Phenobarbital for neonatal seizures:response rate and predictors of refractoriness

Background : Phenobarbital is the first-line choice for neonatal seizures treatment, despite a response rate of approximately 45%. Failure to respond to acute anticonvulsants is associated with poor neurodevelopmental outcome, but knowledge on predictors of refractoriness is limited. Objective : To quantify response rate to phenobarbital and to establish variables predictive of its lack of efficacy. Methods : We retrospectively evaluated newborns with electrographically confirmed neonatal seizures admitted between January 1999 and December 2012 to the neonatal intensive care unit of Parma University Hospital (Italy), excluding neonates with status epilepticus. Response was categorized as complete (cessation of clinical and electrographic seizures after phenobarbital administration), partial (reduction but not cessation of electrographic seizures with the first bolus, response to the second bolus), or absent (no response after the second bolus). Multivariate analysis was used to identify independent predictors of refractoriness. Results : Out of 91 newborns receiving phenobarbital, 57 (62.6%) responded completely, 15 (16.5%) partially, and 19 (20.9%) did not respond. Seizure type (p = 0.02), background electroencephalogram (EEG; p ≤ 0.005), and neurologic examination (p ≤ 0.005) correlated with response to phenobarbital. However, EEG (p ≤ 0.02) and seizure type (p ≤ 0.001) were the only independent predictors. Conclusion : Our results suggest a prominent role of neurophysiological variables (background EEG and electrographic-only seizure type) in predicting the absence of response to phenobarbital in high-risk newborns.

Total factor productivity and the spillover hypothesis: some new evidence

In this paper I test the spillover hypothesis of the endogenous growth literature on a sample of manufacturing firms in Italy, 1989–1994, using a new approach based on the Malmquist index. First, I measure the productivity growth registered by the high- and non-high-tech firms computing the Malmquist index with DEA. I decompose it into technical change and technical efficiency change. Then I test whether the technical change registered by high-tech firms affects productivity growth of non-high-tech firms, after controlling for factors which can potentially affect productivity growth.