Assessment of the External Validity of Dialogue Support for Predicting Lumbar Spine Surgery Outcomes in a US Cohort.

Abstract

External validation using prospectively collected data. To determine the model performance of "Dialogue Support" (DS) in predicting outcomes after lumbar spine surgery. To help clinicians discuss risk versus benefit with patients considering lumbar fusion surgery, DS has been made available online. As DS was created using a Swedish sample, there is a need to study how well DS performs in alternative populations. Preoperative data from patients enrolled in the Quality Outcomes Database were entered into DS. The probability for each patient to report satisfaction, achieve success (leg pain improvement ≥3), or have no leg pain 12 months after surgery was extracted and compared with their actual 12-month postoperative data. The ability of DS to identify patients in the Quality Outcomes Database who report satisfaction, achieve success, or have no leg pain 12 months after surgery was determined using Receiver operating characteristic curve analysis, goodness-of-fit tests, and calibration plots. There was a significant improvement in all outcomes in 23,928 cases included in the analysis from baseline to 12 months postoperative. Most (84%) reported satisfaction, 67% achieved success, and 44% were pain-free 12 months postoperative. Receiver operating characteristic analysis showed that DS had a low ability to predict satisfaction [area under the curve (AUC) = 0.606], success (AUC = 0.546), and being pain-free (AUC = 0.578) at 12 months postoperative; poor fit for satisfaction (<0.001) and being pain-free ( P = 0.004), but acceptable fit for success ( P = 0.052). Calibration plots showed underestimation for satisfaction and success, but acceptable estimates for being pain-free. DS is not directly transferable to predict satisfaction and success after lumbar surgery in a US population. This may be due to differences in patient characteristics, weights of the variables included, or the exclusion of unknown variables associated with outcomes. Future studies to better understand and improve the transferability of these models are needed.