Background and objectiveThe composition of human milk varies widely and impacts the ability to meet nutrient requirements for preterm infants. The purpose of this study is to use a large dataset of milk composition from donors to a milk bank to: (1) describe the macronutrient variability in human milk and how it contributes to the ability to meet the protein and calorie targets for the preterm infant using fortification with commercially available multi-nutrient fortifiers; (2) assess how temporal versus subject effects explain macronutrient variability; (3) determine how macronutrient variability contributes to the nutrient distribution in pooled donor milk.MethodsThis is a retrospective, observational study that analyzes the macronutrient data of 1,119 human milk samples from 443 individual donors to a milk bank. We test fortification strategies with potential basic, intermediate, and high protein and calorie commercial fortifiers. Additionally, we simulate the random pooling of multiple donors to model the impact of macronutrient variability on pooled donor milk.ResultsFat was the most variable nutrient and accounted for 80% of the difference in calories. A subject-effect predicted more of the variability after 4 weeks postpartum in all macronutrients (R2 > = 0.50) than a time-effect (R2 < = 0.28). When pooling multiple donors, variability was reduced by increasing the number of donors randomly selected for a pool or targeted pooling based on macronutrient analysis of donor pools. Over 75% of mature milk samples fortified with a basic protein fortifier did not meet daily protein targets of 3.5 g/kg without exceeding volumes of 160 ml/kg/day.ConclusionThere is a strong individual signature to human milk that impacts the pooling of donor milk, and the ability to meet protein and energy requirements for the preterm infant with basic and intermediate protein and calorie fortifiers.

No abstract

Human breast milk provides nutritional and medicinal benefits that are important to infants, particularly those who are premature or ill. Donor human milk, collected, processed, and dispensed via milk banks, is the standard of care for infants in need whose mothers cannot provide an adequate supply of milk. In this paper, we focus on streamlining donor human milk processing at nonprofit milk banks. On days that milk is processed, milk banks thaw frozen deposits, pool together milk from multiple donors to meet nutritional specifications of predefined milk types, bottle and divide the pools into batches, and pasteurize the batches using equipment with various degrees of labor requirements. Limitations in staffing and equipment and the need to follow strict healthcare protocols require productive, expedient, and frugal pooling strategies. We formulate integer programs that optimize the batching-pasteurizing decisions and the integrated pooling-batching-pasteurizing decisions by minimizing labor and meeting target production goals. We further strengthen these formulations by establishing valid inequalities for the integrated model. Numerical results demonstrate a reduction in the optimality gap through the strengthened formulation versus the basic integer programming formulation. A case study at Mothers’ Milk Bank of North Texas demonstrates significant improvement in meeting milk type production targets and a modest reduction in labor compared with former practice. The model is in use at Mothers’ Milk Bank of North Texas and has effectively improved their production balance across different milk types.