Early prediction of poor outcome in extremely low birth weight infants by classification tree analysis

OBJECTIVE: To predict death or neurodevelopmental impairment (NDI) in extremely low birth weight infants by classification trees with recursive partitioning and automatic selection of optimal cut points of variables. STUDY DESIGN: Data from the Trial of Indomethacin Prophylaxis in Preterms were randomly divided into development (n=784) and validation sets (n=262). Three models were developed for the combined outcome of death (8 days to 18 months) or NDI (cerebral palsy, cognitive delay, deafness, or blindness at 18 months corrected age): antenatal: antenatal data; early neonatal: antenatal+first 3 days data; and first week: antenatal, first 3 days, and 4th to 8th days data. Decision trees were tested on the validation set. RESULTS: Variables associated with death/NDI in each model were: Antenatal: Gestation01 mL/kg/d. First week: Birth weight3 mL/kg/d. Birth weight>787 g: cranial echodense intraparenchymal lesion and transfusion>1 mL/kg/d. Correct classification rates were 61% to 62% for all models. CONCLUSIONS: The ability to predict long-term morbidity/death in extremely low birth weight infants does not improve significantly over the first week of life. Effects of different variables depend on age.

Early prediction of poor outcome in extremely low birth weight infants by classification tree analysis Journal Articles