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Adv Neonatal Care ; 23(5): 435-441, 2023 Oct 01.
Article in English | MEDLINE | ID: mdl-37579004

ABSTRACT

BACKGROUND: For infants requiring intensive care, infant length is a primary gauge of nutrition and forms the basis for important future treatment decisions. However, existing evidence demonstrates discrepancies in infant length measurements between tape measure and length board. PURPOSE: To compare the accuracy and reliability of infant length measurements using tape measure (standard practice) and length board in a neonatal intensive care unit (NICU) at a public academic medical center. METHODS: Using a single-group, quasi-experimental, repeated-measures design, 108 infants (35.58 ± 3.68 weeks, male 59%, 2124.83 ± 881.69 g) admitted to the NICU were prospectively enrolled. Clinical nurses measured infants weekly per unit-based protocol using tape measures. Study team measured length using length boards within 24 hours of tape measurement. Primary outcome was infant length. Length measurements were converted to sex-specific Fenton or WHO growth percentiles. Linear mixed-effects regression, κ coefficient, intraclass correlation coefficient, and positive predictive value were calculated. FINDINGS/RESULTS: The main effect of measurement conditions and time was significant. Predicted length was 0.38 cm lower for length board than for tape measure over time. Weighted κ coefficient and intraclass correlation coefficient indicated good to excellent agreement. More than 37% of infants shifted to a new percentile category based on both measurements. Positive predictive value of length board at time 1 was 63%. IMPLICATIONS FOR PRACTICE: Length board measurements are reliable for accurate classification of an infant's growth curve to influence diagnosis and future plan of care. IMPLICATIONS FOR RESEARCH: Future research warrants replication using a longitudinal design across multiple sites.


Subject(s)
Hospitalization , Intensive Care Units, Neonatal , Infant, Newborn , Female , Infant , Humans , Male , Reproducibility of Results , Linear Models , Critical Care
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