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Home » Lumbar Spine Textbook » Section 4: Epidemiology » Section 4, Chapter 1: Measuring Spine Safety

Section 4, Chapter 1: Measuring Spine Safety

Fady Y. Hijji, Ankur S. Narain, Kern Singh and Jonathan N. Grauer


  • Patient-centered outcomes and safety are central aspects of health care.
    1. Reform will require changes in quality and efficacy of treatment.
  • Surgical treatment for spine disorders is rapidly growing in prevalence and cost – spinal fusion is currently the most expensive procedure performed in hospitals.1
    1. Need for outcomes measurements for true value assessment is on the rise.
    2. In addition to quality metrics, relative value of interventions is being scrutinized.


  • Cost-based versus value-based purchasing in healthcare.
    1. Cost-based
      • Reward lower cost care.
      • Risks de-emphasizing quality of care.
    2. Value-based
      • Quality of care is defined by effectiveness and patient centered outcomes.
      • Both quality and cost measures are utilized to determine physician reimbursement.
      • “Pay-for-performance” method enforces quality improvement by rewarding excellence.
      • Implementation requires accurate recording of patient outcomes.
  • The Patient Protection and Affordable Care Act and the American Recovery and Reinvestment Act promote evidence-based healthcare reform.
    1. Demand measurement of efficacy in clinical practice.
    2. Well-maintained prospective registries are necessary to capture patient-centered data and provide a foundation for evidence-based reform.
  • Public reporting of physician statistics–
    1. Appeals to professional ethos as incentive to improve outcomes.
    2. Patients may vet surgeons based on their available record.
    3. Public reporting in combination with pay for performance has demonstrated greater improvements in hospital outcomes and quality than either method alone.2
    4. Can be biased by confounders (e.g. older, sicker patients).
      • Risk of access issues for more complex issues and/or more medically compromised patients.


  • Randomized Controlled Trials (RCTs)
    1. These are “Gold-standard” for high quality medical evidence.
    2. Due to increasingly specific inclusion/exclusion criteria, RCTs have limited external validity; yet, findings are still applied to patients who may not fit the original study group characteristics.
      • Provides more information on the efficacy of an intervention – outcomes under ideal circumstances.
    3. However, they are time-consuming and not necessarily cost-effective
    4. Also, there can be ethical concerns through the randomization process.3
      • Patients in control arms may be withheld from receiving potentially beneficial care or standard of treatment.
      • There is no clear standard for when a study must be stopped early for apparent benefit of interventions.
  • Administrative Databases–
    1. Well-established nationwide databases4 primarily aim to provide information on the effectiveness of an intervention – outcomes in real-world conditions.
    2. Generally large sample sizes allow for generalizability and analysis of rare outcome measurements and procedure rates.4,5
    3. Data is obtained retrospectively from reimbursement claims filed by providers or hospitals/institutions.
    4. Administrative claims data, such as costs and diagnoses, is regularly obtained so there is no additional burden for hospitals to collect this data.
    5. Data is not collected for quality control/research objectives, and data accuracy can be of concern.
      • Potential conversion to ICD-10 may increase the value of such administrative databases over time, but coding accuracy still of concern.
    6. Typically, these do not follow patients outside the inpatient period, causing low sensitivity for identifying complications in the postoperative period following discharge.
  • Prospective registry databases–
    1. Data is obtained prospectively directly from patient charts, with post-discharge data and high follow-up rate.5
    2. There is the ability to produce high quality evidence through both prognostic analysis as well as characterization of effectiveness of care.
    3. Allows for control of potential confounding variables.
    4. Adds costs and difficulty for hospitals since patient data needs to be selected and collected for quality measurement.


  • Nationwide Inpatient Sample (NIS)–
    1. Administrative database developed by the Agency for Healthcare Research and Quality.4
    2. Conducts 20% random sample of all US hospitals for discharge claims of all payers.
    3. Sampling stratified across five criteria: geographic location, public/private, teaching/nonteaching, urban/rural and bed size.
    4. Data includes demographics, length of stay, ICD-9-CM diagnoses/procedures, hospital charges and discharge disposition.
    5. No longitudinal information (patients cannot be followed for multiple hospitalizations).
  • American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP)–
    1. Registry database developed in 2005.5
    2. Civilian hospitals voluntarily commit into the program.
    3. Prospectively collects data from patients undergoing major operative procedures, following them postoperatively for 30 days for general health complications and adverse events.
    4. Capable of identifying rates and risk factors for postoperative complications.
    5. Lack of disease-specific outcome measures and assessments.
  • National Neurosurgery and Outcomes Database––
    1. Registry database developed in 2012.6
    2. Neurosurgical practice groups voluntarily commit.
    3. Prospectively measures 30-day risk-adjusted morbidity and 1-year outcomes following common lumbar procedures.
    4. Patient enrollment performed through a standardized sampling process based on weekly schedules.


  • Measuring perioperative outcomes establishes normalcy of care.
    1. Utilizing large databases provides substantial population sizes to measure prevalence and incidence of complications, readmissions and associated morbidity/mortality of various perioperative adverse events.
    2. Obtaining these measures helps characterize efficacy and safety for lumbar procedures.
  • Examples:
    1. Lee et al. 7 identified a rate of 5.1% for readmissions following posterior lumbar fusion (PLF) utilizing the ACS-NSQIP registry; most significant postoperative complications included wound complications, pulmonary embolism, sepsis and urinary tract infections.
    2. Bohl et al. 8 and Bohl, et al. 9 identified pneumonia and urinary tract infection (UTI) incidences of 0.5% and 1.77% respectively following PLF.
      • Of mortality cases, 20% occurred postoperatively in patients who had developed pneumonia.
      • There was a 14-fold increase for sepsis with UTI.
    3. Abt et al. 10 demonstrated a complication rate of 6.73% following anterior lumbar interbody fusion, with unplanned reoperations (2.48%), UTI’s (1.55%), surgical site infections (1.41%) and sepsis (1.11%) occurring most commonly.
    4. Using a Medicare claims database, Ong et al. 11 noted a 2-year incidence of 16.9% for reoperation following PLF for spondylolisthesis and spinal stenosis in elderly patients.
    5. Also using Medicare claims data, Deyo et al. 12 demonstrated a rate of 5.6% for life-threatening complications and 30-day rehospitalization rate of 13.0% following complex spinal fusions.


  • Large registries can be utilized to identify risk factors that may predispose patients to perioperative complications or poor outcomes following lumbar surgery.
    1. Allow for preoperative risk stratification, assisting in patient counseling.
    2. Provide opportunity for creation of preemptive protective measures, improving specific hospital quality metrics and patient outcomes.
      • Positively impact costs associated with treatment of complications.
    3. Potentially aid policy makers capable of creating higher reimbursements for newly identified high risk procedures.5
    4. In era of pubic reporting of outcomes and pay-for-performance, they help define adjusted outcome measures.
  • Risk factors currently identified for worse outcomes or complications following lumbar procedures:
    1. Obesity,13
    2. Diabetes mellitus,14
    3. Malnutrition,15
    4. Chronic obstructive pulmonary disease and other comorbidities,9,16
    5. Older age,17
    6. Increased American Society of Anesthesiologists (ASA) scoring,17
    7. Surgical invasiveness, and
    8. Usage of bone morphogenic proteins and other discretionary operative features.18


  • The surgical setting’s effects on perioperative outcomes–
    1. Large databases can identify type of surgical center and corresponding outcomes.
    2. Confirm safety and efficacy of outpatient alternatives to inpatient procedures.
      • Pugely et al. 19 determined that patients undergoing outpatient lumbar discectomy exhibited an overall decreased rate of complication when compared to inpatient.
  • Cost analysis–
    1. Adverse events frequently incur significant costs to hospital resources.
    2. Large databases allow for cost stratification based on type of procedure and complications.
      • Utilizing the Medicare Provider Analysis and Review file registry, Culler et al. 20 demonstrated Medicare beneficiaries accrued incremental costs ranging from $10,000–30,000 to treat adverse events following lumbar spinal fusion.
      • Goz et al. 21 utilized the NIS database to determine that combined interbody fusion approaches and anterior lumbar interbody fusion approaches incur the largest costs on patients.
    3. Allow for analysis of value and cost-effectiveness of surgical interventions and can be compared to medical management alternatives.
      • Mummaneni et al. 22 demonstrated that both lumbar discectomies and single-level lumbar fusions for degenerative spondylolisthesis were both significantly cost effective through direct gains in quality-adjusted life years as well as indirect costs through return-to-work frequency.
      • Parker, et al. 23 reported nonoperative care of degenerative lumbar disease to be associated with significantly increased costs relative to minimal clinical benefit.
    4. Identify risk factors associated with increased costs following lumbar procedures, such as
      • Multiple hospital admissions, increased operative time, sleep apnea and greater than 4 levels of fusion following posterior lumbar fusion.24,25
      • Durotomy following lumbar fusion.26


  • Each type of registry or database exhibits its own unique set of limitations.
  • Administrative and claims data registries:
    1. Inpatient samples contain no post-discharge data, prohibiting any analysis of longer-term postoperative outcomes following lumbar procedures.
    2. Many databases require ICD-9 reliance, which have demonstrated questionable sensitivity and validity in identifying primary diagnoses and comorbidities as well as indications for surgery.27
      • Evidence for claims data to accurately identify surgical indication is conflicting.28,29
      • Claims data appears to be more accurate when reflecting specific diagnoses or type of procedure.30
  • Prospective surgical registries–
    1. Better equipped to accurately identify patient demographics and outcomes.
    2. Require trained surgical clinical reviewers to accurately collect data with high reliability, resulting in high costs to maintain registry and train reviewers.
    3. Voluntary sampling process does not allow for reliable estimates of disease prevalence.
    4. Short postoperative follow-up period may still miss complications that occur beyond time interval.
  • Even prospective registries require data to be reviewed retrospectively, limiting determination of causality
  • Some registries such as ACS-NSQIP were designed for general and vascular surgery, missing important functional measures that are specific to orthopaedics such as pain or range of motion and degree of surgical disease.
  • Questionable outcome assessment in lumbar surgery.
    1. No consensus or normative data available for many subjective outcomes following lumbar surgery.
    2. No consensus on outcomes that are essential to measure.31,32
    3. No standard reference for minimal clinically important difference when comparing Health-Related Quality of Life (HRQoL) scores between different diagnoses.33


  • There is overwhelming evidence indicating a need to establish a uniform nationwide reporting system amongst hospitals in the United States.
    1. Hospital participation has been poor, with minimal implementation of systems and collection/exportation of data.6
  • Marang-van de Mheen et al.34 characterized the process and pitfalls in implementing a nationwide system.
    1. Facilitating factors to create a nationwide system include:
      • Participation of a lead surgeon within departments to make final decisions.
      • Present plan of action to all departments.
      • Create and utilize nationwide definitions of adverse outcomes.
      • Create and utilize nationwide classification of adverse outcomes.
      • Provide software allowing for the exportation of data.
      • Connection with existing hospital databases.
    2. Difficulties in establishing nationwide systems include:
      • Slow implementation of reporting system.
      • Information technology departments having difficulty linking data collected from hospitals to hospital information system.
      • Poor quality of data collected.
    3. Creating financial incentives and congressional mandates will promote implementation of nationwide database systems in hospitals.
  • By implementing a national registry data collection system, clinical practice will continue to improve and a sustainable value-based healthcare system can be achieved.
  • Transparency of outcomes is accepted goal, but appropriate benchmarking and risk adjustment pose challenging obstacles.


  1. Roder C, Muller U, Aebi M. The rationale for a spine registry. Eur Spine J. 2006;15 Suppl 1:S52-56.
  2. Lindenauer PK, Remus D, Roman S, et al. Public reporting and pay for performance in hospital quality improvement. N Engl J Med 2007;356:486-496.
  3. Nardini C. The ethics of clinical trials. Ecancermedicalscience. 2014;8:387.
  4. Pugely AJ, Martin CT, Harwood J, Ong KL, Bozic KJ, Callaghan JJ. Database and registry research in orthopaedic surgery: part I: claims-based data. J Bone Joint Surg Am. 2015;97:1278-1287.
  5. Pugely AJ, Martin CT, Harwood J, Ong KL, Bozic KJ, Callaghan JJ. Database and registry research in orthopaedic surgery: part 2: clinical registry data. J Bone Joint Surg Am. 2015;97:1799-808.
  6. Asher AL, McCormick PC, Selden NR, Ghofawala Z, McGirt MJ. The National Neurosurgery Quality and Outcomes Database and NeuroPoint Alliance: rationale, development, and implementation. Neurosurg Focus 2013;34(1):E2.
  7. Lee NJ, Kothari P, Phan K, et al. The incidence and risk factors for 30-day unplanned readmissions after elective posterior lumbar fusion. Spine. 2018;43(1):41-48.
  8. Bohl DD, Ahn J, Tabaraee E, et al. Urinary tract infection following posterior lumbar fusion procedures: an ACS-NSQIP study. Spine (Phil Pa 1976). 2015;40(22):1785-1791.
  9. Bohl DD, Ahn J, Rossi VJ, Tabaree E, Grauer JN, Singh K. Incidence and risk factors for pneumonia following anterior cervical decompression and fusion procedures: an ACS-NSQIP study. Spine J. 2016;16:335-342.
  10. Abt NB, De la Garza-Ramos R, Olorundare IO, et al. Thirty day postoperative outcomes following anterior lumbar interbody fusion using the national surgical quality improvement program database. Clin Neurol Neurosurg. 2016;143:126-131.
  11. Ong KL, Auerbach JD, Lau E, Schmier J, Ochoa JA. Perioperative outcomes, complications, and costs associated with lumbar spinal fusion in older patients with spinal stenosis and spondylolisthesis. Neurosurg Focus. 2014;36(6):E5.
  12. Deyo RA, Mirza SK, Martin BI, Kreuter W, Goodman DC. Jarvik JG. Trends, major medical complications, and charges associated with surgery for lumbar spinal stenosis in older adults. JAMA. 2010;303:1259-1265.
  13. Buerba RA, Fu MC, Gruskay JA, Long 3rd WD, Graur JN. Obese Class III patients at significantly greater risk of multiple complications after lumbar surgery: an analysis of 10,387 patients in the ACS NSQIP database. Spine J. 2014;14:2008-2018.
  14. Browne JA, Cook C, Pietrobon R,Bethel MA, Richardson WJ. Diabetes and early postoperative outcomes following lumbar fusion. Spine (Phila Pa 1976). 2007;32(20):2214-2219.
  15. Bohl DD, Shen MR, Mayo BC, et al. Malnutrition predicts infectious and wound complications following posterior lumbar spinal fusion. Spine (Phila Pa 1976). 2016; 41(21):1693-1699.
  16. Kimmell KT, Algattas H, Joynt P, et al. Risk modeling predicts complication rates for spinal surgery. Spine (Phila Pa 1976). 2015;40(23):1836-1841.
  17. Basques BA, Fu MC, Buerba RA, Bohl DD, Golinvaux NS, Grauer JN. Using the ACS-NSQIP to identify factors affecting hospital length of stay after elective posterior lumbar fusion. Spine (Phila Pa 1976). 2014;39(6):497-502.
  18. Martin BI, Mirza SK, Franklin GM, Lurie JD, MacKenzie TA, Deyo RA. Hospital and surgeon variation in complications and repeat surgery following incident lumbar fusion for common degenerative diagnoses. Health Serv Res. 2013;48(1):1-25.
  19. Pugely AJ, Martin CT, Gao Y, Mendoza-Lattes SA. Outpatient surgery reduces short-term complications in lumbar discectomy: an analysis of 4310 patients from the ACS-NSQIP database. Spine (Phila Pa 1976). 2013;38(3):264-271.
  20. Culler SD, Jevsevar DS, Shea KG, et al. The incremental hospital cost and length-of-stay associated with treating adverse events among Medicare beneficiaries undergoing lumbar spinal fusion during fiscal year 2013. Spine (Phila Pa 1976). 2017;42(20):1578-1586.
  21. Goz V, Weinreb JH, Schwab F, Lafage V, Errico TJ. Comparison of complications, costs, and length of stay of three different lumbar interbody fusion techniques: an analysis of the Nationwide Inpatient Sample database. Spine J. 2014;14(9):2019-2027.
  22. Mummaneni PV, Whitmore RG, Curran JN, et al. Cost-effectiveness of lumbar discectomy and single-level fusion for spondylolisthesis: experience with the NeuroPoint-SD registry. Neurosurg Focus. 2014;36(6):E3.
  23. Parker SL, Godil SS, Mendenhall SK, Zuckerman, Shau DN, McGirt MJ. Two-year comprehensive medical management of degenerative lumbar spine disease (lumbar spondylolisthesis, stenosis, or disc herniation): a value analysis of cost, pain, disability, and quality of life: clinical article. Journal of neurosurgery. J Neurosurg Spine. 2014;21(2):143-149.
  24. Stundner O, Chiu YL, Sun X, et al. Sleep apnoea adversely affects the outcome in patients who undergo posterior lumbar fusion: a population-based study. Bone Joint J. 2014;96-B(2):242-248.
  25. Eleswarapu A, Mikhael MM, Koh JL. Number of recent inpatient admissions as a risk factor for increased complications, length of stay, and cost in patients undergoing posterior lumbar fusion. Spine (Phila Pa 1976). 2014;39(26):2148-2156.
  26. Buck JS, Yoon ST. The incidence of durotomy and its clinical and economic impact in primary, short-segment lumbar fusion: an analysis of 17,232 cases. Spine (Phila Pa 1976). 2015;40(18):1444-1450.
  27. Bohl DD, Russo GS, Basques BA, et al. 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 Joint Surg Am. 2014;96(23):e193.
  28. Martin BI, Lurie JD, Tosteson AN, et al. Indications for spine surgery: validation of an administrative coding algorithm to classify degenerative diagnoses. Spine (Phila Pa 1976). 2014;39(9):769-779.
  29. Gologorsky Y, Knightly JJ, Chi JH, Groff MW. The Nationwide Inpatient Sample database does not accurately reflect surgical indications for fusion. J Neurosurg Spine. 2014;21(6):984-993.
  30. Kazberouk A, Martin BI, Stevens JP, McGuire KJ. Validation of an administrative coding algorithm for classifying surgical indication and operative features of spine surgery. Spine (Phile Pa 1976) 2015;40(2):114-120.
  31. Mirza SK, Deyo RA, Heagerty PJ, Turner JA, Lee LA, Goodkin R. Towards standardized measurement of adverse events in spine surgery: conceptual model and pilot evaluation. BMC Musculoskelet Disord. 2006;7:53.
  32. Mirza SK, Martin BI, Goodkin R, Hart RA, Anderson PA. Developing a toolkit for comparing safety in spine surgery. Instr Course Lect. 2014;63:271-286.
  33. Zanoli G. Outcome assessment in lumbar spine surgery. Acta Orthop Suppl. 2005;76(318):5-47.
  34. Marang-van de Mheen PJ, Stadlander MC, Kievit J. Adverse outcomes in surgical patients: implementation of a nationwide reporting system. Qual Saf Health Care 2006;15(5):320-324.