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Robert E. Harbaugh, Clinton Devin, Michelle B. Leavy, Zoher Ghogawala, Kristin R. Archer, Mohamad Bydon, Christine Goertz, Doron Dinstein, David R. Nerenz, Guy S. Eakin, William Lavelle, William O. Shaffer, Paul M. Arnold, Charles H. Washabaugh, and Richard E. Gliklich

OBJECTIVE

The development of new treatment approaches for degenerative lumbar spondylolisthesis (DLS) has introduced many questions about comparative effectiveness and long-term outcomes. Patient registries collect robust, longitudinal data that could be combined or aggregated to form a national and potentially international research data infrastructure to address these and other research questions. However, linking data across registries is challenging because registries typically define and capture different outcome measures. Variation in outcome measures occurs in clinical practice and other types of research studies as well, limiting the utility of existing data sources for addressing new research questions. The purpose of this project was to develop a minimum set of patient- and clinician-relevant standardized outcome measures that are feasible for collection in DLS registries and clinical practice.

METHODS

Nineteen DLS registries, observational studies, and quality improvement efforts were invited to participate and submit outcome measures. A stakeholder panel was organized that included representatives from medical specialty societies, health systems, government agencies, payers, industries, health information technology organizations, and patient advocacy groups. The panel categorized the measures using the Agency for Healthcare Research and Quality’s Outcome Measures Framework (OMF), identified a minimum set of outcome measures, and developed standardized definitions through a consensus-based process.

RESULTS

The panel identified and harmonized 57 outcome measures into a minimum set of 10 core outcome measure areas and 6 supplemental outcome measure areas. The measures are organized into the OMF categories of survival, clinical response, events of interest, patient-reported outcomes, and resource utilization.

CONCLUSIONS

This effort identified a minimum set of standardized measures that are relevant to patients and clinicians and appropriate for use in DLS registries, other research efforts, and clinical practice. Collection of these measures across registries and clinical practice is an important step for building research data infrastructure, creating learning healthcare systems, and improving patient management and outcomes in DLS.

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Jeff Ehresman, Daniel Lubelski, Zach Pennington, Bethany Hung, A. Karim Ahmed, Tej D. Azad, Kurt Lehner, James Feghali, Zorica Buser, James Harrop, Jefferson Wilson, Shekar Kurpad, Zoher Ghogawala, and Daniel M. Sciubba

OBJECTIVE

The objective of this study was to evaluate the characteristics and performance of current prediction models in the fields of spine metastasis and degenerative spine disease to create a scoring system that allows direct comparison of the prediction models.

METHODS

A systematic search of PubMed and Embase was performed to identify relevant studies that included either the proposal of a prediction model or an external validation of a previously proposed prediction model with 1-year outcomes. Characteristics of the original study and discriminative performance of external validations were then assigned points based on thresholds from the overall cohort.

RESULTS

Nine prediction models were included in the spine metastasis category, while 6 prediction models were included in the degenerative spine category. After assigning the proposed utility of prediction model score to the spine metastasis prediction models, only 1 reached the grade of excellent, while 2 were graded as good, 3 as fair, and 3 as poor. Of the 6 included degenerative spine models, 1 reached the excellent grade, while 3 studies were graded as good, 1 as fair, and 1 as poor.

CONCLUSIONS

As interest in utilizing predictive analytics in spine surgery increases, there is a concomitant increase in the number of published prediction models that differ in methodology and performance. Prior to applying these models to patient care, these models must be evaluated. To begin addressing this issue, the authors proposed a grading system that compares these models based on various metrics related to their original design as well as internal and external validation. Ultimately, this may hopefully aid clinicians in determining the relative validity and usability of a given model.

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Meng Huang, Avery Buchholz, Anshit Goyal, Erica Bisson, Zoher Ghogawala, Eric Potts, John Knightly, Domagoj Coric, Anthony Asher, Kevin Foley, Praveen V. Mummaneni, Paul Park, Mark Shaffrey, Kai-Ming Fu, Jonathan Slotkin, Steven Glassman, Mohamad Bydon, and Michael Wang

OBJECTIVE

Surgical treatment for degenerative spondylolisthesis has been proven to be clinically challenging and cost-effective. However, there is a range of thresholds that surgeons utilize for incorporating fusion in addition to decompressive laminectomy in these cases. This study investigates these surgeon- and site-specific factors by using the Quality Outcomes Database (QOD).

METHODS

The QOD was queried for all cases that had undergone surgery for grade 1 spondylolisthesis from database inception to February 2019. In addition to patient-specific covariates, surgeon-specific covariates included age, sex, race, years in practice (0–10, 11–20, 21–30, > 30 years), and fellowship training. Site-specific variables included hospital location (rural, suburban, urban), teaching versus nonteaching status, and hospital type (government, nonfederal; private, nonprofit; private, investor owned). Multivariable regression and predictor importance analyses were performed to identify predictors of the treatment performed (decompression alone vs decompression and fusion). The model was clustered by site to account for site-specific heterogeneity in treatment selection.

RESULTS

A total of 12,322 cases were included with 1988 (16.1%) that had undergone decompression alone. On multivariable regression analysis clustered by site, adjusting for patient-level clinical covariates, no surgeon-specific factors were found to be significantly associated with the odds of selecting decompression alone as the surgery performed. However, sites located in suburban areas (OR 2.32, 95% CI 1.09–4.84, p = 0.03) were more likely to perform decompression alone (reference = urban). Sites located in rural areas had higher odds of performing decompression alone than hospitals located in urban areas, although the results were not statistically significant (OR 1.33, 95% CI 0.59–2.61, p = 0.49). Nonteaching status was independently associated with lower odds of performing decompression alone (OR 0.40, 95% CI 0.19–0.97, p = 0.04). Predictor importance analysis revealed that the most important determinants of treatment selection were dominant symptom (Wald χ2 = 34.7, accounting for 13.6% of total χ2) and concurrent diagnosis of disc herniation (Wald χ2 = 31.7, accounting for 12.4% of total χ2). Hospital teaching status was also found to be relatively important (Wald χ2 = 4.2, accounting for 1.6% of total χ2) but less important than other patient-level predictors.

CONCLUSIONS

Nonteaching centers were more likely to perform decompressive laminectomy with supplemental fusion for spondylolisthesis. Suburban hospitals were more likely to perform decompression only. Surgeon characteristics were not found to influence treatment selection after adjustment for clinical covariates. Further large database registry experience from surgeons at high-volume academic centers at which surgically and medically complex patients are treated may provide additional insight into factors associated with treatment preference for degenerative spondylolisthesis.

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Gregory W. Basil, Annelise C. Sprau, Zoher Ghogawala, Jang W. Yoon, and Michael Y. Wang

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Zoher Ghogawala, Shekar Kurpad, Asdrubal Falavigna, Michael W. Groff, Daniel M. Sciubba, Jau-Ching Wu, Paul Park, Sigurd Berven, Daniel J. Hoh, Erica F. Bisson, Michael P. Steinmetz, Marjorie C. Wang, Dean Chou, Charles A. Sansur, Justin S. Smith, and Luis M. Tumialán

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John Paul G. Kolcun, Gregory W. Basil, Zoher Ghogawala, and Michael Y. Wang

Free access

Zoher Ghogawala, Melissa R. Dunbar, and Irfan Essa

OBJECTIVE

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.