Background: Acute postoperative pain is common following many types of surgery, and a significant subset of patients experience severe pain, which can be difficult to manage and result in postoperative complications. Opioid agonists are commonly used to treat severe postoperative pain, but their use has been associated with adverse outcomes. This retrospective study uses data from the Veterans Administration Surgical Quality Improvement Project (VASQIP) database to develop a postoperative Pain Severity Scale (PSS) based on subjective pain reports and postoperative opioid requirements.
Methods: Postoperative pain scores and opioid prescription data were extracted from the VASQIP database for surgeries occurring between 2010 and 2020. Procedures were grouped by surgical Common Procedural Terminology (CPT) codes, and a total of 165,321 surgical procedures were examined, representing 1141 distinct CPT codes. K-means clustering analysis was used to group the surgeries based on 24-h maximum pain, 72-h average pain, and postoperative opioid prescriptions.
Results: K-means clustering analysis showed two optimal grouping strategies; one with 3 and the other with 5 groups. Both clustering strategies produced a PSS that categorized surgical procedures with generally increasing pain scores and opioid requirements. The 5-group PSS accurately captured typical postoperative pain experience across a range of procedures.
Conclusions: K-means clustering produced a Pain Severity Scale that can distinguish typical postoperative pain for a large variety of surgical procedures based on subjective and objective clinical data. The PSS will facilitate research into the optimal postoperative pain management and could be used in the development of clinical decision support tools.
Keywords: Data science; Opioid agonists; Postoperative outcomes; Postoperative pain.
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