Save
Development and validation of machine learning-based preoperative predictive analytics for lumbar spinal stenosis
EANS Academy. Schröder M. 09/26/19; 275921; EP12052
Dr. Marc L. Schröder
Dr. Marc L. Schröder

Access to this content is reserved for EANS members and attendees of this event. Click here to become an EANS member and gain your access to the full content of the EANS Academy


Abstract
Discussion Forum (0)
Rate & Comment (0)
Background: Patient-reported outcome measures (PROMs) following decompression surgery for lumbar spinal stenosis (LSS) demonstrate considerable heterogeneity. Individualized prediction tools can provide valuable insights for shared decision-making.
Methods: Data were derived from a prospective registry. All patients had undergone single- or multilevel mini-open facet-sparing decompression for LSS. We trained prediction models using various machine learning-based algorithms to predict the endpoints of interest. Models were selected by area-under-the-curve (AUC). The endpoints were dichotomize by minimum clinically important difference (MCID), and included 6-week and 12-month numeric rating scales for back (NRS-BP) and leg pain (NRS-LP) severity and the Oswestry Disability Index (ODI), and reoperations.
Results: A total of 635 patients were included. The average age was 62 ± 10 years, and 333 patients (52%) were male. At 6 weeks, MCID was seen in 63%, 76%, and 61% of patients for ODI, NRS-LP, and NRS-BP, respectively. At internal validation, the models predicted MCID in these variables with accuracies of 69%, 76%, and 85% and with AUCs of 0.75, 0.79, and 0.92. At 12-months, 66%, 63%, and 51% of patients reported MCID, and we observed accuracies of 62%, 74%, and 66% as well as AUCs of 0.68, 0.72, and 0.79. Reoperations occurred in 60 patients (9.5%), of which 27 (4.3%) at the index level. Overall and index level reoperations were predicted with 69% and 63% accuracy, respectively, and with AUCs of 0.66 and 0.61.
Conclusions: Preoperative prediction of a range of clinically relevant endpoints in decompression surgery for lumbar spinal stenosis using machine learning is feasible, and may enable enhanced informed patient consent and personalized shared decision-making. Access to individualized preoperative predictive analytics for outcome and treatment risks may represent a further step in the evolution of surgical care for patients with lumbar spinal stenosis.
Background: Patient-reported outcome measures (PROMs) following decompression surgery for lumbar spinal stenosis (LSS) demonstrate considerable heterogeneity. Individualized prediction tools can provide valuable insights for shared decision-making.
Methods: Data were derived from a prospective registry. All patients had undergone single- or multilevel mini-open facet-sparing decompression for LSS. We trained prediction models using various machine learning-based algorithms to predict the endpoints of interest. Models were selected by area-under-the-curve (AUC). The endpoints were dichotomize by minimum clinically important difference (MCID), and included 6-week and 12-month numeric rating scales for back (NRS-BP) and leg pain (NRS-LP) severity and the Oswestry Disability Index (ODI), and reoperations.
Results: A total of 635 patients were included. The average age was 62 ± 10 years, and 333 patients (52%) were male. At 6 weeks, MCID was seen in 63%, 76%, and 61% of patients for ODI, NRS-LP, and NRS-BP, respectively. At internal validation, the models predicted MCID in these variables with accuracies of 69%, 76%, and 85% and with AUCs of 0.75, 0.79, and 0.92. At 12-months, 66%, 63%, and 51% of patients reported MCID, and we observed accuracies of 62%, 74%, and 66% as well as AUCs of 0.68, 0.72, and 0.79. Reoperations occurred in 60 patients (9.5%), of which 27 (4.3%) at the index level. Overall and index level reoperations were predicted with 69% and 63% accuracy, respectively, and with AUCs of 0.66 and 0.61.
Conclusions: Preoperative prediction of a range of clinically relevant endpoints in decompression surgery for lumbar spinal stenosis using machine learning is feasible, and may enable enhanced informed patient consent and personalized shared decision-making. Access to individualized preoperative predictive analytics for outcome and treatment risks may represent a further step in the evolution of surgical care for patients with lumbar spinal stenosis.
Code of conduct/disclaimer available in General Terms & Conditions
Anonymous User Privacy Preferences

Strictly Necessary Cookies (Always Active)

MULTILEARNING platforms and tools hereinafter referred as “MLG SOFTWARE” are provided to you as pure educational platforms/services requiring cookies to operate. In the case of the MLG SOFTWARE, cookies are essential for the Platform to function properly for the provision of education. If these cookies are disabled, a large subset of the functionality provided by the Platform will either be unavailable or cease to work as expected. The MLG SOFTWARE do not capture non-essential activities such as menu items and listings you click on or pages viewed.


Performance Cookies

Performance cookies are used to analyse how visitors use a website in order to provide a better user experience.



Google Analytics is used for user behavior tracking/reporting. Google Analytics works in parallel and independently from MLG’s features. Google Analytics relies on cookies and these cookies can be used by Google to track users across different platforms/services.


Save Settings