Risk Factors for Unplanned Readmissions Following Anterior Cervical Discectomy and Fusion and Posterior Lumbar Fusion Procedures: Comparison of Two National Databases.

World Neurosurg

Mayo Clinic Neuro-Informatics Laboratory, Department of Neurologic Surgery, Mayo Clinic, Rochester, Minnesota, USA; Department of Neurologic Surgery, Mayo Clinic, Rochester, Minnesota, USA. Electronic address:

Published: November 2020

AI Article Synopsis

  • The NSQIP and NRD databases provide insights into healthcare quality but have differing sampling methodologies, potentially leading to conflicting findings in research studies.
  • In a study analyzing 30-day readmissions after anterior cervical discectomy and fusion (ACDF) and posterior lumbar fusion (PLF), significant predictors varied notably between the two databases: NSQIP indicated factors like diagnosis and length of stay, while NRD highlighted age and hypertension for ACDF, and alcohol abuse for PLF.
  • The discrepancies in predictive factors underline the importance of database methodology and suggest the need for further validation to assess the accuracy of these databases for predicting patient outcomes.

Article Abstract

Background: The National Surgical Quality Improvement Program (NSQIP) and National Readmission Database (NRD) are 2 widely used databases that provide valuable information regarding the quality of health care. However, the 2 differ in sampling methodology, which may result in conflicting findings when used for research studies. The objective of this study is to evaluate the differences regarding predictors of 30-day readmissions after anterior cervical discectomy and fusion (ACDF) and posterior lumbar fusion (PLF).

Methods: In this case-control study, NSQIP and NRD were queried for patients undergoing elective ACDF and PLF between 2014 and 2015. The outcome of interest was 30-day readmissions following ACDF and PLF, which were unplanned and related to the index procedure. Subsequently, univariable and multivariable analyses were conducted to determine the predictors of 30-day readmissions using both databases.

Results: For ACDF procedures, diagnosis, outpatient status, American Society of Anesthesiologists class, and length of hospital stay were found to be significant predictors of 30-day readmissions in NSQIP, whereas only age and hypertension were significant in NRD. Among patients undergoing PLF procedures, body mass index, functional status, smoking, steroid use, diabetes, dyspnea, dialysis, emergency, discharge to rehab facility, and length of hospital stay were found to be significant predictors of 30-day readmissions in NSQIP, whereas only alcohol abuse and obesity were significant predictors in NRD.

Conclusions: Two databases differed in terms of significant predictors of 30-day readmissions following ACDF and PLF. This difference may emphasize the differences in the sampling methodology. Further analyses, potentially with an institutional validation, are needed to draw conclusions regarding the accuracy of the 2 databases for predictive analytics.

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http://dx.doi.org/10.1016/j.wneu.2020.08.017DOI Listing

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