Jarinda A. Poppe (1) , Willem van Weteringen (2), Robert B. Flint (1,3,4), Tom G. Goos (1,5), Irwin K.M. Reiss (1), Sinno H.P. Simons (1), Catherijne A.J. Knibbe (6,7), Swantje Völler (6), DINO Research group
(1) Department of Pediatrics, Division of Neonatology, Erasmus University Medical Center - Sophia Children’s Hospital, Rotterdam, The Netherlands, (2) Department of Pediatric Surgery, Erasmus University Medical Center - Sophia Children’s Hospital, Rotterdam, The Netherlands, (3) Department of Pharmacy, Erasmus University Medical Center, Rotterdam, the Netherlands, (4) Department of Pharmacy and Radboud Institute of Health Sciences (RIHS), Radboudumc, Nijmegen, The Netherlands, (5) Department of Biomechanical Engineering, Delft University of Technology, Delft, The Netherlands, (6) Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands , (7) Department of Clinical Pharmacy, St. Antonius Hospital, Nieuwegein, The Netherlands
Objectives: Even at the technically well–equipped intensive care units for newborns infants, drug effect evaluation is mostly based on human interpretation of a selection of patient measurements. Vital parameters from patient’s bedside monitors can nowadays be stored on a second-to-second basis, but their use remains mostly limited to the traditional ‘snapshot’ assessment of a patient’s health status. Continuous quantitative analyses of high frequency patient data could not only allow proper timing of pharmacological interventions, but also help to monitor and evaluate the effects of these interventions. Here, we report on a continuous analysis of data for doxapram, a respiratory stimulant. It is used for the treatment of apnoea of prematurity (AOP) in order to keep arterial oxygen saturation (SpO2) between 89-95% to avoid organ damage by hypo-/hyperoxia [1].
Methods: Second-to-second data on SpO2, respiratory rate and heart rate from bedside monitors of all preterm neonates, stored in the Erasmus Medical Centre in Rotterdam, were available for evaluation. We extracted data from 24 hours before to 144 hours after start of doxapram therapy in 59 preterm neonates treated from 2014 to 2017. Using R, data were processed and the distribution characteristics for each of the physiological parameters were calculated on an hourly basis. As SpO2 is the target measure for treatment of AOP, derived parameters were calculated for SpO2: the cumulative time of SpO2 below the saturation target (89%) per hour [sec/h], the number of times a child dropped below the target value and the area under the curve (AUC) of each saturation dip below the target value. To quantify the immediate effect of doxapram, values in the last hour before and after start of doxapram were compared using a Wilcoxon rank sum test in R. The same was done for 24 h after start of doxapram and 144 h (6 days) after start of doxapram. Potential effect parameters were correlated to the administered dose and the potential differences between oral and intravenous administration, and patients with and without a loading dose were evaluated.
Results: In the hour after start of doxapram treatment, SpO2 increased significantly (p<0.01), while respiratory rate and heart rate remained unaffected. The same was true when comparing the hour before start of treatment to later time points (24 h and 6 days after start of therapy). When looking at the AUC of each saturation dip below the SpO2 target value, the number of dips and the duration of an SpO2-dip below 89%, all three parameters decreased significantly during the first hour of treatment (p<0.01 in all cases). The median decreases were 61.4%, 45.9% and 21.5%, respectively. 144h after start of treatment, the AUC and duration of dips were still significantly lower than before start of therapy, while the number of dips did not differ significantly anymore. Oral administration led to a lower median reduction in AUC during the first hour of treatment than intravenous administration (75.0% vs. 42.7 %) which seems to disappear 24h after start of medication. The administration of a loading dose did not show any pronounced differences in reduction of AUC.
Conclusions: Using high-frequency monitoring data, we showed detailed effects of doxapram over time, which will be linked to pharmacokinetic data in the future. We could objectively determine the respiratory condition and the effects of doxapram treatment in preterm infants on an hourly basis. This type of analysis might help to develop individualized drug treatments with tailored dose adjustments based on real-time physiological monitoring of a patient, using a closed-loop algorithm.
References:
[1] Sakonidou S, Dhaliwal J. The management of neonatal respiratory distress syndrome in preterm infants (European Consensus Guidelines–2013 update). Arch Dis Child Educ Pract Ed. 2015 Oct;100(5):257-9.
Reference: PAGE 27 (2018) Abstr 8464 [www.page-meeting.org/?abstract=8464]
Poster: Drug/Disease Modelling - Paediatrics