Development and validation of a predictive model for 90-day readmission following elective spine surgery

Restricted access

OBJECTIVE

Hospital readmissions lead to a significant increase in the total cost of care in patients undergoing elective spine surgery. Understanding factors associated with an increased risk of postoperative readmission could facilitate a reduction in such occurrences. The aims of this study were to develop and validate a predictive model for 90-day hospital readmission following elective spine surgery.

METHODS

All patients undergoing elective spine surgery for degenerative disease were enrolled in a prospective longitudinal registry. All 90-day readmissions were prospectively recorded. For predictive modeling, all covariates were selected by choosing those variables that were significantly associated with readmission and by incorporating other relevant variables based on clinical intuition and the Akaike information criterion. Eighty percent of the sample was randomly selected for model development and 20% for model validation. Multiple logistic regression analysis was performed with Bayesian model averaging (BMA) to model the odds of 90-day readmission. Goodness of fit was assessed via the C-statistic, that is, the area under the receiver operating characteristic curve (AUC), using the training data set. Discrimination (predictive performance) was assessed using the C-statistic, as applied to the 20% validation data set.

RESULTS

A total of 2803 consecutive patients were enrolled in the registry, and their data were analyzed for this study. Of this cohort, 227 (8.1%) patients were readmitted to the hospital (for any cause) within 90 days postoperatively. Variables significantly associated with an increased risk of readmission were as follows (OR [95% CI]): lumbar surgery 1.8 [1.1–2.8], government-issued insurance 2.0 [1.4–3.0], hypertension 2.1 [1.4–3.3], prior myocardial infarction 2.2 [1.2–3.8], diabetes 2.5 [1.7–3.7], and coagulation disorder 3.1 [1.6–5.8]. These variables, in addition to others determined a priori to be clinically relevant, comprised 32 inputs in the predictive model constructed using BMA. The AUC value for the training data set was 0.77 for model development and 0.76 for model validation.

CONCLUSIONS

Identification of high-risk patients is feasible with the novel predictive model presented herein. Appropriate allocation of resources to reduce the postoperative incidence of readmission may reduce the readmission rate and the associated health care costs.

ABBREVIATIONS ASA = American Association of Anesthesiologists; AUC = area under the receiver operating characteristic curve; BMA = Bayesian model averaging; CMS = Centers for Medicare and Medicaid Services; HRRP = Hospital Readmissions Reduction Program.
Article Information

Contributor Notes

Correspondence Clinton J. Devin: Vanderbilt University Medical Center, Nashville, TN. clinton.j.devin@vanderbilt.edu.INCLUDE WHEN CITING Published online June 15, 2018; DOI: 10.3171/2018.1.SPINE17505.Disclosures Dr. Devin has been a consultant for and has received support from Stryker Spine for non–study-related clinical or research effort. Dr. McGirt has been a consultant for Stryker.

© AANS, except where prohibited by US copyright law.

Headings
References
  • 1

    Asher ALParker SLRolston JDSelden NRMcGirt MJ: Using clinical registries to improve the quality of neurosurgical care. Neurosurg Clin N Am 26:253263ix–x 2015

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 2

    Centers for Medicare and Medicaid Services: Hospital-wide all-cause unplanned readmission (HWR) measure. QualityNet.org.(https://www.qualitynet.org/dcs/ContentServer?c=Page&pagename=QnetPublic%2FPage%2FQnetTier4&cid=1228772504318)[Accessed March 1 2018]

    • Export Citation
  • 3

    Centers for Medicare and Medicaid Services: National Health Expenditure Projections 2011–2021. Baltimore: Centers for Medicare and Medicaid Services2009 (https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData/) [Accessed March 1 2018]

    • Export Citation
  • 4

    Dailey EACizik AKasten JChapman JRLee MJ: Risk factors for readmission of orthopaedic surgical patients. J Bone Joint Surg Am 95:101210192013

  • 5

    Gliklich REDreyer NALeavy MB (eds): Registries for Evaluating Patient Outcomes 3rd edition. A User’s Guide. Rockville, MD: Agency for Healthcare Research and Quality2014 (https://www.ncbi.nlm.nih.gov/books/NBK208616/) [Accessed February 16 2018]

    • Search Google Scholar
    • Export Citation
  • 6

    Hines ALBarrett MLJiang HJSteiner CA: Conditions with the Largest Number of Adult Hospital Readmissions by Payer, 2011. HCUP Statistical Brief #172. Rockville, MD: Agency for Healthcare Research and Quality2014. (https://www.hcup-us.ahrq.gov/reports/statbriefs/sb172-Conditions-Readmissions-Payer.jsp) [Accessed February 16 2018]

    • Export Citation
  • 7

    Jencks SFWilliams MVColeman EA: Rehospitalizations among patients in the Medicare fee-for-service program. N Engl J Med 360:141814282009

  • 8

    Kim BDSmith TRLim SCybulski GRKim JY: Predictors of unplanned readmission in patients undergoing lumbar decompression: multi-institutional analysis of 7016 patients. J Neurosurg Spine 20:6066162014

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 9

    Kocher RPAdashi EY: Hospital readmissions and the Affordable Care Act: paying for coordinated quality care. JAMA 306:179417952011

  • 10

    Lohr KNSchroeder SA: A strategy for quality assurance in Medicare. N Engl J Med 322:7077121990

  • 11

    Lovecchio FHsu WKSmith TRCybulski GKim BKim JY: Predictors of thirty-day readmission after anterior cervical fusion. Spine (Phila Pa 1976) 39:1271332014

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 12

    McCormack RAHunter TRamos NMichels RHutzler LBosco JA: An analysis of causes of readmission after spine surgery. Spine (Phila Pa 1976) 37:126012662012

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 13

    Medicare Payment Advisory Commission: Report to the Congress: Reforming the Delivery System. Washington, DC: Medicare Payment Advisory Commission2008 (http://www.medpac.gov/docs/default-source/reports/Jun08_EntireReport.pdf) [Accessed March 1 2018]

    • Export Citation
  • 14

    Pugely AJCallaghan JJMartin CTCram PGao Y: Incidence of and risk factors for 30-day readmission following elective primary total joint arthroplasty: analysis from the ACS-NSQIP. J Arthroplasty 28:149915042013

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 15

    Schairer WWCarrer ADeviren VHu SSTakemoto SMummaneni P: Hospital readmission after spine fusion for adult spinal deformity. Spine (Phila Pa 1976) 38:168116892013

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 16

    Wang MCShivakoti MSparapani RAGuo CLaud PWNattinger AB: Thirty-day readmissions after elective spine surgery for degenerative conditions among US Medicare beneficiaries. Spine J 12:9029112012

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
TrendMD
Metrics

Metrics

All Time Past Year Past 30 Days
Abstract Views 441 440 85
Full Text Views 115 83 3
PDF Downloads 216 106 5
EPUB Downloads 0 0 0
PubMed
Google Scholar