ObjectivesThe primary objective of this study is to determine the current level of patient medication exposure in Level 3 Neonatal Wards (L3NW). The secondary objective is to evaluate in the first month of life the rate of medication prescription not cited in the Summary of Product Characteristics (SmPC). A database containing all the medication prescriptions is collected as part of a prescription benchmarking program in the L3NW.Material and methodsThe research is a two-year observational cohort study (2017–2018) with retrospective analysis of medications prescribed in 29 French L3NW. Seventeen L3NW are present since the beginning of the study and 12 have been progressively included. All neonatal units used the same computerized system of prescription, and all prescription data were completely de-identified within each hospital before being stored in a common data warehouse.ResultsThe study population includes 27,382 newborns. Two hundred and sixty-one different medications (International Nonproprietary Names, INN) were prescribed. Twelve INN (including paracetamol) were prescribed for at least 10% of patients, 55 for less than 10% but at least 1% and 194 to less than 1%. The lowest gestational ages (GA) were exposed to the greatest number of medications (18.0 below 28 weeks of gestation (WG) to 4.1 above 36 WG) (p<0.0001). In addition, 69.2% of the 351 different combinations of an medication INN and a route of administration have no indication for the first month of life according to the French SmPC. Ninety-five percent of premature infants with GA less than 32 weeks received at least one medication not cited in SmPC.ConclusionNeonates remain therapeutic orphans. The consequences of polypharmacy in L3NW should be quickly assessed, especially in the most immature infants.
The scientific community encourages the use of raw data graphs to improve the reliability and transparency of the results presented in articles. However, the current methods used to visualize raw data are limited to one or two numerical variables per graph and/or small sample sizes. In the behavioral sciences, numerous variables must be plotted together in order to gain insight into the behavior in question. In this paper, we present ViSiElse, an R-package offering a new approach in the visualization of raw data. ViSiElse was developed with the open-source software R to visualize behavioral observations over time based on raw time data extracted from visually recorded sessions of experimental observations. ViSiElse gives a global overview of a process by creating a visualization of the timestamps for multiple actions and all participants into a single graph; individual or group behavior can then be easily assessed. Additional features allow users to further inspect their data by including summary statistics and time constraints.
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