title: [USING INFORMATION FROM COMPUTERIZED MEDICAL FILES TO DETERMINE THE RISK OF POOR OUTCOME IN EXTREMELY PRETERM INFANTS].
abstract: BACKGROUND
  During the last two decades preterm deliveries have been rising with increasing numbers of survivors at risk of abnormal outcomes. Characterization of risk factors for abnormal outcome is important for caregivers and patients' families. We hypothesized that it is possible to evaluate significant risk factors from computerized files early in life.
  
  
  OBJECTIVE
  To evaluate data from computerized files that could help identify children born preterm at increased risk of abnormal outcomes without a neurodevelopmental exam.
  
  
  METHODS
  This is a retrospective cohort study including children born at less than 29 weeks gestation and/or less than 1000 grams. The long term outcome was defined using data from the local child developmental center. Risk factors were retrieved from computerized files from birth until 3 years of age and assessed using univariate and multivariate analysis.
  
  
  RESULTS
  A total of 74 children were included in the study; 30 with abnormal outcomes. The following parameters: Bedouin origin (p = 0.033), low 1 minute Apgar score (p = 0.044), late sepsis (p = 0.017), exposure to x-rays (p = 0.033), hospital based specialist visits (p = 0.017) and hospitalization (p = 0.035) were more common in the abnormal outcome group or increased its risk in univariate analysis. Bedouin origin (OR = 3.81-5.11), late sepsis (OR = 4.07-4.94) and hospital based specialist visits (OR = 4.67, 95% CI = 1.11-19.55) increased more than fourfold the risk of abnormal outcomes in multivariate analysis.
  
  
  CONCLUSIONS
  This study has important implications as Bedouin origin and llate sepsis can be determined before discharge. Furthermore, follow-up of high risk children should be locally accessible. Prevention of late sepsis is of upmost importance, as well as awareness of specialists to the follow-up status of children born preterm.
authors.forename: Michal
authors.initials: M
authors.lastname: Breuer
authors.forename: Eilon
authors.initials: E
authors.lastname: Shany
authors.forename: Tali
authors.initials: T
authors.lastname: Shafat
authors.forename: Nurit
authors.initials: N
authors.lastname: Heiman
authors.forename: Kyla
authors.initials: K
authors.lastname: Marks
mesh_terms: D001034:Apgar Score; D018912:Arabs / Q000706:statistics & numerical data*; D002675:Child, Preschool; D015331:Cohort Studies; D057286:Electronic Health Records / Q000706:statistics & numerical data; D005260:Female; D005500:Follow-Up Studies; D005865:Gestational Age; D006760:Hospitalization / Q000706:statistics & numerical data*; D006801:Humans; D007223:Infant; D062071:Infant, Extremely Premature; D007231:Infant, Newborn; D008297:Male; D015999:Multivariate Analysis; D012189:Retrospective Studies; D012307:Risk Factors; D018805:Sepsis / Q000453:epidemiology*