Institutional databases may underestimate the risk factors for 30-day unplanned readmissions compared to national databases

Shyam J. Kurian, Yagiz Ugur Yolcu, Jad Zreik, Mohammed Ali Alvi, Brett A. Freedman, Mohamad Bydon

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

OBJECTIVE The National Surgical Quality Improvement Program (NSQIP) and National Readmissions Database (NRD) are two widely used databases for research studies. However, they may not provide generalizable information in regard to individual institutions. Therefore, the objective of the present study was to evaluate 30-day readmissions following anterior cervical discectomy and fusion (ACDF) and posterior lumbar fusion (PLF) procedures by using these two national databases and an institutional cohort. METHODS The NSQIP and NRD were queried for patients undergoing elective ACDF and PLF, with the addition of an institutional cohort. 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 by using both databases and the institutional cohort. RESULTS Among all identified risk factors, only hypertension was found to be a common risk factor between NRD and the institutional cohort following ACDF. NSQIP and the institutional cohort both showed length of hospital stay to be a significant predictor for 30-day related readmission following PLF. There were no overlapping variables among all 3 cohorts for either ACDF or PLF. Additionally, the national databases identified a greater number of risk factors for 30-day related readmissions than did the institutional cohort for both procedures. CONCLUSIONS Overall, significant differences were seen among all 3 cohorts with regard to top predictors of 30-day unplanned readmissions following ACDF and PLF. The higher quantity of significant predictors found in the national databases may suggest that looking at single-institution series for such analyses may result in underestimation of important variables affecting patient outcomes, and that big data may be helpful in addressing this concern.

Original languageEnglish (US)
Pages (from-to)845-853
Number of pages9
JournalJournal of Neurosurgery: Spine
Volume33
Issue number6
DOIs
StatePublished - Dec 2020

Keywords

  • Big data
  • NRD
  • NSQIP
  • National Readmissions Database
  • National Surgical Quality Improvement Program
  • Predictive analytics
  • Spinal fusion

ASJC Scopus subject areas

  • Surgery
  • Neurology
  • Clinical Neurology

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