Researchers have developed a model that uses patient-elicited and surgery-specific factors to help predict outpatient use of opioids after gynecological surgical procedures for benign or malignant indications.
Participants in the training cohort of the study (n=158) had been seen at a single institution’s academic gynecologic oncology practice and completed a preoperative survey and weekly postoperative assessments after gynecological surgery. The researchers used this training cohort to consider 39 candidate predictors of opioid use. The final model was validated internally using a separate cohort. Both open and minimally invasive procedures were included.
The majority (98% in cohort 1 and 92% in cohort 2) of participants were prescribed an opioid medication for pain management at discharge. The most prescribed medicine was oxycodone 5 mg (85%). The median number of pills prescribed at discharge was 19 in cohort 1 and 13 in cohort 2.
In all, 38% of participants did not use any opioid medication after discharge. The mean number of opioid pills used was 7.
The final model used seven predictors: age, educational attainment, smoking history, anticipated pain medication use, anxiety regarding surgery, operative time, and preoperative pregabalin administration.
“All 7 predictors are available on the day of surgery, making this model feasible to implement following inpatient and outpatient surgery, and this model is agnostic regarding surgical route, intended for use after either open or minimally invasive abdominal surgery,” the researchers wrote.
The original concordance for predicting use of five or more pills was 0.65; for 10 or more pills, 0.65; and for 15 or more pills, 0.65.
The combined model has been placed into an online calculator for clinical use and prediction of the median number of opioid pills required for patients with certain characteristics.
The researchers are currently validating this model externally as a prescribing intervention in an expanded gynecology cohort.
Reference:
Development and Validation of a Model for Opioid Prescribing Following Gynecological Surgery
