INVITED REVIEW
Comparative Effectiveness Analyses of Intraoperative Neurophysiological Monitoring in Spinal Surgery John P. Ney* and David N. van der Goes†
Summary: Intraoperative neurophysiological monitoring for surgeries of the spine has been performed in clinical practice for several decades, but recent alterations in reimbursement schemes by third party payers have raised issues of the value of these procedures. Decision modeling using comparative effectiveness techniques holds the promise of evidence-based assessment of both cost and meaningful outcomes. In this article, we review the elements of comparative effectiveness analyses followed by a critical appraisal of the small but growing body of cost-effectiveness literature for intraoperative neurophysiological monitoring in spine. Key Words: Comparative effectiveness, cost, value, effectiveness, spine surgery, intraoperative. (J Clin Neurophysiol 2014;31: 112–117)
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onitoring of evoked potentials and electromyogram signal during spine surgeries may avert spinal cord or other neurologic injury at some cost in labor and equipment. Because its utilization as a safety measure during spinal procedures has grown, intraoperative neurophysiological monitoring (IONM) has received considerable scrutiny from third party payers, including a recent readjustment in Medicare-based reimbursement (Federal Register, 2012). In the absence of randomized controlled trial data showing the efficacy of IONM (Nuwer et al., 2012), comparative effectiveness analyses are a method of aggregating the existing evidence to show the benefit of IONM in a framework of cost-effectiveness. The techniques involved in comparative effectiveness analyses (CEAs) may be complex and foreign to physicians used to examine the results of clinical trials; the following is an effort to explain CEAs and their interpretation, followed by a critical review of CEAs involving IONM in spinal procedures.
COMPARATIVE EFFECTIVENESS ANALYSES Cost-effectiveness or CEA refers to a group of methodologies that borrow from economics to aid in decision modeling between 2 or multiple health care interventions. All of these share in the quantification of cost of interventions and subsequent possible sequelae, but may differ in the outcome of interest, the time frame evaluated, and the perspective taken. Determining these factors is of major importance before initiating CEA and can result in dramatic differences in results (Briggs et al., 2008). From the *Department of Neurology, University of Washington, Seattle, Washington, U.S.A.; and †Department of Economics, University of New Mexico, Albuquerque, New Mexico, U.S.A. Address correspondence and reprint requests to John P. Ney, MD, MPH, Comparative Effectiveness, Cost and Outcomes Research Center, University of Washington, UW Box #359455, UW Tower, 14th floor 4333 Brooklyn Avenue NE, Seattle, WA 98195, U.S.A.; e-mail: [email protected]. Copyright Ó 2014 by the American Clinical Neurophysiology Society
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The common thread of CEA is the cost (Gold et al., 1996). A new or existing intervention is frequently compared with the standard of care, which may have associated upfront costs, or none. Determining the initial cost of the intervention (often using Medicare reimbursement schedules) is often the first step in parameterizing a CEA. Subsequent costs postintervention should also be determined,where they differ between the treatment arms and within the limits of the time frame evaluated. For instance, if the intervention is an adjunct to surgery, which does not impact the efficacy of the surgical procedure, the initial cost of the intervention and costs associated with any adverse effects or failure of the intervention should be included, but the costs of the surgery itself and the cost impact of surgical success or failure are assumed to be distributed randomly and evenly between the exposure and control groups, and are removed. After the intervention, costs can be divided into direct costs (hospitalization, outpatient visits, prescriptions, and durable medical equipment) and indirect costs (loss of wages and benefits from disability, missed work or school, reduced productivity, and caretaker expenses). The structure of costs in any given CEA should be well defined in the methodology section. The outcome of interest determines the type of CEA. The choice of outcome may be limited by the available data and the goals of the analysis. The most accurate and specific CEA is the cost– consequences or cost–event analysis, where the outcome is either a beneficial event resulting from application of the intervention (i.e., 50% reduction of pain) or avoidance of a particular adverse consequence (i.e., paralysis). Because the results are usually expressed as a marginal number of cost per beneficial event gained or deleterious event avoided, clinicians may liken it to a number needed to treat or number needed to harm. Although this kind of CEA may provide good information for different interventions that share the same consequences, it generally does not allow for comparisons across disparate conditions and outcomes. A cost–benefit analysis looks purely at costs to determine if an intervention is ultimately cost saving (net negative cost) or cost prohibitive (exceeds cost thresholds). Because all findings are expressed monetarily, this can be used not only across health care interventions and conditions but also for any kind of spending. The difficulty arises in monetizing health states, such as pain and suffering, which can vary greatly from person to person. The cost–utility analysis attempts to quantify both costs and health states to allow for across-disease comparisons. This is the favored approach by the National Institute of Clinical Excellence in Great Britain in health care decision modeling. Cost–utility analyses rely on the determination of a health–utility score (or quality of life score) for a given disease state, which arises from the utility theory of eighteenth century economist–philosophers Jeremy Bentham and John Stuart Mill. For any given person at a particular time, overall health can range from zero (death) to 1 (perfect health), with most persons being between 0.7 and 0.95. The health utility of a particular health
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condition can be ascertained through standardized questionnaires for persons suffering from the disease (e.g., the EuroQOL-5D, Short Form 6-D, Quality Well-Being, or Health Utility Index), or the standard gamble or time trade-off methodologies in at-risk focus groups or persons taken at random (Brazier et al., 2007). These last two determine whether the health condition is valued relative to perfect health, where in the case of the time trade-off a person may be asked how many years of life at a diminished health state they would trade for a year of perfect health. The results of a cost–utility analysis are expressed as a cost per quality-adjusted life year (QALY), a form of cost-effectiveness ratio. The marginal difference in QALYs for an intervention is the mean health utility for each year after the intervention relative to the control. Costs are the mean costs for the intervention and all downstream costs. The cost-effectiveness ratio does not, in itself, indicate whether an intervention is cost-effective (see Evaluating Results of Cost-Effectiveness Models below). The perspective of interest is the lens through which the intervention is viewed. This is the relevant audience for the CEA, which is the decision maker for whether the intervention is made available and paid for (e.g., regulators, policymakers, third party payers, and hospital administrators) or used (physician and patient). This affects the decision to include the direct and indirect costs and the time frame of evaluation. A hospital is largely interested in immediate costsdcost of the intervention and costs associated with the hospital stay. Private health insurers are interested in direct health care costs for 2 to 3 years after the surgery (and indirect costs if they include a disability insurance component) because the average person switches insurance carriers after that time frame. Patients and policy makers are concerned with lifetime costs, both direct and indirect, as the patient and taxpayers will ultimately shoulder the burden. The latter is often referred to as the “societal” perspective because it represents the overall economic impact of the intervention on society. The time frame of evaluation within a decision model (or “time horizon”) is a reflection of the perspective of interest and the type of evaluation. For a cost–consequences model of an intervention that occurs at a single point in time, the time horizon is defined by when the beneficial effect or detrimental event would be expected to occur (for IONM, the immediate postoperative period). A longer time horizon encompassing inpatient stay (additional 30 days for
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assessment of readmission costs) would be of relevance for a hospital perspective in determining a cost–benefit model. Private health insurers would look at direct health care costs over several years, whereas the societal perspective (for policymakers and government insurers) would encompass the lifetime of costs and/or diminished health after the intervention. These are appropriate for both cost– benefit and cost–utility analyses.
CEA DECISION MODELING A decision model is a hypothetical construct ideally based on the best possible information. The model is a mathematical function of its parameters, which can be informed from single or multiple sources. CEA models from a single source are the result of prospectively collected cost and clinical effectiveness data taken alongside observational or randomized controlled trials (Glick et al., 2007). More often, these data do not exist, and parameters should be taken from the best available published and publically sourced information to maximize accuracy and reproducibility. When using multiple sources for a single parameter, meta-analytic methods allow for the evaluation of heterogeneity, and pooling and weighting data based on sample size, for a point estimate and standard error (Pettiti, 2000). Assumptions are defined within the model when even these data are unavailable or for simplicity of modeling, and must be explicitly stated (Philips et al., 2006). A decision model is designed to obtain results for a base case, where the base case describes the average or typical patient through the values of the parameters and assumptions. For models with a multiyear time horizon, base case should include a patient age. The model is generally a tree structure with the initial branch point at the decision to use one or more interventions versus a comparator, dividing into exposed and control arms. The exposure(s) and control experience in parallel with the history of the disorder treated under the intervention (s) and comparator (usually standard care), with the probability of events occurring sourced from the parameters and assumptions at branch points in the tree (event nodes). When all relevant events have been modeled, the tree ends in terminal nodes (Fig. 1). All costs and (in the case of cost–event and cost–utility models) health benefits or detriments are summed for each arm, and the difference between exposure and control is calculated to determine overall costs per overall health effect on the base case (Briggs et al., 2008).
FIG. 1. Decision tree for Intervention A vs. Intervention B. The decision tree consists of a decision node (box) with branch points at subsequent event or chance nodes (circles), with final outcomes depicted in terminal nodes (triangles). The weighted average of the costs and outcomes distal to the decision node for each intervention are summed and compared. Copyright Ó 2014 by the American Clinical Neurophysiology Society
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FIG. 2. Markov model. This shows three health states, A, B, and C, with transition probabilities represented as unidirectional or bidirectional arrows. At the beginning of each cycle, patients can transition from health states (or remain in the same health state) and accrue the associated costs and health benefits of that state for the cycle duration. When a tree structure is insufficient to depict changes in health states over time, a Markov model is used (Briggs and Sculpher, 1998). The Markov depicts the available health states for the subject, such as “normal age-adjusted health,” “paresis,” and death (Fig. 2). The likelihood of changing from one health state to another within a given time frame is the transition probability and can be unidirectional (as with “paresis / death”) or bidirectional (for “normal health 4 paresis”). Transitions occur at each cycle, which could be any defined time period, but generally no longer than 1 year. The model runs until the time horizon is complete or until all possible subjects have died (lifetime time horizon). All costs and benefits are summed in each group. The Markov can be used as a terminal node in a tree model, or as a free-standing model. Once the base case results have been ascertained, sensitivity analyses can evaluate uncertainty in the model. One- and two-way sensitivity analyses are useful in determining the effect of changing one parameter or two simultaneous parameters from the base case (over a range of parameter values) on the model results. A probabilistic sensitivity analysis models all parameters
simultaneously by drawing each parameter at random from within probability distributions (Briggs et al., 2002). Costs are typically depicted on a gamma or log-normal distribution with transition or event probabilities determined using a beta distribution. A Monte Carlo simulation is run for between 1000 and 100,000 iterations, and the data are collected and analyzed.
EVALUATING RESULTS OF COST-EFFECTIVENESS MODELS Cost-effectiveness, for all models except the cost–benefit analysis (where net cost alone is evaluated), is depicted in the format of a ratio of cost per gain in effectiveness. The cost-effectiveness of multiple interventions can be plotted on the cost-effectiveness plane (CE plane), where cost is represented on the vertical axis and effectiveness on the horizontal, and the origin is zero for both quantities (Fig. 3). Both the average cost-effectiveness ratio, the mean cost for mean effect, and the incremental cost-effectiveness ratio, which is the difference in mean costs of two interventions divided by the
FIG. 3. Cost-effectiveness plane. Plots cost (vertical axis) versus effectiveness (horizontal axis). Assumes origin is zero for each. Quadrants I–IV are depicted. 114
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difference in mean effect, can be depicted on the CE plane. For comparison between two interventions, the CE plane is divisible into four quadrants, revisiting the Cartesian plane from algebra. In quadrant I (0–908), the new intervention is more costly than the comparator, but more effective. Quadrants II (908–1808) and III (1808–2708) represent suboptimal states where the new intervention is more costly and less effective than the comparator (quadrant II), or less costly and less effective (quadrant III). A result plotted in quadrant IV indicates that the new intervention is more effective and less costly than the comparator, often referred to as “dominant” (Gray et al., 2010). However, most new interventions that are subject to cost-effectiveness analyses are more costly than standard of care and also more effective, placing them squarely in quadrant I. As an incremental cost-effectiveness ratio landing in quadrant I could show nearly infinite incremental cost for very little incremental beneficial effect over the comparator, determination of actual cost-effectiveness is dependent on the willingness-to-pay threshold for the study audience. This is largely based on precedent, but may also be tempered with more subjective claims. For cost– utility analyses, the expense of dialysis, which costs approximately $50,000/QALY or approximately £30,000/QALY are commonly used thresholds (Drummond et al., 2005). Items that are more costly per QALY mentioned above, these thresholds exceed the willingness-topay for society and are not deemed cost-effective. For cost–event analyses, the precedent of willingness-to-pay for other comparable outcomes is necessary (Fig. 4).
COST-EFFECTIVENESS AND SPINAL SURGERY INTRAOPERATIVE NEUROPHYSIOLOGICAL MONITORING Intraoperative neurophysiological monitoring in spinal surgeries has several features that make comparative effectiveness techniques attractive. The intervention (IONM) occurs at a single point in time and has both immediate (postoperative neurologic complications) and long-term consequences (paralysis and early death). There is no obvious comparator as an adjunct to spine surgery in lieu of IONM, so comparisons are made to surgery without IONM. Center for Medicare and Medicaid Studies (CMS) reimbursement for IONM Current Procedure Terminology-4 codes can be used to determine per-study
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reimbursement (Physician Fee Schedule, 2010). The rate of neurologic complications, paralysis, and other outcomes can be determined from locally available data, or from meta-analysis of large case series. The direct and indirect health care costs for spinal cord injury (SCI) are published in the United States by the National Center for Spinal Cord Injury Statistics in Birmingham, AL (National Center for Spinal Cord Injury Statistics, 2010). There are several studies that address cost-effectiveness of IONM in spine surgery (Table 1). These are based on either singlehospital case series or hypothetical evidence-based models. Ayoub et al. (2010) published a case series of 210 patients in 1 year who had spinal surgeries and Somatosensory Evoked Potentials (SSEP) monitoring. SSEPs were lost and then recovered in three patients after the surgical team was alerted and took appropriate action. The authors assumed that in the absence of SSEP monitoring, the institution would have had at least one patient with a persistent SCI. They derived postoperative costs of neurologic injury from the sum of hospital-based immediate costs and annual age-categorized SCI costs from published data. Costs of SSEP monitoring, including added time for the surgical procedure, cost of neurologist consultation, and costs of total intravenous anesthesia, were estimated at $835. They estimated the cost savings to the hospital as $64,075 to $102,193 in annual health care costs including the costs of $31,564 in summed yearly SSEP monitoring. Although the study suggests using a lifetime time horizon for the determination of costs, the author reports patients’ direct health care costs for 1 year only, likely because of convenience of calculation for their single hypothetically injured patient per year (without monitoring). Traynelis et al. (2012) published a case series of 720 patients undergoing routine cervical spine surgeries collected over 6 years at the University of Iowa Hospitals, none of whom had intraoperative monitoring. The rate of neurologic complications was 0.4%, but the authors note that no neurologic deficit was present on follow-up at 1 year or more. Accompanying the case series was an economic analysis consisting of the reimbursement cost of IONM based on the CMS Current Procedure Terminology-4 fee schedule reimbursement for 2011 multiplied by the number of patients in the series, concluding that the hospital system saved more than a million dollars in IONM costs. The study by Traynelis et al. (2012) was problematic from several vantage points. The exclusion criteria were trauma- or
FIG. 4. Average versus incremental cost-effectiveness. Shows two interventions (A and B) on generic cost and effectiveness scales, where intervention A costs 2 units and is 20% more effective than intervention B, which only costs 1 unit. The average costeffectiveness ratio (ACER) shows a moderate increase in cost per beneficial health effect for intervention B over intervention A, but the incremental cost-effectiveness ratio (ICER) shows 10 times the cost per unit gain in effectiveness. Copyright Ó 2014 by the American Clinical Neurophysiology Society
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Ney (2012) Ney (2013)
Traynelis 2012
IONM, intraoperative neurophysiological monitoring; PSA, probabilistic sensitivity analysis.
One-way, (level of injury) SSEPs only, single hypothetical SCI One-way, (level of injury) Direct health care costs only, scoliosis at age 25 base case Cost–benefit 6.5 years Hospital No IONM saved .$1 million/6.5 years None Descriptive extrapolation of case series with no persistent SCI Cost–event Immediate postop Patient/physician IONM costs $62k/neuro complication averted PSA Multimodal IONM, injury rate approximately 5% Cost–benefit Lifetime Societal IONM cost saving $23k direct/indirect PSA Multimodal IONM, injury rate approximately 5% health costs IONM saves $64k to 102k/year IONM cost-neutral at #$977/operation Hospital Societal Cost–benefit Cost–benefit Ayoub (2012) Sala (2006)
1 year Lifetime
Reported Outcome Perspective Time Horizon Type of Analysis Lead Author (Year)
TABLE 1.
Publications Addressing Cost-Effectiveness of IONM in Spinal Surgeries
Sensitivity Analysis
Notes
J. P. Ney and D. N. van der Goes
tumor-related surgeries, but it is unclear if there was selection bias in nonmonitoring of the selected surgeries. The authors are fortunate that they had no lasting neurologic deficits in retrospective analysis, but their experience may not have been representative of routine cervical spine surgeries nationally. Persistent neurologic deficits of paraparesis or tetraparesis were seen in 0.3% of 4075 patients at the University of Cleveland Hospitals reported by Cramer et al. For a sample to be adequately powered to support the null hypothesis given, an expected complication rate of 0.3% over 1100 patients would be needed for a study with 90% power and alpha of 0.05 (Ney and van der Goes, 2013). In their defense, the authors provided a purely descriptive analysis without the significance testing of results from this case series. Apart from single center studies, several authors have constructed cost-effectiveness models based on the available published evidence. Sala et al. (2007) determined that for a base case paraplegia rate of 0.1% in scoliosis surgery for a 25 year old and using lifetime cost data from the NSCISC, that IONM would be cost saving if it is less than $977 (2006 USD) per surgery. As a sensitivity analysis, the authors calculated maximum benefit in preventing high tetraplegias where cost of IONM outweighed cost of SCI at less than $2,924 (2006 USD). Sala performed an elegant form of cost–benefit analysis, with a limited base case of a 25-year-old patient undergoing scoliosis surgery. The availability of lifetime summed direct health care costs for varying degrees of SCI made this an attractive model. Indirect health care costs and quality of life information were not used to compile this model, and the estimates were not recalculated for non-scoliosis surgeries or persons older than 25 years. The calculations of Sala et al. (2007) also assumed a 100% rate of prevention of injury given an intraoperative monitoring alert. In two successive studies, Ney et al. built hypothetical models for the use of multimodal IONM in spinal surgeries. Using pooled sensitivity, specificity, and rate of injury estimates for multimodal IONM (MEP and SEP) from articles cited in a systematic review by Fehlings et al. (2010), and basing rate of injury prevention given IONM alert from the quasi-controlled study published by Wiedemayer et al. (2002). The outcome of interest was the presence of neurologic complications, and results were based on a cost per neurologic complication avoided. Based on 2009 CMS reimbursement of $1,535 for multimodal IONM, a sensitivity of 94%, specificity of 96%, prevention rate given IONM alert of 52%, and an injury rate of 5%, the cost per neurologic complication prevented was US $63,387 in 2009 (Ney et al., 2012). A Monte Carlo simulation with 10,000 replications provided a 95% confidence interval of $61,939 to $64,836. For comparison purposes, the cost of an asymptomatic carotid ultrasound surveillance program was cited at $290,000 per neurologic injury (stroke) avoided (Cull et al., 2011). Ney et al. (2013) expanded on this cost–event model to extrapolate costs from the immediate postoperative period to a lifetime time horizon using a cost–benefit approach. Using NSCISC data, the base case for direct health care costs of a 50-year-old person with any-level incomplete motor paresis was constructed. Indirect costs in the form of lost wages and benefits were determined based on the formula developed by Miller et al. (1987) for labor-related losses after automobile injury. Monte Carlo simulation with 100,000 replications used a high tetraplegic cord complete cord injury incurred at age 25 as the upper limit of 95% confidence intervals for log-normal distribution costs, with a $100,000 lifetime injury cost as the lower limit. The same IONM diagnostic characteristics, prevention rate given IONM alert, and CMS reimbursement rates were used as per the previous study. The authors calculated that IONM saved $23,189 per surgery for the base case, and remained cost saving when neurologic complication Copyright Ó 2014 by the American Clinical Neurophysiology Society
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rate from surgery exceeded 0.3% and prevention rate after IONM alert was greater than 14.2%. These last two models are limited by the evidence at hand. In particular, they are reliant on the single study by Wiedemayer et al. (2002) wherein all patients who had an IONM alert, which was not acted on (42/42), developed a neurologic deficit, whereas 52.4% (22/ 42) of those where an alert spurred the surgical team to act to prevent injury awoke without new neurologic complications. The study also suggests that acting on the alerts may not have been possible in a number of cases in the control group, suggesting a prevention rate as low as 26%. The cases also included intracranial procedures and tumors and were selected from among more than 15,000 cases and therefore likely not representative of routine spinal surgeries. The other major criticism is that the rate of postoperative neurologic complications is simply too high at 5% (based on pooled metaanalysis of spine surgeries using multimodal IONM) and does not represent the neurologic complication rate of routine spine surgery purported to be less than 1% (Nasser et al., 2010). Despite these limitations, the studies above provide a foundation for further analysis. Given patient-reported quality of life data regarding SCI, the model could be easily modified to a cost–utility analysis reporting cost per quality-adjusted life years. Using Markov modeling, reported lifetime costs would need to be adjusted to provide an estimate of the direct and indirect costs for relevant health states for 1 year or smaller cycle length, then summed for all cycles. The results would be relevant for agencies such as NICE in the United Kingdom or CMS as they continue to evaluate the costs for purported health benefits across a wide range of interventions and disease states. Ultimately, any model should be transparent in its methodology, with parameters based on the best available information, and subject to revision as stronger evidence displaces weaker data. Model validation should be performed because more real-world data are made available. The availability of large observational data sets may help to validate existing CEA models, but should also be carefully examined for biases, particularly selection of IONM and determination of appropriate control groups in cross-sectional analysis. REFERENCES Ayoub C, Zreik T, Sawaya R, et al. Significance and cost-effectiveness of somatosensory evoked potential monitoring in cervical spine surgery. Neurol India 2010;58:424–428. Brazier J, Ratcliffe J, Salomon JA, Tsuchiya A. Measuring and valuing health benefits for economic evaluation. Oxford: Oxford University Press, 2007. Briggs A, Sculpher M. An Introduction to Markov modelling for economic evaluation. Pharmacoeconomics 1998;13:397–409.
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Briggs AH, Goeree R, Blackhouse G, O’Brien BJ. Probabilistic analysis of costeffectiveness models: choosing between treatment strategies for gastroesophageal reflux disease. Med Decis Making 2002;22:290–308. Briggs A, Claxton K, Sculpher M. Decision modelling for health care evaluation. New York: Oxford University Press, 2008. Cramer DE, Maher PC, Pettigrew DB, Kuntz CT. Major neurologic deficit immediately after adult spinal surgery: incidence and etiology over 10 years at a single training institution. J Spinal Disord Tech. 2009;22:565–570. Cull DL, Cole T, Miller B, et al. The value of a carotid duplex surveillance program for stroke prevention. Ann Vasc Surg 2011;25:887–894. Drummond MF, Sculpher MJ, Torrance GW, et al. Methods for the economic evaluation of health care programmes. Oxford: Oxford University Press, 2005. Fehlings MG, Brodke DS, Norvell DC, Dettori JR. The evidence for intraoperative neurophysiological monitoring in spine surgery: does it make a difference? Spine (Phila Pa 1976) 2010;35:S37–S46. Glick HA, Doshi JA, Sonnad SS, Polsky D. Economic evaluation in clinical trials. Oxford: Oxford University Press, 2007. Gold MR, Siegel JE, Russell LB, Weinstein MC. Cost-effectiveness in health and medicine. New York: Oxford University Press, 1996. Gray A, Clarke P, Wolstenholme J, Wordsworth S. Applied methods of cost-effectiveness analysis in healthcare. Oxford: Oxford University Press, 2010. Miller T, Hoskin A, Yalung-Mathews D. A procedure for annually estimating wage losses due to accidents in the U.S. J Saf Res 1987;18:101–119. Nasser R, Yadla S, Maltenfort MG, et al. Complications in spine surgery. J Neurosurg Spine 2010;13:144–157. National Center for Spinal Cord Injury Statistics. Spinal cord injury, facts and figures at a glance. 2010. Available at: https://www.nscisc.uab.edu/. Accessed December 18, 2010. Ney JP, van der Goes DN. Letter to the editor: cervical decompression. J Neurosurg Spine 2013;19:523–525. Ney JP, van der Goes DN, Watanabe JH. Cost-effectiveness of intraoperative neurophysiological monitoring for spinal surgeries: beginning steps. Clin Neurophysiol 2012;123:1705–1707. Ney JP, van der Goes DN, Watanabe JH. Cost-benefit analysis: intraoperative neurophysiological monitoring in spinal surgeries. J Clin Neurophysiol 2013;30:280–286. Nuwer MR, Emerson RG, Galloway G, et al. Evidence-based guideline update: intraoperative spinal monitoring with somatosensory and transcranial electrical motor evoked potentials. Neurology 2012;78:585–589. Pettiti DB. Meta-analysis, decision analysis, and cost-effectiveness analysis. New York: Oxford University Press, 2000. Philips Z, Bojke L, Sculpher M, et al. Good practice guidelines for decision-analytic modelling in health technology assessment: a review and consolidation of quality assessment. Pharmacoeconomics 2006;24:355–371. Physician Fee Schedule, by HCPCS Code. Available at: www.cms.gov. Accessed December 18, 2010. Sala F, Dvorak J, Faccioli F. Cost effectiveness of multimodal intraoperative monitoring during spine surgery. Eur Spine J 2007;16(suppl 2): S229–S231. Traynelis VC, Abode-Iyamah KO, Leick KM, et al. Cervical decompression and reconstruction without intraoperative neurophysiological monitoring. J Neurosurg Spine 2012;16:107–113. Wiedemayer H, Fauser B, Sandalcioglu IE, et al. The impact of neurophysiological intraoperative monitoring on surgical decisions: a critical analysis of 423 cases. J Neurosurg 2002;96:255–262.
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Comparative Effectiveness Analyses of Intraoperative Neurophysiological Monitoring in Spinal Surgery John P. Ney* and David N. van der Goes†
Summary: Intraoperative neurophysiological monitoring for surgeries of the spine has been performed in clinical practice for several decades, but recent alterations in reimbursement schemes by third party payers have raised issues of the value of these procedures. Decision modeling using comparative effectiveness techniques holds the promise of evidence-based assessment of both cost and meaningful outcomes. In this article, we review the elements of comparative effectiveness analyses followed by a critical appraisal of the small but growing body of cost-effectiveness literature for intraoperative neurophysiological monitoring in spine. Key Words: Comparative effectiveness, cost, value, effectiveness, spine surgery, intraoperative. (J Clin Neurophysiol 2014;31: 112–117)
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onitoring of evoked potentials and electromyogram signal during spine surgeries may avert spinal cord or other neurologic injury at some cost in labor and equipment. Because its utilization as a safety measure during spinal procedures has grown, intraoperative neurophysiological monitoring (IONM) has received considerable scrutiny from third party payers, including a recent readjustment in Medicare-based reimbursement (Federal Register, 2012). In the absence of randomized controlled trial data showing the efficacy of IONM (Nuwer et al., 2012), comparative effectiveness analyses are a method of aggregating the existing evidence to show the benefit of IONM in a framework of cost-effectiveness. The techniques involved in comparative effectiveness analyses (CEAs) may be complex and foreign to physicians used to examine the results of clinical trials; the following is an effort to explain CEAs and their interpretation, followed by a critical review of CEAs involving IONM in spinal procedures.
COMPARATIVE EFFECTIVENESS ANALYSES Cost-effectiveness or CEA refers to a group of methodologies that borrow from economics to aid in decision modeling between 2 or multiple health care interventions. All of these share in the quantification of cost of interventions and subsequent possible sequelae, but may differ in the outcome of interest, the time frame evaluated, and the perspective taken. Determining these factors is of major importance before initiating CEA and can result in dramatic differences in results (Briggs et al., 2008). From the *Department of Neurology, University of Washington, Seattle, Washington, U.S.A.; and †Department of Economics, University of New Mexico, Albuquerque, New Mexico, U.S.A. Address correspondence and reprint requests to John P. Ney, MD, MPH, Comparative Effectiveness, Cost and Outcomes Research Center, University of Washington, UW Box #359455, UW Tower, 14th floor 4333 Brooklyn Avenue NE, Seattle, WA 98195, U.S.A.; e-mail: [email protected]. Copyright Ó 2014 by the American Clinical Neurophysiology Society
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The common thread of CEA is the cost (Gold et al., 1996). A new or existing intervention is frequently compared with the standard of care, which may have associated upfront costs, or none. Determining the initial cost of the intervention (often using Medicare reimbursement schedules) is often the first step in parameterizing a CEA. Subsequent costs postintervention should also be determined,where they differ between the treatment arms and within the limits of the time frame evaluated. For instance, if the intervention is an adjunct to surgery, which does not impact the efficacy of the surgical procedure, the initial cost of the intervention and costs associated with any adverse effects or failure of the intervention should be included, but the costs of the surgery itself and the cost impact of surgical success or failure are assumed to be distributed randomly and evenly between the exposure and control groups, and are removed. After the intervention, costs can be divided into direct costs (hospitalization, outpatient visits, prescriptions, and durable medical equipment) and indirect costs (loss of wages and benefits from disability, missed work or school, reduced productivity, and caretaker expenses). The structure of costs in any given CEA should be well defined in the methodology section. The outcome of interest determines the type of CEA. The choice of outcome may be limited by the available data and the goals of the analysis. The most accurate and specific CEA is the cost– consequences or cost–event analysis, where the outcome is either a beneficial event resulting from application of the intervention (i.e., 50% reduction of pain) or avoidance of a particular adverse consequence (i.e., paralysis). Because the results are usually expressed as a marginal number of cost per beneficial event gained or deleterious event avoided, clinicians may liken it to a number needed to treat or number needed to harm. Although this kind of CEA may provide good information for different interventions that share the same consequences, it generally does not allow for comparisons across disparate conditions and outcomes. A cost–benefit analysis looks purely at costs to determine if an intervention is ultimately cost saving (net negative cost) or cost prohibitive (exceeds cost thresholds). Because all findings are expressed monetarily, this can be used not only across health care interventions and conditions but also for any kind of spending. The difficulty arises in monetizing health states, such as pain and suffering, which can vary greatly from person to person. The cost–utility analysis attempts to quantify both costs and health states to allow for across-disease comparisons. This is the favored approach by the National Institute of Clinical Excellence in Great Britain in health care decision modeling. Cost–utility analyses rely on the determination of a health–utility score (or quality of life score) for a given disease state, which arises from the utility theory of eighteenth century economist–philosophers Jeremy Bentham and John Stuart Mill. For any given person at a particular time, overall health can range from zero (death) to 1 (perfect health), with most persons being between 0.7 and 0.95. The health utility of a particular health
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condition can be ascertained through standardized questionnaires for persons suffering from the disease (e.g., the EuroQOL-5D, Short Form 6-D, Quality Well-Being, or Health Utility Index), or the standard gamble or time trade-off methodologies in at-risk focus groups or persons taken at random (Brazier et al., 2007). These last two determine whether the health condition is valued relative to perfect health, where in the case of the time trade-off a person may be asked how many years of life at a diminished health state they would trade for a year of perfect health. The results of a cost–utility analysis are expressed as a cost per quality-adjusted life year (QALY), a form of cost-effectiveness ratio. The marginal difference in QALYs for an intervention is the mean health utility for each year after the intervention relative to the control. Costs are the mean costs for the intervention and all downstream costs. The cost-effectiveness ratio does not, in itself, indicate whether an intervention is cost-effective (see Evaluating Results of Cost-Effectiveness Models below). The perspective of interest is the lens through which the intervention is viewed. This is the relevant audience for the CEA, which is the decision maker for whether the intervention is made available and paid for (e.g., regulators, policymakers, third party payers, and hospital administrators) or used (physician and patient). This affects the decision to include the direct and indirect costs and the time frame of evaluation. A hospital is largely interested in immediate costsdcost of the intervention and costs associated with the hospital stay. Private health insurers are interested in direct health care costs for 2 to 3 years after the surgery (and indirect costs if they include a disability insurance component) because the average person switches insurance carriers after that time frame. Patients and policy makers are concerned with lifetime costs, both direct and indirect, as the patient and taxpayers will ultimately shoulder the burden. The latter is often referred to as the “societal” perspective because it represents the overall economic impact of the intervention on society. The time frame of evaluation within a decision model (or “time horizon”) is a reflection of the perspective of interest and the type of evaluation. For a cost–consequences model of an intervention that occurs at a single point in time, the time horizon is defined by when the beneficial effect or detrimental event would be expected to occur (for IONM, the immediate postoperative period). A longer time horizon encompassing inpatient stay (additional 30 days for
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assessment of readmission costs) would be of relevance for a hospital perspective in determining a cost–benefit model. Private health insurers would look at direct health care costs over several years, whereas the societal perspective (for policymakers and government insurers) would encompass the lifetime of costs and/or diminished health after the intervention. These are appropriate for both cost– benefit and cost–utility analyses.
CEA DECISION MODELING A decision model is a hypothetical construct ideally based on the best possible information. The model is a mathematical function of its parameters, which can be informed from single or multiple sources. CEA models from a single source are the result of prospectively collected cost and clinical effectiveness data taken alongside observational or randomized controlled trials (Glick et al., 2007). More often, these data do not exist, and parameters should be taken from the best available published and publically sourced information to maximize accuracy and reproducibility. When using multiple sources for a single parameter, meta-analytic methods allow for the evaluation of heterogeneity, and pooling and weighting data based on sample size, for a point estimate and standard error (Pettiti, 2000). Assumptions are defined within the model when even these data are unavailable or for simplicity of modeling, and must be explicitly stated (Philips et al., 2006). A decision model is designed to obtain results for a base case, where the base case describes the average or typical patient through the values of the parameters and assumptions. For models with a multiyear time horizon, base case should include a patient age. The model is generally a tree structure with the initial branch point at the decision to use one or more interventions versus a comparator, dividing into exposed and control arms. The exposure(s) and control experience in parallel with the history of the disorder treated under the intervention (s) and comparator (usually standard care), with the probability of events occurring sourced from the parameters and assumptions at branch points in the tree (event nodes). When all relevant events have been modeled, the tree ends in terminal nodes (Fig. 1). All costs and (in the case of cost–event and cost–utility models) health benefits or detriments are summed for each arm, and the difference between exposure and control is calculated to determine overall costs per overall health effect on the base case (Briggs et al., 2008).
FIG. 1. Decision tree for Intervention A vs. Intervention B. The decision tree consists of a decision node (box) with branch points at subsequent event or chance nodes (circles), with final outcomes depicted in terminal nodes (triangles). The weighted average of the costs and outcomes distal to the decision node for each intervention are summed and compared. Copyright Ó 2014 by the American Clinical Neurophysiology Society
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FIG. 2. Markov model. This shows three health states, A, B, and C, with transition probabilities represented as unidirectional or bidirectional arrows. At the beginning of each cycle, patients can transition from health states (or remain in the same health state) and accrue the associated costs and health benefits of that state for the cycle duration. When a tree structure is insufficient to depict changes in health states over time, a Markov model is used (Briggs and Sculpher, 1998). The Markov depicts the available health states for the subject, such as “normal age-adjusted health,” “paresis,” and death (Fig. 2). The likelihood of changing from one health state to another within a given time frame is the transition probability and can be unidirectional (as with “paresis / death”) or bidirectional (for “normal health 4 paresis”). Transitions occur at each cycle, which could be any defined time period, but generally no longer than 1 year. The model runs until the time horizon is complete or until all possible subjects have died (lifetime time horizon). All costs and benefits are summed in each group. The Markov can be used as a terminal node in a tree model, or as a free-standing model. Once the base case results have been ascertained, sensitivity analyses can evaluate uncertainty in the model. One- and two-way sensitivity analyses are useful in determining the effect of changing one parameter or two simultaneous parameters from the base case (over a range of parameter values) on the model results. A probabilistic sensitivity analysis models all parameters
simultaneously by drawing each parameter at random from within probability distributions (Briggs et al., 2002). Costs are typically depicted on a gamma or log-normal distribution with transition or event probabilities determined using a beta distribution. A Monte Carlo simulation is run for between 1000 and 100,000 iterations, and the data are collected and analyzed.
EVALUATING RESULTS OF COST-EFFECTIVENESS MODELS Cost-effectiveness, for all models except the cost–benefit analysis (where net cost alone is evaluated), is depicted in the format of a ratio of cost per gain in effectiveness. The cost-effectiveness of multiple interventions can be plotted on the cost-effectiveness plane (CE plane), where cost is represented on the vertical axis and effectiveness on the horizontal, and the origin is zero for both quantities (Fig. 3). Both the average cost-effectiveness ratio, the mean cost for mean effect, and the incremental cost-effectiveness ratio, which is the difference in mean costs of two interventions divided by the
FIG. 3. Cost-effectiveness plane. Plots cost (vertical axis) versus effectiveness (horizontal axis). Assumes origin is zero for each. Quadrants I–IV are depicted. 114
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difference in mean effect, can be depicted on the CE plane. For comparison between two interventions, the CE plane is divisible into four quadrants, revisiting the Cartesian plane from algebra. In quadrant I (0–908), the new intervention is more costly than the comparator, but more effective. Quadrants II (908–1808) and III (1808–2708) represent suboptimal states where the new intervention is more costly and less effective than the comparator (quadrant II), or less costly and less effective (quadrant III). A result plotted in quadrant IV indicates that the new intervention is more effective and less costly than the comparator, often referred to as “dominant” (Gray et al., 2010). However, most new interventions that are subject to cost-effectiveness analyses are more costly than standard of care and also more effective, placing them squarely in quadrant I. As an incremental cost-effectiveness ratio landing in quadrant I could show nearly infinite incremental cost for very little incremental beneficial effect over the comparator, determination of actual cost-effectiveness is dependent on the willingness-to-pay threshold for the study audience. This is largely based on precedent, but may also be tempered with more subjective claims. For cost– utility analyses, the expense of dialysis, which costs approximately $50,000/QALY or approximately £30,000/QALY are commonly used thresholds (Drummond et al., 2005). Items that are more costly per QALY mentioned above, these thresholds exceed the willingness-topay for society and are not deemed cost-effective. For cost–event analyses, the precedent of willingness-to-pay for other comparable outcomes is necessary (Fig. 4).
COST-EFFECTIVENESS AND SPINAL SURGERY INTRAOPERATIVE NEUROPHYSIOLOGICAL MONITORING Intraoperative neurophysiological monitoring in spinal surgeries has several features that make comparative effectiveness techniques attractive. The intervention (IONM) occurs at a single point in time and has both immediate (postoperative neurologic complications) and long-term consequences (paralysis and early death). There is no obvious comparator as an adjunct to spine surgery in lieu of IONM, so comparisons are made to surgery without IONM. Center for Medicare and Medicaid Studies (CMS) reimbursement for IONM Current Procedure Terminology-4 codes can be used to determine per-study
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reimbursement (Physician Fee Schedule, 2010). The rate of neurologic complications, paralysis, and other outcomes can be determined from locally available data, or from meta-analysis of large case series. The direct and indirect health care costs for spinal cord injury (SCI) are published in the United States by the National Center for Spinal Cord Injury Statistics in Birmingham, AL (National Center for Spinal Cord Injury Statistics, 2010). There are several studies that address cost-effectiveness of IONM in spine surgery (Table 1). These are based on either singlehospital case series or hypothetical evidence-based models. Ayoub et al. (2010) published a case series of 210 patients in 1 year who had spinal surgeries and Somatosensory Evoked Potentials (SSEP) monitoring. SSEPs were lost and then recovered in three patients after the surgical team was alerted and took appropriate action. The authors assumed that in the absence of SSEP monitoring, the institution would have had at least one patient with a persistent SCI. They derived postoperative costs of neurologic injury from the sum of hospital-based immediate costs and annual age-categorized SCI costs from published data. Costs of SSEP monitoring, including added time for the surgical procedure, cost of neurologist consultation, and costs of total intravenous anesthesia, were estimated at $835. They estimated the cost savings to the hospital as $64,075 to $102,193 in annual health care costs including the costs of $31,564 in summed yearly SSEP monitoring. Although the study suggests using a lifetime time horizon for the determination of costs, the author reports patients’ direct health care costs for 1 year only, likely because of convenience of calculation for their single hypothetically injured patient per year (without monitoring). Traynelis et al. (2012) published a case series of 720 patients undergoing routine cervical spine surgeries collected over 6 years at the University of Iowa Hospitals, none of whom had intraoperative monitoring. The rate of neurologic complications was 0.4%, but the authors note that no neurologic deficit was present on follow-up at 1 year or more. Accompanying the case series was an economic analysis consisting of the reimbursement cost of IONM based on the CMS Current Procedure Terminology-4 fee schedule reimbursement for 2011 multiplied by the number of patients in the series, concluding that the hospital system saved more than a million dollars in IONM costs. The study by Traynelis et al. (2012) was problematic from several vantage points. The exclusion criteria were trauma- or
FIG. 4. Average versus incremental cost-effectiveness. Shows two interventions (A and B) on generic cost and effectiveness scales, where intervention A costs 2 units and is 20% more effective than intervention B, which only costs 1 unit. The average costeffectiveness ratio (ACER) shows a moderate increase in cost per beneficial health effect for intervention B over intervention A, but the incremental cost-effectiveness ratio (ICER) shows 10 times the cost per unit gain in effectiveness. Copyright Ó 2014 by the American Clinical Neurophysiology Society
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Ney (2012) Ney (2013)
Traynelis 2012
IONM, intraoperative neurophysiological monitoring; PSA, probabilistic sensitivity analysis.
One-way, (level of injury) SSEPs only, single hypothetical SCI One-way, (level of injury) Direct health care costs only, scoliosis at age 25 base case Cost–benefit 6.5 years Hospital No IONM saved .$1 million/6.5 years None Descriptive extrapolation of case series with no persistent SCI Cost–event Immediate postop Patient/physician IONM costs $62k/neuro complication averted PSA Multimodal IONM, injury rate approximately 5% Cost–benefit Lifetime Societal IONM cost saving $23k direct/indirect PSA Multimodal IONM, injury rate approximately 5% health costs IONM saves $64k to 102k/year IONM cost-neutral at #$977/operation Hospital Societal Cost–benefit Cost–benefit Ayoub (2012) Sala (2006)
1 year Lifetime
Reported Outcome Perspective Time Horizon Type of Analysis Lead Author (Year)
TABLE 1.
Publications Addressing Cost-Effectiveness of IONM in Spinal Surgeries
Sensitivity Analysis
Notes
J. P. Ney and D. N. van der Goes
tumor-related surgeries, but it is unclear if there was selection bias in nonmonitoring of the selected surgeries. The authors are fortunate that they had no lasting neurologic deficits in retrospective analysis, but their experience may not have been representative of routine cervical spine surgeries nationally. Persistent neurologic deficits of paraparesis or tetraparesis were seen in 0.3% of 4075 patients at the University of Cleveland Hospitals reported by Cramer et al. For a sample to be adequately powered to support the null hypothesis given, an expected complication rate of 0.3% over 1100 patients would be needed for a study with 90% power and alpha of 0.05 (Ney and van der Goes, 2013). In their defense, the authors provided a purely descriptive analysis without the significance testing of results from this case series. Apart from single center studies, several authors have constructed cost-effectiveness models based on the available published evidence. Sala et al. (2007) determined that for a base case paraplegia rate of 0.1% in scoliosis surgery for a 25 year old and using lifetime cost data from the NSCISC, that IONM would be cost saving if it is less than $977 (2006 USD) per surgery. As a sensitivity analysis, the authors calculated maximum benefit in preventing high tetraplegias where cost of IONM outweighed cost of SCI at less than $2,924 (2006 USD). Sala performed an elegant form of cost–benefit analysis, with a limited base case of a 25-year-old patient undergoing scoliosis surgery. The availability of lifetime summed direct health care costs for varying degrees of SCI made this an attractive model. Indirect health care costs and quality of life information were not used to compile this model, and the estimates were not recalculated for non-scoliosis surgeries or persons older than 25 years. The calculations of Sala et al. (2007) also assumed a 100% rate of prevention of injury given an intraoperative monitoring alert. In two successive studies, Ney et al. built hypothetical models for the use of multimodal IONM in spinal surgeries. Using pooled sensitivity, specificity, and rate of injury estimates for multimodal IONM (MEP and SEP) from articles cited in a systematic review by Fehlings et al. (2010), and basing rate of injury prevention given IONM alert from the quasi-controlled study published by Wiedemayer et al. (2002). The outcome of interest was the presence of neurologic complications, and results were based on a cost per neurologic complication avoided. Based on 2009 CMS reimbursement of $1,535 for multimodal IONM, a sensitivity of 94%, specificity of 96%, prevention rate given IONM alert of 52%, and an injury rate of 5%, the cost per neurologic complication prevented was US $63,387 in 2009 (Ney et al., 2012). A Monte Carlo simulation with 10,000 replications provided a 95% confidence interval of $61,939 to $64,836. For comparison purposes, the cost of an asymptomatic carotid ultrasound surveillance program was cited at $290,000 per neurologic injury (stroke) avoided (Cull et al., 2011). Ney et al. (2013) expanded on this cost–event model to extrapolate costs from the immediate postoperative period to a lifetime time horizon using a cost–benefit approach. Using NSCISC data, the base case for direct health care costs of a 50-year-old person with any-level incomplete motor paresis was constructed. Indirect costs in the form of lost wages and benefits were determined based on the formula developed by Miller et al. (1987) for labor-related losses after automobile injury. Monte Carlo simulation with 100,000 replications used a high tetraplegic cord complete cord injury incurred at age 25 as the upper limit of 95% confidence intervals for log-normal distribution costs, with a $100,000 lifetime injury cost as the lower limit. The same IONM diagnostic characteristics, prevention rate given IONM alert, and CMS reimbursement rates were used as per the previous study. The authors calculated that IONM saved $23,189 per surgery for the base case, and remained cost saving when neurologic complication Copyright Ó 2014 by the American Clinical Neurophysiology Society
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rate from surgery exceeded 0.3% and prevention rate after IONM alert was greater than 14.2%. These last two models are limited by the evidence at hand. In particular, they are reliant on the single study by Wiedemayer et al. (2002) wherein all patients who had an IONM alert, which was not acted on (42/42), developed a neurologic deficit, whereas 52.4% (22/ 42) of those where an alert spurred the surgical team to act to prevent injury awoke without new neurologic complications. The study also suggests that acting on the alerts may not have been possible in a number of cases in the control group, suggesting a prevention rate as low as 26%. The cases also included intracranial procedures and tumors and were selected from among more than 15,000 cases and therefore likely not representative of routine spinal surgeries. The other major criticism is that the rate of postoperative neurologic complications is simply too high at 5% (based on pooled metaanalysis of spine surgeries using multimodal IONM) and does not represent the neurologic complication rate of routine spine surgery purported to be less than 1% (Nasser et al., 2010). Despite these limitations, the studies above provide a foundation for further analysis. Given patient-reported quality of life data regarding SCI, the model could be easily modified to a cost–utility analysis reporting cost per quality-adjusted life years. Using Markov modeling, reported lifetime costs would need to be adjusted to provide an estimate of the direct and indirect costs for relevant health states for 1 year or smaller cycle length, then summed for all cycles. The results would be relevant for agencies such as NICE in the United Kingdom or CMS as they continue to evaluate the costs for purported health benefits across a wide range of interventions and disease states. Ultimately, any model should be transparent in its methodology, with parameters based on the best available information, and subject to revision as stronger evidence displaces weaker data. Model validation should be performed because more real-world data are made available. The availability of large observational data sets may help to validate existing CEA models, but should also be carefully examined for biases, particularly selection of IONM and determination of appropriate control groups in cross-sectional analysis. REFERENCES Ayoub C, Zreik T, Sawaya R, et al. Significance and cost-effectiveness of somatosensory evoked potential monitoring in cervical spine surgery. Neurol India 2010;58:424–428. Brazier J, Ratcliffe J, Salomon JA, Tsuchiya A. Measuring and valuing health benefits for economic evaluation. Oxford: Oxford University Press, 2007. Briggs A, Sculpher M. An Introduction to Markov modelling for economic evaluation. Pharmacoeconomics 1998;13:397–409.
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Briggs AH, Goeree R, Blackhouse G, O’Brien BJ. Probabilistic analysis of costeffectiveness models: choosing between treatment strategies for gastroesophageal reflux disease. Med Decis Making 2002;22:290–308. Briggs A, Claxton K, Sculpher M. Decision modelling for health care evaluation. New York: Oxford University Press, 2008. Cramer DE, Maher PC, Pettigrew DB, Kuntz CT. Major neurologic deficit immediately after adult spinal surgery: incidence and etiology over 10 years at a single training institution. J Spinal Disord Tech. 2009;22:565–570. Cull DL, Cole T, Miller B, et al. The value of a carotid duplex surveillance program for stroke prevention. Ann Vasc Surg 2011;25:887–894. Drummond MF, Sculpher MJ, Torrance GW, et al. Methods for the economic evaluation of health care programmes. Oxford: Oxford University Press, 2005. Fehlings MG, Brodke DS, Norvell DC, Dettori JR. The evidence for intraoperative neurophysiological monitoring in spine surgery: does it make a difference? Spine (Phila Pa 1976) 2010;35:S37–S46. Glick HA, Doshi JA, Sonnad SS, Polsky D. Economic evaluation in clinical trials. Oxford: Oxford University Press, 2007. Gold MR, Siegel JE, Russell LB, Weinstein MC. Cost-effectiveness in health and medicine. New York: Oxford University Press, 1996. Gray A, Clarke P, Wolstenholme J, Wordsworth S. Applied methods of cost-effectiveness analysis in healthcare. Oxford: Oxford University Press, 2010. Miller T, Hoskin A, Yalung-Mathews D. A procedure for annually estimating wage losses due to accidents in the U.S. J Saf Res 1987;18:101–119. Nasser R, Yadla S, Maltenfort MG, et al. Complications in spine surgery. J Neurosurg Spine 2010;13:144–157. National Center for Spinal Cord Injury Statistics. Spinal cord injury, facts and figures at a glance. 2010. Available at: https://www.nscisc.uab.edu/. Accessed December 18, 2010. Ney JP, van der Goes DN. Letter to the editor: cervical decompression. J Neurosurg Spine 2013;19:523–525. Ney JP, van der Goes DN, Watanabe JH. Cost-effectiveness of intraoperative neurophysiological monitoring for spinal surgeries: beginning steps. Clin Neurophysiol 2012;123:1705–1707. Ney JP, van der Goes DN, Watanabe JH. Cost-benefit analysis: intraoperative neurophysiological monitoring in spinal surgeries. J Clin Neurophysiol 2013;30:280–286. Nuwer MR, Emerson RG, Galloway G, et al. Evidence-based guideline update: intraoperative spinal monitoring with somatosensory and transcranial electrical motor evoked potentials. Neurology 2012;78:585–589. Pettiti DB. Meta-analysis, decision analysis, and cost-effectiveness analysis. New York: Oxford University Press, 2000. Philips Z, Bojke L, Sculpher M, et al. Good practice guidelines for decision-analytic modelling in health technology assessment: a review and consolidation of quality assessment. Pharmacoeconomics 2006;24:355–371. Physician Fee Schedule, by HCPCS Code. Available at: www.cms.gov. Accessed December 18, 2010. Sala F, Dvorak J, Faccioli F. Cost effectiveness of multimodal intraoperative monitoring during spine surgery. Eur Spine J 2007;16(suppl 2): S229–S231. Traynelis VC, Abode-Iyamah KO, Leick KM, et al. Cervical decompression and reconstruction without intraoperative neurophysiological monitoring. J Neurosurg Spine 2012;16:107–113. Wiedemayer H, Fauser B, Sandalcioglu IE, et al. The impact of neurophysiological intraoperative monitoring on surgical decisions: a critical analysis of 423 cases. J Neurosurg 2002;96:255–262.
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