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J Neurosurg Spine. 2019 Jun 1;30(6):729-735. doi: 10.3171/2019.2.SPINE18751.

Lumbar spondylolisthesis: modern registries and the development of artificial intelligence.

Author information

1
1Alan L. and Jacqueline B. Stuart Spine Research Center, Department of Neurosurgery, Lahey Hospital & Medical Center, Burlington, Massachusetts.
2
2Department of Neurosurgery, Tufts University School of Medicine, Boston, Massachusetts; and.
3
3College of Computing, Georgia Institute of Technology, Atlanta, Georgia.

Abstract

OBJECTIVEThere are a wide variety of comparative treatment options in neurosurgery that do not lend themselves to traditional randomized controlled trials. The object of this article was to examine how clinical registries might be used to generate new evidence to support a particular treatment option when comparable options exist. Lumbar spondylolisthesis is used as an example.METHODSThe authors reviewed the literature examining the comparative effectiveness of decompression alone versus decompression with fusion for lumbar stenosis with degenerative spondylolisthesis. Modern data acquisition for the creation of registries was also reviewed with an eye toward how artificial intelligence for the treatment of lumbar spondylolisthesis might be explored.RESULTSCurrent randomized controlled trials differ on the importance of adding fusion when performing decompression for lumbar spondylolisthesis. Standardized approaches to extracting data from the electronic medical record as well as the ability to capture radiographic imaging and incorporate patient-reported outcomes (PROs) will ultimately lead to the development of modern, structured, data-filled registries that will lay the foundation for machine learning.CONCLUSIONSThere is a growing realization that patient experience, satisfaction, and outcomes are essential to improving the overall quality of spine care. There is a need to use practical, validated PRO tools in the quest to optimize outcomes within spine care. Registries will be designed to contain robust clinical data in which predictive analytics can be generated to develop and guide data-driven personalized spine care.

KEYWORDS:

AI = artificial intelligence; EHR = electronic health record; ML = machine learning; NIS = National Inpatient Sample; PRO = patient-reported outcome; PROMIS = Patient-Reported Outcomes Measurement Information System; RCT = randomized controlled trial; SID = State Inpatient Databases; SVM = support vector machine; artificial intelligence; lumbar spondylolisthesis; machine learning; patient-reported outcomes; predictive analytics; registry

PMID:
31153155
DOI:
10.3171/2019.2.SPINE18751

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