Post-Discharge Opioid Prescribing and Use after Common Surgical Procedure.

BACKGROUND: The number of deaths from prescription opioids in the US continues to increase and remains a major public health concern. Opioid-related deaths parallel prescribing trends, and postoperative opioids are a significant source of opioids in the community. Our objective was to identify opioid prescribing and use patterns after surgery to inform evidence-based practices. STUDY DESIGN: Data from a 340-bed academic medical institution and its affiliated outpatient surgical facility included retrospective medical record data and prospective telephone questionnaire and medical record data. Retrospective data included patients discharged after 1 of 19 procedure types, from July 2015 to June 2016 (n = 10,112). Prospective data included a consecutive sample of general and orthopaedic surgery and urology patients undergoing 1 of 13 procedures, from July 2016 to February 2017 (n = 539). Primary outcomes were the quantity of opioid prescribed and used in morphine milligram equivalents (MME), and the proportion of patients receiving instructions on disposal and nonopioid strategies. RESULTS: In the retrospective dataset, 76% of patients received an opioid after surgery, and 87% of prescriptions were prescribed by residents or advanced practice providers. Median prescription size ranged from 0 to 503 MME, with wide interquartile ranges (IQR) for most procedures. In the prospective dataset, there were 359 participants (67% participation rate). Of these, 92% of patients received an opioid and the median proportion used was 27%, or 24 MME (IQR 0 to 96). Only 18% of patients received disposal instructions, while 84% of all patients received instructions on nonopioid strategies. CONCLUSIONS: Median opioid use after surgery was 27% of the total prescribed, and only 18% of patients reported receiving disposal instructions. Significant variability in opioid prescribing and use after surgery warrants investigation into contributing factors.

Computational gene expression modeling identifies salivary biomarker analysis that predict oral feeding readiness in the newborn.

OBJECTIVE: To combine mathematical modeling of salivary gene expression microarray data and systems biology annotation with reverse-transcription quantitative polymerase chain reaction amplification to identify (phase I) and validate (phase II) salivary biomarker analysis for the prediction of oral feeding readiness in preterm infants. STUDY DESIGN: Comparative whole-transcriptome microarray analysis from 12 preterm newborns pre- and postoral feeding success was used for computational modeling and systems biology analysis to identify potential salivary transcripts associated with oral feeding success (phase I). Selected gene expression biomarkers (15 from computational modeling; 6 evidence-based; and 3 reference) were evaluated by reverse-transcription quantitative polymerase chain reaction amplification on 400 salivary samples from successful (n = 200) and unsuccessful (n = 200) oral feeders (phase II). Genes, alone and in combination, were evaluated by a multivariate analysis controlling for sex and postconceptional age (PCA) to determine the probability that newborns achieved successful oral feeding. RESULTS: Advancing PCA (P < .001) and female sex (P = .05) positively predicted an infant's ability to feed orally. A combination of 5 genes, neuropeptide Y2 receptor (hunger signaling), adneosine-monophosphate-activated protein kinase (energy homeostasis), plexin A1 (olfactory neurogenesis), nephronophthisis 4 (visual behavior), and wingless-type MMTV integration site family, member 3 (facial development), in addition to PCA and sex, demonstrated good accuracy for determining feeding success (area under the receiver operator characteristic curve = 0.78). CONCLUSIONS: We have identified objective and biologically relevant salivary biomarkers that noninvasively assess a newborn's developing brain, sensory, and facial development as they relate to oral feeding success. Understanding the mechanisms that underlie the development of oral feeding readiness through translational and computational methods may improve clinical decision making while decreasing morbidities and health care costs.