Abstract
There are inconsistent recommendations in available guidelines and uncertainty regarding the exact nutrient requirements for preterm infants, partly because of inconsistent outcome reporting in nutrition intervention studies. To support development of a minimum reporting set, we undertook a scoping review to identify measures of nutrition intake and growth outcomes reported in recent preterm nutrition studies. We searched for publications from 2018 to 2023 reporting individuals born preterm at any gestational age and study location whose nutrition intake was assessed before first hospital discharge and whose growth was assessed at any age. One reviewer screened articles and extracted the measures. Any uncertainties were resolved by a second reviewer. The results were tabulated and analyzed descriptively. We identified 6365 records, and included 250 studies. Only 10 of 236 studies (4%) reported how nutrition intake was calculated. There were five different ways of reporting weight and 13 of weight gain, each at many different time points. Furthermore, 27 of 105 studies (26%) did not report how weight gain was calculated, and those that did reported five different calculation methods. Our findings demonstrate significant variation and incomplete reporting of nutrition intake and growth outcomes in preterm nutrition studies. This lack of standardization limits the utility of existing evidence and highlights the need for a standardized reporting framework to improve comparability and support evidence‐based guidelines in preterm nutrition.
Keywords: enteral nutrition, life cycle, neonates, nutrition, parenteral nutrition, pediatrics
INTRODUCTION
Optimizing nutrition is needed to achieve optimal growth, neurodevelopment, and long‐term health outcomes for preterm infants. 1 , 2 However, there are inconsistent recommendations in available guidelines, 3 and more information regarding the precise nutrient requirements for preterm infants and their relationship with measures of growth and longer‐term outcomes is needed.
Several systematic reviews have reported gaps in reporting of nutrition intake and growth outcomes in preterm nutrition studies, including inconsistency in calculating nutrient intake, 4 disparate growth outcome measures that were not universally reported, 5 incomplete reporting of denominators of weight gain, 6 and different times of assessment. 7 These gaps complicate summarizing the evidence in meta‐analyses.
This scoping review aims to identify the range and variability of measures of nutrition intake and growth outcomes recently reported in preterm nutrition studies. The findings will be used in a Delphi process to develop a minimum reporting set and a standard reporting framework for such measures.
METHODS
Design and eligibility criteria
This review follows the Preferred Reporting Items for Systematic Reviews and Meta‐Analysis extension for Scoping Reviews (PRISMA‐ScR) checklist 8 (Supporting Information: Data). The study protocol was registered with OSF Registries (https://doi.org/10.17605/OSF.IO/AZXNM).
Our research question formulated using PCC (population, concept, and context) criteria was as follows: What are methods used to estimate, calculate, and report nutrition intakes (concept) and growth outcomes (concept) for individuals born preterm (population) in preterm nutrition and growth studies (context)?
The inclusion criteria were intervention, observational, and methodology studies (outcome measures or validation studies) related to nutrition intake before first hospital discharge or in the first 28 days and growth outcomes assessed at any age in individuals born preterm, in any language, available as full text, and published between 2018 and September 18, 2023. We excluded reviews, animal studies, and studies published >5 years ago to ensure relevance.
Search strategy
A systematic search of the literature was conducted in accordance with the Joanna Briggs Institute methodology for scoping reviews. 9 An initial limited search of Ovid MEDLINE was undertaken to identify relevant articles. Words contained in titles and abstracts and the index terms used to describe the articles were then used to develop a full search strategy for the databases Ovid MEDLINE, EMBASE, and Cochrane CENTRAL (Supporting Information: Data). We did not search for unpublished studies or gray literature.
Screening and data extraction
Records identified from the searches were uploaded to Covidence. 10 One reviewer screened results by title and abstract (first stage) and full text (second stage). Relevant data were extracted using a data extraction spreadsheet, including details about the participants, concept, context, study methods, and key findings. Any uncertainties were resolved with a second reviewer.
Data analysis and presentation
The results were tabulated, and measures of nutrition intakes were grouped by types of nutrients, types of milk, routes of administration, reporting units, intake calculation methods, and the timing of assessment. Measures of growth were grouped based on the types of outcomes, the timing of assessment, growth measurement and calculation methods, and growth charts used.
RESULTS
The search strategy identified 6365 records. After duplicate removal, 4704 records were screened for title and abstract, and 4116 excluded. Full‐text screening of 588 records (585 studies) resulted in 250 studies included in this scoping review (Figure 1). The number of included studies published each year ranged from 35 to 53 (Supporting Information: Table S3). Among these, 190 (76%) were observational studies, 44 (17.6%) were intervention studies, and 16 (6.4%) were methodology studies (Supporting Information: Table S4).
Figure 1.
PRISMA flow diagram.
Measures of nutrition intake
Enteral and parenteral nutrition intakes
Of the 250 studies, 233 (93%) reported enteral nutrition intakes (Table 1; Supporting Information: Table S5). Most studies reported that preterm infants were fed breastmilk, fortified breastmilk, or preterm formula, although half of the studies lacked information about the nutrient composition. Breastmilk was reported as the enteral nutrition by 214 studies, but 51 of these (23.8%) did not mention whether the source was mother's own milk or donor human milk. Among 119 studies with reported breastmilk composition, 34 of 119 (28.6%) analyzed the breastmilk, 36 of 119 (30.3%) cited 23 different references (of which four references were institutional databases or had irretrievable URLs), and 49 of 119 (41.2%) did not cite a source. Over half of the studies reported actual enteral intake, whereas about 10% neither reported nutrient intake nor specified if the intake was actual or prescribed.
Table 1.
Summary of main findings of measures of nutrition intake.
| Outcome | Number of studies reporting outcome, n (%) | Most frequently reported outcome measure | Number of studies reporting the most frequently reported outcome measure, n (%) |
|---|---|---|---|
| Enteral nutrition intake (n = 233) | |||
| Types of milk | 174 (75%) | Breastmilk | 172 (74%) |
| Unit used for reporting milk composition | 122 (52%) | Per 100 ml | 119 (51%) |
| Nutrient content reported | 210 (90%) | Protein | 129 (55%) |
| Nutrient intake reported | 212 (91%) | Actual | 137 (59%) |
| Unit of nutrient intake reported | 204 (88%) | Per kg∙day−1 | 193 (83%) |
| How intake calculated | 15 (6%) | Divided by current weight at each measurement | 8 (3%) |
| Time period | 184 (79%) | From birth/day 1/admission to day/week/month after birth | 66 (28%) |
| Parenteral nutrition intake (n = 203) | |||
| Unit used for reporting parenteral solution composition | 41 (20%) | Percentage | 41 (20%) |
| Nutrient content reported | 159 (78%) | Protein (amino acids) | 140 (69%) |
| Nutrient intake reported | 161 (79%) | Actual | 114 (56%) |
| Fluids or other items included or excluded from the intake calculation | 7 (3%) | Transfused blood product excluded | 4 (2%) |
| Unit of nutrient intake reported | 157 (77%) | Per kg∙day−1 | 140 (69%) |
| How intake calculated | 11 (5%) | Divided by current weight at each measurement | 4 (2%) |
| Time period | 152 (75%) | From birth/day 1/admission to day/week/month after birth | 62 (31%) |
Note: Percentages are calculated based on the total number of studies reporting either enteral or parenteral nutrition intake.
Parenteral nutrition intakes were reported by 203 of 250 (81%) studies (Table 1; Supporting Information: Table S6). Most did not provide nutrient composition of the parenteral solution with slightly over half reporting actual parenteral nutrition intake. Only 37 studies reported parenteral amino acids and lipid while reporting nutrient composition, and of these, 11 (29.7%) did not specify the type or brand of amino acids or intravenous lipid emulsion. Missing details on nutrient reporting were more frequent in parenteral (21.7%) vs enteral intakes (9.9%).
Intake calculation method
Only 15 of 233 (6.4%) of studies reporting enteral and 11 of 203 (5.4%) of studies reporting parenteral nutrition intake detailed calculation methods (Table 1; Supporting Information: Tables S5 and S6). Only 7 of 203 (3.4%) reported the fluids or other items excluded from the parenteral nutrition intake calculation.
Timing of assessment
Nutrition intakes were reported either at specific times (eg, at 1 week after birth or 36 weeks' postmenstrual age) or over specific time periods (eg, from birth to discharge or from the start to the end of study), but around one‐fifth of studies did not specify timing (Table 1; Supporting Information: Tables S5 and S6).
Measures of growth outcomes
Weight, length/height, and head circumference
Weight, length/height, and head circumference were reported in 244 of 250 (97%), 166 of 250 (66%), and 179 of 250 (72%) studies, respectively (Table 2; Supporting Information: Tables S7, S8, and S10). Weight, length/height, and head circumference were mostly reported in g or cm and z scores. Weight gain was mostly reported in g∙kg−1∙day−1, changes in z scores, and g∙day−1. Length and head circumference gain were mostly reported in cm∙week−1 and as changes in z scores. In addition to head circumference, 6 of 179 studies (3.4%) reported brain growth using brain structure and volume measurements assessed by ultrasound or magnetic resonance imaging.
Table 2.
Summary of main findings of measures of growth outcomes.
| Outcome | Number of studies reporting outcome, n (%) | Most frequently reported outcome measure | Number of studies reporting the most frequently reported outcome measure, n (%) | Most frequently reported measurement time point | Number of studies reporting the most frequently reported outcome measure at this time point (n, %) |
|---|---|---|---|---|---|
| Weight (n = 244) | |||||
| Weight | 180 (74) | Weight in g | 125 (51) | Discharge | 59 (24) |
| Weight gain | 168 (69) | Weight gain in g∙kg−1∙day−1 | 105 (43) | From birth/day 1/admission to discharge | 32 (13) |
| Postnatal growth failure | 49 (20) | Weight percentile using different thresholds | 25 (10) | Discharge | 18 (7) |
| Postnatal weight loss | 32 (13) | Postnatal weight loss in | 25 (10) | Weight nadir | 22 (9) |
| Birthweight recovery | 47 (19) | Time to regain birthweight in days | 47 (19) | Days when birthweight regained | 47 (19) |
| Weight gain calculation method | 78 (32) | Exponential 2‐point method | 56 (23) | N/A | N/A |
| Measurement instrument | 77 (32) | Electronic scales | 77 (32) | N/A | N/A |
| Length/height (n = 166) | |||||
| Length/height | 133 (80) | Length/height‐for‐age z score | 86 (52) | Discharge | 37 (22) |
| Length/height gain | 89 (54) | Length gain in cm∙week−1 | 49 (30) | From birth/day 1/admission to discharge | 22 (13) |
| Postnatal growth failure | 9 (5) | Decline in length‐for‐age z score using different thresholds | 5 (3) | From birth/day 1/admission to discharge | 4 (2) |
| Measurement instrument | 69 (42) | Length board | 38 (23) | N/A | N/A |
| Measurement method | 3 (2) | Length measured using two legs | 2 (1) | N/A | N/A |
| Measurement assessor | 12 (7) | Two persons | 10 (6) | N/A | N/A |
| Head circumference (n = 179) | |||||
| Head circumference | 140 (78) | Head circumference‐for‐age z score | 92 (51) | Discharge | 42 (23) |
| Brain growth | 6 (3) | Brain volume | 4 (2) | Weeks/months' postmenstrual age | 4 (2) |
| Head circumference gain | 106 (59) | Head circumference‐for‐age z score change | 60 (34) | From birth/day 1/admission to discharge | 26 (15) |
| Postnatal growth failure | 10 (6) | Decline in head circumference‐for‐age z score using different thresholds | 6 (3) | From birth/day 1/admission to discharge | 4 (2) |
| Measurement instrument | 71 (40) | Tape measure | 68 (38) | N/A | N/A |
| Body composition (n = 63) | |||||
| Body mass index | 20 (32) | Body mass index‐for‐age z score | 15 (24) | Months/years' corrected age | 8 (13) |
| Weight‐for‐length | 8 (13) | Weight‐for‐length/height z score | 7 (11) | Months/years' corrected age | 4 (6) |
| Fat mass | 37 (59) | fat mass | 30 (48) | Weeks/months' postmenstrual age | 20 (32) |
| Fat‐free mass | 39 (62) | Fat‐free mass in kg | 31 (49) | Weeks/months' postmenstrual age | 22 (35) |
| Skinfold thickness measurement | 7 (11) | Subscapular skinfold thickness (in cm) | 7 (11) | Weeks/months' postmenstrual age | 4 (6) |
| Circumference measurement | 9 (14) | Mid‐upper arm circumference (in cm) | 5 (8) | Weeks/months' postmenstrual age | 3 (5) |
| Bone growth | 8 (13) | Bone mass density (in kg∙m−2) | 6 (10) | Months/years' corrected age | 5 (8) |
| Adipose tissue volume and distribution | 3 (5) | 16 different measures | 1 for each measure (2) | Weeks/months' postmenstrual age | 1 (2) |
| Body mass index change | 4 (6) | Body mass index‐for‐age z score change | 4 (6) | From birth/day 1/admission to weeks/months' postmenstrual age | 2 (3) |
| Weight‐for‐length change | 1 (2) | Weight‐for‐length/height z score change | 1 (2) | From birth/day 1/admission to discharge | 1 (2) |
| Fat mass change | 2 (3) | 4 different measures | 1 for each (2) | 2 different time periods | 1 for each (2) |
| Fat‐free mass change | 2 (3) | 4 different measures | 1 for each (2) | 2 different time periods | 1 for each (2) |
| Change in bone growth | 1 (2) | Bone mass density change (in kg∙m−2) and bone mineral content change (in kg) | 1 (2) | From weeks/months' postmenstrual age to months/years' corrected age | 1 (2) |
| Measurement instrument | 63 (100) | Air displacement plethysmography | 29 (46) | N/A | N/A |
Note: Percentages are calculated based on the total number of studies for each growth category (weight, length/height, head circumference, or body composition).
Abbreviation: N/A, not applicable.
Body composition
Body composition measurements were reported by 63 of 250 studies (25%) (Table 2; Supporting Information: Table S11). Changes in body composition metrics and adipose tissue volume/distribution were rarely addressed. Only 2 of 7 studies (28.6%) measuring skinfold thickness used skinfold thickness equations to predict body fat.
Weight gain calculation method
One hundred and five studies reported weight gain in g∙kg−1∙day−1, using methods such as the exponential 2‐point, 11 , 12 average 2‐point, 13 , 14 early 1‐point, 15 undefined 2‐point, 16 and generalized reduced gradient. 17 Some did not report how weight gain was calculated (27 of 105; 25.7%). A total of 4 of 56 studies that used the exponential 2‐point method, 5 of 14 that used the average 2‐point method, and 3 of 5 that used the early 1‐point method did not provide a reference for the method used.
Measurement instrument and method
Approximately two‐thirds of studies reporting weight, length/height, and head circumference did not report the measurement instruments (Table 2). When reported, most reporting weight (77 of 244; 31.6%) used electronic scales, and the remainder used a scale integrated in the incubator or air displacement plethysmography instrument. Of studies reporting length, most used length boards (38 of 166; 22.9%) (Supporting Information: Table S9), but almost none reported how length was measured or how many assessors did the measurement. Most studies reporting head circumference used a tape measure (68 of 179; 38.0%), whereas small numbers used magnetic resonance imaging, cranial ultrasound, and SCANIFY, a handheld point‐and‐shoot 3D scanner. Air displacement plethysmography was most often used to measure body composition (Supporting Information: Table S12). Most of the 44 studies using machines to measure body composition reported the machine manufacturer and model/type, but only 7 (15.9%) reported the software algorithm used.
Timing of assessment
Timing of assessment of growth ranged widely, with the largest proportion assessing weight, length/height, and head circumference at discharge and body composition at specific weeks/months of postmenstrual age (Table 2; Supporting Information: Tables S7, S8, S10, and S11). When measuring changes in size, most studies used birth/day 1/admission as the start time and discharge or a specific week of postmenstrual age as the end time.
Postnatal growth failure
Postnatal growth failure was reported by approximately 20% of studies reporting weight, but only by around 5% of studies reporting length/height and head circumference (Table 2; Supporting Information: Tables S7, S8, and S10). The term “extrauterine growth restriction” was used most often, but this was frequently used interchangeably with “postnatal growth failure,” “postnatal growth restriction,” “growth retardation,” “growth failure,” or “poor growth.” Three studies did not provide the definition of postnatal growth failure used. Among those that did, it was inconsistently defined, using different thresholds for a number of parameters, the most common of which were weight percentiles (eg, percentile <10th), weight‐for‐age z scores (eg, z score less than −2 SD), or decline in weight‐for‐age z scores (eg, decline ≥1.28 SD).
Growth chart used
The included studies used 21 growth charts for weight, 19 for length/height, 17 for head circumference, and 15 for body composition (Supporting Information: Table S13).
The most frequently used growth charts for these measures were Fenton 2013 18 and WHO child growth standards. 19 , 20 Most studies measuring body composition used WHO child growth standards for body mass index, skinfold thickness, and circumferences, and Norris 2019 21 for fat and fat‐free mass.
The growth chart used was not reported by 36 of 202 (17.8%) for weight, 25 of 89 (21.4%) for length/height, 28 of 125 (22.4%) for head circumference, and 10 of 27 (37.0%) for body composition.
DISCUSSION
Our review of nutrition intake and growth measures in preterm nutrition studies over the last 5 years identified the range and variability of measures reported and found incomplete and inconsistent reporting (Tables 1 and 2). This review is intended to describe contemporary measures used by researchers worldwide, which can then be included in a Delphi survey to develop a minimum reporting set and standard reporting framework for these measures for widespread use.
Most studies of nutrition interventions lacked reporting of nutrient composition in enteral and parenteral nutrition. Measuring nutrient composition can be labor‐intensive and time consuming. For example, some studies did not distinguish between mother's own milk and donor human milk, despite their nutrition and biological differences. Variations in human milk composition complicate precise intake estimates, often relying on published values which vary significantly (eg, protein content: 1.4 g∙100 ml−1 for preterm donor milk 22 vs 1.9 g∙100 ml−1 23 or 1.5 g∙100 ml−1 24 for early preterm milk). Similarly, most studies did not specify the type of parenteral amino acids and lipid administered to infants, although formulations for preterm formulas, fortifiers, and parenteral solutions differ across brands, may change over time, and have different associated health benefits and risks. 25 , 26 , 27 Transparent reporting of parenteral nutrition composition could facilitate comparative analyses to determine optimal nutrient formulations.
Most studies also did not report how intake of enteral and parenteral nutrition was calculated. One key piece of information needed is the weight used as the denominator for the calculation. Measuring weights daily is labor intensive and often neither feasible nor clinically advisable, and we found that many researchers used birthweight, current weight, or average weekly weights to estimate the daily nutrient intake. However, using these different weights as the denominator could result in important differences in the estimated nutrient intake. For example, for an infant weighing 1500 g at birth and 1800 g on day 21 with an average weight from day 14 to 21 of 1740 g and total fluid intake of 360 ml on day 21, the calculated intakes on day 21 using birth weight, actual weight on day 21, and average weekly weight after birth weight was regained were 240, 200, and 206 ml∙kg−1∙day−1.
Almost half of studies either reported the prescribed intake or did not specify whether the reported intake was prescribed or actual. They also did not report whether nonnutritional fluids were included or excluded from the calculation. Actual intake as prescribed is challenging to achieve during early life, and the actual intakes are commonly less than prescribed. 28 The reasons for this are multifactorial and include medical examination and treatment. Preterm infants, especially the sicker ones, often receive additional nonnutrition fluids such as saline, transfused blood products, and medications, which may contribute to the total fluid intake but not nutrient intake. This can result in a potentially large difference in the reported fluid intake depending on whether these fluids are included or excluded. At the same time, interruption of the infusion of nutrition fluids to give these nonnutrition fluids can result in a large difference between prescribed and actual nutrient intakes. For example, babies with necrotizing enterocolitis commonly receive three antibiotics which may require 2 h to administer, causing parenteral nutrition infusion to be given over 22 h instead of 24 h, potentially reducing the actual intake to 92% of the prescribed amount. 29 Detailed reporting on the actual nutrition intakes and what is excluded and included in the intake calculation is essential for readers to be able to interpret the nutritional interventions actually received.
Some studies did not report the method of calculating weight gain, and the remainder used several different methods. Weight gain calculated per unit body weight depends on the denominator used, and this showed the same variation as noted above for calculating nutritional intake. There was also variation in the times of assessment or period over which the weight gain was calculated. We found that almost all studies using the exponential method used birthweight as the starting point, but two studies used weight nadir. Using different methods and timing of calculations results in different outcomes. For example, Patel 11 found that the exponential method results in very accurate (mean absolute errors of 0.02% to 0.10%) estimation of weight gain or growth velocity (g∙kg−1∙day−1) regardless the starting point (day of birth or day of regaining birthweight). However, the 2‐points and linear models using birthweight as the denominator were very inaccurate (mean absolute errors of 50.3% to 96.4%), but when using average weight, they were fairly accurate (mean absolute errors of 0.10% to 8.97%). A later paper 14 recommended a 2‐point model using average weight and the starting point at 7 days, as they found estimations for the exponential method and 2‐points model were not significantly different when using weight nadir as starting point. Although more than half of studies we reviewed have implemented the suggestion from these two papers, the methods used to calculate weight gain are still not universally reported, making interpretation difficult.
We also found incomplete reporting and variation of the measuring instrument used for length measurement. Inaccuracy is often found in length measurement because it involves straightening the infant's body and legs. A cross sectional study in a large tertiary maternity hospital in Sydney compared neonatal length measured using standard practice, commonly involving one examiner using a length board or tape measure, with length measured using the gold standard method involving two examiners using a length board. 30 They found discrepancies of between −3.06 cm and 2.67 cm between the standard practice and the gold standard measurement, with 59% outside the acceptable 0.5 m margin and 34% outside a more lenient 1 cm margin. These discrepancies in length measurements caused incorrect plotting of percentiles of more than half of the neonates. Although training in the gold standard method can improve the accuracy of length measurements, using this method is not always feasible in practice. 30 , 31 Nevertheless, consistent reporting of the method used would allow the reader to better interpret any findings and particularly the likely variability of the measurements.
Body composition may be a better measure of growth than weight, and greater fat‐free mass but not fat mass gain during hospitalization has been associated with better neurodevelopment at 12 months' corrected age. 32 However, body composition was the least frequently measured growth outcome, reported by only 25% of studies. Part of the reason may be the difficulty of measurement of body composition in babies, particularly because air displacement plethysmography, the most frequently used approach, is expensive, not portable, and can only be used in babies who are free of instrumentation and below a maximum size limit. 33 In addition, the second most frequently used method, dual X‐ray absorptiometry, requires radiation exposure and the child to keep still. Movement during assessment may result in substantial error, and over or underestimation of fat and fat‐free mass may occur because of the techniques used to keep the child from moving. 33
We found studies reported measuring skinfold thickness in several different locations, and most did not use skinfold thickness equations to predict body fat. Although using skinfold thickness equations to predict body composition is portable, cheaper, and less time consuming than other body composition assessment methods, they are not very accurate and may not be suitable for different ethnic groups, ages, medical conditions, or sexes. 34 Development and validation of new skinfold thickness equations which are more suitable for specific populations may increase the utility of such equations to predict body composition in preterm infants and children.
We found three different definitions of postnatal growth failure were used in the included studies, usually defined using an arbitrary statistical cutoff. 35 Most studies defined postnatal growth failure as weight less than a certain percentile at one time point, which may misclassify infants with biological variations in size due to genetics or morbidities 35 and be misleading regarding the adequacy of the administered nutrition. Recommended indicators to identify malnutrition in preterm and neonatal populations as a screening tool were published in 2018, including declines in weight‐for‐age z scores (>0.8, >1.2 to 2, or >2), lower weight gain velocity than the expected rate to maintain desired growth rate, and/or consecutive days of protein/energy intake less than that required to maintain desired growth rate. 36 However, it is too early to expect changes as a result of these recommendations in the studies we reviewed because the included studies were likely to have finalized their protocols and data collection by the time the recommendation was published.
There was a wide range of growth charts used in the preterm nutrition studies we included, and some studies did not report which charts they used. The Fenton 2013 chart is comparable to the WHO growth chart at 50 weeks' gestational age, has a large preterm birth sample size of 4 million infants, and is based on growth data of fetuses and term infants. 18 WHO child growth standards are the international standards of healthy child growth which are useful to monitor growth when there are no country‐specific growth charts available. 37 The Norris 2019 chart was developed for assessment at birth and across the first 6 months, using a large sample size and air displacement plethysmography, and provides sex‐specific centiles for fat mass (kg), fat‐free mass (kg), and percentage body fat of preterm infants. 21 Other than these three most used growth charts, many studies used local child growth charts. Child growth in a specific population may deviate significantly from the WHO standards, and a local growth chart representing the population more accurately may be preferable for use by researchers. 37 However, different growth charts may result in different classifications of postnatal growth. A study on extremely low birthweight infants found that fetal references (growth curves which are based on in utero growth such as Fenton 2013 and UK‐WHO growth charts) are similar to each other in classifying postnatal growth but different from the preterm growth standards (growth curves which describes what postnatal growth should be, such as INTERGROWTH‐21). 38 It is therefore essential that researchers report the growth charts they used to allow interpretation of the different classifications of postnatal growth.
The timing of measurement of nutrition intakes were mostly starting from birth/day 1/admission until a specified day/week/month after birth. Growth measures were mostly reported either at discharge or a specific week/month of postmenstrual age, whereas changes in growth measures were mostly reported from birth/day 1/admission to discharge or to a specific week/month of postmenstrual age. Assessing growth at birth and discharge allows clinicians to monitor progress, compare the measures before and after an in‐hospital intervention, and set a baseline for postdischarge care. However, standardizing the end time of measurement at discharge for all infants is likely to be impractical because infants can be discharged at different times depending on the discharge criteria, which change over time and between units, and may even include weight, so are not independent of infant growth. Many studies also measured growth at 36 weeks' postmenstrual age because at this age most babies have not yet been discharged.
Strengths of this scoping review include providing an up‐to‐date summary of measures of nutrition intake and growth outcomes reported in preterm nutrition studies, as well as the inclusion of all languages of publication and study locations to ensure global coverage. Although limiting the types of study design included in this review might have excluded some relevant preterm nutrition studies, we included the designs that are likely contribute the highest quality evidence, and including all study designs was not feasible because of the high number of publications identified by our search strategy. Likewise, we limited the search strategy to reports published in the last 5 years to ensure they reflected current reporting practice and also for logistic reasons because of the large numbers involved.
This scoping review highlights the incomplete and inconsistent reporting of nutrition intake and growth outcomes in preterm nutrition studies over the past 5 years. Such variability hinders data synthesis and limits the ability to generalize findings in neonatal nutrition research. 39
Systematic review and meta‐analysis play a crucial role in synthesizing evidence for clinical practice, determining the certainty of evidence, and identifying research priorities to advance the field. 3 , 39 Several systematic reviews in neonatal nutrition have highlighted challenges in data synthesis because of incomplete and inconsistent outcome reporting. Examples include gaps in documenting actual protein intake in trials comparing high vs low protein intake, 40 incomplete reporting of dosing and timing of zinc supplementation, 41 and inconsistency in reporting growth, with studies variously using either absolute values or z scores. 42 Standardizing reporting practices would help facilitate meta‐analysis and other forms of evidence synthesis to inform clinical guidelines and practice, ultimately improving neonatal nutrition interventions and health outcomes for preterm infants.
Among the gaps identified in this review, we suggest that high priority should be given to improving the reporting of actual nutrition intake in preterm infants and ensuring growth outcomes are presented using both absolute values and z scores. We are currently identifying the key measures and developing a standardized reporting framework for preterm nutrition studies, based on consensus from a multidisciplinary stakeholder group. This framework aims to ensure that reported measures are clinically relevant for patients, caregivers, researchers, and healthcare professionals while remaining feasible for implementation. Because this process is ongoing, researchers can refer to previously suggested standardizing reporting in neonatal nutrition studies. 4 , 36
AUTHOR CONTRIBUTIONS
Jane E. Harding, Frank H. Bloomfield, Luling Lin, and Meiliana Meiliana contributed to the conception of the study; Meiliana Meiliana screened records and extracted data, Jane E. Harding, Frank H. Bloomfield, Luling Lin, and Meiliana Meiliana contributed to the analysis and interpretation of data, as well as drafted the manuscript; All authors critically revised the manuscript and provided their final approval.
CONFLICT OF INTEREST STATEMENT
None declared.
Supporting information
Supporting information.
ACKNOWLEDGMENTS
Meiliana Meiliana is funded by a University of Auckland doctoral scholarship; Jane E. Harding and Frank H. Bloomfield are funded in part by the Health Research Council of New Zealand; Luling Lin is funded by the Aotearoa Foundation. Open access publishing facilitated by The University of Auckland, as part of the Wiley ‐ The University of Auckland agreement via the Council of Australian University Librarians.
Meiliana M, Bloomfield FH, Harding JE, Lin L. Measures of nutrition intake and growth reported in preterm nutrition studies: a scoping review. J Parenter Enteral Nutr. 2025;49:806‐815. 10.1002/jpen.2768
REFERENCES
- 1. Harding JE, Cormack BE, Alexander T, Alsweiler JM, Bloomfield FH. Advances in nutrition of the newborn infant. Lancet. 2017;389(10079):1660‐1668. [DOI] [PubMed] [Google Scholar]
- 2. Tremblay G, Boudreau C, Bélanger S, et al. Body composition in very preterm infants: role of neonatal characteristics and nutrition in achieving growth similar to term infants. Neonatology. 2017;111(3):214‐221. [DOI] [PubMed] [Google Scholar]
- 3. Meiliana M, Alexander T, Bloomfield FH, et al. Nutrition guidelines for preterm infants: a systematic review. JPEN J Parenter Enteral Nutr. 2024;48(1):11‐26. [DOI] [PubMed] [Google Scholar]
- 4. Cormack BE, Embleton ND, van Goudoever JB, Hay WW, Bloomfield FH. Comparing apples with apples: it is time for standardized reporting of neonatal nutrition and growth studies. Pediatr Res. 2016;79(6):810‐820. [DOI] [PubMed] [Google Scholar]
- 5. Amissah EA, Brown J, Harding JE. Protein supplementation of human milk for promoting growth in preterm infants. Cochrane Database Syst Rev. 2020;9(9):CD000433. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Osborn DA, Schindler T, Jones LJ, Sinn JK, Bolisetty S. Higher versus lower amino acid intake in parenteral nutrition for newborn infants. Cochrane Database Syst Rev. 2018;3(3):CD005949. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Webbe JWH, Ali S, Sakonidou S, et al. Inconsistent outcome reporting in large neonatal trials: a systematic review. Arch Dis Child Fetal Neonatal Ed. 2020;105(1):69‐75. [DOI] [PubMed] [Google Scholar]
- 8. Tricco AC, Lillie E, Zarin W, et al. PRISMA extension for scoping reviews (PRISMA‐ScR): checklist and explanation. Ann Intern Med. 2018;169(7):467‐473. [DOI] [PubMed] [Google Scholar]
- 9. Peters M, Godfrey C, McInerney P, Munn Z, Tricco AC, Khalil H. Chapter 11: scoping reviews (2020 version). In: Aromataris E, Munn Z, eds, JBI Manual for Evidence Synthesis. JBI; 2024. https://synthesismanual.jbi.global [Google Scholar]
- 10.Veritas Health Innovation. Covidence Systematic Review Software. Accessed June 13, 2022. https://app.covidence.org/reviews/active
- 11. Patel AL, Engstrom JL, Meier PP, Kimura RE. Accuracy of methods for calculating postnatal growth velocity for extremely low birth weight infants. Pediatrics. 2005;116(6):1466‐1473. [DOI] [PubMed] [Google Scholar]
- 12. Patel AL, Engstrom JL, Meier PP, Jegier BJ, Kimura RE. Calculating postnatal growth velocity in very low birth weight (VLBW) premature infants. J Perinatol. 2009;29(9):618‐622. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Fenton TR, Anderson D, Groh‐Wargo S, Hoyos A, Ehrenkranz RA, Senterre T. An attempt to standardize the calculation of growth velocity of preterm infants‐evaluation of practical bedside methods. J Pediatr. 2018;196:77‐83. [DOI] [PubMed] [Google Scholar]
- 14. Fenton TR, Griffin IJ, Hoyos A, et al. Accuracy of preterm infant weight gain velocity calculations vary depending on method used and infant age at time of measurement. Pediatr Res. 2019;85(5):650‐654. [DOI] [PubMed] [Google Scholar]
- 15. Martin CR, Brown YF, Ehrenkranz RA, et al. Nutritional practices and growth velocity in the first month of life in extremely premature infants. Pediatrics. 2009;124(2):649‐657. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Fu TT, Schroder PE, Poindexter BB. Macronutrient analysis of target‐pooled donor breast milk and corresponding growth in very low birth weight infants. Nutrients. 2019;11(8):1884. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Lasdon LS, Fox RL, Ratner MW. Nonlinear optimization using the generalized reduced gradient method. Revue Française D'automatique, Informatique, Recherche Opérationnelle. Recherche Opérationnelle. 1974;8:73‐103. [Google Scholar]
- 18. Fenton TR, Kim JH. A systematic review and meta‐analysis to revise the fenton growth chart for preterm infants. BMC Pediatr. 2013;13(1):59. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Turck D, Michaelsen KF, Shamir R, et al. World health organization 2006 child growth standards and 2007 growth reference charts: a discussion paper by the committee on nutrition of the European Society for Pediatric Gastroenterology, Hepatology, and Nutrition. J Pediatr Gastroenterol Nutr. 2013;57(2):258‐264. [DOI] [PubMed] [Google Scholar]
- 20. Ebrahim GJ. WHO child growth standards: head circumference‐for‐age, arm circumference‐for‐age, triceps skin fold‐for‐age and sub scapular skin fold‐for‐age. J Trop Pediatr. 2008;54(3):214‐215. [Google Scholar]
- 21. Norris T, Ramel SE, Catalano P, et al. New charts for the assessment of body composition, according to air‐displacement plethysmography, at birth and across the first 6 mo of life. Am J Clin Nutr. 2019;109(5):1353‐1360. [DOI] [PubMed] [Google Scholar]
- 22. Ballard O, Morrow AL. Human Milk Composition. Pediatr Clin North Am. 2013;60(1):49‐74. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Bauer J, Gerss J. Longitudinal analysis of macronutrients and minerals in human milk produced by mothers of preterm infants. Clin Nutr. 2011;30(2):215‐220. [DOI] [PubMed] [Google Scholar]
- 24. Gidrewicz DA, Fenton TR. A systematic review and meta‐analysis of the nutrient content of preterm and term breast milk. BMC Pediatr. 2014;14:216. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Department for Health and Wellbeing, Government of South Australia. South Australian Neonatal Medication Guidelines—Nutrient delivery comparison tables: Preterm infants. Accessed February 29, 2024. https://www.sahealth.sa.gov.au/wps/wcm/connect/14e027d5-a21b-40d1-84e8-3b03d9b1bc8a/Nutrient+delivery+comparison+tables_Preterm+Infants_Neo_v1.0.pdf?MOD=AJPERES&
- 26. Bolisetty S, Osborn D, Sinn J, et al. Australasian Neonatal Parenteral Nutrition Consensus Group . Standardised neonatal parenteral nutrition formulations—An Australasian group consensus 2012. BMC Pediatr. 2014;14:48. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Frazer LC, Martin CR. Parenteral lipid emulsions in the preterm infant: current issues and controversies. Arch Dis Child Fetal Neonatal Ed. 2021;106(6):676‐681. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Corpeleijn WE, Vermeulen MJ, van den Akker CH, van Goudoever JB. Feeding very‐low‐birth‐weight infants: our aspirations versus the reality in practice. Ann Nutr Metab. 2011;58(suppl 1):20‐29. [DOI] [PubMed] [Google Scholar]
- 29. Cormack BE, Bloomfield FH. Audit of feeding practices in babies less than 1200 g or 30 weeks gestation during the first month of life. J Paediatr Child Health. 2006;42(7‐8):458‐463. [DOI] [PubMed] [Google Scholar]
- 30. Wood AJ, Raynes‐Greenow CH, Carberry AE, Jeffery HE. Neonatal length inaccuracies in clinical practice and related percentile discrepancies detected by a simple length‐board. J Paediatr Child Health. 2013;49(3):199‐203. [DOI] [PubMed] [Google Scholar]
- 31. Johnson TS, Engstrom JL, Haney SL, Mulcrone SL. Reliability of three length measurement techniques in term infants. Pediatr Nurs. 1999;25(1):13‐17. [PubMed] [Google Scholar]
- 32. Ramel SE, Gray HL, Christiansen E, Boys C, Georgieff MK, Demerath EW. Greater early gains in fat‐free mass, but not fat mass, are associated with improved neurodevelopment at 1 year corrected age for prematurity in very low birth weight preterm infants. J Pediatr. 2016;173:108‐115. [DOI] [PubMed] [Google Scholar]
- 33. Gallagher D, Andres A, Fields DA, et al. Body composition measurements from birth through 5 years: challenges, gaps, and existing & emerging technologies—A national institutes of health workshop. Obes Rev. 2020;21(8):13033. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34. Asadi S, Bloomfield FH, Alexander T, McKinlay CJD, Rush EC, Harding JE. Utility of published skinfold thickness equations for prediction of body composition in very young New Zealand children. Br J Nutr. 2020;124(3):349‐360. [DOI] [PubMed] [Google Scholar]
- 35. Fenton TR, Cormack B, Goldberg D, et al. “Extrauterine growth restriction” and “postnatal growth failure” are misnomers for preterm infants. J Perinatol. 2020;40(5):704‐714. [DOI] [PubMed] [Google Scholar]
- 36. Goldberg DL, Becker PJ, Brigham K, et al. Identifying malnutrition in preterm and neonatal populations: recommended indicators. J Acad Nutr Diet. 2018;118(9):1571‐1582. [DOI] [PubMed] [Google Scholar]
- 37. Ziegler EE, Nelson SE. The WHO growth standards: strengths and limitations. Curr Opin Clin Nutr Metab Care. 2012;15(3):298‐302. [DOI] [PubMed] [Google Scholar]
- 38. Nyakotey DA, Clarke AM, Cormack BE, et al. ProVIDe Study Group . Postnatal growth and neurodevelopment at 2 years' corrected age in extremely low birthweight infants. Pediatr Res. 2024;96(2):436‐449. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39. Willhelm C, Girisch W, Gottschling S, Gräber S, Wahl H, Meyer S. Systematic Cochrane reviews in neonatology: a critical appraisal. Pediatr Neonatol. 2013;54(4):261‐266. [DOI] [PubMed] [Google Scholar]
- 40. Das S, McClintock T, Cormack BE, Bloomfield FH, Harding JE, Lin L. High protein intake on later outcomes in preterm children: a systematic review and meta‐analysis. Pediatr Res. 2025;97(1):67‐80. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41. Sinha B, Dudeja N, Chowdhury R, et al. Enteral zinc supplementation in preterm or low birth weight infants: a systematic review and meta‐analysis. Pediatrics. 2022;150(suppl 1):e2022057092J. [DOI] [PubMed] [Google Scholar]
- 42. Lin L, Amissah E, Gamble GD, Crowther CA, Harding JE. Impact of macronutrient supplements on later growth of children born preterm or small for gestational age: a systematic review and meta‐analysis of randomised and quasirandomised controlled trials. PLoS Med. 2020;17(5):e1003122. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supporting information.