Palivizumab prophylaxis during nosocomial outbreaks of respiratory syncytial virus in a neonatal intensive care unit: Predicting effectiveness with an artificial neural network model

Loai M. Saadah, Fares D. Chedid, Muhammad R. Sohail, Yazied M. Nazzal, Mohammed R. Al Kaabi, Aiman Y. Rahmani

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

Study Objective: To identify subgroups of premature infants who may benefit from palivizumab prophylaxis during nosocomial outbreaks of respiratory syncytial virus (RSV) infection. DESIGN: Retrospective analysis using an artificial intelligence model. SETTING: Level IIIB, 35-bed, neonatal intensive care unit (NICU) at a tertiary care hospital in the United Arab Emirates. PATIENTS: 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. MEASUREMENTS AND MAIN 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. CONCLUSION: 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.

Original languageEnglish (US)
Pages (from-to)251-259
Number of pages9
JournalPharmacotherapy
Volume34
Issue number3
DOIs
StatePublished - 2014

Fingerprint

Neural Networks (Computer)
Respiratory Syncytial Viruses
Neonatal Intensive Care Units
Disease Outbreaks
Premature Infants
Heart Diseases
Extremely Low Birth Weight Infant
United Arab Emirates
Oxygen
Respiratory Syncytial Virus Infections
Mortality
Artificial Intelligence
Tertiary Healthcare
Tertiary Care Centers
Birth Weight
Gestational Age
Palivizumab
Demography

Keywords

  • Artificial
  • Neural networks
  • Nosocomial
  • Outbreak
  • Palivizumab
  • Premature
  • Respiratory syncytial virus

ASJC Scopus subject areas

  • Pharmacology (medical)

Cite this

Palivizumab prophylaxis during nosocomial outbreaks of respiratory syncytial virus in a neonatal intensive care unit : Predicting effectiveness with an artificial neural network model. / Saadah, Loai M.; Chedid, Fares D.; Sohail, Muhammad R.; Nazzal, Yazied M.; Al Kaabi, Mohammed R.; Rahmani, Aiman Y.

In: Pharmacotherapy, Vol. 34, No. 3, 2014, p. 251-259.

Research output: Contribution to journalArticle

Saadah, Loai M. ; Chedid, Fares D. ; Sohail, Muhammad R. ; Nazzal, Yazied M. ; Al Kaabi, Mohammed R. ; Rahmani, Aiman Y. / Palivizumab prophylaxis during nosocomial outbreaks of respiratory syncytial virus in a neonatal intensive care unit : Predicting effectiveness with an artificial neural network model. In: Pharmacotherapy. 2014 ; Vol. 34, No. 3. pp. 251-259.
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