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Ann Thorac Surg 2007;83:1257-1264
© 2007 The Society of Thoracic Surgeons

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Original Articles: General Thoracic

Defining the Optimal Treatment of Locally Advanced Esophageal Cancer: A Systematic Review and Decision Analysis

^a Department of Surgery, Division of Thoracic Surgery, University of Calgary, Institute of Health Economics, Calgary, Alberta
^b Department of Community Health Sciences, University of Calgary, Institute of Health Economics, Calgary, Alberta
^c Department of Medicine, University of Calgary, Institute of Health Economics, Calgary, Alberta
^d Division of Thoracic Surgery, University of British Columbia, Vancouver, British Columbia, Canada

Accepted for publication November 13, 2006.

^_* Address correspondence to Dr Graham, Foothills Medical Centre, 1403 29th St NW, Calgary, Alberta, Canada T2N 2T9 (Email: andrew.graham{at}calgaryhealthregion.ca).

	Abstract

Top Abstract Introduction Material and Methods Results Comment References

 
Background: The objective of this study was to combine systematic review and decision analytic techniques to determine the optimal treatment strategy for patients with locally advanced esophageal cancer.

Methods: We performed a systematic review of all randomized trials of patients with locally advanced esophageal cancer that included one of the following strategies compared with surgery alone: chemoradiotherapy followed by surgery, chemotherapy followed by surgery, or surgery with adjuvant chemoradiotherapy. Using the estimates of relative risk for mortality and overall quality of life we constructed a decision model. The outcome of interest was expected quality-adjusted life-years (QALY).

Results: The meta-analysis showed for the first year, the relative risk (95% confidence interval) of death for treatments compared with surgery were 0.87 (0.75 to 1.02) for chemoradiotherapy followed by surgery, 0.94 (0.82 to 1.08) for chemotherapy followed by surgery, and 1.33 (0.93 to 1.93) for surgery with adjuvant chemoradiotherapy. The QALYs gained for surgery alone, chemoradiotherapy followed by surgery, chemotherapy followed by surgery, and surgery with adjuvant chemoradiotherapy strategies were 2.07, 2.18, 2.14, and 1.99, respectively. If the reduction in utility for multimodality treatment was increased to 21%, the QALYs gained for surgery alone, chemoradiotherapy followed by surgery, chemotherapy followed by surgery, and surgery with adjuvant chemoradiotherapy were 2.07, 2.03, 1.99, and 1.85, respectively.

Conclusions: Chemoradiotherapy followed by surgery appears to be associated with the best survival and the largest expected gain in QALYs. However, the improvement in quality-adjusted life expectancy is modest at 40 days, and surgery alone becomes the preferred strategy if the reduction in utility associated with multimodality treatment is increased to 21%.

	Introduction

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The treatment of locally advanced esophageal cancer results in 5-year survival rates of 15% to 20%. The traditional standard of care has been surgery alone, but a number of systematic reviews have shown that multimodal approaches to therapy may improve survival [1–4]. Owing to the intensity of these treatment strategies, however, each may increase early mortality and may also be associated with reductions in health-related quality of life (HRQOL) during their administration. The optimal treatment strategy is thus widely debated. The present treatment strategies include surgery alone, neoadjuvant chemotherapy or chemoradiotherapy followed by surgery, surgery with adjuvant chemoradiotherapy, or chemoradiotherapy alone. For fit patients, the most widely used treatment strategies have involved surgery alone or in combination with neoadjuvant or adjuvant treatment.

Advocates of evidence-based medicine describe decision analysis as one of two strategies to move from evidence to action. The reported benefits of decision analysis are that it uses explicit quantitative methods to analyze decisions under conditions of uncertainty and allow clinicians to compare the expected outcomes of different strategies [5]. Decision analysis is thought to be most relevant when there are high-stake decisions (each strategy involving intensive treatment and potential complications), there does not exist a clearly superior treatment strategy, and quality of life is critical [6]. The selection of treatment strategy for locally advanced esophageal cancer is such a situation.

Previous meta-analyses of multimodality treatments of esophageal cancer have shown small survival advantages but with increased risk of postoperative mortality compared with surgery alone [1–3]. However, multimodality treatment is associated with increased side effects and has been shown to reduce HRQOL [7]. Given the reduced life expectancy associated with esophageal cancer, HRQOL during a patient’s remaining time is important and should be incorporated into medical decision making. Decision analysis allows incorporation of both survival and HRQOL data and thus is well suited to examine the selection of treatment for esophageal cancer.

The purpose of this study was to (1) perform a systematic review of all randomized intervention trials of patients with locally advanced esophageal cancer that included at least one of the following strategies compared with surgery alone: chemoradiotherapy followed by surgery, chemotherapy followed by surgery, or surgery with adjuvant chemoradiotherapy; and (2) perform a decision analysis to incorporate the effects of the treatments on both survival and quality of life, thus enabling selection of the optimal therapy for patients with locally advanced esophageal cancer. For the purposes of the study, we defined locally advanced esophageal cancer as malignant disease limited to the esophagus or gastroesophageal junction and regional lymph nodes.

	Material and Methods

Top Abstract Introduction Material and Methods Results Comment References

 
Systematic Review and Meta-Analysis
Population
In the baseline analysis, we evaluated a cohort of patients with locally advanced esophageal cancer based on the patients enrolled in the randomized clinical trials that were included in the meta-analysis. Consistent with the inclusion criteria for these studies, patients for whom only palliative treatment was planned were not included.

Treatment strategies
The following treatment strategies were considered within this study: surgery alone, chemoradiotherapy followed by surgery, or chemotherapy followed by surgery or adjuvant chemoradiotherapy.

Efficacy of treatments for locally advanced esophageal cancer
A systematic search of published studies by an experienced medical librarian was carried out to identify abstracts of randomized clinical trials or systematic reviews of esophageal neoplasm treatment. The following databases were searched: Medline/PubMed 1966 to October 2004, EMBASE 1980 to October 2004, CINAHL 1982 to October 2004, the Cochrane Central Register of Controlled Trials, and the Cochrane Database of Systematic Reviews. The exact search strategies are available upon request. Additional methods of retrieval included cross-referencing bibliographies of retrieved articles and consultation with content experts.

All abstracts were reviewed by a single author (A.G.) to determine if the studies met the selection criteria of being randomized controlled trials studying locally advanced esophageal cancer. For the purposes of the study, we defined locally advanced esophageal cancer as malignant disease limited to the middle or lower third of the esophagus or gastroesophageal junction and regional lymph nodes. Thus, the clinical stages included T2N0M0, T3N0M0, T1N1M0, T2N1M0, and T3N1M0. For those abstracts meeting the selection criteria, the individual studies were retrieved. Those studies still meeting selection criteria provided survival data for the meta-analysis. Two authors (A.G., S.G.) extracted the following data: the treatment strategy (classified as chemoradiotherapy followed by surgery, chemotherapy followed by surgery, surgery alone, or surgery with adjuvant chemoradiotherapy), the number of patients in each arm, the proportion of patients alive at 6-month intervals from 6 months to 36 months from the published survival curves. All data were double entered. Any discrepancies were resolved by consensus.

The quality of each study was assessed independently by two of the authors (A.G., F.S.) using the Jadad score, a widely used grading system for randomized controlled trials [8]. Jadad scores range from 0 to 5, with 5 being the maximum score possible. The type of surgical procedure, histologic type of cancer, and details of neoadjuvant or adjuvant protocol were recorded.

A meta-analysis of survival for those undergoing surgery was then carried out for each 6-month interval to 36 months. Statistical analysis was done using Stata version 8.0 (Stata Corp, College Station, Texas) and SAS version 8.1 (SAS Institute, Cary, North Carolina). Relative risks were calculated for 0 to 6 months, 6 to 12 months, and given the small number of surviving patients past 12 months and the flattening of survival curves for all of the treatment strategies, we calculated an overall relative risk for each treatment strategy for the 12- to 36-month period. A random effects model was used in all cases.

Decision Analysis
Model
We used decision analysis to model the impact of the different strategies on survival and expected quality-adjusted life-years (QALYs) gained. A Markov process [9] was used to model transitions, over recurring 6-month cycles, between the different clinical states that were considered. We analyzed this Markov model using cohort simulation. In the base case, the model was run over a 3-year time horizon. In sensitivity analysis, other time horizons were considered (5 and 10 years).

In the baseline analysis, the expected QALYs gained were discounted at an annual rate of 5% [10]. We rank ordered strategies based on an objective of maximizing expected QALYs gained, assuming that patients were risk neutral (ie, were willing to accept a risk of early mortality if outweighed by a higher chance of prolonged survival). All analyses were performed using DATA PRO (TreeAge Software, Williamstown, MA).

Utilities
To incorporate information on HRQOL into decision analysis, a single measure of HRQOL called a utility (ranging from 0, equivalent to death, to 1, representing perfect health) is required. After a thorough review of the literature, we were unable to locate published utility scores for patients receiving all the treatment strategies that we considered. We were, however, able to find several studies reporting on HRQOL of esophageal cancer patients that used the Medical Outcomes Study 36-Item Short-Form Health Survey (SF-36) scores. Short Form-36 scores are not a utility measure, but quantitative methods of linking SF-36 data to utility scores have been described [11]. As such, after determining SF-36 scores for patients receiving each of the treatment modalities (see below), we used this published algorithm to convert SF-36 data to utility scores appropriate for use in the model.

The most comprehensive study that reported SF-36 scores for patients receiving most of the treatment strategies of interest was an observational study that has currently been published only in abstract form [12]. The study, which included 67 patients overall, measured HRQOL repeatedly over time using the SF-36 with patients who had esophageal cancer. We were able to use the full dataset for this study, which included patients who were treated with surgery, chemoradiotherapy, and radiotherapy followed by surgical resection. Short Form-36 data were available at baseline for 64 patients, among whom 35 underwent surgery. 40 were alive and had HRQOL data available at 6 months (23 of whom underwent surgery). Thus, an overall baseline utility score (ie, before treatment) was determined for all patients, regardless of eventual treatment received. A utility decrement was then applied to those undergoing surgery based on the difference between the mean baseline and mean 6-month score in the surgery only group. To account for a possible survivor bias, we repeated the calculation including only those patients with data at both time points. However, the decrement was not notably different; therefore, all available data were included. Because data were not available for the other three interventions, we assumed that HRQOL would be equally affected by multimodality treatment and calculated the decrement for all patients receiving multimodality treatment again based on the difference between the mean baseline and mean 6-month score. We maintained the decrement for the first year in line with other published studies indicating that lower HRQOL associated with multimodality treatment persist for approximately 9 months [7, 13].

Utility scores after the first year were interpolated from the calculated decrement to a combined estimate at 2 years after treatment from two published studies examining long-term HRQOL in patients after treatment [14, 15]. This interpolation is supported by the Medical Research Council study [16] comparing surgery alone with surgery plus preoperative chemotherapy, and showing no difference in dysphagia scores and performance status at 1 year after treatment.

Utility scores for 2 years and beyond were obtained from SF-36 data from McLarty and colleagues [14] and de Boer and coworkers [15].

Sensitivity Analysis
As any model involves assumptions and uncertainties, extensive sensitivity analyses were carried out. These analyses assessed the effect of varying baseline estimates for mortality and the relative risk (RR) of mortality associated with the various treatment regimens within the 95% confidence interval (CI). We also varied the utility scores for survivors within clinically plausible ranges, and varied the duration of assumed treatment benefit. Monte Carlo simulation enables simultaneous sensitivity analysis of all uncertain variables. It does so by replacing estimates of probabilities and utilities with specific probability distributions, which are based on the reported means and variances of each variable. The analysis is then repeated 25,000 times, sampling different values from the appropriate distributions for each of the variables. In such a way, a statistical distribution is built up around the QALYs gained, giving a better reflection of the uncertainty inherent in the analysis. In this case, we used Monte Carlo simulation to sample estimates from statistical distributions that were created around the RR of mortality for the different treatments.

	Results

Top Abstract Introduction Material and Methods Results Comment References

 
Survival
A total of 156 studies were identified, of which 14 randomized controlled trials met our selection criteria. The treatment strategies used, histology, surgical approach, and Jadad study quality score are reported for each of these 14 randomized controlled trials in Table 1. Table 2 reports the meta-analysis of cumulative mortality and mortality within consecutive 6-month intervals for patients treated with surgery alone. The cumulative mortality rate at 6 months was 17.5%, and at 36 months it was 67.4%. There was statistically significant heterogeneity across studies (² p = 0.001), and for this reason, we used random effects models to pool results across studies (see below). The mortality rates within consecutive 6-month intervals ranged from 12.5% (in the last 6-month interval) to 21.6%, with most of the 6-month mortality rates being close to 20%; these consecutive 6-month interval mortality rates were the input values used in our Markov model.

Decision Model
Survival analysis
Using the combined baseline survival probabilities for the surgery arm of all randomized controlled trials, and applying the RR of death for each of the alternative treatment strategies over time, we were able to construct modeled survival curves for each of the strategies (Fig 1, panel A). These reveal that the chemoradiotherapy followed by surgery multimodality strategy is associated with the best modeled survival at 3 years. Meanwhile, the modeled survival curve for the surgery with adjuvant chemoradiotherapy strategy demonstrates an initial decrement in survival relative to the surgery alone strategy between 6 and 12 months, that subsequently reverses as the later survival advantages associated with the surgery with adjuvant chemoradiotherapy strategy accrue. Figure 1 (panel B) presents the same survival relationships across treatment strategies, but with utilities taken into account. In this case, the y axis of the plot represents QALYs, and the plots depict the decrement in QALYs that result over time as increasing numbers of patients die.

View larger version (13K): [in this window] [in a new window]   Fig 1. Modeled survival curves resulting from the Markov cohort analysis. (A) Survival. (B) Quality-adjusted life-years gained. (Light solid line = surgery; dotted line = chemoradiation therapy plus surgery; heavy dashed line = chemotherapy plus surgery; heavy solid line = surgery plus chemoradiation therapy.)

	Comment

Top Abstract Introduction Material and Methods Results Comment References

 
We found that the optimal treatment strategy for patients with locally invasive esophageal cancer was chemoradiotherapy followed by surgery. It was associated with a mean improvement in quality-adjusted life expectancy of 0.11 QALY (40 days) compared with surgery, 0.04 QALY (15 days) compared with chemotherapy followed by surgery, and 0.19 QALY (69 days) compared with surgery with adjuvant chemoradiotherapy. Previous studies have suggested that a quality-adjusted life expectancy of 2 months or more is a clinically significant gain, although many recent advances in cancer have provided mean increases in life expectancy of less than 30 days [17]. The modest increase in QALYs of 40 days with multimodality treatment provides important information beyond survival data alone.

We found that clinically plausible uncertainty in the estimates of the utility scores for patients undergoing multimodality treatment could change the preferred strategy, suggesting that more accurate measurement of HRQOL in patients undergoing different treatment modalities for esophageal cancer would be an important future research initiative.

One of the limitations of our analysis, therefore, is that the estimates of utilities are taken from a variety of sources, some of which are of relatively low methodologic validity. For example, owing to the absence of comparative studies, an assumption has been made that the HRQOL is the same for all multimodality strategies. Although they represent the best available evidence, they are subject to potential error as they were not derived from randomized controlled trials comparing the treatment strategies of interest. Another potential limitation of our analysis was that the only randomized controlled trial comparing surgery with surgery followed by chemoradiotherapy was the Southwest Oncology Group-9008 study [18], which enrolled patients with adenocarcinoma of the stomach and gastroesophageal junction. The patients with carcinoma of the cardia represented only 20% of the patients.

A further limitation is that we have not modeled a decision option of chemoradiotherapy alone as primary treatment, which has been tested in two recent studies. One of the studies has only been reported in abstract form, however, and the second enrolled patients with upper third malignancies and was not compared with surgery alone [19, 20]. For these reasons, the studies of chemoradiotherapy alone did not meet our inclusion criteria. It should be noted that both of these studies showed no survival difference between chemoradiotherapy followed by surgery and chemoradiotherapy alone.

The strengths of our study include our use of a systematic search for data and rigorous meta-analysis to provide baseline and relative survival probabilities. Another strength is the model design, which allows us to model both the early intensive phase of treatment and the latter posttreatment phase separately, thus enhancing validity. Finally, the use of clinical decision analysis methods allows us to determine the optimal treatment strategy in a situation where it cannot be determined from a single study or a single parameter. We used data on both expected survival and quality of life, combining this into a single composite measure, thus enabling comparison of strategies with a common metric.

Previous meta-analyses have been performed comparing chemoradiotherapy followed by surgery and surgery alone, and have shown no survival advantage at 1 and 2 years but a small survival advantage at 3 years [1–4]. Meta-analyses comparing chemotherapy followed by surgery and surgery alone have shown mixed results. Urschel and associates [21] reported no survival difference but did not include the large Medical Research Council study, which reported superior survival for patients receiving chemotherapy followed by surgery compared with surgery alone [16]. A subsequent meta-analysis by Malthaner and Fenlon [22] included this Medical Research Council study and showed no statistically significant survival advantage in years 1 to 4, but a 9% absolute survival advantage by 5 years after treatment. The finding that there is at best a small survival advantages at 3 to 5 years after treatment and that the absolute number of surviving patients is small is consistent with our hypothesis that quality of life during treatment could alter the preferred choice of treatment from adjuvant treatment followed by surgery to surgery alone.

We did not incorporate the costs of the different treatments, which is an important consideration for health care decision makers. However, considering the intensity of treatments required for multimodal therapies, it would be expected that costs would be higher for these approaches. Whether this additional expenditure is worth the small expected increases in life expectancy and quality-adjusted life expectancy will await the results of a formal economic evaluation.

In conclusion, our study demonstrates that quality-adjusted life expectancy is maximized by the use of chemoradiotherapy followed by surgery for locally advanced esophageal cancer. However, in patients more likely to have toxicity and reduced quality of life from multimodal therapy, surgery alone may be the preferred strategy.

	References

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This Article Abstract Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Add to Personal Folders Download to citation manager Author home page(s): Andrew J. Graham Sean C. Grondin Richard J. Finley Joanne Clifton Permission Requests Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Graham, A. J. Articles by Clifton, J. Search for Related Content PubMed PubMed Citation Articles by Graham, A. J. Articles by Clifton, J. Related Collections Esophagus - cancer Related Article