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Cochrane Database of Systematic Reviews Protocol - Intervention

Targeted or adjustable versus standard diet fortification for growth and development in very low birth weight infants receiving human milk

Authors' declarations of interest

Version published: 04 November 2019 Version history

https://doi.org/10.1002/14651858.CD013465

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Abstract

This is a protocol for a Cochrane Review (Intervention). The objectives are as follows:

To determine the effect of standard versus adjustable versus targeted diet fortification for nutritional support on growth, and other clinical, nutritional, and neurodevelopmental outcomes in very low birth weight infants receiving human milk, and in birth weight subgroups (< 1000 grams; 1000 to 1499 grams).

Background

Description of the condition

Growth failure in preterm infants

Preterm birth is a major cause of mortality and morbidity worldwide. A major morbidity faced by preterm infants is extrauterine growth restriction (EUGR), defined as weight at discharge less than the tenth percentile of expected intrauterine growth at the corresponding gestational age (Clark 2003; Ehrenkranz 2014; Hu 2019). Although rates of EUGR are decreasing, it remains a significant problem among very low birthweight (VLBW) infants in reports from large multicenter cohorts in North America and Israel (Griffin 2016; Horbar 2015; Ofek Shlomai 2014). Growth failure in VLBW infants results from the complex interaction of many factors of which inadequate nutrition, especially during the first weeks of life, appears critically important (Embleton 2001). Growth failure during neonatal intensive care unit (NICU) hospitalization is associated with neurodevelopmental outcomes including incidence of cerebral palsy, scores less than 70 on the Bayley II Mental Development and Psychomotor Development Indices, and abnormal neurologic examinations at 18 to 22 months (Ehrenkranz 1999); and abnormal performance in IQ and verbal flexibility, visual memory, and visual flexibility composite scores at a mean age of 25 years (Sammallahti 2014).

Fortification of human milk for preterm infants

Human milk is the infant feeding method recommended by the American Academy of Pediatrics due to the improved maternal and infant health outcomes which result (AAP 2012). These include: decreased infections in the first year of life; reduced risk of sudden infant death syndrome; protective effect against asthma, atopic dermatitis, and eczema; reduction in certain gastrointestinal diseases, obesity, childhood leukemia and lymphoma; and improved neurodevelopmental outcomes. Specifically, human milk protects against sepsis and necrotizing enterocolitis (NEC) in preterm infants, and is associated with fewer hospitalizations in the year after NICU discharge, lower rates of severe retinopathy of prematurity, and lower rates of metabolic syndrome and blood pressure in adolescence (AAP 2012). Human milk in preterm infants is also associated with improved neurodevelopmental outcomes, including mental, motor, and behavior testing (AAP 2012). It is common practice, therefore, to use mother’s own milk and donor human milk, as needed, for VLBW infants to improve outcomes, especially to decrease the risk of NEC (Hagadorn 2016).

Although human milk has been established as the preferred enteral feeding option for preterm infants, its nutritional content is not sufficient to maintain the pace of intrauterine nutrient accretion. Intake of both protein and energy are crucial for the growth of preterm infants, and human milk does not adequately provide the recommended amounts at the average feeding volumes, typically between 135 to 200 ml/kg/day (Arslanoglu 2019). Thus, fortification of human milk can be utilized in the NICU setting to optimize nutritional intake and improve growth outcomes for preterm infants (Agostoni 2010; Ehrenkranz 2006). Bovine‐ or human‐milk‐derived multi‐nutrient fortifier is typically introduced once the infant has demonstrated tolerance of enteral feeds advanced beyond minimal volumes. These fortifiers attempt to increase the protein and energy levels of enteral feeds to goals of 3.5 g/kg/day to 4.5 g/kg/day and 105 kcal/kg/day to 135 kcal/kg/day, respectively (Arslanoglu 2019). Fortifier is usually continued until the infant approaches discharge.

Fortification of human milk varies due to baseline human milk nutrient content. When measured both within and among mothers, the macronutrient composition of human milk varies considerably (Wu 2018). In addition, the majority of banked donor milk is pooled from mothers of term infants and, when compared to preterm maternal milk, differs in macronutrient composition. (Lawrence 2011; Radmacher 2013; Saarela 2005).

Description of the intervention

This review will compare three approaches to human milk fortification for preterm infants: standard, adjustable, and targeted (Adamkin 2014; Alan 2013; Radmacher 2017). Standard fortification, the most commonly used approach, assumes that all breast milk has an average caloric content and macronutrient composition and then fortifies with a predetermined amount of fortifier. With adjustable fortification, addition of fortifying nutrients is individualized using the infant’s metabolic response to enteral protein intake, as measured by blood urea nitrogen (BUN) (Alan 2013). Adjustable fortification typically increases protein content as tolerated using cut off BUN levels typically around 9 mg/dl to 16 mg/dl, adding extra protein if BUN levels remain low (Arslanoglu 2019). Targeted fortification individualizes fortification using the results of human milk analysis, specifically adding extra protein, fat, or carbohydrate based on the macronutrient concentration measured (Arslanoglu 2019). Milk analyzers assess breast milk content of carbohydrates, fat, protein, total solids and energy, and may help healthcare providers meet the needs of infants requiring additional nutrients because of preterm birth or other health conditions. In 2018, the FDA approved a human milk analyzer for clinical use (US Food and Drug Administration 2018). NICUs are incorporating analyzers into clinical care (Wake Forest/Baptist Medical Center 2018).

How the intervention might work

The primary goal of human milk fortification for preterm infants is to support postnatal growth at a velocity similar to in utero growth (AAP 1977). Standard fortification practice fails to account for variation in composition of mother’s milk and donor’s milk and is associated with post‐natal growth failure. By individualizing nutritional support, adjustable or targeted fortification strategies may improve growth failure and, secondarily, the neurodevelopmental outcomes associated with growth.

Why it is important to do this review

Given the known variation in human milk macronutrient composition prior to fortification, a systematic assessment of standard versus adjustable versus targeted diet fortification of VLBW infant feedings is warranted. This review will be clearly distinct from existing reviews of topics involving human milk and preterm infants (donor milk versus formula, banked preterm milk versus banked term milk, maternal breast milk versus formula), and will incorporate sophisticated advances in human milk feeding techniques for which an important literature is just emerging. In addition, this review will make summary results of randomized controlled trials of different fortification strategies available as they emerge, supporting management and promoting optimal VLBW outcomes.

Infrared human milk analyzers efficiently provide accurate macronutrient profiles for individual specimens of human milk. They have reached the market, they are cost‐effective, and they are being used in descriptive research studies to examine the composition of mother’s own milk and donor human milk (Radmacher 2013; Rochow 2013; Sauer 2011). In 2018, the FDA approved a human milk analyzer for clinical use. Analyzers will therefore allow for targeted human milk fortification — i.e. tailored to individual infants and milk specimens — in clinical care. The impact of routine use of analyzers upon nutritional support, clinical outcomes, or long‐term neurodevelopment for VLBW infants receiving human milk is yet to be seen in the NICU setting. Similarly, the comparative merits of these fundamentally different approaches to fortification have not been well defined.

Objectives

To determine the effect of standard versus adjustable versus targeted diet fortification for nutritional support on growth, and other clinical, nutritional, and neurodevelopmental outcomes in very low birth weight infants receiving human milk, and in birth weight subgroups (< 1000 grams; 1000 to 1499 grams).

Methods

Criteria for considering studies for this review

Types of studies

We will consider randomized controlled trials (RCTs), quasi‐RCTs, and cluster‐RCTs for inclusion. We plan to exclude cross‐over trials.

Types of participants

Very low birth weight (VLBW) infants (birth weight < 1500 grams) fed human milk exclusively, either mother’s own milk or donor human milk, or a combination of mother’s milk and donor milk.

Types of interventions

The interventions will be human milk fortification methods. We will make comparisons between each of the three fortification approaches: targeted and adjustable fortification; adjustable and standard fortification; targeted and standard fortification. We will consider studies examining any use of fortification in eligible infants for a minimum duration of two weeks, initiated at any time during enteral feeding, and with any regimen of human milk feeding.

Types of outcome measures

Primary outcomes

  1. In‐hospital growth outcomes (at 36 weeks' postmenstrual age; at hospital discharge)

    1. Weight (g or Z score)

    2. Length (cm or Z score)

    3. Head circumference (cm or Z score)

    4. Growth velocity in weight (grams/kg/day), length (cm/week) and head circumference (cm/week)

    5. Body mass index

    6. Ponderal index

    7. Incidence of growth < 10th percentile for postmenstrual age

Weight growth velocity will be expressed as g/day, as g/kg/day, and will also be calculated as follows: growth velocity = 1000 × Ln(Wt2/Wt1)/(D2 − D1) where Wt1 and Wt2 are the weights measured on days D1 (Birth) and D2 (discharge), respectively (Patel 2005).

Secondary outcomes

  1. Post‐discharge growth outcomes (up to 6 months' post‐term; beyond 6 months' post‐term)

    1. Weight (g or Z score)

    2. Length (cm or Z score)

    3. Head circumference (cm or Z score)

    4. Growth velocity in weight (g/kg/day), length (cm/week) and head circumference (cm/week)

    5. Body mass index

    6. Ponderal index

    7. Incidence of growth < 10th percentile for corrected age

  2. Other growth outcomes

    1. Time to regain birth weight (days)

  3. Clinical feeding/nutritional outcomes

    1. Time to establishment of full enteral feedings (days)

    2. Duration of parenteral nutrition (days)

    3. Feeding intolerance defined as the number of days when feeds were stopped or reduced and parenteral nutrition was either commenced or increased during hospital stay secondary to the inability to digest enteral feeds as indicated by gastric residual volume of more than 50%, abdominal distension or emesis or both, or as defined by study authors (Moore 2011).

  4. In‐hospital clinical outcomes

    1. In‐hospital mortality

    2. NEC stage ≥ 2 (Bell 1978)

    3. Culture‐proven sepsis

    4. Any retinopathy of prematurity

    5. Retinopathy of prematurity treated with retinal ablation or vascular endothelial growth factor (VEGF) inhibitor

    6. Late‐onset sepsis

    7. Bronchopulmonary dysplasia at 28 days of life and 36 weeks' postmenstrual age (Jobe 2001)

    8. Length of hospitalization

  5. Severe neurodevelopmental disability defined after 12 months' corrected age as presence of one or more of the following: non‐ambulatory cerebral palsy; neurodevelopmental delay (Bayley Scales of Infant Development) (Bayley 1993; Bayley 2005); auditory impairment (any impairment requiring or unimproved by amplification); and visual impairment (visual acuity < 6/60)

Search methods for identification of studies

We will use the criteria and standard methods of Cochrane and Cochrane Neonatal.

Electronic searches

We will conduct a comprehensive search including: Cochrane Central Register of Controlled Trials (CENTRAL 2019, current issue) in the Cochrane Library; MEDLINE Ovid Epub Ahead of Print, In‐Process & Other Non‐Indexed Citations, MEDLINE(R) Ovid Daily and MEDLINE(R) Ovid (1946 to current); MEDLINE via PubMed for the previous year; and CINAHL (1981 to current). We will not apply language restrictions. We include the MEDLINE search (Appendix 1), which will be adapted for the other databases using the standard search strategy of Cochrane Neonatal (Appendix 2).

We will search clinical trial registries for ongoing or recently completed trials. We will search the World Health Organization’s International Clinical Trials Registry Platform (ICTRP) (www.who.int/ictrp/search/en) and the U.S. National Library of Medicine’s ClinicalTrials.gov via Cochrane CENTRAL. Additionally, we will search the ISRCTN Registry for any unique trials not found through the Cochrane CENTRAL search.

Searching other resources

We will contact authors who have published in this field for possible unpublished work, and handsearch reference lists of identified clinical trials.

Data collection and analysis

We will use the standard methods of the Cochrane Neonatal Review Group.

Selection of studies

Authors will independently search for trials and identify studies for inclusion into this review. Two authors will review titles, abstracts, and studies. We will resolve disagreements in opinion through discussion. In instances where resolution is not achieved, we will contact relevant study authors for further clarification.

Data extraction and management

All authors will extract data using an extraction form created for this study. Two authors, assigned randomly, will extract data from each included study.

Assessment of risk of bias in included studies

Two review authors (JIH, JMT) will independently assess the risk of bias (low, high, or unclear) of all included trials using Cochrane’s ‘Risk of bias’ tool, as described in the Cochrane Handbook for Systematic Reviews of Interventions, for the following domains (Higgins 2011).

  1. Random sequence generation (selection bias)

  2. Allocation concealment (selection bias)

  3. Blinding of participants and personnel (performance bias)

  4. Blinding of outcome assessment (detection bias)

  5. Incomplete outcome data (attrition bias)

  6. Selective reporting (reporting bias)

  7. Any other bias

We will resolve any disagreements by discussion or by consulting a third author. See Appendix 3 for a more detailed description of criteria used to assess each domain.

Measures of treatment effect

We will use the standard methods of the Cochrane Neonatal Review Group. If we identify multiple studies, analyses will be performed using the most recent version of the statistical package Review Manager 5 (Review Manager 2014). We plan to assess dichotomous data using risk ratio (RR) and risk difference (RD) with corresponding 95% confidence intervals (CI). If we detect a statistically significant difference, we will calculate the number needed to treat for an additional beneficial outcome (NNTB) and the number needed to treat for an additional harmful outcome (NNTH). We will present means, standard deviations, weighted mean differences (WMD) and corresponding 95% CIs for continuous outcomes. If included studies assess the same outcome but measure it in a variety of ways, we will use the standardized mean difference as a summary statistic in meta‐analysis. We will assume a fixed‐effect model. We will extract primary outcomes and all planned subgroup analyses first; we may conduct further post hoc subgroup analyses if appropriate. If meta‐analysis includes 10 or more studies, we will evaluate potential reporting bias using a funnel plot. We may conduct sensitivity analyses if enough studies are identified for inclusion in this review.

Unit of analysis issues

For each study, we will report whether the unit of randomization, and hence the unit of analysis, occurred at the individual level or at the cluster level. If we identify and include cluster‐randomized trials, we plan to extract a direct estimate of the required effect measure (for example, an odds ratio with its confidence interval) from an analysis that properly accounts for the cluster design. Such an analysis might be based on a ‘multilevel model’, a ‘variance components analysis’ or may use ‘generalized estimating equations (GEEs)’, among other techniques (Hagadorn 2019). If multilevel analysis is not feasible, we will conduct analysis at the same level as the allocation, using a summary measurement from each cluster to avoid unit‐of‐analysis errors.

Dealing with missing data

We will, if possible, obtain data from the primary investigator for unpublished trials or when published data were incomplete.

Assessment of heterogeneity

We will evaluate heterogeneity of studies by the I² statistic, using the following cut‐offs and labels for heterogeneity.

  • Less than 25% indicates no heterogeneity

  • 25% to 49% indicates low heterogeneity

  • 50% to 74% indicates moderate heterogeneity

  • 75% and above indicates high heterogeneity

Assessment of reporting biases

We will identify potential reporting bias using funnel plots (in the absence of publication bias, the plot will represent an inverted funnel). Alternate plots may suggest potential publication bias.

Data synthesis

We plan to assess dichotomous data using risk ratio (RR) and risk difference (RD) with corresponding 95% confidence intervals (CI). If we detect a statistically significant difference, we will calculate the number needed to treat for an additional beneficial outcome (NNTB) and the number needed to treat for an additional harmful outcome (NNTH). We will present means, standard deviations, weighted mean differences (WMD) and corresponding 95% CIs for continuous outcomes.

Quality of evidence

We will use the GRADE approach, as outlined in the GRADE Handbook (Schünemann 2013), to assess the certainty of evidence of the following (clinically relevant) outcomes: in‐hospital growth; mortality; NEC stage 2 or more; sepsis; retinopathy of prematurity; bronchopulmonary dysplasia; post‐discharge growth; and severe neurodevelopmental disability.

Two authors (JIH, JMT) will independently assess the certainty of the evidence for each outcome. We will use the GRADEpro GDT Guideline Development Tool to create a ‘Summary of findings’ table to report the certainty of the evidence (GRADEpro GDT).

The GRADE approach results in an assessment of the certainty of a body of evidence as one of four grades.

  1. High: We are very confident that the true effect lies close to that of the estimate of the effect.

  2. Moderate: We are moderately confident in the effect estimate: the true effect is likely to be close to the estimate of the effect, but there is a possibility that it is substantially different.

  3. Low: Our confidence in the effect estimate is limited: the true effect may be substantially different from the estimate of the effect.

  4. Very low: We have very little confidence in the effect estimate: the true effect is likely to be substantially different from the estimate of effect.

We will consider evidence from randomized controlled trials as high certainty but downgrade the evidence one level for serious (or two levels for very serious) limitations based upon the following: design (risk of bias), consistency across studies, directness of the evidence, precision of estimates, and presence of publication bias.

Subgroup analysis and investigation of heterogeneity

Planned subgroup analysis will consist of comparisons of standard versus adjusted versus targeted human milk fortification by birth weight (< 1000 grams; ≥ 1000 and < 1500 grams) and donor breast milk versus mother’s own milk if feasible.

Sensitivity analysis

If a sufficient number of trials are included in this review, we will perform sensitivity analyses by excluding unblinded trials and those without adequate treatment allocation concealment.