Anzeige
Erschienen in:
05.02.2018 | Original Research
Prediction of Future Chronic Opioid Use Among Hospitalized Patients
Erschienen in: Journal of General Internal Medicine | Ausgabe 6/2018
Einloggen, um Zugang zu erhaltenAbstract
Background
Opioids are commonly prescribed in the hospital; yet, little is known about which patients will progress to chronic opioid therapy (COT) following discharge. We defined COT as receipt of ≥ 90-day supply of opioids with < 30-day gap in supply over a 180-day period or receipt of ≥ 10 opioid prescriptions over 1 year. Predictive tools to identify hospitalized patients at risk for future chronic opioid use could have clinical utility to improve pain management strategies and patient education during hospitalization and discharge.
Objective
The objective of this study was to identify a parsimonious statistical model for predicting future COT among hospitalized patients not on COT before hospitalization.
Design
Retrospective analysis electronic health record (EHR) data from 2008 to 2014 using logistic regression.
Patients
Hospitalized patients at an urban, safety net hospital.
Main Measurements
Independent variables included medical and mental health diagnoses, substance and tobacco use disorder, chronic or acute pain, surgical intervention during hospitalization, past year receipt of opioid or non-opioid analgesics or benzodiazepines, opioid receipt at hospital discharge, milligrams of morphine equivalents prescribed per hospital day, and others.
Key Results
Model prediction performance was estimated using area under the receiver operator curve, accuracy, sensitivity, and specificity. A model with 13 covariates was chosen using stepwise logistic regression on a randomly down-sampled subset of the data. Sensitivity and specificity were optimized using the Youden’s index. This model predicted correctly COT in 79% of the patients and no COT correctly in 78% of the patients.
Conclusions
Our model accessed EHR data to predict 79% of the future COT among hospitalized patients. Application of such a predictive model within the EHR could identify patients at high risk for future chronic opioid use to allow clinicians to provide early patient education about pain management strategies and, when able, to wean opioids prior to discharge while incorporating alternative therapies for pain into discharge planning.