Neonatal intensive care unit: predictive models for length of stay

J Perinatol. 2013 Feb;33(2):147-53. doi: 10.1038/jp.2012.62. Epub 2012 Jun 7.


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.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Benchmarking
  • Birth Weight
  • Cohort Studies
  • Female
  • Gestational Age
  • Humans
  • Infant, Newborn
  • Infant, Newborn, Diseases / diagnosis*
  • Infant, Newborn, Diseases / therapy
  • Intensive Care Units, Neonatal / statistics & numerical data*
  • Length of Stay / statistics & numerical data*
  • Male
  • Models, Statistical*
  • Multivariate Analysis
  • New England
  • Predictive Value of Tests
  • Regression Analysis
  • Retrospective Studies
  • Risk Factors
  • Severity of Illness Index