Abstract
In orthopaedic surgery, clinical decisions must often be made with imperfect information from observational studies and limited resources. Decision analysis and cost-effectiveness analysis have emerged as evidence-based tools to assist in making choices in situations in which uncertainty exists. This review demonstrates how decision-analysis and cost-effectiveness-analysis tools can be used to expand on published observational studies within the context of a specific clinical scenario. Critical evaluation of clinical and economic data is of increasing importance in today's health-care delivery climate. The use of decision analysis and cost-effectiveness analysis as tools to augment observational studies can assist clinicians, patients, and policy makers in choosing techniques that will optimize benefits. A clear understanding of and the ability to use and apply these tools will allow surgeons to participate effectively in health-policy decisions to enhance the overall quality and efficiency of care that is delivered.
The term evidence-based medicine came into use in clinical medicine in the 1980s and has become a common term in the medical literature1-3. Sackett et al. and others have emphasized levels of evidence and patient-centered outcomes assessment to promote careful application of diagnostic and therapeutic technologies in the management of patients4-9. Increasingly, the focus is on the effectiveness and more recently the efficiency and cost-effectiveness of clinical care10-18. Physicians, patients, health-care managers, and policy makers are paying more attention to synthesizing the best evidence from health-care research to provide guidance for the treatment of typical patients in practice.
Study design is important in assessing the strength of available evidence, and the strength of available evidence drives the robustness of the resulting recommendations3,5,16,19,20. Analytic studies include observational studies (cohort, case-control, and cross-sectional studies) and experimental studies (randomized and nonrandomized clinical trials). In observational studies, researchers observe patient groups without allocating the treatment(s). These studies may be retrospective, meaning that the events occurred before the study began, or prospective, meaning that the events occur after the onset of the study. Cross-sectional studies survey a defined population at a single point in time, and longitudinal studies follow a defined population across multiple points in time5.
Randomized controlled trials have long been the so-called gold standard in evaluations of evidence-based medicine, mainly because randomization is the best way to minimize the effect of bias in a therapeutic study6,16,19,21,22. However, surgery presents some unique issues with regard to the ability to perform randomized trials. Randomized trials related to surgical procedures are challenging to design and perform. The following are matters of concern: surgeon skill set, the training required to change a technique, and the need for specialty training as compared with general training. The randomization of patients to certain types of risk factors and treatments is cause for ethical concern19,23. In addition, the sample size may be too large or the follow-up too long to make a randomized controlled trial logistically or economically feasible. As a result of these limitations of randomized trials, a large amount of evidence in relation to orthopaedic surgical procedures has been, and will continue to be, accumulated through nonrandomized studies24. Certain questions can be answered in study designs other than randomized controlled trials, which yield the highest level of evidence25. Although observational studies have often been considered to be inferior to randomized controlled trials for the purpose of evaluating the efficacy of therapeutic interventions, sometimes observational studies can be used to generate hypotheses or to identify where the greatest amount of uncertainty exists within a treatment algorithm19,25.
The primary goal of clinical research is to provide accurate answers to clinically relevant questions through the use of methods that will allow as much application as possible in real-life situations. In orthopaedic surgery, clinical decisions must often be made under circumstances of uncertainty, with information from observational studies at best and with limited resources. Decision analysis and cost-effectiveness analysis have emerged as evidence-based tools that can be used to assist in making choices in situations in which uncertainty exists. These tools are now being used in an expanded capacity for the assessment of difficult choices in the field of health technology and to help generate treatment algorithms or guidelines10,15,26,27.
This review will provide a brief overview of decision analysis and cost-effectiveness analysis within the context of a clinical example: pedicle screw fixation for spinal deformity in patients with adolescent idiopathic scoliosis. We will further attempt to demonstrate how decision analysis and cost-effectiveness analysis can be used to expand on existing knowledge gained from observational studies in a situation in which a randomized trial would be difficult or impossible to conduct.
Decision analysis was initially developed as a tool to help businesses find the most profitable option among a series of strategic alternatives. However, it has been gathering momentum in health-care research and has been used increasingly in orthopaedic surgery15,27-31. The technique can be applied at the level of individual patient evaluation or at the level of population health. The goal is to quantitatively analyze information and optimize decision-making to provide the most benefit for patient and population health with a finite amount of resources.
The first step in a decision analysis is to develop a model. Usually this is in the form of a decision tree, with the choice under consideration at the base of the tree. All clinically relevant outcomes resulting from this choice are entered into the tree at successive branches. Developers should explicitly describe which outcomes, both beneficial and harmful, are considered important and what value they place on them. This process will reveal specific questions and identify evidence gaps that demand additional research. By convention, the tree is built from left to right, and the points at which branches are formed are named "nodes." A decision node branching to all possible treatment options marks the beginning of the tree. Chance nodes represent situations in which there is uncertainty about the outcome; for example, a complication may or may not occur or a patient may or may not die (Fig. 1). Each branch has a specific probability of the outcome occurring. These outcomes should come from the best available evidence (experimental or observational trials, registries, claims databases, and case studies), as found through a systematic review of the literature. This may be a search already developed by others, such as the Cochrane Collaboration (), or, if a search has not previously been performed, it may be an organized literature search done specifically for the identified question. Specific criteria should be developed for including evidence, and a standard consideration should be done of study quality. Quality evaluation should include a detailed examination of the design and execution of the study and the resulting evidence. Inclusion and exclusion criteria for studies should be established pre hoc to minimize bias and confounding variables. Even if only observational studies are available, within this group a difference exists between a well-designed case-control study and a small case series. Once potential studies are identified by a search of computerized databases, the articles identified should be reviewed by multiple investigators to determine whether they meet the inclusion criteria. The review of the literature should identify any available patient outcomes for the defined health states as well as identify the probability that each of the outcomes may occur; then, once again, some gaps in the original decision tree may be highlighted and the tree may be revised. When possible, a summary statistic of the articles should be calculated with use of a meta-analytic type of technique, although the lack of sufficient data often precludes this calculation. If the latter situation exists, an explanation needs to be provided as to the reason for choosing the point estimate that was used, and an appropriate clinically relevant range should be provided to reinforce the uncertainty in the estimate. In the absence of peer-reviewed evidence, it may be necessary to rely on the consensus of experts for certain variables.
Determining health outcomes can often be a difficult process. Objective measures of health outcome, such as life years gained or infections successfully treated, have the advantage of objectivity but fail to incorporate the value of such outcomes from a patient or societal perspective. Many argue that such preferences need to be considered, because a year of life spent in a particular health state may be preferred to one spent in another state; for example, a year in perfect health is valued by patients differently than a year with a chronic disability13,32-34. Incorporating patient or societal preferences into an analysis is one of the most challenging issues in decision and economic analyses.
Ideally, we would like to measure benefit in quality-adjusted life years33-35. This would be the health benefit reported as an overall global measure of health-related quality of life, or a utility. Utility states are measures of an individual's or society's preference for a particular set of health outcomes. Typically, this is a value determined by patients or their proxies on a scale from zero to one (with zero indicating death and one indicating perfect health). Several different methods have been used to generate utilities for health states in the orthopaedic literature27. The most common technique is to use clinical judgment without any other formal methodology; however, often a generic tool, such as the EuroQol36 or the Health Utilities Index37,38, is applied. Time trade-off, standard gamble, and visual analog scale techniques, the details of which are beyond the scope of this discussion, have also been used to assign utility to specific health states39. Increasingly, surgeons are using crosswalk techniques to transform the Short Form-36 into a utility estimate27,40,41. It is most often this portion of the analysis, along with the statistical evaluation, that will require a multidisciplinary approach.
Once the input variables have been defined, the next step in determining the optimal treatment path is a process of so-called "folding back the tree." Software is used to multiply the outcomes by the probabilities back through the tree to choose the optimum pathway. The basic steps are to multiply the utility of each outcome with its probability. An expected value is calculated for each branch, and rational decision-making favors the decision with the highest expected value.
In the final step of decision analysis, sensitivity analysis should be used to vary the inputs, and this should be done on the basis of confidence intervals from clinical trials, if available. The range of values considered in the sensitivity analysis should be greater in cases where limited evidence exists regarding clinical outcome probabilities. For example, in a recent cost-effectiveness analysis exploring alternate bearing surfaces in total hip arthroplasty, osteoarthritis that was present before the patient underwent primary total hip arthroplasty was assigned a utility value of 0.515. The authors considered a relatively wide range of values (0.32 to 0.85) on the basis of their review of the literature. In addition, three-way sensitivity analysis was performed to investigate the relationship between incremental implant cost, patient age, and the reduction in twenty-year failure rates on lifetime cost savings15.
Rules for building trees and guidelines for their appropriate appraisal have been published in greater detail elsewhere42-47.
Cost-effectiveness analysis is performed to assess the value of one health intervention compared with another. It is often an extension of decision analysis in that it not only explores the added health benefit from one intervention compared with an alternative therapy, but it also explores the incremental cost. The type of cost-effectiveness analysis that reports benefit as quality-adjusted life years is often referred to as cost-utility analysis and presents the results as a ratio of the incremental costs over the incremental benefit, or as cost per quality-adjusted life years gained. Many other methodological recommendations exist, most aimed at creating a reference case that can be compared between studies33,34,48,49.
Cost-effectiveness analysis is often an extension of the process for decision analysis; the added step from decision analysis is the identification and valuing of the costs30,33,34,48-50. Appropriate costs to include depend on the perspective of the study. If the perspective is that of the health-care payer, then only direct costs (the goods, services, and other resources consumed in the provision of care) need to be included. However, it is recommended that the analyst assumes a societal perspective, which includes the indirect costs (costs to the patient or family to partake in the care, such as the cost of taking time off from work or the cost of transportation) as well. After all of the important costs have been identified, the model needs to be evaluated to determine if all economically relevant outcomes have been included. Once this has been comprehensively established, the costs need to be valued. Charges need to be converted to a cost estimate. When costs are from different years, the data should be adjusted for inflation so that all costs are measured in current dollars. In addition, costs are usually discounted with time. When people borrow money, they pay interest payments, and when they lend or save money, they receive interest payments. Thus, a dollar in the future is worth less than a dollar today. In order to make a valid comparison between the costs and benefits of a program, future costs must be adjusted back to their present values51,52. If benefits are not discounted, then simply delaying interventions would make them appear economically more attractive53. The current standard is to discount both costs and benefits at a rate of 5% per year33,34.
The purpose of the example below is to illustrate the process of decision analysis and cost-effectiveness analysis alongside observational studies with use of the example of pedicle screw fixation for the surgical treatment of adolescent idiopathic scoliosis. As with many areas of orthopaedic surgery, application of evidence-based medicine analysis to spinal instrumentation is challenging. Spinal surgical techniques have evolved rapidly, and changes have not been limited to specific instrumentation techniques. The number of surgeons with experience is small due to the complexity of surgery for spinal deformity and the extensive training required to safely manage this problem. Much of the variation in practice may be due to variations in skill, experience, and patient population. Still, in a health-care system with limited resources, it is important to determine whether or not the widespread adoption of this technique has led to improved patient outcomes, as the implants are associated with a considerable increase in cost. We have used decision-analysis and cost-effectiveness techniques to improve our understanding and to add to previously published information.
Question
Are pedicle screw constructs cost-effective in the treatment of adolescent idiopathic scoliosis?
Model
The model (Fig. 1) compares two choices: (1) a pedicle screw construct, or (2) the traditional technique involving hooks and/or wires for deformity correction in patients with adolescent idiopathic scoliosis. Since both techniques are assumed to have equivalent anesthesia, positioning, monitoring, and intraoperative imaging and mortality, these factors are no longer considered in the model. Pedicle screw constructs often require an additional computerized tomography scan preoperatively to improve imaging of the pedicles; this is included in the cost of the pedicle screw treatment branch. Intraoperatively or postoperatively, the patient may or may not experience a complication, and, of the complications observed, the one likely to be the most severe with the largest health and cost effects is a neurological injury resulting in a deficit. For this reason, complications were subdivided.
Literature Search and Probabilities Used in the Model
We used a recent systematic review that examined an evidence-based medicine analysis of pedicle screw constructs in adolescent idiopathic scoliosis54. This review searched Ovid Medline and PubMed with use of combinations of the terms pedicle screws, adolescent idiopathic scoliosis, child, scoliosis, and spine. Each article was screened by abstract, and the reference lists of the major papers were searched to identify any new articles missed in the initial search. Forty articles were identified that met the inclusion criteria, all of which were observational studies. The overall complication rate associated with pedicle screw instrumentation ranged from 0% to 25%. The most common complication was malposition of a screw, reported to be from 1.5% to 10.4%. The prevalence of neurological injury was reported as small; however, this was believed to be subject to reporting bias. Mulpuri et al. did not report the complication rate associated with other constructs; however, a review of their selected articles gave estimates ranging from 0% to 14%54. The two-year postoperative outcomes were only available from five studies. Only one paper noted that those with pedicle screws had better scores on postoperative self-image and function questionnaires. The reported added benefits of the screw constructs were an improvement in apical vertebral translation, an improvement in tilt with the placement of instrumentation on the lower instrumented vertebrae, and an average savings of 1.1 motion segments. Although, theoretically, the importance of these improvements may be substantial, this importance has not yet been demonstrated in the literature. The probabilities of transition between clinical states were taken from this review (Table I). Ideally this information should be supplemented with additional literature searches that may identify useful outcome data from studies that did not meet the inclusion criteria for the initial study. In addition, expert opinion can be sought to determine if there is a reporting bias in the literature and to determine a more realistic estimate of the rate of neurological injury. For costs, surgical intervention with use of screw constructs had an additional cost of $1711 (U.S. dollars), on the average. In order to estimate the utility of spinal cord injury and the associated costs, the costs used in this analysis were drawn from the values identified in the review by Mulpuri et al. and a published cost-effectiveness analysis of cervical spine injuries54,55. To add strength to this analysis, a retrospective cost-identification cohort design could be conducted to collect and analyze economic data associated with consecutive spinal instrumentation and fixation procedures. This would involve looking retrospectively, over a specific time period, at the patients who were treated for adolescent idiopathic scoliosis at an institution, and using the costs incurred by these patients again over a specified time period. Often, the values that are provided from institutions will need careful analysis to determine if they need to be converted from charges to costs. If so, a description of how this can be accomplished, along with a breakdown of what is included in the values, may be needed, as the value of all resources consumed as part of the treatment should be included in the analysis.
We used the decision-analysis software DATA Pro (TreeAge Software, Williamstown, Massachusetts) to analyze the model. With use of the available values from our focused literature search, we found that the use of pedicle screw constructs in spinal surgery for the treatment of adolescent idiopathic scoliosis is associated with an increased cost (average $1700, 2002 U.S. dollars) and a decreased benefit. On the average, hook-and-wire constructs provide 0.0004 quality-adjusted life years more than those provided by pedicle screw constructs. This result of a higher cost and a lower benefit is described in health-care economic terminology as being dominated; hook-and-wire construct surgery dominates pedicle screw fixation in the surgical correction of adolescent idiopathic scoliosis.
The results were not sensitive to one-way and two-way analysis of the complication rate, spinal cord injury rate, or costs. Only when the complication rate that was associated with hook-and-wire constructs was 5% higher than that associated with pedicle screw fixation was pedicle screw fixation deemed to be more beneficial (an incremental benefit of 0.00001 quality-adjusted life years), and it remained more expensive.
If we are to use $50,000 (U.S. dollars) per quality-adjusted life year gained as a value that society is willing to pay, then, in this analysis, pedicle screw constructs would have to be associated with an overall improvement of 0.034 quality-adjusted life years, or twelve quality-adjusted days, in order to be considered more cost-effective than hook-and-wire constructs.
In this example, we have provided a very simple model to demonstrate how the tools of decision analysis and cost-effectiveness analysis can be used alongside observational studies to inform clinical and policy decision-making. Ideally, this exercise would involve a more detailed literature review and some further elicitation of quality of life; however, even with this simple exercise, much useful information can be extracted.
Using information from a systematic review of the currently available literature, we determined that pedicle screw constructs are more expensive and provide no clear clinical benefit over hook-and-wire constructs. However, our current outcome instruments may not be sensitive enough to detect a difference in quality of life between patients. This analysis highlights the fact that the added incremental benefit in terms of quality-adjusted life years gained over the lifetime of the patient could be relatively small, and still justify the increased costs involved. Future research on adolescent idiopathic scoliosis surgery should include better information on patient utility or preference. While prospective use of such a tool will further identify the limitations of these patients, the differences between the groups will likely be small and not easily reflected through utility elicitation. The process, however, could begin with the use of information from focus groups of patients, their proxies, and expert panels to identify the aspects of scoliosis surgery that are most important. Longer term follow-up of these patients is also needed to determine the benefit of operating on fewer vertebral levels and of improving curve correction.
Another issue that affects the relevance of this cost-effectiveness analysis is that the information from the literature may be too general; thus, researchers will need to subdivide patients depending on the type and location of the spinal curve. Many experts may believe that some curves could be treated with hook-and-wire constructs; however, they may select pedicle screws in these situations simply to maintain their skills.
While the results of our study were not sensitive to our range of published complication rates, it appears likely that a bias exists toward not reporting such catastrophic complications as spinal cord injury. In addition, the complications that have been reported are from major centers where large numbers of surgeries are performed. Institutions that operate on a smaller volume of patients with scoliosis may have higher rates of complications. The development of a central scoliosis registry that allows surgeons to report their outcomes and complications may be useful in tracking changes in these rates over time.
It needs to be stressed that, while this analysis favors hook-and-wire constructs, there are many limitations in the data. It would be inappropriate to use this information to influence clinical practice. What this exercise can do is highlight the areas in which further research is needed.
Many factors have led to an increasing focus on evidence-based medicine in orthopaedic surgery2,3,56,57. In orthopaedic surgery, the reality may be that randomized trials, if they are feasible, will take years to complete and cost millions of dollars to perform. By the time results are available, medicine may have moved on to other pertinent standards of care. The use of decision analysis and cost-effectiveness analysis as tools to augment observational studies can assist clinicians, patients, and policy makers in choosing among treatment options. One added benefit to using models to look at observational studies is that the process will often highlight the greatest areas of uncertainty. This information can be used to focus future research questions so as to produce the most valuable results that will have the greatest impact on clinical care. The process can also estimate both the population size and the time line needed to perform a randomized trial, which could be useful in designing better studies that will make the most efficient use of scarce health-care resources.
Critical evaluation of clinical and economic data is of increasing importance in today's health-care delivery climate. Orthopaedic surgeons are increasingly being asked to play a role in health-technology assessment. With rising health-care costs, there is a growing recognition that health-care resources should be managed more efficiently. A clear understanding of and the ability to use and apply decision-analysis techniques will allow surgeons to participate effectively in health-policy decisions to enhance the overall quality and efficiency of care that is delivered. 
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