin Nutr. 2015;34(1):53-9. https://doi.org/10.1016/j.clnu.2014.01.003
» https://doi.org/10.1016/j.clnu.2014.01.003 -
42. Carter L, Hulst JM, Afzal N, Jeejeebhoy K, Brunet-Wood K. Update to the pediatric Subjective Global Nutritional Assessment (SGNA). Nutr Clin Pract. 2022;37(6):1448-57. https://doi.org/10.1002/ncp.10859
» https://doi.org/10.1002/ncp.10859
