Risk modeling predicts complication rates for spinal surgery

Kristopher T. Kimmell*, Hanna Algattas, Patrick Joynt, Tyler Schmidt, Babak S. Jahromi, Howard J. Silberstein, G. Edward Vates

*Corresponding author for this work

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

Study Design. Retrospective review of clinical data registry. Objective. In the current era of quality reporting and pay for performance, neurosurgeons must develop models to identify patients at high risk of complications. We sought to identify risk factors for complications in spine surgery and to develop a score predictive of complications. Summary of Background Data. We examined spinal surgeries from the American College of Surgeons National Surgical Quality Improvement Project (ACS-NSQIP) database. 22,430 cases were identified based on common procedural terminology. Methods. Univariate analysis followed by multivariate regression was used to identify significant factors. Results. The overall complication rate for the cohort was 9.9%. The most common complications were postoperative bleeding requiring transfusion (4.1%), nonwound infections (3.1%), and wound-related infections (2.2%). Multivariate regression analysis identified 20 factors associated with complications. Assigning 1 point for the presence of each factor a risk model was developed. The range of scores for the cohort was 0 to 13 with a median score of 4. Complication rates for a risk score of 0 to 4 was 3.7% and for scores 5 to 13 was 18.5%. The risk model robustly predicted complication rates, with complication rate of 1.2% for score of 0 (n=412, 1.8% of total) and 63.6% and 100% for scores of 12 and 13 (n=22 patients, 0.1% of total cohort) respectively (P < 0.001). The risk score also correlated strongly with total length of stay, mortality, and total work relative value units for the case. Conclusion. Patient-specific risk factors including comorbidities are strongly associated with surgical complications, length of stay, cost of care, and mortality in spine surgery and can be used to develop risk models that are highly predictive of complications.

Original languageEnglish (US)
Pages (from-to)1836-1841
Number of pages6
JournalSpine
Volume40
Issue number23
DOIs
StatePublished - Jan 1 2015

Fingerprint

Length of Stay
Spine
Multivariate Analysis
Incentive Reimbursement
Mortality
Wound Infection
Quality Improvement
Terminology
Registries
Comorbidity
Regression Analysis
Databases
Hemorrhage
Costs and Cost Analysis
Infection
Neurosurgeons

Keywords

  • ACA
  • ACS-NSQIP
  • Clinical registries
  • Complications
  • Cost reduction
  • Length of stay
  • Quality improvement
  • Risk modelling
  • SCIP
  • Spine

ASJC Scopus subject areas

  • Orthopedics and Sports Medicine
  • Clinical Neurology

Cite this

Kimmell, K. T., Algattas, H., Joynt, P., Schmidt, T., Jahromi, B. S., Silberstein, H. J., & Vates, G. E. (2015). Risk modeling predicts complication rates for spinal surgery. Spine, 40(23), 1836-1841. https://doi.org/10.1097/BRS.0000000000001051
Kimmell, Kristopher T. ; Algattas, Hanna ; Joynt, Patrick ; Schmidt, Tyler ; Jahromi, Babak S. ; Silberstein, Howard J. ; Vates, G. Edward. / Risk modeling predicts complication rates for spinal surgery. In: Spine. 2015 ; Vol. 40, No. 23. pp. 1836-1841.
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abstract = "Study Design. Retrospective review of clinical data registry. Objective. In the current era of quality reporting and pay for performance, neurosurgeons must develop models to identify patients at high risk of complications. We sought to identify risk factors for complications in spine surgery and to develop a score predictive of complications. Summary of Background Data. We examined spinal surgeries from the American College of Surgeons National Surgical Quality Improvement Project (ACS-NSQIP) database. 22,430 cases were identified based on common procedural terminology. Methods. Univariate analysis followed by multivariate regression was used to identify significant factors. Results. The overall complication rate for the cohort was 9.9{\%}. The most common complications were postoperative bleeding requiring transfusion (4.1{\%}), nonwound infections (3.1{\%}), and wound-related infections (2.2{\%}). Multivariate regression analysis identified 20 factors associated with complications. Assigning 1 point for the presence of each factor a risk model was developed. The range of scores for the cohort was 0 to 13 with a median score of 4. Complication rates for a risk score of 0 to 4 was 3.7{\%} and for scores 5 to 13 was 18.5{\%}. The risk model robustly predicted complication rates, with complication rate of 1.2{\%} for score of 0 (n=412, 1.8{\%} of total) and 63.6{\%} and 100{\%} for scores of 12 and 13 (n=22 patients, 0.1{\%} of total cohort) respectively (P < 0.001). The risk score also correlated strongly with total length of stay, mortality, and total work relative value units for the case. Conclusion. Patient-specific risk factors including comorbidities are strongly associated with surgical complications, length of stay, cost of care, and mortality in spine surgery and can be used to develop risk models that are highly predictive of complications.",
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Kimmell, KT, Algattas, H, Joynt, P, Schmidt, T, Jahromi, BS, Silberstein, HJ & Vates, GE 2015, 'Risk modeling predicts complication rates for spinal surgery', Spine, vol. 40, no. 23, pp. 1836-1841. https://doi.org/10.1097/BRS.0000000000001051

Risk modeling predicts complication rates for spinal surgery. / Kimmell, Kristopher T.; Algattas, Hanna; Joynt, Patrick; Schmidt, Tyler; Jahromi, Babak S.; Silberstein, Howard J.; Vates, G. Edward.

In: Spine, Vol. 40, No. 23, 01.01.2015, p. 1836-1841.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Risk modeling predicts complication rates for spinal surgery

AU - Kimmell, Kristopher T.

AU - Algattas, Hanna

AU - Joynt, Patrick

AU - Schmidt, Tyler

AU - Jahromi, Babak S.

AU - Silberstein, Howard J.

AU - Vates, G. Edward

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AB - Study Design. Retrospective review of clinical data registry. Objective. In the current era of quality reporting and pay for performance, neurosurgeons must develop models to identify patients at high risk of complications. We sought to identify risk factors for complications in spine surgery and to develop a score predictive of complications. Summary of Background Data. We examined spinal surgeries from the American College of Surgeons National Surgical Quality Improvement Project (ACS-NSQIP) database. 22,430 cases were identified based on common procedural terminology. Methods. Univariate analysis followed by multivariate regression was used to identify significant factors. Results. The overall complication rate for the cohort was 9.9%. The most common complications were postoperative bleeding requiring transfusion (4.1%), nonwound infections (3.1%), and wound-related infections (2.2%). Multivariate regression analysis identified 20 factors associated with complications. Assigning 1 point for the presence of each factor a risk model was developed. The range of scores for the cohort was 0 to 13 with a median score of 4. Complication rates for a risk score of 0 to 4 was 3.7% and for scores 5 to 13 was 18.5%. The risk model robustly predicted complication rates, with complication rate of 1.2% for score of 0 (n=412, 1.8% of total) and 63.6% and 100% for scores of 12 and 13 (n=22 patients, 0.1% of total cohort) respectively (P < 0.001). The risk score also correlated strongly with total length of stay, mortality, and total work relative value units for the case. Conclusion. Patient-specific risk factors including comorbidities are strongly associated with surgical complications, length of stay, cost of care, and mortality in spine surgery and can be used to develop risk models that are highly predictive of complications.

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Kimmell KT, Algattas H, Joynt P, Schmidt T, Jahromi BS, Silberstein HJ et al. Risk modeling predicts complication rates for spinal surgery. Spine. 2015 Jan 1;40(23):1836-1841. https://doi.org/10.1097/BRS.0000000000001051