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


Study design:

External validation using prospectively collected data.


Objectives:

To determine model performance of Dialogue Support in predicting outcomes after lumbar spine surgery.


Summary of background data:

To help clinicians discuss risk versus benefit with patients considering lumbar fusion surgery, “Dialogue Support” (DS) has been made available on-line. As DS was created using a Swedish sample, there is a need to study how well DS performs in alternative populations.


Methods:

Pre-op data from patients enrolled in the Quality Outcomes Database (QOD) 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 were extracted and compared to their actual 12 month post-op data. The ability of DS to identify patients in QOD who report satisfaction, achieve success or have no leg pain 12 months after surgery was determined using ROC Curve Analysis, goodness-of-fit tests and calibration plots.


Results:

There was a significant improvement in all outcomes in 23,928 cases included in the analysis from baseline to 12 months post-op. Most (84%) reported satisfaction, 67% achieved success and 44% were pain free 12 months post-op. ROC analysis showed that DS had a low ability to predict satisfaction (AUC=0.606), success (AUC=0.546) and being pain free (AUC=0.578) at 12 months post-op; 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.


Conlcusion:

Dialogue Support 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 exclusion of unknown variables associated with outcomes. Future studies to better understand and improve transferability of these models are needed.

Share on facebook
Facebook
Share on twitter
Twitter
Share on linkedin
LinkedIn
Share on vk
VK
Share on pinterest
Pinterest
Close Menu