Study highlights benefits of patient-reported outcome tool to predict pain relief after lumbar surgery

According to recently published results, a patient-reported prediction tool derived from population-level data was able to explain most of the pain relief and improvements in function after patients underwent adult lumbar surgery.

Researchers identified adult lumbar surgery candidates using patient-reported outcome data from the Spine Surgical C are and Outcomes Assessment Program and the survey center at the Comparative Effectiveness Translational Network. Nearly 2,000 candidates completed baseline surveys prior to surgery and completed a follow-up survey within a 3 - year s pan after their procedure . Patie nt-repor ted outcome improvements 1 year after lumbar fusion surgery from 85% of the data set were used to develop prediction models. T he models were validated by using the remaining 15% of the dataset. The main outcomes included functional improvement , defined as a 15-point minimum reduction in the Oswestry Disability Index score, and back pain and leg pain improvement , defined as a minimum two-point reduction in the numeric rating scale score .

O f the 1,965 adult lumbar surgery candidates, 1,583 candidates underwent elective lumbar fusion procedures ; 1,223 candidates had stenosis ; and 1,033 had spondylolisthesis. The participation rate after 12 months for each outcome w as between 66% to 70%. At 12 months, there were improvements in function for 3 06 out of 528 surgical patients ; back pain improvements were found for 616 of 899 patients ; and leg pain improvements were found for 355 of 464 patients.

Of the nonoperative patients , 35 had improvements in function ; 47 had improvements in back pain ; and 53 had improvements in leg pain . D ata in the final prediction models included age, sex, race, insurance status, American Society of Anesthesiologists score, smoking status, diagnoses, prior surgery, prescription opioid use, asthma and baseline patient-reported outcome scores. In the validation cohort, the models had a good predictive performance and were included into a patient-facing, web-based interactive tool. – by Monica Jaramillo

Disclosures: The research was supported by the Life Sciences Discovery Fund (grant 4593311) and the Agency for Healthcare Research and Quality (grant R01HS022959). Please see the full study for a list of all other authors’ relevant financial disclosures.

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