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

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  • 1 Mayo Clinic Neuro-Informatics Laboratory, Department of Neurologic Surgery;
  • 2 Department of Neurologic Surgery, Mayo Clinic;
  • 3 Mayo Clinic Alix School of Medicine; and
  • 4 Department of Orthopedic Surgery, Mayo Clinic, Rochester, Minnesota
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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.

ABBREVIATIONS ACDF = anterior cervical discectomy and fusion; ASA = American Society of Anesthesiologists; CHF = congestive heart failure; CPT = Current Procedural Terminology; GERD = gastroesophageal reflux disease; NRD = National Readmissions Database; NSQIP = National Surgical Quality Improvement Program; PLF = posterior lumbar fusion.

Supplementary Materials

    • Supplemental Tables 1–8 (PDF 613 MB)

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Contributor Notes

Correspondence Mohamad Bydon: Mayo Clinic, Rochester, MN. bydon.mohamad@mayo.edu.

INCLUDE WHEN CITING Published online July 31, 2020; DOI: 10.3171/2020.5.SPINE20395.

Disclosures The authors report no conflict of interest concerning the materials or methods used in this study or the findings specified in this paper.

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