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African Health Sciences
Makerere University Medical School
ISSN: 1680-6905
EISSN: 1680-6905
Vol. 12, No. 2, 2012, pp. 166-173
Bioline Code: hs12028
Full paper language: English
Document type: Research Article
Document available free of charge

African Health Sciences, Vol. 12, No. 2, 2012, pp. 166-173

 en Predicting mortality and length-of-stay for neonatal admissions to private hospital neonatal intensive care units: a Southern African retrospective study
Pepler, P.T.; Uys, D.W. & Nel, D.G.

Abstract

Objectives To predict neonatal mortality and length of stay (LOS) from readily available perinatal data for neonatal intensive care unit (NICU) admissions in Southern African private hospitals.
Methods: Retrospective observational study using perinatal data from a large multicentre sample. Fifteen participating NICU centres in the Medi-Clinic private hospital group in Southern Africa. We used 2376 infants born between 1 January − 31 December 2008 to build the regression models, and a further 1 578 infants born between 1 January − 31 December 2007 to test the models. Outcome measures were mortality and length of hospital stay for NICU admissions.
Results: Of the infants included in the 2008 dataset, ninety-one (3.8%) died after being admitted to NICU centres. The median LOS for non-transferred survivors was 11 days. An analysis of the structural peculiarities of the data showed high correlations between groups of the perinatal variables pertaining to the size and Apgar scores of the newborn infants, respectively. The logistic regression model to predict neonatal mortality had a good fit (AUC: 0.8507, misclassification rate: 13.6%), but the low positive predictive value of this model reduces its usefulness. The poisson log-linear model to predict LOS had a good fit (predicted R2: 0.7027).
Conclusions: Apgar score at one minute, birth weight, and delivery mode significantly influence the odds of neonatal death and are associated with significant effects on LOS.

Keywords
length of stay, neonatal mortality, neonatal intensive care units, principal component analysis, logistic regression, log-linear models.

 
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