Electronic Health Record–Embedded Decision Support Platform for Morphine Precision Dosing in Neonates. Issue 1 (11th December 2019)

• Record Type: Journal Article
• Authors: Vinks, Alexander A.; Punt, Nieko C.; Menke, Frank; Kirkendall, Eric; Butler, Dawn; Duggan, Thomas J.; Cortezzo, DonnaMaria E.; Kiger, Sam; Dietrich, Tom; Spencer, Paul; Keefer, Rob; Setchell, Kenneth D.R.; Zhao, Junfang; Euteneuer, Joshua C.; Mizuno, Tomoyuki; Dufendach, Kevin R.
• Is Part Of: Clinical pharmacology & therapeutics. Volume 107:Issue 1(2020)
• Page Start: 186
• Page End: 194
• Publication Date: 2019-12-11
• Subjects: Pharmacology -- Periodicals; Therapeutics -- Periodicals; 615.5
• DOI: 10.1002/cpt.1684
• Languages: English
• ISSNs: 0009-9236

Abstract

Abstract : Morphine is the opioid most commonly used for neonatal pain management. In intravenous form, it is administered as continuous infusions and intermittent injections, mostly based on empirically established protocols. Inadequate pain control in neonates can cause long‐term adverse consequences; however, providing appropriate individualized morphine dosing is particularly challenging due to the interplay of rapid natural physiological changes and multiple life‐sustaining procedures in patients who cannot describe their symptoms. At most institutions, morphine dosing in neonates is largely carried out as an iterative process using a wide range of starting doses and then titrating to effect based on clinical response and side effects using pain scores and levels of sedation. Our background data show that neonates exhibit large variability in morphine clearance resulting in a wide range of exposures, which are poorly predicted by dose alone. Here, we describe the development and implementation of an electronic health record–integrated, model‐informed decision support platform for the precision dosing of morphine in the management of neonatal pain. The platform supports pharmacokinetic model‐informed dosing guidance and has functionality to incorporate real‐time drug concentration information. The feedback is inserted directly into prescribers' workflows so that they can make data‐informed decisions. The expected outcomes are better clinical efficacy and safety with fewer side effects in the neonatal population.