Early prediction of poor outcome in extremely low birth weight infants by classification tree analysis Academic Article uri icon

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abstract

  • 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.

authors

  • Ambalavanan, N
  • Baibergenova, A
  • Carlo, WA
  • Saigal, Saroj
  • Schmidt, B
  • Thorpe, KE

publication date

  • April 2006