Adrienne L Williamson1,2, Josip Derado1,3, Bradley J Barney1,4, Greg Saunders1, Irene E Olsen5, Reese H Clark6, M Louise Lawson7. 1. Departments of Statistics and Analytical Sciences and. 2. Department of Psychology, College of Humanities and Social Sciences, Kennesaw State University, Kennesaw, Georgia. 3. Mathematics, College of Science and Mathematics, and. 4. Department of Statistics, College of Physical and Mathematical Sciences, Brigham Young University, Provo, Utah. 5. Department of Nutrition Sciences, College of Arts and Sciences, Drexel University, Philadelphia, Pennsylvania; and. 6. Pediatrix Medical Group, Inc, Sunrise, Florida. 7. Departments of Statistics and Analytical Sciences and mlawso16@kennesaw.edu.
Abstract
BACKGROUND: Longitudinal growth curves, based on repeated measurements from the same group of infants, exist for preterm infant weight and length but not for BMI. Our existing BMI (weight divided by length squared) curves are based on cross-sectional birth data obtained from a different group of infants at each gestational age (GA). METHODS: We calculated BMI over time for 68 693 preterm infants between 24 and 36 weeks GA. Stratifying infants by sex, GA at birth, and quintiles based on birth BMI, we created longitudinal median curves using R and validated the resulting curves for empirical fit, proper classification, and normality of z scores. RESULTS: We created 2 sets of BMI growth charts. The first set displays fitted median curves for all 5 percentile groups in each GA group by sex. The second set displays fitted median curves with their corresponding third and 97th percentiles by percentile group, GA, and sex. In the validation analysis, percentage of daily observations below the median curve approximated the expected 50th percentile after the initial 3 days. Unlike the cross-sectional curves, the longitudinal curves reveal the pattern of change corresponding to nadir; postnadir, these curves remained consistently below the cross-sectional curves and varied by GA and sex as expected. The percentage of observations falling below the 50th percentile for cross-sectional curves (revealing optimal growth) was generally much higher than for longitudinal curves (revealing actual growth). CONCLUSIONS: These new longitudinal curves provide clinicians data on how premature infants' body proportionality changes over time.
BACKGROUND: Longitudinal growth curves, based on repeated measurements from the same group of infants, exist for preterm infant weight and length but not for BMI. Our existing BMI (weight divided by length squared) curves are based on cross-sectional birth data obtained from a different group of infants at each gestational age (GA). METHODS: We calculated BMI over time for 68 693 preterm infants between 24 and 36 weeks GA. Stratifying infants by sex, GA at birth, and quintiles based on birth BMI, we created longitudinal median curves using R and validated the resulting curves for empirical fit, proper classification, and normality of z scores. RESULTS: We created 2 sets of BMI growth charts. The first set displays fitted median curves for all 5 percentile groups in each GA group by sex. The second set displays fitted median curves with their corresponding third and 97th percentiles by percentile group, GA, and sex. In the validation analysis, percentage of daily observations below the median curve approximated the expected 50th percentile after the initial 3 days. Unlike the cross-sectional curves, the longitudinal curves reveal the pattern of change corresponding to nadir; postnadir, these curves remained consistently below the cross-sectional curves and varied by GA and sex as expected. The percentage of observations falling below the 50th percentile for cross-sectional curves (revealing optimal growth) was generally much higher than for longitudinal curves (revealing actual growth). CONCLUSIONS: These new longitudinal curves provide clinicians data on how premature infants' body proportionality changes over time.
Authors: Jiajun Lyu; Haifeng Li; Lei Wang; John A Groeger; Anna L Barnett; Jiajia Zhang; Wenchong Du; Jing Hua Journal: BMC Med Date: 2022-08-08 Impact factor: 11.150