Estimates of length of neonatal stay for very premature babies in the UK.
OBJECTIVE
To describe simple estimates of likely duration of stay for very premature babies born in the UK and discharged home.


METHODS
Statistical modelling of data from thirty neonatal units in a geographically defined region of the UK.


METHODS
All babies born at 23 to 32 completed weeks of gestation in 2005, 2006 and 2007 who were discharged home with the expectation that they would survive.


METHODS
Total duration of stay in the neonatal service.


RESULTS
5528 babies were initially identified. 558 (10.1%) who died or who did not follow a normal care pathway were excluded. In a further 27, data were either missing or inadequate, leaving a study population of 4702 babies. As expected, gestation and birthweight exhibited strong influence on length of stay. Of the other variables tested, initial reason for admission (need for early respiratory support) showed the most consistent association. These factors were combined to produce predictive tables. The predictive performance of the tables was found to fit the data well for various groups, with the exception of multiple births who tended to have longer stays. However, when tested against individual units, much greater variation was seen independent of unit size and case mix.


CONCLUSIONS
The prediction tables should permit parents to make sensible estimates about the duration of their baby's stay in the neonatal service; however, there appear to be important differences between units. The variation noted in length of stay between otherwise similar units merits further investigation.
Department of Health Sciences, University of Leicester, Leicester LE1 6TP, UK.
B
B
Manktelow
E S
ES
Draper
C
C
Field
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D
Field
D001724:Birth Weight; D004739:England; D005865:Gestational Age; D019538:Health Care Surveys; D006801:Humans; D007231:Infant, Newborn; D007234:Infant, Premature; D015931:Intensive Care, Neonatal / Q000706:statistics & numerical data*; D007902:Length of Stay / Q000706:statistics & numerical data*; D015233:Models, Statistical; D011379:Prognosis; D012121:Respiration, Artificial