Chapter 2. Assessing Cost Effectiveness in the Intensive Care Unit

• The care of critically ill patients in the modern ICU results in a large societal burden in terms of both manpower and monetary cost.
• The high cost of critical care can largely be attributed to high overhead costs (e.g., need for experienced staff and expensive equipment), high resource utilization (e.g., drugs, lab tests, and complex imaging procedures), and an ever-growing demand for ICU services.
• Complex economic analysis of health care decision making can be reduced to two fundamental questions that need to be answered: First, “Is a therapy worth using when compared with the alternatives?" The second question is broader and asks “Should a portion of available health care resources be allocated to a given therapy or program?”
• Routine incorporation of Cost Effectiveness studies is important to the conduct of high quality clinical trials research in the intensive care unit. Inclusion of cost effectiveness gives the clinician the opportunity to fully assess the effect of a new therapy in the ICU.

The care of critically ill patients in the modern ICU results in a large societal burden in terms of both manpower and monetary cost. The high cost of critical care can largely be attributed to: high overhead costs (e.g., need for experienced staff and expensive equipment), high resource utilization (e.g., drugs, lab tests, and complex imaging procedures), and an ever-growing demand for ICU services. With the continuing rise in health care costs, there is an ever-increasing need to establish whether new therapies are not only efficacious, but also cost effective. The need for cost-effectiveness evaluations is spread throughout medicine, but the issue of cost effectiveness is especially important in critical care medicine. While ICU beds account for only about 10% of all inpatient hospital beds, ICU costs in the U.S. exceed $60 billion annually, and consume up to one third of all hospital costs.1 Furthermore, attempts to reduce ICU costs by other mechanisms, such as reduction in length of stay, have proved to be difficult.2 While access to new therapies is being tied increasingly to cost, physicians continue to be skeptical and suspicious of cost analyses. In large part, this skepticism is prompted by the variable quality of earlier cost analyses. However, skepticism is also due to the lack of familiarity many physicians have with the general principles of health economics. The U.S. Public Health Service (USPHS) attempted to set standards for the conduct of rigorous cost-effectiveness analyses in medicine.3–5 In critical care medicine, the American Thoracic Society (ATS) established specific guidelines for the conduct of cost-effectiveness analyses based on the USPHS recommendations.6 These standards have the potential to greatly improve the quality of future cost-effectiveness studies. However, if clinicians do not understand and embrace the principles of cost effectiveness, it is likely that such studies will continue to be viewed simply as ammunition for nonclinician administrators in the battle to restrict physician freedom of choice and to control clinical decision making.

In this chapter, we will cover many of the principal aspects of cost-effectiveness analyses and consider how such studies ought to be conducted on potential new therapies. We will use drotrecogin alfa (activated) as a working example of the type of therapy often utilized in the modern ICU that is expensive, but has potentially life-saving effects. The overall goal of this chapter is to encourage all of us to familiarize ourselves with cost analyses and to consider cost-effectiveness analysis as simply another tool in our evaluation toolkit, along with case reports, animal studies, and randomized clinical trials of potential new therapies. We have previously reviewed this topic,7 and here will further describe the utility of economic analyses in critical care. For a more detailed in-depth discussion of economic analysis in health care, the reader is referred to the excellent text by Gold and associates.8

One can compress all of health economics down to two main questions. First, “Is a therapy worth using when compared with alternatives?” For example, what is the worth of a new antisepsis agent like drotrecogin alfa (activated) that has recently demonstrated a beneficial effect in a large randomized clinical trial? We can consider that worth represents some trade-off of cost and benefit (or effect). The second question is broader and asks “Should a portion of available health care resources be allocated to a given therapy or program?” This is principally a social policy issue and requires considering the worth of new therapies not only within a given disease, but also in comparison to other therapies in other diseases. For example, although drotrecogin alfa (activated) might be deemed worthwhile in the treatment of sepsis, a state Medicaid agency might be forced to compare its value to that of a hepatitis B vaccination program for newborns, or to influenza vaccinations for the elderly. These types of choices are becoming increasingly common in situations in which health care resources are limited and must be equitably distributed among multiple programs. In other words, we would wish to know if drotrecogin alfa (activated) is not only cost effective with respect to standard management for sepsis, but also whether we can afford it as a society.

There are a variety of different analytic approaches that address all or some part of these two questions. Though the approaches appear similar, and have similar names, there are key differences. In order to understand the strengths and weaknesses of each approach, we must first describe the various methodologies used to evaluate health care costs. There are essentially four types of cost analyses: cost minimization, cost benefit, cost effectiveness, and cost utility. The fourth, cost utility, is best viewed as a special case of cost effectiveness. In addition, there are situations in which a cost-effectiveness analysis can produce a cost-minimization statement.

Cost-Minimization Analysis

The cost studies that are most familiar to clinicians are cost minimization studies, and these consider only how much a drug costs to stock in the pharmacy. They are essentially studies of drug acquisition cost, and are most frequently conducted by hospital pharmacy departments. When comparing different products (e.g., two sulfonamides), each product is assumed to have equal efficacy and to equally impact all other aspects of treatment (although this may or may not be true). Drug effects such as shortened length of stay, reduced need for other therapies, and improved quality of life after illness are not considered in cost-minimization analyses. The preferred therapy is simply the one that costs the hospital less money per unit of treatment (e.g., per day of therapy, or per dose).

Additionally, a cost-minimization analysis can result when a formal cost-effectiveness analysis (CEA, see below) with sophisticated assessment of all potential changes in costs and effects between two programs demonstrates no difference in effect. However, in the situation in which there is no difference in effect, there may be significant differences in cost between the programs. This result does not produce a cost-effectiveness ratio (since one would be dividing the change in costs by zero), but does allow accurate assessment of the true differences in cost between two programs with comparable treatment effects. This explicit evaluation of costs and effects, as opposed to an assumption of no difference in effect, is in contrast to traditional cost-minimization studies done in the past.

Cost-Benefit Analysis

The term “cost benefit” is frequently confused with cost effectiveness.9 In fact, a cost-benefit analysis is a very specific analysis, rarely conducted today, that expresses all costs and effects in monetary units. This means that a dollar value must be placed on all effects. For example, a life saved must be converted into a monetary value. This conversion of life into economic terms can be problematic and very nonintuitive. After conversion of all effects into monetary units, one then adds up all the costs (expressed in dollars) and subtracts them from all the benefits (effects, expressed in dollars). If the final total is negative, the costs outweigh the benefits, and vice versa. Although the final output is attractive in its simplicity, the manipulations required to convert all effects into dollar values are often very controversial. Because of this controversy concerning the “value” of effects, this type of analysis has largely fallen out of favor for health economic evaluations.

Cost-Effectiveness Analysis

Cost effectiveness is simply a ratio of the net change in costs (dollars) associated with two different programs or therapies divided by the net change in effects (health outcome). The denominator represents the gain in health (life-years gained, number of additional survivors, or cases of disease averted), while the numerator reflects the cost (in dollars) of affecting the gain in health. Since the units used are different for the numerator and denominator, the typical cost-effectiveness expression will take a form such as cost (dollars) per year of life saved. Cost-effectiveness analyses are the current dominant form of cost evaluation, and were endorsed by both the USPHS Panel on Cost-Effectiveness in Health and Medicine (PCEHM), and more recently by the ATS as the primary method by which to measure the costs and effects of health care programs and medical therapies.4,6

Deciding whether a therapy is cost effective is a subjective evaluation of the cost-effectiveness ratio. If $100,000 per year of life gained is deemed the threshold for effectiveness, then a new therapy with a cost-effectiveness ratio of $82,000 per year of life gained is viewed as cost effective. Though there is no absolute cut-off, there is general consensus that a level somewhere between $50,000 and $100,000 per year of life gained is acceptable in the U.S. today. However, the way in which both costs and effects are calculated can have profound effects on the resulting ratio, and therein lies much of the controversy over CEA.

The typical CEA requires the collection of a significant amount of information on costs and effects, much of which may be gathered from widely varying sources. Interpretation of this information is often difficult, and a decision analysis model is usually constructed that mimics the key clinical decisions and events. This model can most easily be represented by a tree in which each branch point is calibrated with a probability of occurrence and a cost. At its simplest, the tree will contain only branches for treatment allocation (e.g., drotrecogin alfa [activated] or standard therapy) and outcome (e.g., alive or dead). To calibrate such a tree, we would need to know only the probability of living or dying, depending on whether a given patient received the new therapy or not, and the average cost of care for survivors and nonsurvivors in the two treatment arms (Fig. 2-1).

Alternatively, we may be interested in understanding the key events that drive either morbidity or cost (e.g., mechanical ventilation or hemodialysis). This could be important for a variety of reasons; there may be evidence that the study population has a far lower rate of mechanical ventilation than is expected for septic patients in general. Therefore, the extent to which differences in cost are the result of the number of patients undergoing mechanical ventilation may be important when estimating the cost effectiveness of the new therapy in the real world. Similarly, need for hemodialysis can be a significant cost driver. A new therapy that reduces the need for mechanical ventilation or hemodialysis may be expensive, but the cost of the therapy can be offset by the reduced need for supportive care, and hence deemed cost effective by CEA. In contrast, cost-benefit analysis would take into account only the expense of the new therapy, without considering the reduction in supportive care. However, considering changes in other care modalities leads to the addition of branch points for mechanical ventilation and hemodialysis, and thereby expands the model dramatically, creating 16 branches to consider. For each branch, we must know a patient's likelihood of entering such an arm and the average costs (Fig. 2-2).

Cost-Utility Analysis

A cost-utility analysis is a form of CEA in which the effects are converted into common units of utility. Typically, this approach involves adjusting the number of years of survival for the “quality” of that survival, in which a person living for one year with a quality-of-life score of 80% would be “awarded” 0.8 years of quality-adjusted survival. The advantage of this approach is that it allows comparison of different programs across different diseases. In this way we can compare the number of quality-adjusted life years (as opposed simply to the number of lives saved), and perhaps can more equitably compare drotrecogin alfa (activated) for critically ill adults to a hepatitis B vaccination program in newborns.

Good cost-effectiveness analysis design requires consideration of multiple elements in order to both adequately explore the relationship between costs and effects, and to determine the robustness of the conclusions and the comparability of the results to those of other studies. Each of these elements is outlined in Table 2-1 with reference to both the PCEHM and ATS guidelines, and discussed individually in more detail below.

Perspective

The costs considered in a CEA can vary depending on whose perspective is considered. As an example, consider the issue of early discharge from the hospital after childbirth. From the hospital's or managed care organization's perspective, cost may be reduced by early discharge. In contrast, from a societal perspective, the cost savings for the health care system may be offset by additional costs to the patient, such as extra time off work for the husband who must stay home to care for the new mother. Cost studies conducted to date have often been hampered by a lack of consistent perspective either within or among studies. Failure to maintain a consistent perspective hampers comparison of results across studies and threatens the validity of the study itself. Both the PCEHM and ATS recommend adoption of the societal perspective when conducting cost-effectiveness studies.

Outcomes (Effects)

This is an exceedingly difficult problem for CEA for a variety of reasons. First, information on outcomes usually comes from randomized clinical trials (RCTs), which often do not reflect the actual clinical practice of medicine. Conversely, the implications of a CEA are intended for real-world practice. A cost-effectiveness ratio is intended to capture the expected relationship between the costs incurred and the effects gained in actual practice. Conversely, an RCT is usually designed to maximize the likelihood of finding an effect. As such, an RCT can represent a rather idealized situation, which is quite distinctly different from the real world. For example, only specific patients may be selected, the dosage and timing of therapy will likely be optimized, and other aspects of care may be protocolized and carefully controlled. The effect size generated under such rigorous situations is termed a therapy's efficacy (or maximal effect). In the real world, the effect of a new therapy is likely diluted by less appropriate patient selection, changes in dosing and timing, and increased variability in other aspects of patient care. The effect of a new therapy under these real-world conditions is termed a therapy's effectiveness. The more RCTs are refined, the further removed they are from the reality of using a therapy in clinical practice.10 Thus, the relationship between cost and effect in some RCTs becomes increasingly distorted.

A cost analysis conducted using efficacy from an RCT might better be termed a cost-efficacy study, rather than a cost-effectiveness study. However, there are no clear guidelines on how to reduce the bias introduced by using efficacy data instead of effectiveness data. One possibility is to consider adding an open-label, open-enrollment arm to clinical trials in which a CEA is being conducted.11 However, this presents both many logistic and ethical problems. The more accepted alternative is to expose the cost model to varying estimates of reduced effect from those seen in the RCT during sensitivity analysis (see below).

Another problem encountered when determining effect or outcome is that the outcome measure evaluated in the RCT may not be directly relevant in the cost analysis. The PCEHM recommends, and the ATS agrees, that quality-adjusted life years (QALYs) be used as the units of effect, or utility. However, most RCTs in critical care use short-term (day 28 or hospital) mortality as the primary end point, and still others use indices such as “organ failure–free days” as outcome measures.12 Although short-term survival likely correlates with long-term quality-adjusted survival, the relationship is not explicitly clear. Whether there is any relationship between organ failure–free days and long-term quality of life is even less clear. A recent study by Clermont and associates showed that patients who develop acute organ dysfunction are at risk for poor long-term quality of life (QOL), but that the risk is largely due to poor baseline health status, and not directly to organ failure in the ICU.13

This problem is only slowly evolving. While the PCEHM recommends long-term outcome, a National Institutes of Health (NIH)–sponsored workshop on sepsis studies recommended day 28 mortality.12 More recently, the United Kingdom Medical Research Council workshop still recommended that day 28 mortality was an appropriate primary end point, but recommended follow-up to ≥90 days, and whenever possible to ≥6 months.14 Recent successful trials in sepsis reported mortality at widely varying time points: 28 days (drotrecogin alfa [activated]),15 28 days and one year (steroids),16 60 days (early-goal directed therapy),17 and a recent study of ARDS (Low Tidal Volume)18 reported mortality to 180 days.

Proponents of short-term outcome state that longer follow-up is too expensive and not necessarily related to the therapy being studied. Advocates of longer follow-up state that short-term survival, of indeterminate quality of life, and possibly with death a short time thereafter, is of little utility to society.8 They further argue that the ability to prioritize health care spending on the basis of value requires that we compare the long-term value of alternative programs for alternative disease processes. Many health care programs are administered, and/or have effects lasting, over a long period of time, making long term follow-up of patients enrolled in these programs essential.

There is currently relatively little long-term follow-up information on ICU patients. However, the available evidence does suggest that there is considerable mortality and morbidity beyond hospital discharge, supporting the notion that we should consider longer follow-up.19–21 Quartin and colleagues showed that continuing mortality occurs in sepsis patients for many months after discharge from the hospital.19 Studies exploring quality of life after ICU care have yielded conflicting results, but certainly several suggest considerable diminution of quality of life that appears to be sustained over time.22

Thus, until more evidence is available, studies of new ICU therapies upon which CEAs are to be performed should have some mechanism (e.g., a subset study or parallel cohort) to incorporate mortality follow-up for 6 to 12 months with an accompanying quality-of-life assessment.

Costs

Which costs should be included? Debates over this subject can be very contentious and can resemble debates over whether to give colloids or crystalloids to hypotensive patients. The subject is further complicated by economic terms such as direct versus indirect costs and tangible versus intangible costs. We will attempt to avoid using too many accounting terms and to suggest alternative ways to understand this issue.

Let us reconsider the cost-effectiveness ratio. It is a ratio of net costs divided by net effects. Thereby, regardless of whether the costs of any given element seem important, if they are distributed equally in both comparison groups, the net difference will be zero and we therefore need not worry about them. In other words, we need consider only those costs we believe to be relevant and likely to differ between the treatment groups. As an example, the PCEHM believed that the intangible costs of pain and suffering were relevant costs that should be measured in CEAs, but we have never measured these costs in any critical care CEA. Therefore, if a new therapy is unlikely to cause either more or less pain, then we can continue to ignore such intangible costs, even though they are considered relevant. The caveat here is that we have now made the important assumption of no difference in pain, which may or may not be true.

We have of course glossed over the term “relevant.” Which costs are relevant? All costs to society could be considered relevant from the societal perspective. Utilizing this perspective, one could argue that the costs of lost wages while a patient is sick are relevant. In response to this issue, the PCEHM recognized there are no correct answers. However, in order to promote standardization of CEA methodologies, they recommend inclusion of all health-related costs, and the ATS concurs that this is the current best approach. They also recommended including opportunity costs, and suggested that lost wages, not only as a postdischarge consequence of the illness but also during hospitalization, represents an example of an opportunity cost. Direct application of these guidelines to critical care is not easy. But one way to consider them is to think about a health care system without drotrecogin alfa (activated) and a health care system with the new therapy. We then need to include all possible cost elements that could differ between these two health care worlds.

Estimating, Measuring, or Guessing Costs

Not all costs included in a CEA are necessarily measured empirically. The CEA is a model that is often calibrated using estimates. Some of these estimates come from measurements. For example, the estimate of differences in the mortality rate between a drug and placebo often is derived from the effect of size in an RCT. Other estimates can be based on expert opinion, or some combination of measurement and opinion. For example, the cost of the actual therapy is usually unknown since the therapy is often not yet approved, and no price has been set by the company that manufactures the therapy. One is therefore forced to estimate on the basis of an educated “best guess,” perhaps with some knowledge of preliminary pricing from the company. While one might be alarmed at this notion of educated guesswork, it is important to appreciate that such estimates can be wildly erroneous, yet have minimal impact on the cost-effectiveness ratio. In order to test how sensitive a CEA ratio is to various estimates in the cost model, the completed CEA model is exposed to a rigorous sensitivity analysis (see below). In this way, we can decide to include many costs in a CEA, yet only measure specifically some portion of that total. As long as the estimated costs have little impact on the overall final CEA conclusions, the strategy regarding which costs to measure and which to estimate can be considered robust.

For How Long Do We Measure Costs?

When the cost of therapy is computed, the duration of the costs attributed to the therapy must also be considered. For example, if our new therapy allows more people to leave the ICU, but causes a higher incidence of renal failure requiring long-term dialysis, shouldn't all the costs of dialysis be attributed to that therapy? The answer is yes.

Although most intensivists do not accept this concept of blaming therapy received in the ICU for incurred long-term costs, it is difficult to argue to the contrary. In producing a survivor, one must also take responsibility for the cost of maintaining survival, which means following the cost streams for a significant length of time. Furthermore, if chronic renal failure leads to a lower quality of life, the new therapy will be doubly penalized, both for the cost of the dialysis and for the reduced quality-adjusted survival.

How Should Costs Be Measured?

For those costs that we choose to measure, we must decide what represents true cost. When we consider hospital costs, true costs are generally assumed to be those generated by formal cost-accounting mechanisms. For example, the cost of a complete blood count includes the wage rate for and time spent by the employee who drew the blood, the cost of the tube, and some tiny amortized fraction of the cost of the equipment upon which the test is run. However, detailed information such as this is rarely available as part of a CEA. Another frequently used approach is to collect hospital charges and adjust them by the hospital- or department-specific cost-to-charge ratios. The relationship between hospital charges and costs has long been a source of skepticism for physicians. However, recent work by Shwartz and associates comparing department-specific cost-to-charge ratio-adjusted charges to estimates generated from a formal cost-accounting system, found good correlation when assessing patients in groups.23 Agreement was much worse when comparing individual patients and when using hospital-specific ratios. However, CEAs rely on average grouped estimates of costs, and therefore department-specific cost-to-charge ratios appear adequate for estimating hospital costs.

Other proxy measures of cost, such as the Therapeutic Intervention Scoring System (TISS) or length of stay, can also be used.24,25 As stated above, their value will depend on how sensitive the conclusions are to variations in the relationship between these measures and true costs.

Defining Standard Care (Comparators)

When comparing a new therapy, the choice of comparator, or standard therapy, is also critical. For example, the cost-effectiveness ratio of a 1-year cervical cancer screening program is quite different than that of 2-year or 3-year programs. Similarly, a tissue thromboplastin activator has a different cost-effectiveness ratio when compared to standard acute myocardial infarction therapy with no thrombolytic therapy as opposed to standard therapy with streptokinase. The PCEHM recommended that the control therapy used for comparative purposes be the least expensive available standard therapy. However, in the field of critical care this view is currently changing. In the treatment of sepsis, should standard care include early goal-directed therapy, steroids, and/or drotrecogin alfa (activated), even though these may be expensive? If so, do we consider all treatments to be standard therapy, or just one or two? The ATS Guidelines recommend that standard care isn't always “best practice,” and that best practice should be the comparator of choice in critical care.

Discounting (Time)

Discounting costs due to time is another important factor to consider when conducting a CEA. When we borrow money, we must pay it back with interest. This is because money is worth more now than it will be in the future. Therefore, $10 is more valuable now than $10 delivered at a rate of $1 per year for the next ten years. Thus, to pay back $10 that we just received over the next 10 years, we would be required to pay back more than $1 per year. Worldwide economic growth is occurring at approximately 3% per year, and therefore the PCEHM has recommended that all costs be discounted at a 3% rate per annum, and the ATS has agreed with this recommendation.

But what about effects; should they also be discounted? Are ten people living for one year more valuable than one person living for ten years? Although this issue may seem inhumane, consideration of this point is vital. Discounting costs without discounting effects will incur the Keeler-Cretin procrastination paradox wherein we would forever favor health care programs that take place some time in the future.26 This situation would have us forever putting off until tomorrow that which could be done today, and therefore we also discount effects at 3%, the same rate as costs.

Robustness and Uncertainty

When we perform an RCT, our primary conclusion is a statement of effect: did the new therapy change the outcome of interest? While it is highly likely that the outcome rates will be different (rarely would the mortality rates in both trial arms be identical), we rely on statistical significance to tell us whether the observed difference is due to a true effect of the therapy and not chance alone. We traditionally infer statistical significance when the p-value is <0.05. In this instance, we are 95% certain that the observed difference did not occur by chance alone. If we are interested only in effect, then we care only about which therapy arm is better, not how much better.

It is important to appreciate, however, that the p-value does not confirm the magnitude of effect. Consider the case of drotrecogin, for which a recent large RCT found a mortality rate in the treatment arm of 25%, as opposed to a placebo rate of 31%, with a p = 0.006.15 This does not mean that six lives are saved per 100 persons treated. Rather, it tells us that our best estimate is that six lives are saved. If we presume a binomial distribution around the mortality rates, we can generate confidence intervals around the two estimates. These confidence intervals might now tell us that new therapy saves between 2 and 10 lives per 100 persons treated, but cannot tell us where the true value falls within that range. The p-value simply confirms the likelihood that there are lives saved by the new therapy, not how many lives.

In CEAs, however, we must quantify the magnitude of effect (and cost) so that we can generate a ratio. The general principle is to first take our best point estimates of cost and effect to generate a base case. Thereafter, we vary all our measures and estimates across their range of probabilities (e.g., 95% CI) in order to determine the extent to which the cost-effectiveness ratio varies. This is a sensitivity analysis and can be done either with one or multiple variables simultaneously. In one respect, the sensitivity analysis can be considered analogous to the p-value in that it allows us to explore the robustness of our conclusions. In other words, if, despite varying several or all variables across their stochastic distributions, there is minimal change in the final ratio, then one can have considerable confidence in the CE ratio estimate.

Another aspect of the sensitivity analysis is that it can be used to determine which model estimates must be the most accurate. For example, the CE ratio may be exquisitely sensitive to the estimate of ICU costs, but relatively insensitive to the expected costs of postdischarge health care resource use. In this situation, one might need to measure ICU costs very carefully, yet rely only on approximate estimates of postdischarge resource use. A comprehensive sensitivity analysis can in fact often be considered more powerful than a p-value, because it can be used to graphically show all of the uncertainties inherent in the underlying assumptions of the CEA model.

Figure 2-3 shows the base case cost effectiveness and reference case cost-effectiveness ratio estimates for drotrecogin alfa (activated) generated by running 1000 simulations.27 This is a common graphic representation of the output from a rigorously conducted CEA. The x axis shows incremental effects and the y axis incremental costs. Quadrants to the right of the y axis represent where treatment with drotrecogin alfa (activated) was associated with a net gain in effect. Quadrants above the x axis represent a net increase in cost. The majority of the simulation estimates fall within the upper right hand quadrant, indicating a net gain in effect with an associated increase in cost (more costly, more effective). The dashed lines represent thresholds of cost, with regions below and to the right of the thresholds being more cost effective than regions above and to the left.

The PCEHM also recommended that all future CEAs produce a reference case. This is accomplished by generation of the CE ratio by a standardized approach to estimating and measuring each of the important elements of the CEA (see Table 2-1). This includes the perspective chosen, determination of costs and effects, study time horizon, and measurement of uncertainty and sensitivity analyses. Use of this standardized approach allows for comparison of CEA results across studies. Comparison of reference cases between CEAs allows us to make inferences about the cost effectiveness of a new sepsis therapy like drotrecogin alfa (activated) vs. a therapy used to fight breast cancer. It allows us to compare apples to oranges and bananas and not just apples to apples. Figure 2-4 shows comparisons of costs for a variety of treatments in different diseases. These include both interventions against specific disease states (e.g., myocardial infarction, stroke),28,29 and interventions designed to prevent injury or illness (e.g., airbags).30 The ATS Guidelines also recommend the generation of a reference case, and further recommend the presentation of a data-rich case from the results of the randomized trial. This case would be generated using a minimal number of model assumptions and the maximum amount of available data from the clinical trial.

Decision making based on the results of a CEA is founded on the idea of social utilitarianism. This valuation is in turn based on the assumptions that: (1) good is determined by consequences at the community level, these consequences being the sum of individual utilities (health and happiness); (2) all utilities are equal within the metric used to measure them; and (3) loss of benefit to some individuals is balanced by benefit to others. As a simple example, consider the decision to fund a childhood immunization program rather than a radical chemotherapy program to treat a rare cancer. This decision assumes that spending resources on immunizations will maximize the community's utility (health) more than money spent on treating a rare cancer. Social utilitarianism acts to maximize the health and happiness (utility) of the community, and consequently leads to maximum efficiency in use of health care resources for community benefit. CEA is designed to result in a ranked list of community benefits resulting from given cost outlays. While CEAs can inform us about where to spend money to improve utility, they cannot inform about how much money should be spent to improve health care overall.

The overall goal of CEAs is to provide decision makers with information that can be used to choose between medical care options when all options are not financially feasible. If monies are unlimited, the relevant question becomes “what treatment options minimize patient morbidity and mortality?” and CEAs are unnecessary. If funds are limited, the question becomes “what provides the best value?” and CEAs can help to answer this question. This was the overall conclusion of the ATS and the workshop further recommended that critical care researchers utilize the PCEHM Guidelines and conduct reference case cost-effectiveness analyses as part of the evaluation of ICU interventions. The rigorous application of this CEA methodology will allow more informative health care policy in the care of the critically ill.

The conduct of a rigorous CEA is clearly challenging. There are many methodologic complexities to consider within the study, and the analysis is likely to be highly dependent on the availability and quality of information from other studies (e.g., a phase III RCT). Much of the information required for thorough conduct of a CEA may in fact be missing, and therefore the analysis will require sophisticated modeling techniques to explore the impact of various assumptions and estimates. In order for the reader to be confident in the results of a CEA, the analysis must not only be robust, but must also be perceived as such. Accomplishing this requires clear reporting from the analyst and a commitment on the part of the reader to embrace cost-effectiveness analyses as a legitimate approach to determining the value of new therapies in the treatment of the critically ill.