Comparing short-term AIS post-operative complications between ACS-NSQIP and a surgeon study group

Abstract

Study design

Prospective cohort review.

Objective

To compare two AIS databases to determine if a performance improvement-based surgeon group has different outcomes compared to a national database.

Summary of background data

The American College of Surgeon’s National Quality Improvement Program (ACS-NSQIP) and a surgeon study group (SG) collect prospective data on AIS surgery outcomes. NSQIP offers open enrollment to all institutions, and SG membership is limited to 15 high-volume institutions, with a major initiative to improve surgeon performance. While both provide important outcome benchmarks, they may reflect outcomes that are not relatable nationwide.

Methods

The ASC-NSQIP Pediatric Spine Fusion and SG database were queried for AIS 30- and 90-day complication data for 2014 and 2015. Prospective enrollment and a dedicated site coordinator with rigorous data quality assurance protocols existed for both registries. Outcomes were compared between groups with respect to superficial and deep surgical site infections (SSI), neurologic injury, readmission, and reoperation.

Results

There were a total of 2927 AIS patients included in the ASC-NSQIP data and 721 in the SG database. Total complication rate was 9.4% NSQIP and 3.6% SG. At 90 days, there were fewer surgical site infections reported by SG than ASC-NSQIP (0.6% vs. 1.6%, p = 0.03). Similarly, there were less spinal cord injuries (0.8% vs 1.5%, p = 0.006), 30-day readmissions (0.8% vs. 2.6%, p = 0.002), and 30-day reoperations (0.6% vs. 1.7%, p = 0.02) in the SG cohort.

Conclusions

Comparison of these two data sets suggests a range of complications and readmission rates, with the SG demonstrating lower values. These results are likely multi-factorial with the performance improvement initiative of the SG playing a role. Understanding the rate and ultimate risk factors for readmission and complications from big data sources has the potential to further drive quality improvement.

Level of evidence

III.

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References

  1. 1.

    VanLare JM, Conway PH (2012) Value-based purchasing–national programs to move from volume to value. N Engl J Med 367:292–295

    CAS  PubMed  Google Scholar 

  2. 2.

    Rihn JA, Currier BL, Phillips FM et al (2013) Defining the value of spine care. J Am Acad Orthop Surg 21:419–426

    PubMed  Google Scholar 

  3. 3.

    Kraemer K, Cohen ME, Liu Y et al (2016) Development and evaluation of the American College of Surgeons NSQIP Pediatric Surgical Risk Calculator. J Am Coll Surg 223(5):685–693

    PubMed  Google Scholar 

  4. 4.

    Sebastian AS (2016) Database research in spine surgery. Clin Spine Surg 29(10):427–429

    PubMed  Google Scholar 

  5. 5.

    Sebastian AS (2017) National database research in spine surgery: limitations in the current literature. Clin Spine Surg 30(1):27–29

    PubMed  Google Scholar 

  6. 6.

    Bohl DD, Singh K, Grauer JN (2016) Nationwide databases in orthopaedic surgery research. J Am Acad Orthop Surg 24(10):673–682

    PubMed  Google Scholar 

  7. 7.

    Lee NS, Guzman JZ, Kim J et al (2016) A comparative analysis among the SRS M&M, NIS, and KID Databases for the Adolescent Idiopathic Scoliosis. Spine Deform 4:420–424

    CAS  PubMed  Google Scholar 

  8. 8.

    Bohl DD, Russo GS, Basques BA et al (2014) Variations in data collection methods between national databases affect study results: a comparison of the Nationwide Inpatient Sample and National Surgical Quality Improvement Program databases for lumbar spine fusion procedures. J Bone Jt Surg Am 97:e193

    Google Scholar 

  9. 9.

    Golvinaux NS, Bohl DD, Basques BA et al (2014) Administrative database concerns: accuracy of international classification of diseases, ninth revision coding is poor for preoperative anemia in patients undergoing spinal fusion. Spine 39:2019–2023

    Google Scholar 

  10. 10.

    ACS-NSQIP. 2015 PUF User Guide. Oct 2016; https://www.facs.org/~/media/files/quality%2520programs/nsqip/nsqip_puf_user_guide_2015.ashx. Accessed 4 February, 2016.

  11. 11.

    Fletcher ND, Glotzbecker MP, Marks MC et al (2017) Development of consensus-based best practice guidelines for postoperative care following posterior spinal fusion for adolescent idiopathic scoliosis. Spine 42(9):E547–E554

    PubMed  Google Scholar 

  12. 12.

    Fletcher ND, Marks MC, Asghar JK et al (2018) Development of consensus based best practice guidelines for perioperative management of blood loss in patients undergoing posterior spinal fusion for adolescent idiopathic scoliosis. Spine Deform 6(4):424–429

    PubMed  Google Scholar 

  13. 13.

    Pugely AJ, Martin CT, Gao Y et al (2014) The incidence and risk factors for short-term morbidity and mortality in pediatric deformity spinal surgery: an analysis of the NSQIP pediatric database. Spine 39(15):1225–1234

    PubMed  Google Scholar 

  14. 14.

    Basques BA, Bohl DD, Golinvaux NS et al (2014) Patient factors are associated with poor short-term outcomes after posterior fusion for adolescent idiopathic scoliosis. Clin Orthop Relat Res 473:286–294

    PubMed  PubMed Central  Google Scholar 

  15. 15.

    Patil CG, Santarelli J, Lad SP et al (2008) Inpatient complications, mortality, and discharge disposition after surgical correction of idiopathic scoliosis: a national perspective. Spine J 8:904–910

    PubMed  Google Scholar 

  16. 16.

    Theologis AA, Sing DC, Chekeni F et al (2017) Idiopathic scoliosis: analysis of a national estimate of 60,108 children from the National Inpatient Sample over a 13-year time period in the United States. Spine Deform 5:56–65

    PubMed  Google Scholar 

  17. 17.

    Menger RP, Kalakoti P, Pugely AJ et al (2017) Adolescent idiopathic scoliosis: risk factors for complications and the effect of hospital volume on outcomes. Neurosurg Focus 43(4):E3

    PubMed  Google Scholar 

  18. 18.

    De la Garza RR, Goodwin CR, Abu-Bonsrah N et al (2016) Patient and operative factors associated with complications following adolescent idiopathic scoliosis surgery: an analysis of 36,335 patients from the Nationwide Inpatient Survey. J Neurosurg Pediatr 25(6):730–736

    Google Scholar 

  19. 19.

    Carreon LY, Puno RM, Lenke LG et al (2007) Non-neurologic complications following surgery for adolescent idiopathic scoliosis. J Bone Jt Surg Am 89:2427–2432

    Google Scholar 

  20. 20.

    Mueller FJ, Gluch H (2009) Adolescent idiopathic scoliosis (AIS) treated with arthrodesis and posterior titanium instrumentation: 8–12 years follow up without late infection. Scoliosis 12(4):16

    Google Scholar 

  21. 21.

    Mignemi M, Tran D, Ramo B, Richards BS (2018) Repeat surgical interventions following “definitive” instrumentation and fusion for idiopathic scoliosis: 25-year update. Spine Deform 6(4):409–416

    PubMed  Google Scholar 

  22. 22.

    Roddy E, Diab M (2017) Rates and risk factors associated with unplanned hospital readmission after fusion for pediatric spinal deformity. Spine J 17(3):369–379

    PubMed  Google Scholar 

  23. 23.

    Burton DC, Carlson BB, Place HM et al (2016) Results of the scoliosis research society morbidity and mortality database 2009–2012: a report from the morbidity and mortality committee. Spine Deform 4:338–343

    PubMed  Google Scholar 

  24. 24.

    Mackenzie WGS, Matsumoto H, Williams BA et al (2013) Surgical site infection following spinal instrumentation for scoliosis: a multicenter analysis of rates, risk factors, and pathogens. J Bone Joint Surg Am. 95:800-6.S1-2

    Google Scholar 

  25. 25.

    Reames DL, Smith JS, Fu KG et al (2011) Complications in the surgical treatment of 19360 cases of pediatric scoliosis: a review of the scoliosis research society morbidity and mortality database. Spine 36(18):1484–91

    PubMed  Google Scholar 

  26. 26.

    Ahmed SI, Bastrom TP, Yaszay B et al (2017) 5-Year reoperation risk and causes for revision after idiopathic scoliosis surgery. Spine Deform 42(13):99–1005

    Google Scholar 

  27. 27.

    Minhas SV, Chow I, Feldman DS et al (2016) A predictive risk index for 30-day readmissions following surgical treatment of pediatric scoliosis. J Pediatr Orthop 36:187–192

    PubMed  Google Scholar 

  28. 28.

    Martin CT, Pugely AJ, Gao Y, Weinstein SL (2015) Causes and risk factors for 30-day unplanned readmissions after pediatric spinal deformity surgery. Spine 40(4):238–246

    PubMed  Google Scholar 

  29. 29.

    Bohl DD, Basques BA, Golvinaux NS et al (2014) Nationwide inpatient sample and national surgical quality improvement program give different results in hip fracture studies. Clin Orthop Relat Res 472:1672–1680

    PubMed  PubMed Central  Google Scholar 

  30. 30.

    Samuel AM, Lukasiewicz AM, Webb ML et al (2016) Do we really know our patient population in database research? A comparison of the femoral shaft fracture patient populations in three commonly used national databases. Bone Jt J. 98-B(3):425–32

    CAS  Google Scholar 

  31. 31.

    Webb ML, Lukasiewicz AM, Samuel AM et al (2015) Overall similar infection rates reported in the physician-reported Scoliosis Research Society database and the chart-abstracted American College of Surgeons National Surgical Quality Improvement Program database. Spine 40:1431–1435

    PubMed  Google Scholar 

  32. 32.

    Martin CT, Pugely AJ, Yubo G et al (2016) Reliability of a surgeon-reported morbidity and mortality database: a comparison of short-term morbidity between the Scoliosis Research Society and National Surgical Quality Improvement Program Databases. Iowa Orthop J 36:147–154

    PubMed  PubMed Central  Google Scholar 

  33. 33.

    Richards BS, Hasley BP, Casey VF (2006) Repeat surgical interventions following “Definitive” instrumentation and fusion for idiopathic scoliosis. Spine 31(26):3018–3026

    PubMed  Google Scholar 

  34. 34.

    Ramo BA, Richards BS (2012) Repeat surgical interventions following “definitive” instrumentation and fusion for idiopathic scoliosis: fire-year update on a previously published cohort. Spine 37:1211–1217

    PubMed  Google Scholar 

  35. 35.

    Ho C, Sucato DJ, Richards BS (2007) Risk factors for the development of delayed infections following posterior spinal fusion and instrumentation in adolescent idiopathic scoliosis patients. Spine 32(20):2272–2277

    PubMed  Google Scholar 

  36. 36.

    Fu KM, Smith JS, Polly DW et al (2011) Morbidity and mortality associated with spinal surgery in children: a review of the Scoliosis Research Society morbidity and mortality database. J Neurosurg Pediatr 7:37–41

    PubMed  Google Scholar 

  37. 37.

    Basques BA, McLynn RP, Fice MP et al (2017) Results of database studies in spine surgery can be influenced by missing data. Clin Orthop Rel Res 472(12):2893–2904

    Google Scholar 

  38. 38.

    Tsai TC, Joynt KE, Orav EJ et al (2013) Variation in surgical-readmission rates and quality of hospital care. N Engl J Med 369:1134–1142

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Hollis RH, Graham LA, Richman JS et al (2017) Hospital readmissions after surgery: how important are hospital and specialty factors? J Am Coll Surg 224(4):515–523

    PubMed  Google Scholar 

  40. 40.

    Paul JC, Lonner BS, Vira S et al (2015) High-volume hospitals and surgeons experience fewer early reoperation events after adolescent idiopathic scoliosis surgery. Spine Deform 3(5):496–501

    PubMed  Google Scholar 

  41. 41.

    Paul JC, Lonner BJ, Gos V et al (2015) Complication rates are reduced for revision adult spine deformity surgery among high-volume hospitals and surgeons. Spine J 15:1963–1972

    PubMed  Google Scholar 

  42. 42.

    Skovrlj B, Cho SK, Caridi JM et al (2015) Association between surgeon experience and complication rates in adult scoliosis surgery: a review of 5117 cases from the scoliosis research society database 2004–2007. Spine 40(15):1200–1205

    PubMed  Google Scholar 

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Funding

This study was supported in part by Grants to the Setting Scoliosis Straight Foundation in support of Harms Study Group research from DePuy Synthes Spine, EOS imaging, K2M, Medtronic, NuVasive and Zimmer Biomet.

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Contributions

Concept/design: JMB, SAS, AFS, the Harms Study Group. Acquisition/analysis/interpret data: JMB, SAS, PDS, AFS, PON, MC, BSL, BY, the Harms Study Group. Draft/revise: JMB, SAS, PDS, AFS, PON, MC, BSL, BY. Final approval: JMB, SAS, PDS, AFS, PON, MC, BSL, BY, the Harms Study Group.

Corresponding author

Correspondence to Jennifer M. Bauer.

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Bauer, J.M., Shah, S.A., Sponseller, P.D. et al. Comparing short-term AIS post-operative complications between ACS-NSQIP and a surgeon study group. Spine Deform (2020). https://doi.org/10.1007/s43390-020-00170-w

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Keywords

  • AIS
  • Pediatric spine fusion
  • Spine complications
  • NSQIP
  • Pediatric scoliosis