Big data analyses for continuous evaluation of pharmacotherapy: A proof of principle with doxapram in preterm infants

Background: Drug effect evaluation is often based on subjective interpretation of a selection of patient data. Continuous analyses of high frequency patient monitor data are a valuable source to measuring drug effects. However, these have not yet been fully explored in clinical care. We aim to evaluate the usefulness and applicability of high frequency physiological data for analyses of pharmacotherapy. Methods: As a proof of principle, the effects of doxapram, a respiratory stimulant, on the oxygenation in preterm infants were studied. Second-to-second physiological data were collected from 12 hours before until 36 hours after start of doxapram loading dose plus continuous maintenance dose in seven preterm infants. Besides physiological data, plasma concentrations of doxapram and keto-doxapram were measured. Results: Arterial oxygen saturation (SpO2) increased after the start of doxapram treatment alongside an increase in heart rate. The respiratory rate remained unaffected. The number of saturation dips and the time below a saturation of 80%, as well as the area under the 80%-saturation-time curve (AUC), were significantly lowered after the start of doxapram. The AUC under 90% saturation also significantly improved after start of doxapram. Plasma concentrations of doxapram and keto-doxapram were measured. Conclusion: Using high-frequency monitoring data, we showed the detailed effects over time of pharmacotherapy. We could objectively determine the respiratory condition and the effects of doxapram treatment in preterm infants. This type of analysis might help to develop individualized drug treatments with tailored dose adjustments based on a closed-loop algorithm.