Suplementación con carbohidratos de la leche materna para promover el crecimiento en lactantes prematuros
Resumen
Antecedentes
Los lactantes prematuros nacen con reservas de glucógeno bajas y necesitan de una ingesta mayor de glucosa para igualar las tasas de almacenamiento fetal. A pesar de los innumerables beneficios de la leche materna para los niños prematuros, es posible que no satisfaga suficientemente las necesidades de estos niños de rápido crecimiento. La administración de suplementos de carbohidratos a la leche materna podría ser de ayuda. Sin embargo, hay escasez de datos sobre la evaluación de los efectos beneficiosos o perjudiciales de la administración de suplementos de carbohidratos a la leche materna para promover el crecimiento en los lactantes prematuros. Esta es una actualización de 2020 de una revisión Cochrane publicada por primera vez en 1999.
Objetivos
Determinar si la leche materna a la que se le añaden suplementos de carbohidratos para los lactantes prematuros, comparada con la leche materna sin suplementos, mejora el crecimiento, la composición corporal y los desenlaces cardiometabólicos y del desarrollo nervioso, sin efectos adversos significativos.
Métodos de búsqueda
Se utilizó la estrategia de búsqueda estándar del Grupo Cochrane de Neonatología en el Registro Cochrane Central de Ensayos Controlados (CENTRAL 2019, Número 8) en la Biblioteca Cochrane y en MEDLINE vía PubMed el 22 de agosto de 2019. También se buscaron ensayos controlados aleatorizados y cuasialeatorizados en bases de datos de ensayos clínicos y en las listas de referencias de artículos obtenidos.
Criterios de selección
Los ensayos controlados publicados y no publicados fueron elegibles si utilizaron métodos aleatorios o cuasialeatorios para asignar a los lactantes prematuros alimentados con leche materna en el hospital a la administración de suplementos con carbohidratos adicionales o a ninguna administración de suplementos.
Obtención y análisis de los datos
Dos autores de la revisión, de manera independiente, resumieron los datos y evaluaron la calidad de los ensayos y la calidad de la evidencia a nivel de los desenlaces mediante los criterios Grading of Recommendations Assessment, Development and Evaluation (GRADE). Se planificó realizar metanálisis mediante los riesgos relativos (RR) para los datos dicotómicos y las diferencias de medias (DM) para los datos continuos, con sus intervalos de confianza (IC) del 95% respectivos. Se planificó utilizar un modelo de efectos fijos y explorar las posibles causas de heterogeneidad mediante análisis de sensibilidad. Se estableció contacto con los autores de los estudios para obtener información adicional.
Resultados principales
Fue elegible para inclusión en esta revisión un ensayo controlado cuasialeatorizado sin cegamiento, que evaluó los efectos de la administración de suplementos de carbohidratos en la leche materna en forma de un prebiótico en 75 lactantes prematuros. Se identificaron dos publicaciones del mismo ensayo que informaron metodologías diferentes con respecto al cegamiento y la asignación al azar. Los autores del estudio confirmaron que estas publicaciones se refieren al mismo ensayo, pero todavía no han aclarado qué metodología es la correcta. Los análisis mostraron evidencia de certeza muy baja de un efecto de la administración de suplementos con carbohidratos sobre el peso a los 30 días de edad (DM 160,4 gramos, IC del 95%: 12,4 a 308,4 gramos), y ningún efecto sobre el riesgo de intolerancia alimentaria (RR 0,64, IC del 95%: 0,36 a 1,15) ni de enterocolitis necrosante (ECN) (RR 0,2, IC del 95%: 0,02 a 1,3). La duración de la estancia hospitalaria fue más corta en el grupo de prebióticos que en el grupo control (mediana de diferencia de 9 días ). No hubo datos disponibles para la evaluación de los efectos de la administración de suplementos de carbohidratos sobre el crecimiento a largo plazo ni el desarrollo nervioso.
Conclusiones de los autores
No se sabe con certeza si la administración de suplementos con carbohidratos de la leche materna afecta a cualquier desenlace en lactantes prematuros. El único ensayo incluido en esta revisión presentó evidencia de muy baja calidad y los autores del estudio no proporcionaron información clara acerca de la metodología y el análisis del estudio. La evidencia puede estar limitada con respecto a su aplicabilidad porque los investigadores incluyeron una muestra pequeña de lactantes prematuros de un único centro. Los ensayos futuros podrían evaluar la seguridad y la eficacia de los diferentes tipos y concentraciones de suplementos de carbohidratos para los lactantes prematuros alimentados con leche materna. No obstante, no se prevé que se realicen ensayos adicionales de carbohidratos digeribles, pues actualmente es un componente de la leche materna enriquecida con varios nutrientes.
PICO
Resumen en términos sencillos
Suplementación con carbohidratos de la leche materna para promover el crecimiento en lactantes prematuros
Pregunta de la revisión
¿Añadir carbohidratos extra a la leche materna con la que se alimentan los lactantes prematuros, en comparación con no añadir carbohidratos, mejora el crecimiento, la grasa corporal, la obesidad, los problemas cardíacos, el aumento del azúcar en sangre y el desarrollo del cerebro sin causar efectos secundarios significativos?
Antecedentes
La ingesta insuficiente de carbohidratos en los lactantes prematuros podría dar lugar a un crecimiento y un desarrollo deficientes. La leche materna es el mejor alimento para los bebés prematuros, pero alimentarlos sólo con leche materna puede ser insuficiente desde el punto de vista nutricional. Añadir carbohidratos a la leche materna podría ser de ayuda. Sin embargo, no es posible encontrar suficientes datos para evaluar los efectos beneficiosos y perjudiciales de agregar carbohidratos a la leche materna para favorecer el crecimiento en los lactantes prematuros.
Características de los estudios
Esta revisión sistemática encontró un ensayo con 75 lactantes prematuros con evidencia de muy baja calidad sobre los efectos de agregar carbohidratos prebióticos (un tipo de carbohidrato) extra a la leche materna en los lactantes prematuros. Una segunda publicación realizada por los mismos autores informó métodos diferentes con respecto a la ocultación y a la asignación al azar del ensayo. Los autores del estudio confirmaron que estas publicaciones describen el mismo ensayo, pero todavía no han aclarado qué método es el exacto. No fue posible reproducir los análisis de los datos presentados. La búsqueda está actualizada hasta agosto de 2019.
Resultados clave
La administración de suplementos de carbohidratos prebióticos aumentó la media del peso de los lactantes prematuros a los 30 días y dio lugar a una estancia hospitalaria más corta en comparación con el control. No hay evidencia que muestre una diferencia clara en el riesgo de intolerancia alimentaria ni en el de enterocolitis necrosante entre los grupos que recibieron o no recibieron suplementos de prebióticos. No hubo otros datos disponibles sobre los efectos de añadir carbohidratos extra a la leche materna en el crecimiento a corto y largo plazo, la grasa corporal, la obesidad, el desarrollo del cerebro y los problemas cardíacos.
Conclusiones
Falta evidencia sobre los efectos a corto y a largo plazo de la administración de suplementos de carbohidratos a la leche materna en los lactantes prematuros. Esta revisión sistemática encontró evidencia de muy baja calidad sobre los efectos de agregar carbohidratos prebióticos a la leche materna en lactantes prematuros, así como incertidumbres acerca de las metodologías y el análisis. El único ensayo incluyó una muestra pequeña de lactantes prematuros iraníes, por lo que la evidencia podría considerarse no generalizable. Sin embargo, los resultados evaluados son comunes a todos los lactantes prematuros y el ensayo muestra que es posible agregar carbohidratos prebióticos a la leche materna en los países en desarrollo. Se necesitan estudios de investigación adicionales para evaluar los efectos beneficiosos y perjudiciales de diferentes tipos y concentraciones de suplementos de carbohidratos para los lactantes prematuros alimentados con leche materna. Actualmente, a los lactantes prematuros se les proporcionan suplementos de carbohidratos digeribles como un componente de la leche materna enriquecida con varios nutrientes.
Authors' conclusions
Summary of findings
| Carbohydrate supplementation compared to control in preterm infants | ||||||
| Patient or population: preterm infants | ||||||
| Outcomes | Anticipated absolute effects* (95% CI) | Relative effect | № of participants | Certainty of the evidence | Comments | |
|---|---|---|---|---|---|---|
| Risk with control | Risk with carbohydrate supplementation | |||||
| Growth/Weight at day 30 | Mean weight 1542.4 g | Mean weight increased by 160.4 g | ‐ | 75 (1 RCT) | ⊕⊝⊝⊝ | No other data were reported on growth except for weight at day 30. |
| Neurodevelopmental outcomes | ‐ | ‐ | ‐ | ‐ | ‐ | No data were reported for this outcome in the included trial. |
| Duration of hospital stay | ‐ | ‐ | ‐ | 75 | ⊕⊝⊝⊝ | The trial reported data on duration of hospital stay using median (range) for the prebiotic‐supplemented and unsupplemented groups as 16 (9 to 45) (95% CI 15.34 to 24.09 days) and 25 (11 to 80) (95% CI 25.52 to 34.39 days), respectively. We have reported this outcome in the text of the review. |
| Feeding Intolerance | 560 per 1000 | 358 per 1000 | RR 0.64 | 75 | ⊕⊝⊝⊝ | Study authors defined feeding intolerance as "gastric residue, i.e. the presence of milk in the stomach two hours after completion of a feeding". However another reported outcome was "requiring to cut off milk", which was similar to our pre‐specified definition of feeding intolerance, i.e. resulting in cessation or reduction in enteral feeding. Thus, we used "requiring to cut off milk" in the analysis of feeding intolerance. |
| Necrotising enterocolitis | 220 per 1000 | 40 per 1000 | RR 0.18 | 75 | ⊕⊝⊝⊝ | Definition was suspected NEC, which was based on clinical assessment |
| *The risk in the intervention group (and its 95% confidence interval) is based on the assumed risk in the comparison group and the relative effect of the intervention (and its 95% CI). | ||||||
| GRADE Working Group grades of evidence | ||||||
| aDowngraded one level for risk of bias: the trial lacked methodological details and caregivers were not masked. bDowngraded one level for indirectness: we did not get any response from study authors for clarification on dosage, frequency, and duration of administration of the intervention. cDowngraded two levels for serious imprecision: small sample size, few events, and wide confidence intervals. | ||||||
Background
Description of the condition
Infants born preterm have low glycogen reserves because they are born before the phase of rapid glycogen accumulation in the third trimester of pregnancy (Velaphi 2011). To compensate for prenatal and postnatal deficits, and to match fetal accretion rates, preterm infants require higher glucose intakes and have higher glucose synthetic rates than full‐term infants (Fenton 2013). For example, the rate of glucose synthesis among preterm infants at 28 weeks’ gestation is 6 to 8 mg/min/kg compared to 3 to 5 mg/min/kg in full‐term infants (Hay 2008).
Lactose, the most abundant carbohydrate in human milk, is the least variable among milk macronutrients (Ballard 2013). However, its concentration decreases in human milk with decreasing gestational age (Mahajan 2017). Lactose facilitates the absorption of two essential minerals ‐ calcium and magnesium (Gregory 2005; Martin 2016), and it provides about 40% of the caloric intake of preterm infants (Elzouki 2012). Lactose is a disaccharide that is metabolised in the small intestine by brush border lactase to glucose and galactose (Cowett 2012). Of these two metabolites, glucose is the main endogenous substrate for energy production in the brain, and galactose is essential for the production of galactolipids, which are required for the infant’s brain development (Elzouki 2012).
Human milk oligosaccharides (HMOs), the second most abundant carbohydrate in human milk, function as immunological agents (prebiotics) to promote the growth of selective commensal gut bacteria (Ballard 2013). They exhibit anti‐infective properties by serving as soluble decoys that alter bacterial adhesion to intestinal walls (Jantscher‐Krenn 2012), thereby reducing the risk of sepsis. Specific HMOs act to enhance gastrointestinal immunity and decrease the risk of necrotising enterocolitis (NEC) (Bode 2012). Insufficient consumption of these carbohydrates by the preterm infant may, therefore, have adverse effects on growth, intestinal colonisation, immune maturation, and neurological functioning (Morrow 2011).
Human milk, the ideal enteral nutrition for preterm infants, optimises neurodevelopmental outcomes (Belfort 2016). It decreases the incidence of non‐specific gastrointestinal tract infections by 64% (Duijts 2010), and it reduces the incidence of NEC by 77%, compared with cow's milk‐based infant formula (Sullivan 2010). Human milk feeding is associated with fewer re‐hospitalisations in the first year of life (Vohr 2006), and unlike infant formula, human milk contains a wide range of HMOs (Bode 2012).
However, in spite of its numerous benefits, unsupplemented human milk may not meet the nutritional needs of preterm infants. First, its HMO content is variable. It shows intraindividual and interindividual variations (Blank 2012), and it varies according to maternal genetics (i.e. secretor and Lewis blood group status) and by stage of lactation. HMO concentration is highest in colostrum and decreases with lactational changes from transitional to mature milk (Bode 2012; Gabrielli 2011).
Second, the developmental deficiency of the lactase enzyme in preterm infants may interfere with complete digestion of lactose from breast milk (Ayede 2011). For instance, infants born at between 28 and 34 gestational weeks have only 30% of the lactase activity of term infants (Fanaroff 2012). This deficiency could limit their ability to maximally derive energy from lactose in breast milk for optimal growth (Blackburn 2017). Although this inefficiency of dietary energy utilisation is salvageable in the colon through fermentation of undigested lactose, some of the energy is lost as heat (Erasmus 2002).
Therefore, for preterm infants who need larger amounts of energy and carbohydrate, carbohydrate supplements are sometimes added to human milk.
Description of the intervention
Carbohydrate supplements occur as monosaccharides, disaccharides, oligosaccharides, or polysaccharides. They may be derived from cow's milk, human milk, or soy milk or may be synthetically made. They are commercially available as modular supplements or as components of multi‐nutrient supplements in liquid or powder form. Carbohydrate supplements may also be found in mixed forms designed to achieve an intended effect (e.g. a disaccharide/polysaccharide mix of glucose polymers and lactose to enhance carbohydrate absorption because of concerns about limited lactase activity in preterm infants) (Duggan 2008). Glucose polymers provide high caloric densities without increasing the osmotic load (Hay 2017), and unlike lactose, they are better absorbed, as they do not require lactase for digestion but are dependent on glucoamylase, which is available in sufficient quantities (Cowett 2012). However, in preterm infants, it is recommended that lactose should account for 40% to 100% of the carbohydrate intake (Klein 2002), as it is needed to aid mineral absorption and to foster prebiotic and lactase activities (Blackburn 2017).
Carbohydrate supplements are fed enterally to preterm infants once they begin to tolerate breast milk feeds. Like other macronutrients, they are commonly administered as a fixed dose per unit volume of breast milk, also known as 'standardised fortification' (Mangili 2017).
How the intervention might work
Carbohydrate digestion in the preterm infant is dependent on the composition of the carbohydrate ingested, the functional maturity of intestinal hydrolytic enzymes, and the gastrointestinal system (Elzouki 2012). Unlike other hydrolytic enzymes such as glucoamylase, which are embedded deeper in villus enterocytes, lactase is located at the tips of intestinal villi, making it vulnerable to intestinal mucosal injury (Duggan 2008). Thus, carbohydrate supplementation is expected to increase concentrations of carbohydrate in human milk and may increase mucosal uptake in cases of intestinal injury (Blackburn 2017). Higher concentrations of carbohydrate fed to preterm infants increase caloric density and thus could contribute to faster growth (Brown 2014).
Why it is important to do this review
Carbohydrate supplementation of human milk could enhance the optimal growth and health of preterm infants. However, it confers potential risks. For example, excessive intake may result in hyperglycaemia and transient symptoms of malabsorption including diarrhoea, flatulence, abdominal distension, and pain (Heine 2017). Thus, a systematic review of available evidence on the efficacy and safety of carbohydrate supplements in preterm infants is needed to clarify the uncertainties surrounding this intervention. This is an update of a previously published Cochrane Review (Kuschel 2000).
Objectives
To determine whether human milk supplemented with carbohydrate compared with unsupplemented human milk fed to preterm infants improves growth, body composition, and cardio‐metabolic and neurodevelopmental outcomes without causing significant adverse effects.
Methods
Criteria for considering studies for this review
Types of studies
We considered published and unpublished controlled trials utilising random or quasi‐random patient allocation for inclusion in this review. We excluded cross‐over trials.
Types of participants
Preterm infants (< 37 weeks' gestation) receiving enteral feeding of human milk within a hospital setting.
Types of interventions
Human milk with or without additional carbohydrate supplementation.
Types of outcome measures
Primary and secondary outcomes for this review were aligned with the outcomes of the Cochrane Review titled "Multi‐nutrient fortification of human milk for preterm infants" (Brown 2016).
Primary outcomes
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Growth: weight, length, head circumference, skinfold thickness, body mass index, and measures of body composition (lean/fat mass) and growth restriction (proportion of infants who remain < 10th percentile for the index population distribution for weight, length, or head circumference)
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Researchers assessed growth parameters from birth to hospital discharge, at or after two years’ corrected age, during adolescence, and during adulthood
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Neurodevelopmental outcomes
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Neurodevelopmental outcomes after 12 months post term included neurological evaluations, developmental scores, and classifications of disability, including auditory and visual disability. We defined neurodevelopmental impairment as the presence of one or more of the following: non‐ambulant cerebral palsy, developmental quotient greater than two standard deviations below the population mean, blindness (visual acuity < 6/60), or deafness (any hearing impairment requiring or unimproved by amplification)
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Secondary outcomes
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Duration of hospital admission
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Feeding intolerance that results in cessation of or reduction in enteral feeding
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Necrotising enterocolitis (NEC)
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Hyperglycaemia
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Diarrhoea
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Gastrointestinal disturbance
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Long‐term measures of cardio‐metabolic health such as insulin resistance, obesity, diabetes, and hypertension
Search methods for identification of studies
We used the criteria and standard methods of Cochrane and Cochrane Neonatal (see the Cochrane Neonatal search strategy for specialised register).
Electronic searches
We conducted a comprehensive search including: Cochrane Central Register of Controlled Trials (CENTRAL 2019, Issue 8) in the Cochrane Library and MEDLINE via PubMed (2018 to 22 August 2019). We have included the search strategies for each database in Appendix 1. We did not apply language restrictions.
We searched clinical trial registries for ongoing or recently completed trials (ISRCTN Registry). 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 (clinicaltrials.gov) were searched via Cochrane CENTRAL.
This search updates the searches conducted for previous versions of the review (Amissah 2018, Kuschel 1999).
Searching other resources
We searched the reference lists of articles included in this review to identify additional relevant articles. We did not search any additional conference proceedings.
Data collection and analysis
We used the guidelines and standardised methods of Cochrane and its Neonatal Review Group to assess the methodological quality of the included trial. Two review authors (EA and JH) independently extracted data, compared data, and resolved differences. We used the standard method of Cochrane Neonatal to synthesise data and expressed results as relative risk and weighted mean difference.
Selection of studies
For the 2018 update, review authors carried out the study selection process independently as follows: two review authors (EA and JB) independently screened the titles and abstracts of records identified by the searches. We resolved conflicts through discussion or by consultation with a third review author (JH). We retrieved the full text of all potentially relevant articles and linked together reports of the same study. Two review authors (EA and JB) independently assessed full‐text articles for inclusion or exclusion using the review eligibility criteria and resolved conflicts by discussion. We used Covidence during the study selection and data collection process.
For the 2020 update, Cochrane Neonatal screened the titles and abstracts identified by the search independently and in duplicate in consultation with a review author (JH).
Data extraction and management
We developed a data extraction form before gathering data to enable two review authors (EA and JH) to independently extract information from study reports. We extracted data such as source details, study eligibility, study design, participant characteristics, intervention and control details, and outcomes. We resolved conflicts in the data extraction and management process by discussion. We then exported the data into Cochrane's statistical software, Review Manager 3 (RevMan 2014).
Assessment of risk of bias in included studies
Two review authors (EA and JH) independently assessed the risk of bias (low, high, or unclear) of all included trials using the Cochrane ‘Risk of bias’ tool for the following domains (Higgins 2017).
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Sequence generation (selection bias).
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Allocation concealment (selection bias).
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Blinding of participants and personnel (performance bias).
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Blinding of outcome assessment (detection bias).
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Incomplete outcome data (attrition bias).
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Selective reporting (reporting bias).
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Any other bias.
We resolved disagreements by discussion. See Appendix 2 for a detailed description of risk of bias for each domain. We contacted the primary author of the included trial for confirmation that the two publications describe the same trial, and for clarification of study methods, specifically, methods related to randomisation and blinding during the trial.
Measures of treatment effect
We used the numbers of events in control and intervention groups of each study to calculate risk ratios (RRs) with 95% confidence intervals (CIs) for dichotomous data. We planned to calculate mean differences (MDs) between treatment groups when outcomes were measured in the same way for continuous data. However, this was not possible because data for our pre‐defined outcomes were scarce. Trial investigators measured duration of hospital stay using median and range values. We opted to report their findings narratively rather than convert values to mean and standard deviation because of the skewness of the data. We did not need to use standardised mean differences (SMDs) in this update as there was only one included trial. We reported 95% CIs for all outcomes. We did not calculate number needed to treat for an additional beneficial outcome or number needed to treat for an additional harmful outcome because data were insufficient.
Unit of analysis issues
We did not identify any unit of analysis issues.
Dealing with missing data
We noted levels of attrition. We carried out analyses on an intention‐to‐treat basis, when possible, for all outcomes. We analysed all participants, when possible, in the treatment group to which they were randomised, regardless of the treatment received. We did not contact the primary author regarding missing data. We were unable to conduct sensitivity analyses and were unable to address the potential impact of missing data on findings of the review because data were insufficient.
Assessment of heterogeneity
We planned to assess whether the clinical and methodological characteristics of included studies were sufficiently similar for meta‐analysis to provide a clinically meaningful summary. We planned to do this by assessing statistical heterogeneity using the Chi² test and the I² statistic. An I² measurement greater than 50% and a low P value (< 0.10) in the Chi² test for heterogeneity were taken to indicate moderate to high heterogeneity. When we detected moderate to high heterogeneity, we planned to explore possible explanations through sensitivity and/or subgroup analyses. We planned to take statistical heterogeneity into account when interpreting trial results, especially if we noted any variation in the direction of effect. We were unable to perform any of these assessments, as we included only one trial.
Assessment of reporting biases
Some types of reporting bias (e.g. publication bias, multiple publication bias, language bias) reduce the likelihood that all studies eligible for a review will be retrieved. If all eligible studies are not retrieved, the review may be biased. We aimed to conduct a comprehensive search for eligible studies and were alert for duplication of data. We were unable to assess publication bias, as we found insufficient studies for any of the outcomes (10 or more trials required).
Data synthesis
We planned to use the GRADE approach, as outlined in the GRADE Handbook (Schünemann 2013), to assess the quality of evidence for the following clinically relevant outcomes: growth, neurodevelopment, duration of hospital admission, feeding intolerance that results in cessation or reduction in enteral feeding, and NEC. However, because of lack of data, we could assess quality using GRADE only for feeding intolerance and NEC.
Two review authors (EA and JB) independently assessed the quality of the evidence for each of the outcomes above. We considered evidence from RCTs as high quality but downgraded 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 evidence, precision of estimates, and presence of publication bias. We used the GRADEpro GDT Guideline Development Tool to create a ‘Summary of findings’ table to report the quality of the evidence.
The GRADE approach yields an assessment of the quality of a body of evidence by one of four grades.
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High: we are very confident that the true effect lies close to that of the estimate of the effect.
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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.
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Low: our confidence in the effect estimate is limited: the true effect may be substantially different from the estimate of the effect.
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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.
Subgroup analysis and investigation of heterogeneity
We planned to perform subgroup and sensitivity analyses if we noted moderate to high heterogeneity. We planned to consider whether an overall summary was meaningful, and if it was, we planned to use a random‐effects model to analyse it. We planned to carry out the following subgroup analyses to evaluate differences in outcomes: gestational age subgroups (< 30 vs 30 to < 34 vs 34 to < 37 completed weeks), birth weight subgroups (< 1 kg vs ≥ 1 kg), male versus female sex, and types of carbohydrate supplements (lactose vs other forms). However, data were insufficient for us to conduct any subgroup analyses.
Sensitivity analysis
We planned to conduct sensitivity analysis by examining only trials considered to have low risk of bias for allocation concealment and randomisation. We were unable to do this, as we included only one trial in this review.
Results
Description of studies
Please refer to the tables sections for study details (Included studies; Excluded studies).
Results of the search
Using search methods, we identified 548 records after duplicates had been removed. After trial and abstract screening, we excluded 545 records and retrieved three full‐text articles for further analysis (Figure 1). We identified one trial (two publications) as eligible for inclusion in this review (Armanian 2014).
Study flow diagram: review update
Included studies
One trial published in English met our inclusion criteria (Armanian 2014). This two‐armed, single‐centre study was carried out at a tertiary neonatal intensive care unit in Iran and included a total of 75 preterm infants. One publication stated that the trial was quasi‐randomised and unblinded (Armanian 2014), but another publication by the same study authors stated that the study was randomised and blinded (Armanian 2016). It was conducted between December 2012 and November 2013 and reported on effects of prebiotic supplementation of human milk on preterm infants. We have summarised details of the included study in the Characteristics of included studies table.
Participants
Preterm infants involved in this trial were at ≤ 34 gestational weeks with birth weight ≤ 1500 grams. They had no asphyxia, major congenital anomalies, congenital cyanotic heart disease, gastrointestinal system anomalies, proven sepsis, or infection before the start of the study and were not transferred to other departments. Preterm infants in both intervention and control groups entered the study when their milk intake reached 30 mL/kg/d.
Interventions
Researchers used a non‐human short‐chain galacto‐oligosaccharides/long‐chain fructo‐oligosaccharides (GOS/ FOS) supplement in a 9:1 mixture. Trialists initially gave incremental doses of the supplement until the infant’s milk intake reached 150 mL/kg/d. However, it was not clear if these initial doses were given separately from breast milk. They then mixed a single dose of 1.5 g/kg/d of supplement with breast milk and fed this to preterm infants in the intervention group for a day or two. We sought clarification from study authors on dosing and mode of administration of the intervention, but we have not yet received a response. Investigators fed only human milk to preterm infants in both intervention and control groups throughout the study. However, it is not clear if the milk was maternal or donor human milk or both.
Comparators
The control group received unsupplemented human milk.
Outcomes
The trial evaluated weight at day 30 but did not report our pre‐defined outcome of weight gain in g/kg/d. Study authors also reported duration of hospital stay, feeding intolerance, and NEC, sepsis, intraventricular haemorrhage, patent ductus arteriosus, time to full enteral feeds, and death. They provided no data on short‐ and long‐term growth, body mass index, body composition, or neurodevelopmental and cardio‐metabolic outcomes.
Excluded studies
We excluded one trial, for which the intervention was not relevant to our review, as it involved comparing a synbiotic versus no intervention (Nandhini 2016). We were unable to identify any ongoing trials involving carbohydrate supplementation of human milk in preterm infants. See Characteristics of excluded studies for details on exclusions.
Risk of bias in included studies
Please see Characteristics of included studies and the risk of bias graph and summary (Figure 2; Figure 3) for details.
Risk of bias graph: review authors' judgements about each risk of bias item presented as percentages across all included studies.
Risk of bias summary: review authors' judgements about each risk of bias item for each included study.
Allocation
Random sequence generation was performed through the use of odd or even file numbers; therefore we judged this as high risk. In addition, although unequal allocation of two controls to one case, as was done in this trial, may be scientifically desirable (Hey 2014), study authors did not report how this was done by using odd and even file numbers. We also judged allocation concealment as high risk because caregivers were not blinded and the allocation sequence was therefore easy to predict.
Blinding
This trial was not blinded, as study authors stated, "care providers were not blinded to an infant's protocol". We judged blinding of participants and personnel as high risk, as knowledge of the allocated intervention was not concealed from clinicians nor participants. We judged blinding of outcome assessors as unclear risk owing to insufficient methodological detail.
Incomplete outcome data
Study authors clearly reported reasons for withdrawals and dropouts and provided the missing numbers for each group. One reason given for missing numbers in each group was transfer to other departments. However, numbers transferred differed between groups, and study authors did not report why infants were transferred, as this potentially could be related to study outcomes. Study authors also reported no comparison of baseline characteristics between included and excluded participants. It is unclear whether researchers used an intention‐to‐treat approach, as they included four participants lost from both groups in the analysis and excluded the 13 randomised infants who were transferred. We judged attrition bias to be at unclear risk owing to insufficient methodological details.
Selective reporting
We viiewed no protocol. However, we included in the Results section all outcomes mentioned in the Methods section. We judged this to be of low risk.
Other potential sources of bias
We judged this as high risk owing to the publication of two reports for the same trial that reported different methods and different numbers of included infants. We were unable to reproduce the reported analyses from published data.
Effects of interventions
See: Summary of findings 1 Carbohydrate supplementation compared to control in preterm infants
1 Carbohydrate supplementation versus control
1.1 Growth/Weight
1.1.1 Weight at day 30
One randomised controlled trial including 75 infants contributed data (Armanian 2014). Prebiotic supplementation of human milk led to an increase in weight at day 30 compared with unsupplemented human milk (MD 160.4 grams, 95% CI 12.4 to 308.4 grams; one RCT, N = 75 infants; very low‐quality evidence). We downgraded the evidence for risk of bias, as methodological information provided was insufficient for judgement of the risk of bias, participants and events were few, and confidence intervals were wide.
1.2 Feeding intolerance
Armanian 2014 contributed data providing no clear evidence that prebiotic supplementation increased the risk of feeding intolerance (RR 0.64, 95% CI 0.36 to 1.15; one RCT, N = 75 infants; very low‐quality evidence). We downgraded the evidence for risk of bias as methodological information was insufficient for judgement of risk of bias and participants and events were few.
1.3 Necrotising enterocolitis
One trial reported data on the incidence of necrotising enterocolitis (Armanian 2014), which showed no evidence of a clear difference in risk between prebiotic‐supplemented and unsupplemented groups (RR 0.2, 95% CI 0.02 to 1.3; one RCT, N = 75 infants; very low‐quality evidence). We downgraded the quality of evidence for risk of bias as methodological information was insufficient for judgement of risk of bias, participants and events were few, and confidence intervals were wide.
1.4 Duration of hospital stay
One trial reported on duration of hospital stay (Armanian 2014), noting that the prebiotic‐supplemented group had a shorter hospital stay than the unsupplemented group. The median (range) length of hospital stay was 16 (9 to 45) days (95% CI 15.34 to 24.09) and 25 (11 to 80) days (95% CI 25.52 to 34.39), respectively. We downgraded the evidence for risk of bias to very low quality, as methodological information was insufficient for judgement of risk of bias, patients and events were few, and confidence intervals were wide.
Study authors also reported on sepsis, intraventricular haemorrhage (IVH), patent ductus arteriosus (PDA), time to full feeds, and death, which were not among our pre‐defined outcomes. However, they provided no data for any of our other pre‐specified primary or secondary outcomes, including long‐term growth, body mass index, body composition, and neurodevelopmental and cardio‐metabolic outcomes. We were unable to conduct our pre‐specified subgroup analysis owing to insufficient data.
Discussion
Summary of main results
We identified one trial for inclusion in this review (Armanian 2014). In this trial, the prebiotic‐supplemented group showed an increase in mean weight at 30 days of age and shorter length of hospital stay compared to the control group. Study authors provided no evidence of a clear difference in risk of feeding intolerance and necrotising enterocolitis (NEC) between intervention and control groups. Except for weight at day 30, no data were available for assessment of effects of carbohydrate supplementation on short‐ and long‐term growth, body mass index (BMI), body composition, and neurodevelopmental and cardio‐metabolic outcomes.
Overall completeness and applicability of evidence
The only trial included in this review is limited in applicability because it was conducted among a small sample of preterm infants in Iran. However, the outcomes assessed are common to all preterm infants. This trial shows that prebiotic carbohydrate supplementation of human milk may be feasible in developing countries.
Quality of the evidence
The included trial had high risk of selection and performance bias owing to quasi‐randomisation and lack of blinding of caregivers. In addition, two reports of this trial were inconsistent regarding methods and numbers of included infants, and we were unable to reproduce analyses of findings for NEC and weight at day 30, as reported in the publication. The overall quality of evidence for outcomes assessed according to GRADE was very low owing to insufficient methodological details, high risk of bias, small sample size, few events, and variable precision.
Potential biases in the review process
The comprehensive search strategy, use of appropriate search terminology, and lack of language restriction in this review minimised bias and increased the likelihood of identifying all relevant articles. Additionally, involvement of multiple authors in the review process to independently screen records for inclusion and extract data for analysis limited the introduction of bias into the review. However, there is always potential for publication bias. Unfortunately, we were unable to create funnel plots and evaluate this potential risk because we included only one trial in the review.
Agreements and disagreements with other studies or reviews
To the best of our knowledge, this is the only systematic review covering this topic, and our previous review found no trials eligible for inclusion.
Study flow diagram: review update
Risk of bias graph: review authors' judgements about each risk of bias item presented as percentages across all included studies.
Risk of bias summary: review authors' judgements about each risk of bias item for each included study.
Comparison 1: Carbohydrate supplementation versus control, Outcome 1: Growth/Weight
Comparison 1: Carbohydrate supplementation versus control, Outcome 2: Feeding intolerance
Comparison 1: Carbohydrate supplementation versus control, Outcome 3: Necrotising enterocolitis
| Carbohydrate supplementation compared to control in preterm infants | ||||||
| Patient or population: preterm infants | ||||||
| Outcomes | Anticipated absolute effects* (95% CI) | Relative effect | № of participants | Certainty of the evidence | Comments | |
|---|---|---|---|---|---|---|
| Risk with control | Risk with carbohydrate supplementation | |||||
| Growth/Weight at day 30 | Mean weight 1542.4 g | Mean weight increased by 160.4 g | ‐ | 75 (1 RCT) | ⊕⊝⊝⊝ | No other data were reported on growth except for weight at day 30. |
| Neurodevelopmental outcomes | ‐ | ‐ | ‐ | ‐ | ‐ | No data were reported for this outcome in the included trial. |
| Duration of hospital stay | ‐ | ‐ | ‐ | 75 | ⊕⊝⊝⊝ | The trial reported data on duration of hospital stay using median (range) for the prebiotic‐supplemented and unsupplemented groups as 16 (9 to 45) (95% CI 15.34 to 24.09 days) and 25 (11 to 80) (95% CI 25.52 to 34.39 days), respectively. We have reported this outcome in the text of the review. |
| Feeding Intolerance | 560 per 1000 | 358 per 1000 | RR 0.64 | 75 | ⊕⊝⊝⊝ | Study authors defined feeding intolerance as "gastric residue, i.e. the presence of milk in the stomach two hours after completion of a feeding". However another reported outcome was "requiring to cut off milk", which was similar to our pre‐specified definition of feeding intolerance, i.e. resulting in cessation or reduction in enteral feeding. Thus, we used "requiring to cut off milk" in the analysis of feeding intolerance. |
| Necrotising enterocolitis | 220 per 1000 | 40 per 1000 | RR 0.18 | 75 | ⊕⊝⊝⊝ | Definition was suspected NEC, which was based on clinical assessment |
| *The risk in the intervention group (and its 95% confidence interval) is based on the assumed risk in the comparison group and the relative effect of the intervention (and its 95% CI). | ||||||
| GRADE Working Group grades of evidence | ||||||
| aDowngraded one level for risk of bias: the trial lacked methodological details and caregivers were not masked. bDowngraded one level for indirectness: we did not get any response from study authors for clarification on dosage, frequency, and duration of administration of the intervention. cDowngraded two levels for serious imprecision: small sample size, few events, and wide confidence intervals. | ||||||
| Outcome or subgroup title | No. of studies | No. of participants | Statistical method | Effect size |
| 1.1 Growth/Weight Show forest plot | 1 | Mean Difference (IV, Fixed, 95% CI) | Subtotals only | |
| 1.1.1 Weight at day 30 (g) | 1 | 75 | Mean Difference (IV, Fixed, 95% CI) | 160.40 [12.41, 308.39] |
| 1.2 Feeding intolerance Show forest plot | 1 | 75 | Risk Ratio (M‐H, Fixed, 95% CI) | 0.64 [0.36, 1.15] |
| 1.3 Necrotising enterocolitis Show forest plot | 1 | 75 | Risk Ratio (M‐H, Fixed, 95% CI) | 0.18 [0.02, 1.33] |
