Predicting mortality and length-of-stay for neonatal admissions to private hospital neonatal intensive care units: a Southern African retrospective study.
OBJECTIVE
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 R(2): 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.
Genetics Department, University of Stellenbosch, South Africa. tpepler@sun.ac.za
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D000328:Adult; D000353:Africa, Southern / Q000453:epidemiology; D005260:Female; D005865:Gestational Age; D016304:Hospitals, Private; D006801:Humans; D007223:Infant; D007226:Infant Mortality; D007230:Infant, Low Birth Weight; D007231:Infant, Newborn; D007363:Intensive Care Units, Neonatal / Q000706:statistics & numerical data*; D007902:Length of Stay / Q000706:statistics & numerical data*; D016015:Logistic Models; D008297:Male; D017063:Outcome Assessment, Health Care / Q000706:statistics & numerical data*; D010343:Patient Admission / Q000706:statistics & numerical data*; D011237:Predictive Value of Tests; D025341:Principal Component Analysis; D012372:ROC Curve; D012189:Retrospective Studies; D015996:Survival Rate