Nutritional assessment: new tools and knowledge translation LISA SAMSON-FANG Intermountain Healthcare – Pediatrics, Salt Lake City, UT, USA. doi: 10.1111/dmcn.12437 This commentary is on the original article by Oeffinger et al. on pages 475-481 of this issue.
Recently, an 8-year-old male with cerebral palsy (CP) was referred to me for nutritional concerns. The referring physician suggested that I discuss the insertion of a feeding tube with the child’s parents. The child appeared very thin. Because of a pathological fracture, a colleague had ordered a dual energy X-ray absorptiometry (DXA) scan. The DXA scan showed his body fat stores to be in the obese range. Although this child had nutritional concerns (e.g. micronutrient deficiencies), inadequate caloric intake was not one of them. Clinical observations are combined with data to develop an assessment and plan. However, assessing the nutritional status of a child with CP creates problems for the clinician. Owing to altered body composition, our clinical gestalt is often incorrect. In brief, ‘we cannot believe our eyes’. Additionally, the tools traditionally used to provide data (including weight for height centile, body mass index centile, and percentage ideal body weight) are not valid in this population. Oeffinger et al.1 have published research validating the use of the Gurka equations and Bioelectric Impedance Analysis (BIA) to assess body fat stores in children with CP. This article should play a pivotal role in shifting how nutritional status is assessed in this group of children by solidifying the utility of the equations derived by Gurka et al.2 The list of validated tools for assessment of nutrition in children with CP include equations to calculate height from segmental measures including knee height, tibial length, and ulnar length;3–5 Gurka equations which estimate body fat from skinfold measures; and BIA and DXA. These tools are not perfect: specialized equipment is required; new skills must be learned; some tools apply to select age, sex, or disability classification; DXA and BIA are not reliable in children who have implanted hardware, such as baclofen pumps or metal rods used for spinal fixation.
Regardless, these tools are very useful to the clinician. While the Gurka equations estimate fat stores with up to a 2% error, from a clinical standpoint this error doesn’t matter if the child’s fat is solidly in the healthy range. Despite inherent error in height estimation, we can still be reassured that a child has a good linear growth trend through time and the height estimate can be used to calculate body composition from conductance when doing BIA. It is time to apply these tools in our clinics. In my experience, the clinician using a selection of these tools will meet friendly resistance. Clinicians unfamiliar with this literature may find it hard to accept that their clinical gestalt is wrong. Some are resistant to accepting that the tools they have relied upon are not valid. Some will not have needed equipment (at minimum a skinfold caliper and tape measure; optimally, multiple calipers for measuring different limb lengths, a BIA device, and a DXA at a local pediatric referral center). Training or time constraints can affect the ability to use these tools. Some will raise concern for the costs of DXA although the cost seems quite reasonable when compared to frequently used imaging. Dr Iona Novak presented information on knowledge translation at the 2013 annual meeting of the American Academy for Cerebral Palsy and Developmental Medicine. The transfer of evidence-based practice into the clinic remains a challenge and evidence for which strategies are most effective is limited. Multifaceted collaborative quality improvement approaches, interactive workshops, and influential opinion leaders were among strategies that may have positive impact. It is up to us to meet the knowledge translation challenges and implement these tools in our clinics. Patients and families can also contribute to system change by asking questions such as, ‘Should my child’s fat stores be assessed using a skinfold measure or a DXA scan?’ Twenty years ago we learned that poor nutrition was an overlooked clinical concern in children with CP. Then we learned that the traditional tools used to assess nutrition were often invalid. Now we have the tools. It is time to learn to use these tools ourselves, implement them in our clinical settings, and focus on knowledge translation!
REFERENCES 1. Oeffinger DJ, Gurka MJ, Kuperminc M, Hassani MS, Buhr NB, Tylkowski C. Accuracy of skinfold and bioelec-
ing body fat in children with cerebral palsy. Dev Med
and children with application to mobility-impaired or
Child Neurol 2010; 52: e35–41.
handicapped persons. J Am Diet Assoc 1994; 94:
trical impedance assessments of body fat percentage in
3. Stevenson RD. Use of segmental measures to estimate
ambulatory individuals with cerebral palsy. Dev Med Child
stature in children with cerebral palsy. Arch Pediatr Adolesc
Neurol; 2014; 56: 475–81.
Med 1995; 149: 658–62.
2. Gurka MJ, Kuperminc MN, Busby MG, et al. Assessment
4. Chumlea WC, Guo SS, Steinbaugh ML. Prediction of
and correction of skinfold thickness equations in estimat-
stature from knee height for black and white adults
416 Developmental Medicine & Child Neurology 2014, 56: 413–419
1385–8. 5. Gauld LM, Kappers JB, Carlin J, Robertson CF. Height prediction from ulna length. Dev Med Child Neurol 2004; 46: 478–80.
Recently, an 8-year-old male with cerebral palsy (CP) was referred to me for nutritional concerns. The referring physician suggested that I discuss the insertion of a feeding tube with the child’s parents. The child appeared very thin. Because of a pathological fracture, a colleague had ordered a dual energy X-ray absorptiometry (DXA) scan. The DXA scan showed his body fat stores to be in the obese range. Although this child had nutritional concerns (e.g. micronutrient deficiencies), inadequate caloric intake was not one of them. Clinical observations are combined with data to develop an assessment and plan. However, assessing the nutritional status of a child with CP creates problems for the clinician. Owing to altered body composition, our clinical gestalt is often incorrect. In brief, ‘we cannot believe our eyes’. Additionally, the tools traditionally used to provide data (including weight for height centile, body mass index centile, and percentage ideal body weight) are not valid in this population. Oeffinger et al.1 have published research validating the use of the Gurka equations and Bioelectric Impedance Analysis (BIA) to assess body fat stores in children with CP. This article should play a pivotal role in shifting how nutritional status is assessed in this group of children by solidifying the utility of the equations derived by Gurka et al.2 The list of validated tools for assessment of nutrition in children with CP include equations to calculate height from segmental measures including knee height, tibial length, and ulnar length;3–5 Gurka equations which estimate body fat from skinfold measures; and BIA and DXA. These tools are not perfect: specialized equipment is required; new skills must be learned; some tools apply to select age, sex, or disability classification; DXA and BIA are not reliable in children who have implanted hardware, such as baclofen pumps or metal rods used for spinal fixation.
Regardless, these tools are very useful to the clinician. While the Gurka equations estimate fat stores with up to a 2% error, from a clinical standpoint this error doesn’t matter if the child’s fat is solidly in the healthy range. Despite inherent error in height estimation, we can still be reassured that a child has a good linear growth trend through time and the height estimate can be used to calculate body composition from conductance when doing BIA. It is time to apply these tools in our clinics. In my experience, the clinician using a selection of these tools will meet friendly resistance. Clinicians unfamiliar with this literature may find it hard to accept that their clinical gestalt is wrong. Some are resistant to accepting that the tools they have relied upon are not valid. Some will not have needed equipment (at minimum a skinfold caliper and tape measure; optimally, multiple calipers for measuring different limb lengths, a BIA device, and a DXA at a local pediatric referral center). Training or time constraints can affect the ability to use these tools. Some will raise concern for the costs of DXA although the cost seems quite reasonable when compared to frequently used imaging. Dr Iona Novak presented information on knowledge translation at the 2013 annual meeting of the American Academy for Cerebral Palsy and Developmental Medicine. The transfer of evidence-based practice into the clinic remains a challenge and evidence for which strategies are most effective is limited. Multifaceted collaborative quality improvement approaches, interactive workshops, and influential opinion leaders were among strategies that may have positive impact. It is up to us to meet the knowledge translation challenges and implement these tools in our clinics. Patients and families can also contribute to system change by asking questions such as, ‘Should my child’s fat stores be assessed using a skinfold measure or a DXA scan?’ Twenty years ago we learned that poor nutrition was an overlooked clinical concern in children with CP. Then we learned that the traditional tools used to assess nutrition were often invalid. Now we have the tools. It is time to learn to use these tools ourselves, implement them in our clinical settings, and focus on knowledge translation!
REFERENCES 1. Oeffinger DJ, Gurka MJ, Kuperminc M, Hassani MS, Buhr NB, Tylkowski C. Accuracy of skinfold and bioelec-
ing body fat in children with cerebral palsy. Dev Med
and children with application to mobility-impaired or
Child Neurol 2010; 52: e35–41.
handicapped persons. J Am Diet Assoc 1994; 94:
trical impedance assessments of body fat percentage in
3. Stevenson RD. Use of segmental measures to estimate
ambulatory individuals with cerebral palsy. Dev Med Child
stature in children with cerebral palsy. Arch Pediatr Adolesc
Neurol; 2014; 56: 475–81.
Med 1995; 149: 658–62.
2. Gurka MJ, Kuperminc MN, Busby MG, et al. Assessment
4. Chumlea WC, Guo SS, Steinbaugh ML. Prediction of
and correction of skinfold thickness equations in estimat-
stature from knee height for black and white adults
416 Developmental Medicine & Child Neurology 2014, 56: 413–419
1385–8. 5. Gauld LM, Kappers JB, Carlin J, Robertson CF. Height prediction from ulna length. Dev Med Child Neurol 2004; 46: 478–80.
