Edward C. Benzel and Zoher Ghogawala
JNSPG 75th Anniversary Invited Review Article
Zoher Ghogawala, Melissa R. Dunbar, and Irfan Essa
There 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.
The 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.
Current 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.
There 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.
Anthony L. Asher, Matthew J. McGirt, and Zoher Ghogawala
John Paul G. Kolcun, Gregory W. Basil, Zoher Ghogawala, and Michael Y. Wang
Zoher Ghogawala, Daniel K. Resnick, Steven D. Glassman, James Dziura, Christopher I. Shaffrey, and Praveen V. Mummaneni
Gregory W. Basil, Annelise C. Sprau, Zoher Ghogawala, Jang W. Yoon, and Michael Y. Wang
Paul M. Arnold, Zoher Ghogawala, and Candan Tamerler
Khoi D. Than, Jill N. Curran, Daniel K. Resnick, Christopher I. Shaffrey, Zoher Ghogawala, and Praveen V. Mummaneni
To date, the factors that predict whether a patient returns to work after lumbar discectomy are poorly understood. Information on postoperative work status is important in analyzing the cost-effectiveness of the procedure.
An observational prospective cohort study was completed at 13 academic and community sites (NeuroPoint–Spinal Disorders [NeuroPoint-SD] registry). Patients undergoing single-level lumbar discectomy were included. Variables assessed included age, sex, body mass index (BMI), SF-36 physical function score, Oswestry Disability Index (ODI) score, presence of diabetes, smoking status, systemic illness, workers' compensation status, and preoperative work status. The primary outcome was working status within 3 months after surgery. Stepwise logistic regression analysis was performed to determine which factors were predictive of return to work at 3 months following discectomy.
There were 127 patients (of 148 total) with data collected 3 months postoperatively. The patients' average age at the time of surgery was 46 ± 1 years, and 66.9% of patients were working 3 months postoperatively. Statistical analyses demonstrated that the patients more likely to return to work were those of younger age (44.5 years vs 50.5 years, p = 0.008), males (55.3% vs 28.6%, p = 0.005), those with higher preoperative SF-36 physical function scores (44.0 vs 30.3, p = 0.002), those with lower preoperative ODI scores (43.8 vs 52.6, p = 0.01), nonsmokers (83.5% vs 66.7%, p = 0.03), and those who were working preoperatively (91.8% vs 26.2%, p < 0.0001). When controlling for patients who were working preoperatively (105 patients), only age was a statistically significant predictor of postoperative return to work (44.1 years vs 51.1 years, p = 0.049).
In this cohort of lumbar discectomy patients, preoperative working status was the strongest predictor of postoperative working status 3 months after surgery. Younger age was also a predictor. Factors not influencing return to work in the logistic regression analysis included sex, BMI, SF-36 physical function score, ODI score, presence of diabetes, smoking status, and systemic illness.
Clinical trial registration no.: 01220921 (clinicaltrials.gov)