Predictive modeling of long-term opioid and benzodiazepine use after intradural tumor resection

Abstract

Background context: Despite increased awareness of the ongoing opioid epidemic, opioid and benzodiazepine use remain high after spine surgery. In particular, long-term co-prescription of opioids and benzodiazepines have been linked to high risk of overdose-associated death. Tumor patients represent a unique subset of spine surgery patients and few studies have attempted to develop predictive models to anticipate long-term opioid and benzodiazepine use after spinal tumor resection.

Methods: The IBM Watson Health MarketScan Database and Medicare Supplement were assessed to identify admissions for intradural tumor resection between 2007 and 2015. Adult patients were required to have at least 6 months of continuous preadmission baseline data and 12 months of continuous postdischarge follow-up. Primary outcomes were long-term opioid and benzodiazepine use, defined as at least 6 prescriptions within 12 months. Secondary outcomes were durations of opioid and benzodiazepine prescribing. Logistic regression models, with and without regularization, were trained on an 80% training sample and validated on the withheld 20%.

Results: A total of 1,942 patients were identified. The majority of tumors were extramedullary (74.8%) and benign (62.5%). A minority of patients received arthrodesis (9.2%) and most patients were discharged to home (79.1%). Factors associated with postdischarge opioid use duration include tumor malignancy (vs benign, B=19.8 prescribed-days/year, 95% confidence interval [CI] 1.1-38.5) and intramedullary compartment (vs extramedullary, B=18.1 prescribed-days/year, 95% CI 3.3-32.9). Pre- and perioperative use of prescribed nonsteroidal anti-inflammatory drugs and gabapentin/pregabalin were associated with shorter and longer duration opioid use, respectively. History of opioid and history of benzodiazepine use were both associated with increased postdischarge opioid and benzodiazepine use. Intramedullary location was associated with longer duration postdischarge benzodiazepine use (B=10.3 prescribed-days/year, 95% CI 1.5-19.1). Among assessed models, elastic net regularization demonstrated best predictive performance in the withheld validation cohort when assessing both long-term opioid use (area under curve [AUC]=0.748) and long-term benzodiazepine use (AUC=0.704). Applying our model to the validation set, patients scored as low-risk demonstrated a 4.8% and 2.4% risk of long-term opioid and benzodiazepine use, respectively, compared to 35.2% and 11.1% of high-risk patients.

Conclusions: We developed and validated a parsimonious, predictive model to anticipate long-term opioid and benzodiazepine use early after intradural tumor resection, providing physicians opportunities to consider alternative pain management strategies.

Keywords: Benzodiazepine; Intradural; Opioid; Pain; Predictive modeling; Tumor.