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Chapter 10 Outcomes Research for Spinal Disorders 163
An interesting feature of case-control studies includes their
ability to be “nested” within a dened cohort. is is referred
to as a nested case-control or nested case-cohort design. Here,
the cohort has typically been assembled for another purpose
and, as such, has previously stored images, specimens, and
other data, or has been assembled de novo to answer the
research question. e investigator would typically measure
the outcome variable in this cohort to distinguish cases from
controls, and then measure the predictor variables in the
banked specimens and images. is allows for comparison of
risk factors in the cases and controls. e main advantage of
this subtype of case-control study design is that it is especially
useful for minimizing costly measurements on serum and
other specimens (since these will only have to be conducted
on all the cases and a sample of the controls, instead of the
entire cohort). Additionally, this design also preserves the
advantages of cohort studies that result from collecting predictor variables prior to the occurrence of the outcome.
Although case-control studies have rarely been used in the
spine literature, a hypothetical example would be the comparison of the rate of exposure to nonsteroidal antiinammatory
drugs (NSAIDs) between patients with a lumbar pseudarthrosis (the cases) and patients with a successful fusion (controls).
is approach might prove useful in evaluating the hypothesis
that NSAID use might aect the likelihood of successful
fusion. Sampling bias (e.g., dierential rates of smoking, diabetes, and other risk factors for nonunion between the cases
and controls) and recall bias (patients with pseudarthroses
might be more likely to report exposure to NSAIDs) would
have to be taken into account.
Case Series
Case series are reports of outcomes for patients undergoing a
treatment without any control group. e spine literature is
replete with this type of study. No inferential conclusions can
be made from case series because there is no control group
with which to compare outcomes. Case series should be based
on a consecutive series of patients to avoid selection bias in
which the investigator includes only patients with desirable
outcomes. ese studies are useful for hypothesis generating
or reporting outcomes on rare conditions. ey should not be
used to develop treatment guidelines because they lack a
control group and do not allow for the assessment of eective-
ness of a treatment.
Levels of Evidence
Investigators have created a hierarchy of study designs based
on the quality of causal inference that one can make with
each study design (Fig. 10.7). Well-controlled RCTs and
meta-analyses of such studies are at the pinnacle of the
hierarchy. ese studies have been labeled level I evidence.59
Observational cohort studies or RCTs with methodologic
shortcomings are level II evidence. Case-control studies are
considered level III evidence. Descriptive studies such as case
Meta-
analysis
RCT
Cohort study
Case-control study
Case series/case
Expert opinion
FIG. 10.7 Hierarchy of research designs in evidence-based medicine.
series and case reports are level IV evidence. Expert opinion
is considered level V evidence. As shown by the SPORT IDH
RCT, all questions are not best answered with a randomized
design. In addition, when modern observational studies are
compared with RCTs, it has been shown that they do not
usually overstate the treatment eect of an intervention.
Whereas the hierarchy of evidence is useful for comparing
study designs in general, the merits of each individual study
should be assessed, and high-quality, observational studies
should not be discounted.
57,60
Cost-Eectiveness Analysis
Given the ever-increasing costs of health care, policymakers
have recommended that medical interventions be evaluated
for their cost-eectiveness.61 Cost-eectiveness analysis aims
to determine the cost to society for the incremental health
benet derived from an intervention that is more costly than
an alternative, less eective treatment. Although an RCT can
show the ecacy of a treatment, further economic analysis can
be performed alongside the RCT to evaluate how much society
must pay for the treatment eect. To compare the costeectiveness of a wide variety of treatments across many
medical specialties, a universal scale of health must be used to
measure preference for health outcomes. In cost-eectiveness
analysis, the quality-adjusted life-year (QALY), which com-
bines length and quality of life in a single number, is the recommended measure of health benet.
To estimate QALYs, a utility, which is a numeric preference
rating of health ranging from 0 (equivalent to death) to 1
(perfect health) is used to value the health states associated
with a treatment. Classically, utilities have been derived using
techniques such as the time trade-o, which essentially determines how much time in a state of suboptimal health people
would be willing to trade for a lesser amount of time in perfect
health.61 More recently, techniques have been developed to
determine utilities based on standard questionnaires such as
the SF-36.62 QALYs are determined by multiplying utility for
each health state by the length of time in each health state and
summing up over time. For example, 2 years spent in a poor
health state with a utility of 0.5 followed by 10 years in a good
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164 BASIC SCIENCE
health state with a utility of 0.8 would be equivalent to 9
QALYs (2 × 0.5 + 10 × 0.8). To compare the benets of various
interventions, cost-eectiveness analysis determines the gain
in QALYs associated with an intervention. e advantage of
QALYs is that they allow for the comparison of very dierent
health states across medical disciplines.
e other half of the cost-eectiveness equation is cost.
Although an intervention typically produces a health benet,
that benet comes at a cost to society. Determining the cost
of an intervention is challenging; thus, many cost-eectiveness
analyses use gross costing based on average reimbursements
for various procedures. Aer determining the costs and ben-
ets associated with an intervention, these must be compared
with another treatment. is is done by determining the incre-
mental cost-eectiveness ratio (ICER). e ICER is dened as
the dierence in cost between two treatments divided by the
dierence in utility. Tosteson and colleagues63 reported that
discectomy resulted in a gain of 0.21 QALYs compared with
nonoperative treatment at an additional cost of $14,137, which
yielded an ICER of $67,319/QALY ($14,137/0.21 QALY).
Traditionally, an ICER of $50,000/QALY has been used as
a cost-eectiveness threshold because this was the ICER for
hemodialysis, an intervention for which society has decided
to pay.64 More recently, some authors have suggested that
$100,000/QALY is a more realistic cost-eectiveness threshold
because many frequently used interventions fall in the $50,000
to $100,000/QALY range.
63,65,66
eoretically, cost-eectiveness analysis should allow
societies to maximize the value of their health care expenditures; the United Kingdom has set ICER thresholds to determine which services should be provided by their National
Health Service.67 ere is little evidence, however, that health
care systems, including systems that currently ration care,
have been consistently using cost-eectiveness analysis to
guide rational treatment guidelines.68 As health care spending
comes under greater scrutiny and decisions regarding which
treatments to provide are made, evaluating the costeectiveness of an intervention will be essential.
Future of Outcomes Research:
Patient-Specic Recommendations
Since the prior edition of this textbook, the quality of spine
outcomes research has improved markedly alongside a more
sophisticated understanding of the issues surrounding outcomes research within the spine community. A study of
spine-related clinical trials published in 2007 reported that
60% were performed and reported in an acceptable fashion, a
result that is better than seen in a general orthopaedic journal.69
Although this percentage is likely an improvement from years
prior, there is further work to be done on the quality of the
spine literature. e 4-year and 8-year outcome data from the
SPORT IDH study, the largest scale outcomes research study
ever performed in the eld of spinal disorders, have been
published more recently.
scale trials are able to determine the treatment eect of an
intervention for the “average” patient, it can be dicult to
70,71
Although this and most large-
apply the results to clinical practice, in which no patient is
“average.”
In the case of IDH, there are clearly patients who fail surgical treatment and others who are very successful with nonoperative treatment. Blind application of the results of SPORT to
all patients who met the inclusion criteria (symptoms lasting
at least 6 weeks, the presence of neurologic ndings, and
imaging consistent with their symptoms) would result in
surgery for all such patients. Although this approach would
result in greater clinical improvement than nonoperative
treatment, on average, surgery would be performed on some
patients who would have improved to an acceptable, and even
to a greater, degree with nonoperative treatment. Other
patients would fail to improve with surgery and perhaps
experience an additional decrease in their quality of life.
To avoid unnecessary surgery on these two groups of
patients, models that take individual characteristics and values
into account when predicting outcomes are needed. When
suciently powerful models are developed, individual baseline characteristics, physical ndings, and results from imaging
studies can be entered into such models to determine the
likelihood of success with surgery or nonoperative treatment.
Individual patient values should also be considered in dening
success and assigning utilities to the various possible outcomes. Such an approach represents the true integration of
evidence-based medicine with shared decision making at the
level of the individual patient and should be the next step in
spine outcomes research.
KEY POINTS
1. With constantly increasing health care costs, policymakers are
demanding outcomes research to show the eectiveness of
treatments, especially in elds that require expensive
technology, such as spine surgery.
2.
There is substantial geographic variation in the rates of spine
surgery across the United States, indicating that further research
is needed to determine which patients are served best with
surgery.
3.
Chance, bias, and confounding all threaten the validity of
conclusions based on clinical research and need to be
addressed in study design and data analysis.
4.
RCTs can yield the highest level of evidence, although many
surgical questions are not amenable to this type of study
design. In these cases, well-designed observational studies may
be more appropriate.
5.
Cost-eectiveness analysis will become more important as
decisions need to be made about the use of scarce health care
resources.
KEY REFERENCES
1. Fisher ES, Wennberg DE, Stukel TA, et al. The implications of
regional variations in Medicare spending. Part 2: health
outcomes and satisfaction with care. Ann Intern Med.
2003;138:288-298.
This study showed wide variation in Medicare spending across
hospital referral regions with no measurable improvement in
outcomes in areas with the highest levels of spending.
2.
Weinstein JN, Lurie JD, Olson PR, et al. United States’ trends and
regional variations in lumbar spine surgery: 1992-2003. Spine.
2006;31:2707-2714.
Chapter 10 Outcomes Research for Spinal Disorders 165
This small area analysis showed the increase in the rate of lumbar
fusion in the Medicare population throughout the 1990s and the
wide geographic variation in fusion rates in this population.
3.
Bombardier C. Outcome assessments in the evaluation of
treatment of spinal disorders. Introduction. Spine.
2000;25:3097-3099.
This article reviews the dierences between dierent outcome
measures used in the spine literature and introduces an issue
dedicated to this topic.
4.
Kocher MS, Zurakowski D. Clinical epidemiology and
biostatistics: a primer for orthopaedic surgeons. J Bone Joint Surg
Am. 2004;86:607-620.
This is a good review of basic epidemiology and biostatistics as they
apply to orthopedics.
5.
Benson K, Hartz AJ. A comparison of observational studies and
randomized, controlled trials. N Engl J Med. 2000;342:1878-1886.
This article convincingly shows that well-designed observational
trials yield similar results to randomized controlled trials.
REFERENCES
1. Webster’s New World Medical Dictionary. 3rd ed. Hoboken, NJ:
Wiley Publishing; 2008.
2. Musgrove P, Zeramdini R, Carrin G. Basic patterns in national
health expenditure. Bull World Health Organ. 2002;80:134-142.
3. Wilkinson RG, Pickett KE. Income inequality and population
health: a review and explanation of the evidence. Soc Sci Med.
2006;62:1768-1784.
4. Wennberg JE, Gittelsohn A. Health care delivery in Maine, I:
patterns of use of common surgical procedures. J Maine Med
Assoc. 1975;66:123-130, 149.
5. Wennberg JE, Freeman JL, Culp WJ. Are hospital services
rationed in New Haven or over-utilised in Boston? Lancet.
1987;1:1185-1189.
6. Fisher ES, Wennberg DE, Stukel TA, et al. e implications
of regional variations in Medicare spending. Part 1: the
content, quality, and accessibility of care. Ann Intern Med.
2003;138:273-287.
7. Fisher ES, Wennberg DE, Stukel TA, et al. e implications
of regional variations in Medicare spending. Part 2: health
outcomes and satisfaction with care. Ann Intern Med.
2003;138:288-298.
8. Weinstein JN, Lurie JD, Olson PR, et al. United States’ trends
and regional variations in lumbar spine surgery: 1992-2003.
Spine. 2006;31:2707-2714.
9. Fisher ES, Wennberg JE. Health care quality, geographic
variations, and the challenge of supply-sensitive care. Perspect
Biol Med. 2003;46(1):69-79.
10. Wennberg DE, Wennberg JE. Addressing variations: is there
hope for the future. Health A (Millwood). 2003;10.
11. Wennberg JE. Unwarranted variations in healthcare
delivery: implications for academic medical centres. BMJ.
2002;325(7370):961.
12. Wennberg JE. Practice variations and health care reform:
connecting the dots. Health A. 2004;23:VAR-140.
13. Wennberg JE. Time to tackle unwarranted variations in
practice. BMJ. 2011;342.
14. Fuchs VR. More variation in use of care, more at-of-the-curve
medicine. Health A. 2004;VAR104-VAR107.
15. Atlas SJ, Deyo RA, Keller RB, et al. e Maine Lumbar Spine
Study, Part II: 1-year outcomes of surgical and nonsurgical
management of sciatica. Spine. 1996;21:1777-1786.
16. Atlas SJ, Deyo RA, Keller RB, et al. e Maine Lumbar
Spine Study, Part III: 1-year outcomes of surgical and
nonsurgical management of lumbar spinal stenosis. Spine.
1996;21:1787-1795.
17. Schafer J, O’Connor D, Feinglass S, et al. Medicare Evidence
Development and Coverage Advisory Committee Meeting on
lumbar fusion surgery for treatment of chronic back pain from
degenerative disc disease. Spine. 2007;32:2403-2404.
18. Glassman SD, Polly DW, Bono C, et al. Outcome of lumbar
fusion in patients over 65 years old. Presented at American
Academy of Orthopaedic Surgeons Annual Meeting, Las Vegas,
NV, 2009.
19. Bombardier C. Outcome assessments in the evaluation
of treatment of spinal disorders. Introduction. Spine.
2000;25:3097-3099.
20. Weber H. Lumbar disc herniation: a controlled, prospective
study with ten years of observation. Spine. 1983;8:131-140.
21. Ware JE Jr, Sherbourne CD. e MOS 36-item short-form
health survey (SF-36): I. Conceptual framework and item
selection. Med Care. 1992;30:473-483.
22. Brooks R. EuroQol: the current state of play. Health Policy
(New York). 1996;37:53-72.
23. Bergner M, Bobbitt RA, Carter WB, et al. e Sickness Impact
Prole: development and nal revision of a health status
measure. Med Care. 1981;19:787-805.
24. Cella D, Yount S, Rothrock N, et al. e Patient-Reported
Outcomes Measurement Information System (PROMIS):
progress of an NIH Roadmap cooperative group during its rst
two years. Med Care. 2007;45(5 suppl 1):S3.
25. Reeve BB, Hays RD, Bjorner JB, et al. Psychometric evaluation
and calibration of health-related quality of life item banks:
plans for the Patient-Reported Outcomes Measurement
Information System (PROMIS). Med Care. 2007;45(5):S22-S31.
26. Fairbank JC, Couper J, Davies JB, et al. e Oswestry low back
pain disability questionnaire. Physiotherapy. 1980;66:271-273.
27. Roland M, Morris R. A study of the natural history of back
pain. Part I: development of a reliable and sensitive measure of
disability in low-back pain. Spine. 1983;8:141-144.
28. Lurie J. A review of generic health status measures in patients
with low back pain. Spine. 2000;25:3125-3129.
29. Zanoli G, Stromqvist B, Padua R, et al. Lessons learned
searching for a HRQoL instrument to assess the results
of treatment in persons with lumbar disorders. Spine.
2000;25:3178-3185.
30. Atlas SJ, Deyo RA, Patrick DL, et al. e Quebec Task Force
Classication for Spinal Disorders and the severity, treatment,
and outcomes of sciatica and lumbar spinal stenosis. Spine.
1996;21:2885-2892.
31. Deyo RA, Battie M, Beurskens AJ, et al. Outcome measures for
low back pain research: a proposal for standardized use. Spine.
1998;23:2003-2013.
32. Ware JE Jr. SF-36 health survey update. Spine.
2000;25:3130-3139.
33. Ader DN. Developing the patient-reported outcomes
measurement information system (PROMIS). Med Care.
2007;45(5):S1-S2.
34. DeWalt DA, Rothrock N, Yount S, Stone AA. Evaluation of
item candidates: the PROMIS qualitative item review. Med
Care. 2007;45(5 suppl 1):S12.
35. Wainer H, Dorans NJ, Flaugher R, Green BF, Mislevy RJ.
Computerized Adaptive Testing: A Primer. Abingdon-onames, UK: Routledge; 2000.
36. van der Linden WJ, Hambleton RK. Handbook of Modern Item
Response eory. Berlin: Springer Science & Business Media;
2013.
SECTION
I
166 BASIC SCIENCE
37. Rothrock NE, Hays RD, Spritzer K, et al. Relative to the
general US population, chronic diseases are associated with
poorer health-related quality of life as measured by the
Patient-Reported Outcomes Measurement Information
System (PROMIS). J Clin Epidemiol. 2010;63(11):
1195-1204.
38. Hung M, Hon SD, Franklin JD, et al. Psychometric properties
of the PROMIS physical function item bank in patients with
spinal disorders. Spine. 2014;39(2):158-163.
39. Rose M, Bjorner JB, Becker J, Fries J, Ware J. Evaluation of
a preliminary physical function item bank supported the
expected advantages of the Patient-Reported Outcomes
Measurement Information System (PROMIS). J Clin Epidemiol.
2008;61(1):17-33.
40. Garcia SF, Cella D, Clauser SB, et al. Standardizing
patient-reported outcomes assessment in cancer clinical trials:
a patient-reported outcomes measurement information system
initiative. J Clin Oncol. 2007;25(32):5106-5112.
41. Fries JF, Krishnan E, Rose M, Lingala B, Bruce B. Improved
responsiveness and reduced sample size requirements of
PROMIS physical function scales with item response theory.
Arthritis Res er. 2011;13(5):R147.
42. Fries J, Bruce B, Cella D. e promise of PROMIS: using item
response theory to improve assessment of patient-reported
outcomes. Clin Exp Rheumatol. 2005;23(5):S53.
43. Roland M, Fairbank J. e Roland-Morris Disability
Questionnaire and the Oswestry Disability Questionnaire.
Spine. 2000;25:3115-3124.
44. Weinstein JN, Tosteson TD, Lurie JD, et al. Surgical vs
nonoperative treatment for lumbar disk herniation. e Spine
Patient Outcomes Research Trial (SPORT): a randomized trial.
JAMA. 2006;296:2441-2450.
45. Roland M, Morris R. A study of the natural history of low-back
pain. Part II: development of guidelines for trials of treatment
in primary care. Spine. 1983;8:145-150.
46. Kopec JA. Measuring functional outcomes in persons with
back pain: a review of back-specic questionnaires. Spine.
2000;25:3110-3114.
47. Fritzell P, Hagg O, Wessberg P, et al. 2001 Volvo Award
Winner in Clinical Studies. Lumbar fusion versus nonsurgical
treatment for chronic low back pain: a multicenter randomized
controlled trial from the Swedish Lumbar Spine Study Group.
Spine. 2001;26:2521-2534.
48. Brox JI, Sorensen R, Friis A, et al. Randomized clinical trial
of lumbar instrumented fusion and cognitive intervention
and exercises in patients with chronic low back pain and disc
degeneration. Spine. 2003;28:1913-1921.
49. Hulley SB, Newman TB, Cummings SR. Choosing the study
subjects: specication, sampling, and recruitment. In: Designing
Clinical Research. 2nd ed. Philadelphia: Lippincott Williams &
Wilkins; 2001:25-35.
50. Spratt K. Statistical relevance. In: Garn S, Abitbolet J, eds.
Orthopaedic Knowledge Update: Spine. Rosemont, IL: American
Academy of Orthopaedic Surgeons; 2002:497-505.
51. Kocher MS, Zurakowski D. Clinical epidemiology and
biostatistics: a primer for orthopaedic surgeons. J Bone Joint
Surg Am. 2004;86:607-620.
52. Birkmeyer NJ, Weinstein JN, Tosteson AN, et al. Design of
the Spine Patient Outcomes Research Trial (SPORT). Spine.
2002;27:1361-1372.
53. Newman TB, Browner WS, Hulley SB. Enhancing causal
inference in observational studies. In: Designing Clinical
Research. 2nd ed. Philadelphia: Lippincott Williams & Wilkins;
2001:125-137.
54. Dawson B, Trapp RG. Basic and Clinical Biostatistics. 4th ed.
New York: McGraw-Hill; 2004.
55. Flum DR. Interpreting surgical trials with subjective
outcomes: avoiding UnSPORTsmanlike conduct. JAMA.
2006;296:2483-2485.
56. Weinstein JN, Lurie JD, Tosteson TD, et al. Surgical vs
nonoperative treatment for lumbar disk herniation: the Spine
Patient Outcomes Research Trial (SPORT) observational
cohort. JAMA. 2006;296:2451-2459.
57. Benson K, Hartz AJ. A comparison of observational
studies and randomized, controlled trials. N Engl J Med.
2000;342:1878-1886.
58. Newman TB, Browner WS, Cummings SR, et al. Designing an
observational study: cross-sectional and case-control studies.
In: Designing Clinical Research. 2nd ed. Philadelphia: Lippincott
Williams & Wilkins; 2001:107-123.
59. Brighton B, Bhandari M, Tornetta P 3rd, et al. Hierarchy of
evidence: from case reports to randomized controlled trials.
Clin Orthop Relat Res. 2003;413:19-24.
60. Concato J, Shah N, Horwitz RI. Randomized, controlled trials,
observational studies, and the hierarchy of research designs.
N Engl J Med. 2000;342:1887-1892.
61. Gold MG, Siegel JE, Russell LB, et al. Cost-Eectiveness in
Health and Medicine. New York: Oxford University Press; 1996.
62. Brazier J, Roberts J, Deverill M. e estimation of a
preference-based measure of health from the SF-36. J Health
Econ. 2002;21:271-292.
63. Tosteson AN, Skinner JS, Tosteson TD, et al. e cost
eectiveness of surgical versus nonoperative treatment for
lumbar disc herniation over two years: evidence from the
Spine Patient Outcomes Research Trial (SPORT). Spine.
2008;33:2108-2115.
64. Winkelmayer WC, Weinstein MC, Mittleman MA, et al. Health
economic evaluations: the special case of end-stage renal
disease treatment. Med Decis Making. 2002;22:417-430.
65. Laupacis A, Feeny D, Detsky AS, et al. How attractive does a
new technology have to be to warrant adoption and utilization?
Tentative guidelines for using clinical and economic
evaluations. Can Med Assoc J. 1992;146:473-481.
66. Tosteson AN, Lurie JD, Tosteson TD, et al. Surgical
treatment of spinal stenosis with and without degenerative
spondylolisthesis: cost-eectiveness aer 2 years. Ann Intern
Med. 2008;149:845-853.
67. Rawlins MD, Culyer AJ. National Institute for Clinical
Excellence and its value judgments. BMJ. 2004;329:224-227.
68. Appleby J, Devlin N, Parkin D, et al. Searching for
cost eectiveness thresholds in the NHS. Health Policy.
2009;91:239-245.
69. Dodwell E, Fischer CG, Reilly CW, et al. A quality assessment of
randomized controlled trials in the spine literature. Presented at
American Academy of Orthopaedic Surgeons Annual Meeting,
Las Vegas, NV, 2009.
70. Weinstein JN, Lurie JD, Tosteson TD, et al. Surgical versus
nonoperative treatment for lumbar disc herniation: four-year
results for the Spine Patient Outcomes Research Trial (SPORT).
Spine. 2008;33:2789-2800.
71. Lurie JD, Tosteson TD, Tosteson AN, et al. Surgical versus
non-operative treatment for lumbar disc herniation: eight-year
results for the Spine Patient Outcomes Research Trial (SPORT).
Spine. 2014;39(1):3.
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11
CHAPTER
Introduction
Computational and numeric methods have been used to
assess the biomechanical behaviors of biologic systems. e
advent and continuous development of powerful computing
systems in addition to improvement of the emerging computational packages in computer-aided engineering (CAE) with
enhanced modeling features have enabled scientists to develop
more rigorous models of biologic systems to predict the
behavior of these systems under dierent biologic conditions.
In orthopaedic biomechanics, the latest advances in medical
imaging technologies have helped researchers obtain a better
resolution of geometric and anthropometric specications of
individual organs in the human body, including micro-scale
computational models of the knee, hip, spine, and bone itself.
In addition to ethical considerations, the practical diculties,
restrictions, and cost involved in experimental in vitro and in
vivo studies highlight the utility of computational models as
complementary tools for studies in orthopaedic biomechanics.
e future challenge will be how to apply these approaches
to those areas of science that are not yet considered and how
to further improve present generation models to better simulate the couplings and nonlinearities that occur in physical
incidents.
One of the most applicable approaches used in biomechanical studies is nite element analysis (FEA), in which the
object or system is represented by a geometrically similar
model consisting of multiple linked representations of discrete
regions. e basic idea for the nite element (FE) method
originated from advances made in aircra structural analysis
in the 1940s. Since then, the FE method has become a powerful tool for the numeric solution to a wide range of engineering
problems. In this method of analysis, a complex region den-
ing a continuum is broken into simple geometric shapes called
nite elements. Material properties and governing relationships are assigned to the elements. With the advances in
computational power and computer-aided drawing (CAD)
Finite Element Analysis
Emily Walsh
M. Saeid Asadollahi
Raj Nangunoori
Jie Zheng
Daniel Cook
Boyle C. Cheng
Vijay K. Goel
systems, complex problems can now be solved with relative
ease. Specialized FE method soware—such as Abaqus
(Simulia), ANSYS, COMSOL Multiphysics, and others—
provides linear, nonlinear, static, and dynamic solutions to
many industrial problems.
In general, FEA includes three main steps: preprocessing,
analysis, and postprocessing. Preprocessing is the rst step in
FEA, in which an FE model of the structure is created. Most
FEA packages require a topological description of the structure’s geometric features as input, which can be in one-, two-,
or three-dimensional (3D) form, representing line, surface, or
structural elements, respectively. However, 3D models are
used in most cases. Design les, CAD models, and preexisting
digital scans can be imported into an FEA environment to be
utilized for an FEA. Once the FE geometric model is developed, a meshing procedure is used to dene and divide the
model into small discrete elements. An FE model is dened
by creation of a mesh network, which includes the geometric
arrangement of elements and nodes. When the FE model is
created, the material properties are assigned to the individual
parts, and proper interactions and constraints are dened
between interacting part and surfaces. Finally, the boundary
conditions and loads are assigned to the model.
e next step is analysis, which solves the model to converge
for solutions within the predened boundary, load conditions,
and constraints. To better simulate the physical conditions
of the problem, dierent types of analysis, such as static or
dynamic (time-dependent) simulation, can be considered. In
static simulation, the inputs and outputs are independent of
time and the system is solved to balance the load and boundary conditions. In dynamic simulation, time can aect the
input and output parameters and the behavior of the model
varies over the time. Examples of dynamic simulations are
dynamic loading, impact, and long-term creep (or wear).
Once the simulation is nished, postprocessing of the data
can be performed using visualization tools. e analysis
outputs can be in the form of nodal outputs, including
I
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168 BASIC SCIENCE
FIG. 11.1 Steps of creating the nite element (FE) model of spine. The computed tomographic images are
used to develop a grid at each transverse plane. The coordinates of the nodes are imported to the FE package,
and the elements are created. The material properties are dened for individual elements; the loads, boundary
conditions, and constraints are dened as the last step in modeling. The model is run and various FE outputs,
such as stresses and nodal displacements, are calculated for sections of interest.
displacement at each node and element outputs, such as
stresses and strains (Fig. 11.1).
Finite Element Modeling of the Spine
Low Back Pain
e human spine is a complex structure that supports the
upper trunk weight and external loads applied to the human
body. e spine column also protects the delicate spinal cord
and provides adequate exibility and stiness to perform
various daily activities. e stability of the ligamentous spine
is signicantly reduced in vivo due to absence of muscles.
Low back pain (LBP) is one of the common musculoskeletal
disorders that aects the functionality of the spine. While
there are many causes of back pain, the most prevalent causes
are muscle strain, degenerative disc disease, spinal stenosis,
disc herniation, facet hypertrophy, isthmic spondylolisthesis,
degenerative spondylolisthesis, or (rarely) a spinal tumor.
In the United States, LBP is one of the major reasons for
disability for people younger than 45 years. More than 80% of
Americans suer from LBP at some point in life. According
to an estimate, LBP costs between $100 and $200 billion annually, two-thirds of which is a result of decreased wages and
productivity.1 us, there is a need to study the origin of pain
associated with the lumbar spine and search for simple, costeective, and safe treatment options. While there are a number
of avenues that researchers are pursing in this direction, the
role of mechanical factors in back disorders is signicant.
us, it is prudent to undertake biomechanical studies of the
spine in various conditions: intact-normal, degenerated,
surgery, and then “stabilization” of some sort. ese issues
have been investigated using cadaver models, animal models,
and numeric tools. is chapter focuses on the application of
numeric approaches in understanding the biomechanics of
the human spine, especially the analyses of spinal implants.
e human spine is composed of highly specic tissues
and structures, which together provide an extensive range
of motion (ROM) and considerable load-carrying capacity.
1
FIG. 11.2 A motion segment in the lumbar spine.
Alteration of the form of these structures with increasing age,
injury, or any other reason can have a profound inuence
on the quality of life. Low back pain is generally associated
with degenerative changes occurring in the spine. Mechanical property changes resulting from degeneration are likely
contributors to lumbar spine instability that may lead to other
pathologies. is instability may be accelerated by injuries
or deformities.2 Vertebral body degeneration and ligament
degeneration are degenerative diseases that can occur with
age.3 e intervertebral disc and two facet joints form a threejoint complex and share the majority of the load on the spine.4
Due to this, degenerative changes of the spine can be initiated
as disc degeneration or facet joint osteoarthritis.
5
In a spine segment (Fig. 11.2), the intervertebral disc
and facet joints are the main load-bearing structures in the
spine, and thus are most susceptible to mechanical wear and
tear. Back pain arising from degenerative disease could be
Chapter 11 Finite Element Analysis 169
discogenic or may be directly due to diseased facet joints. e
advanced stage of disc degeneration or facet degeneration may
call for replacement surgery to achieve spinal stability and
symptom relief. Disc arthroplasty and facet joint replacement
technologies aim to restore the normal kinematics of the spine
by acting as load-bearing devices.5 Surgery for these devices
is highly invasive. Replacing either the disc or the facet joint
would be considered a partial joint replacement. As loadbearing structures, there is also a possibility of wear of the
device, leading to osteolysis. ere is a lack of literature on
the kinematic eect of these replacement technologies on the
remaining structures of motion segments.
Many cadaveric and experimental studies have been formulated to compare the biomechanical behavior of the intact
spine versus the injured or implanted spine. Motion across the
segments as well as disc pressure measurements and facet
loads have been quantied. However, there are many factors
that come into play, such as specimen variability and errors
involved in experimental testing. In addition, prototyping of
many implants at once is not physically easy. Currently, mathematical models are being widely used to quantify forces and
moments acting on the lumbar spine during various activities
in life. ey can be used to quantify stresses at any area and
thus point to the area where a fracture might occur. Almost
all results seen in experimental testing can be quantied by
mathematical modeling. Hence, the use of a mathematical
method such as the FE method is justied for the study of the
lumbar spine.
Because of the diculty in analyzing the spine as a whole,
it has been divided into a number of regions (e.g., cervical,
thoracic, lumbar), and each area has been analyzed separately
in dierent studies. For example, numerous studies of the
lumbar spine have applied the FE models of the entire ligamentous lumbosacral spine (LI–S1) or individual lumbar
functional spinal units (FSUs; each functional unit consists of
two adjacent vertebrae with connecting ligaments and intervertebral disc) to investigate the biomechanical behavior of
the spine.
Some of the previous FE models of the lumbar spine have
studied only the response of the motion segments, neglecting
posterior elements,6 while the others did the entire motion
segment with posterior elements7 or multimotion segments or
the whole ligamentous lumbosacral spine.8 It is crucial to bear
in mind that the simplications made in the development of
the model—such as in geometry, material denitions, load
and boundary conditions, and so on—will directly aect the
accuracy of predictions.
Once the FE model is developed, it must be validated with
relevant in vivo and in vitro test data. e model to be validated should replicate the crucial features and characteristics
of the real specimen in order to be able to be compared to
experimental data that may help in ne-tuning the FE model.
For components such as ligaments, the experimental test data
are required to dene the true mechanical behavior as well as
failure modes and hysteresis characteristics of the corresponding element in the model. All these will enhance the precision
of the FE simulations and outputs and will make them comparable with real behavior of the spine.
9
ere are some specic considerations that should be taken
into account in FE modeling of the spine in order to make the
FE prediction reliable under a specic loading or motion
condition. Following are some of these characteristics10:
e geometry of specic features should match the real
•
case, in which the shape of the construct aects the
biomechanical outputs; examples are geometry of discs
at dierent levels with the proper boundary prole in
the sagittal plane and the proper simulation of the
anterior/posterior disc height and the angle between
vertebral endplates at each segment.
e correct simulation of the lordosis angle due to its
•
main role in the stability of the spine under loads and
load sharing across the segments.
e geometry of the posterior bone and partitioning of
•
the bony elements into cancellous and cortical with
proper thickness of cortical bone across the vertebrae.
Proper denition of the element types for specic fea-
•
tures, such as ligaments, which can be dened either by
two-dimensional (2D) truss or beam elements connecting to nodes of the model with correct moment arm
with respect to the center of the segment. Ligaments can
be modeled either as a bundle with appropriate crosssection or as a combination of individual truss elements
with cross-section of unity.
e geometry of the facet articulations with their gap
•
distance and likely asymmetry.
Contact pattern between the articulating surfaces of
•
facets as a nite large-displacement contact problem
with proper sliding (with or without friction) and
normal (so or hard) behavior.
e disc should be modeled as a nonhomogeneous
•
composite structure, including an amorphous matrix
(protoglycan and water) reinforced by collagenous
bers. Proper element types should be used for each
area of the disc, that is, the nucleus can be modeled
with noncompressible uid elements, while the anulus
can be modeled with solid elastic elements with bers at
proper angles by considering the radial variation of the
collagenous bers’ mechanical properties and volume
fracture.
In case a nonstatic simulation—such as dynamic loading,
•
impact, long-term creep and wear—is being considered,
the time-dependent behavior of elements and materials
needs to be specied.
e denition of loads and boundary conditions for
•
example, if the study of kinematics of L3–S1 spine is of
interest, S1 is xed in all degrees of freedom and the
compressive loads and bending moments are applied at
L3 level.
Modeling of the Lumbar Spine
e rst intact FE model of the ligamentous lumbar spine that
was developed consisted of two motion segments (L3–L5).11
e geometric data for the L3–L4 motion segment were
acquired from 1.5-mm thick computed tomography (CT)
scans (transverse slices) of a cadaveric ligamentous spine
SECTION
I
170 BASIC SCIENCE
specimen. Radiographs and dual-energy x-ray absorptiometry
(DEXA) were used to ensure that no osseous abnormalities
existed with the specimen and that the bone quality was good.
Each region was then divided into several quadrilaterally
shaped elements. e four nodes characterizing a particular
element were digitized to obtain their X and Z coordinates
with respect to the global axes system. e Y coordinate
equaled the depth of the corresponding transverse slice on the
CT lm. e transverse cross-sectional shape of an intact
normal specimen is symmetric about the midsagittal plane.
us, only one-half of the model was digitized; the other half
was simulated as a reection of the rst half automatically by
the FE soware. e coordinate data of elements from dier-
ent cross-sections were then assembled to generate threedimensional meshes. e midtransverse plane of the L3–L4
disc was horizontal. A lordotic curve of approximately 9
degrees was simulated at the L4–L5 level of the FE model,
based on the anthropometric data. e model validation study
was undertaken by Kong.
12
en, the L5–S1 disc and the S1 vertebral body were added
to the existing L3–L5 model to construct an L3–S1 segment.
A total lordotic curve of approximately 27 degrees was simulated across the L3–S1 level with the mid-L3–L4 disc kept
horizontal. e concluding L3–S1 model has a total of 27,540
elements and 32,946 nodes (Fig. 11.3). e number of elements and the material properties of the intact L3–S1 model
are presented in Table 11.1.
vertebral bodies were modeled as a cancellous (porous) bone
core surrounded by a 0.5-mm thick cortical (dense) bone
shell. e appropriate isotropic material properties were
dened for the respective regions (see Table 11.1).
Intervertebral Disc
e intervertebral disc was modeled as the anulus brosus and
nucleus pulposus. e anulus brosus was modeled as a
composite solid ground substance, reinforced with embedded
bers. e ground substance was made up of 3D solid hexagonal elements. e REBAR option (Abaqus) was used to dene
the bers, which were oriented at alternating angles ±30
degrees to the horizontal. e “no compression” option was
used for the REBAR elements such that they could transmit
Vertebral Body and Posterior Bone
e vertebral body and posterior bony regions were dened
using three-dimensional solid continuum hexagonal elements
with eight nodes, each possessing six degrees of freedom. e
TABLE 11.1 Element Types and Material Properties for the Intact L3–S1 Finite Element Spine Model
Element Set Elements (n) Element Type Young’s Modulus (MPa) Poisson’s Ratio Cross-Sectional Area (mm2)
Bony Regions
Vertebral cortical bone 3312 C3D8 12,000 0.30
Vertebral cancellous 10,608 C3D8 100 0.20
Posterior cortical bone 3632 C3D8 12,000 0.30
Posterior cancellous 1834 C3D8 100 0.20
Intervertebral Disc
Anulus (ground) 5376 C3D8 1.2 0.45
Anulus bers 2685 REBAR 357.5–550 0.30 0.00601–0.00884
Nucleus pulposus 1920 C3D8 1.0 0.4999
Joints
Apophyseal joints 216 GAPUNI Softened, 12,000
Ligaments
Anterior longitudinal 216 T3D2
Posterior longitudinal 144 T3D2
Intertransverse 30 T3D2
Ligamentum avum 21 T3D2
Interspinous 21 T3D2
Supraspinous 9 T3D2
Capsular 84 T3D2
7.8 (<12%), 20.0 (>12%)
10.0 (<11%), 20.0 (>11%)
10.0 (<18%), 58.7 (>18%)
15.0 (<6.2%),19.5 (>6.2%)
9.8 (<14%), 12.0 (>14%)
8.8 (<20%), 15.0 (>20%)
8.48 (<25%), 32.9 (>25%)
FIG. 11.3 Finite element model of the ligamentous L3–S1 segment.
0.30 74
0.30 14.4
0.30 1.8
0.30 40
0.30 40
0.30 30
0.30 34
GAPUNI, two-node unidirectional gap element.
From Dooris A. Experimental and Theoretical Investigations Into the Eects of Articial Disc Implantation. Doctoral dissertation. Iowa City: University of Iowa; 2001.
Chapter 11 Finite Element Analysis 171
only tension; also, the ber thickness and stiness increased
in the radial direction. An overall collagenous ber content of
16% of the annular volume was distributed in the anulus.
e nucleus pulposus was modeled with C3D8 hexagonal
elements. Isotropic material property with a stiness of 1 MPa
and near incompressibility simulated with a Poisson’s v =
0.4999 was assigned to the nucleus to simulate its hydrostatic
characteristics.
Apophyseal (Facet) Joint
Simulation of the facet joints is crucial for the spine model
since it drastically aects the outcome of the analysis. In this
model, the facet joints were simulated using 20 threedimensional gap elements (GAPUNI). e facets were oriented
at an inclination of 72 degrees from the horizontal plane. An
initial gap of 0.5 mm was specied between these elements.
Force is transmitted by using the Abaqus “soened contact,”
which exponentially adjusts the force transfer as the gap is
closed. At full closure, the joint assumes the same stiness as
the posterior bone (Fig. 11.4).
Ligaments
All seven major ligaments—interspinous, supraspinous, intertransverse, capsular, posterior longitudinal, anterior longitudinal, and ligamentum avum—were simulated in the model.
e ligaments were modeled using three-dimensional twonode truss elements (T3D2). Hypoelastic material properties
were assigned to each of these ligaments, allowing a “neutral
zone” to be incorporated in which the ligament provided little
stability under minimally applied external loads. e hypoelastic material denition was given by specifying varying
Young’s modulus and Poisson’s ratio along with the strain
invariants at the specied strain rate. e material properties
of ligaments were taken from the literature, including our
own experimental data.13 e dening elements were aligned
along the respective ligament ber orientation. Although the
ligamentum avum and the longitudinal ligaments experience
a prestress at rest, all ligaments were assumed to be unstressed
initially. Modeling the ligaments causes nonlinearity in kinematics of the spine model (see Fig. 11.3).
Medial
Lateral
e entire process of developing the FE models of the spine
has been simplied due to the advances in technology.
e reconstruction of so tissue and hard tissue geometry
from magnetic resonance imaging (MRI) and CT scans is
completed through the use of Mimics v15.0 (Materialise). MRI
is capable of imaging both hard and so tissue (vertebrae and
intervertebral discs) through manual segmenting. CT scans
are capable of autosegmenting hard bone tissue but are dicult
to use for reconstruction of so tissue. e segmented geometry is exported in the form of a standard triangle language
(STL) surface le. ese les (especially manually segmented
geometries) are not smooth and require further processing.
Surface processing of the reconstructed geometry is
conducted in Geomagic Studio 2014. is soware is a
reverse-engineering soware that provides surface geometry
modications. Aer importing the geometry, there are various
functions that allow the surface to be precisely smoothed and
edited. e smoothing is utilized to be able to achieve uniform
meshes that are critical for achieving accurate and stable FE
solutions. e editing that is conducted is used to adjust the
overlapping surfaces to satisfy the requirements of the FE
analysis soware.
e prepared surface geometry is then imported into
HyperMesh 14.0 (Altair) to create the three-dimensional
meshes. ese meshes are created such that they have optimal
Jacobian and aspect ratios. Multiple mesh densities are created
of each body to then perform mesh convergence. Mesh convergence is used to show that the solution of the FE problem
is independent of mesh density. is method allows us to
utilize accurate mesh with the lowest possible computational
expense.
e converged mesh is then imported into Abaqus 6.14
(Simulia), and constructed. e construction of the model
consists of assigning material properties to all geometries.
Abaqus has the capability of assigning a variety of material
types and constitutive models to accurately simulate the linear
elastic, plastic, and hyperelastic properties of bones, implants,
and so tissue. Interactions are then generated to tie surfaces
together, such as the vertebral endplate to the intervertebral
disc. Interactions are also used to generate contact denitions,
such as the articulation between a cage and endplate, to accurately simulate the contact force, contact area, stress, displacement, and so on. e appropriate one-dimensional elements
are then added to the model to represent the small so-tissue
restraints that are not able to be reconstructed through MRI or
CT data. Loading and boundary conditions are then applied to
simulate various clinically relevant conditions. e outputs of
the model are ROM, stress, strain, force, and contact mechanics in all of the reconstructed and instrumented geometries.
Complex subroutines are available to simulate growth and
deformities of the spine. Last, both static and dynamic loading
scenarios are available to fully capture all studies of interest.
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I
FIG. 11.4 Facet joint in the nite element model of the L3–S1 spine.
Validation of the Lumbar Model
Formulating an FE model for a biologic system oen involves
making justiable assumptions. Validation of an FE model is
essential in order to indicate whether the model predictions
172 BASIC SCIENCE
are similar to the experimental predictions. Previous biomechanical studies using the intact two-segment FE model were
validated with experimental in vitro studies.
12,14–17
Axial compressive preload acts as the major component
of the in vivo preload. e exact degree and magnitude of the
net preload relies on the degree of lumbar lordosis and the
posture of the individual during physiologic loading. Axial
compressive preload aects the load displacement character-
istics of the joint.4 e model was subjected to an axial preload
of 400 N as a follower load since it is physiologic. In addition
to the axial preload, motion was predicted for all six degrees
of freedom with a moment of 10 Nm. Schultz et al.12 have
reported the load-displacement properties in all principal
directions with a compressive preload of 400 N. e compressive preload of 400 N was maintained while moments (4.7 Nm
and 10 Nm) about the three principal axes were applied
individually. Table 11.2 provides a comparison of the model
predictions in response to bending and torsional moments
compared to those reported by Schultz et al.12 e predicted
facet loads were compared with Yang and King,
and Drouin,
17b
and Kim.15 Radial disc bulge predictions at
17a
Shirazi-Adl
L4–L5, measured at the disc mid-height, were compared to
previous experimental and analytic results. In response to the
TABLE 11.2 Comparison of Intact L3–L5 and L3–S1 Finite Element
Predictions and Results From Schultz et al.
L3/5 FE Model
Predictions
Rotation (Degrees) From 4.7 Nm Moment + 400 N Compression
Flexion L3–L4: 3.29
L4–L5: 3.36
Extension L3–L4: 1.84
L4–L5: 1.62
Right lateral bending L3–L4: 2.33
L4–L5: 2.31
Left lateral bending L3–L4: 2.33
L4–L5: 2.31
Right axial rotation L3–L4: 1.28
L4–L5: 1.25
Rotation (Degrees) From 10.6 Nm Moment + 400 N Compression
Flexion L3–L4: 5.32
L4–L5: 5.08
Extension L3–L4: 3.45
L4–L5: 3.35
Right lateral bending L3–L4: 5.13
L4–L5: 5.18
Left lateral bending L3–L4: 5.13
L4–L5: 5.18
Right axial rotation L3–L4: 2.98
L4–L5: 2.75
Finite element model predictions fall within one standard deviation of in vitro results.
12
L3/S1 FE Model
Predictions
L3–L4: 3.20
L4–L5: 3.32
L5–S1: 4.45
L3–L4: 1.67
L4–L5: 1.40
L5–S1: 0.59
L3–L4: 2.32
L4–L5: 2.13
L5–S1: 1.63
L3–L4: 2.32
L4–L5: 2.13
L5–S1: 1.63
L3–L4: 1.34
L4–L5: 1.20
L5–S1: 1.00
L3–L4: 5.19
L4–L5: 5.00
L5–S1: 6.45
L3–L4: 3.83
L4–L5: 3.80
L5–S1: 3.72
L3–L4: 5.15
L4–L5: 4.91
L5–S1: 3.73
L3–L4: 5.15
L4–L5: 4.91
L5–S1: 3.73
L3–L4: 3.17
L4–L5: 2.97
L5–S1: 2.56
In Vitro
Results
5.13 ± 1.86
2.12 ± 0.98
4.47 ± 1.63
4.32 ± 1.47
0.69 ± 0.33
5.51 ± 1.00
2.99 ± 1.02
5.64 ± 1.22
4.90 ± 0.79
1.50 ± 0.67
compressive preload, the mean lateral disc bulge was 0.12 mm
for a 400 N load. Although these values are somewhat low, the
values were in the range reported by Dooris et al.7 Ligament
strains were predicted in response to an axial compressive load
of 400 N, coupled with bending moments of 5 and 10 Nm
about the three principal axes. e trends seen were close to
the in vitro work reported by Panjabi et al.
17
e FE solutions are dependent on the mesh renement.
Using a large number of elements is known to reduce error,
and the mesh should be rened until a stage is reached at
which the results from the current renement iteration are
similar to the results obtained by the previous renement
iterations. Such a mesh would be an optimized mesh, which
enables the model to predict correct results. Further renement beyond this point can theoretically induce more errors.
us, the mesh of the L3–L5 FE model was further rened and
the L5–S1 segment was added. e results between the L3–L5
model and L5–S1 model exhibited a strong correlation (see
Table 11.2).
To further validate the L3–S1 model, a cadaveric study was
undertaken recently. e study involved comparing experimentally predicted load-displacement behavior using the
Optotrak with FE model predictions.18 Five fresh ligamentous
lumbar L1–S1 spine specimens were used for the experimental
tests. Specimens were potted in a rigid base secured to the
sacrum and a loading frame likewise was secured to the L1
vertebral body. To determine the load-displacement behavior
of the specimen, a set of three light-emitting diodes (LEDs)
was attached to each vertebral body (Fig. 11.5). e Optotrak
motion measuring system (Northern Digital Inc.) was used to
track the spatial location of the LED markers secured rigidly
to the vertebral bodies, including the base, during the loaddisplacement evaluation. e specimen was loaded to a
maximum of 9 Nm in all six degrees of freedom. e intact
FE model was also subjected to similar loading of 9 Nm as the
cadaveric testing. e angular displacement data for the
FIG. 11.5 The ligamentous L1–S1 segment with light-emitting diodes, used
to predict angular displacements.