title: Palivizumab prophylaxis during nosocomial outbreaks of respiratory syncytial virus in a neonatal intensive care unit: predicting effectiveness with an artificial neural network model.
abstract: OBJECTIVE
  To identify subgroups of premature infants who may benefit from palivizumab prophylaxis during nosocomial outbreaks of respiratory syncytial virus (RSV) infection.
  
  
  METHODS
  Retrospective analysis using an artificial intelligence model.
  
  
  METHODS
  Level IIIB, 35-bed, neonatal intensive care unit (NICU) at a tertiary care hospital in the United Arab Emirates.
  
  
  METHODS
  One hundred seventy six premature infants, born at a gestational age of 22-34 weeks, and hospitalized during four RSV outbreaks that occurred between April 2005 and July 2007.
  
  
  RESULTS
  We collected demographic and clinical data for each patient by using a standardized form. Input data consisted of seven categoric and continuous variables each. We trained, tested, and validated artificial neural networks for three outcomes of interest: mortality, days of supplemental oxygen, and length of NICU stay after the index case was identified. We compared variable impacts and performed reassignments with live predictions to evaluate the effect of palivizumab. Of the 176 infants, 31 (17.6%) received palivizumab during the outbreaks. All neural network configurations converged within 4 seconds in less than 400 training cycles. Infants who received palivizumab required supplemental oxygen for a shorter duration compared with controls (105.2 ± 7.2 days vs 113.2 ± 10.4 days, p=0.003). This benefit was statistically significant in male infants whose birth weight was less than 0.7 kg and who had hemodynamically significant congenital heart disease. Length of NICU stay after identification of the index case and mortality were independent of palivizumab use.
  
  
  CONCLUSIONS
  Palivizumab may be an effective intervention during nosocomial outbreaks of RSV in a subgroup of extremely low-birth-weight male infants with hemodynamically significant congenital heart disease.
authors.affiliation: Department of Pharmacy, Zayed Military Hospital, Abu Dhabi, United Arab Emirates.
authors.forename: Loai M
authors.initials: LM
authors.lastname: Saadah
authors.forename: Fares D
authors.initials: FD
authors.lastname: Chedid
authors.forename: Muhammad R
authors.initials: MR
authors.lastname: Sohail
authors.forename: Yazied M
authors.initials: YM
authors.lastname: Nazzal
authors.forename: Mohammed R
authors.initials: MR
authors.lastname: Al Kaabi
authors.forename: Aiman Y
authors.initials: AY
authors.lastname: Rahmani
mesh_terms: D061067:Antibodies, Monoclonal, Humanized / Q000008:administration & dosage*; D000998:Antiviral Agents / Q000008:administration & dosage; D003428:Cross Infection / Q000175:diagnosis / Q000188:drug therapy* / Q000453:epidemiology; D004196:Disease Outbreaks / Q000517:prevention & control*; D005260:Female; D006801:Humans; D007230:Infant, Low Birth Weight / Q000502:physiology; D007231:Infant, Newborn; D007363:Intensive Care Units, Neonatal; D008297:Male; D016571:Neural Networks, Computer; D000069455:Palivizumab; D011237:Predictive Value of Tests; D018357:Respiratory Syncytial Virus Infections / Q000175:diagnosis / Q000188:drug therapy* / Q000453:epidemiology; D012136:Respiratory Syncytial Viruses / Q000187:drug effects / Q000302:isolation & purification; D012189:Retrospective Studies; D016896:Treatment Outcome
chemical_list: D061067:Antibodies, Monoclonal, Humanized; D000998:Antiviral Agents; D000069455:Palivizumab
keywords: artificial; neural networks; nosocomial; outbreak; palivizumab; premature; respiratory syncytial virus