Generating expected growth curves and Z-scores for premature infants.

OBJECTIVE: To establish a standard for expected growth of premature infants and generate Z-scores based on the standard. STUDY DESIGN: Multiple regression and analysis of variance were used to evaluate whether the 10th, 50th, and 90th percentiles of birth weight, head circumference, and length from other studies were statistically different from the percentiles from Riddle. Z-scores were generated from the 10th, 50th, and 90th percentiles. RESULT: The growth charts from Mead-Johnson, Babson, Williams, Alexander, Usher, Yudkin, and Fenton were not coincident with the values reported by Riddle. The percentiles reported by Thomas were statistically coincident with the percentiles reported by Riddle. CONCLUSION: The regression equations for the 10th, 50th, and 90th percentile for weight, head circumference, and length provide expected growth of premature infants. The equations can be used to generate expected growth curves and Z-scores for weight, head circumference, and length of premature infants.

Equations describing percentiles for birth weight, head circumference, and length of preterm infants.

OBJECTIVE: To describe growth of prematurely born infants and create a growth chart adequate to assess growth of infants with less than 29 completed weeks of gestation. STUDY DESIGN: Birth weight, head circumference and length measurements of 7,425 liveborn preterm infants from 1985 to 1997 were retrieved from a longitudinal database maintained by the neonatology division. The 3rd, 5th, 10th, 15th, 25th, 50th, 75th, 85th, 90th, 95th and 97th percentiles of each measurement were determined and used for mathematical modeling. RESULTS: Birth weight was described with an exponential function while head circumference and length were described with linear functions. A preterm growth chart for the 10th, 50th and 90th percentiles for birth weight, weight growth, head circumference and length was generated. CONCLUSION: The mathematical models of growth provide smooth representations of the percentiles across gestational ages.

Equations to support predictive automated postnatal growth curves for premature infants.

Growth charts are used in pediatric medicine to plot anthropomorphic measurements over time, serving as a screen for diseases related to a patient's nutritional and general health status. Whereas reference data for term infants are available from the Center for Disease Control, reference data for premature infants in a neonatal intensive care unit have not been established. Predictive curves for preterm patients, which are based on a patient's postmenstrual age and anthropomorphic measurements at birth, cannot be easily implemented with traditional paper-based methods. Preterm growth charts can be generated in an electronic health record system, but doing so requires mathematical equations or computer-readable tables. This report examines published perinatal growth curves and presents equations for predicted postnatal weight, head circumference and length in preterm infants.

Carnitine status at birth of newborn infants of varying gestation.

This study assessed and compared the plasma and red blood cell concentrations of carnitine in cord blood samples from preterm (less than or equal to 36 wk, n = 53) and term (greater than or equal to 37 wk, n = 72) neonates. The mean (+/- S.E.) plasma carnitine concentration (PL[C]) was significantly higher in preterm than in term neonates (29.0 +/- 1.8 versus 22.4 +/- 0.8 nmole/ml; P less than 0.001). Likewise, the mean (+/- S.E.) red blood cell carnitine concentration (RBC[C]) was significantly higher in preterm than in term neonates (0.24 +/- 0.02 versus 0.14 +/- 0.01 nmole/mg Hgb; P less than 0.001). Both PL[C] and RBC[C] were particularly elevated in extremely immature neonates (less than or equal to 33 wk gestation). Linear regression analysis showed a significantly negative correlation between PL[C] and gestational age (r = -0.332; P less than 0.001), and between RBC[C] and gestational age (r = -0.531; P less than 0.001). Approximately 72.2 +/- 1.1% (mean +/- S.E.) of carnitine in blood was estimated to be contained in the RBC, and 27.8 +/- 1.1% (mean +/- S.E.) of carnitine was estimated to be in the plasma.