Neonatal intensive care unit: Predictive models for length of stay

G. J. Bender*, D. Koestler, Hernando Ombao, M. Mccourt, B. Alskinis, L. P. Rubin, J. F. Padbury

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

Objective:Hospital length of stay (LOS) is important to administrators and families of neonates admitted to the neonatal intensive care unit (NICU). A prediction model for NICU LOS was developed using predictors birth weight, gestational age and two severity of illness tools, the score for neonatal acute physiology, perinatal extension (SNAPPE) and the morbidity assessment index for newborns (MAIN).Study Design:Consecutive admissions (n=293) to a New England regional level III NICU were retrospectively collected. Multiple predictive models were compared for complexity and goodness-of-fit, coefficient of determination (R 2) and predictive error. The optimal model was validated prospectively with consecutive admissions (n=615). Observed and expected LOS was compared.Result:The MAIN models had best Akaike's information criterion, highest R 2 (0.786) and lowest predictive error. The best SNAPPE model underestimated LOS, with substantial variability, yet was fairly well calibrated by birthweight category. LOS was longer in the prospective cohort than the retrospective cohort, without differences in birth weight, gestational age, MAIN or SNAPPE.Conclusion:LOS prediction is improved by accounting for severity of illness in the first week of life, beyond factors known at birth. Prospective validation of both MAIN and SNAPPE models is warranted.

Original languageEnglish (US)
Pages (from-to)147-153
Number of pages7
JournalJournal of Perinatology
Volume33
Issue number2
DOIs
StatePublished - Feb 1 2013

Keywords

  • benchmarking
  • length of stay
  • neonatal intensive care units
  • projections and predictions
  • score for neonatal acute physiology
  • severity of illness index

ASJC Scopus subject areas

  • Pediatrics, Perinatology, and Child Health
  • Obstetrics and Gynecology

Fingerprint Dive into the research topics of 'Neonatal intensive care unit: Predictive models for length of stay'. Together they form a unique fingerprint.

Cite this