This study synthesized published evidence on Lynch syndrome screening and expanded that evidence to match the decision needs of internal decision makers.
To characterize the current state of evidence and apply simulation modeling to support decision making about provision and coverage of a Lynch syndrome (LS) screening program among colorectal cancer (CRC) patients in our integrated healthcare delivery system.
Application of multiple methods for synthesizing evidence guided by needs of our clinical and administrative decision makers.
Narrative and focused reviews, computerized simulation models of multiple screening options, queries of our electronic data warehouse, and extensive communication with decision makers.
Review of published evidence at the time of the study period revealed that screening unselected CRC patients for LS would likely cost less than $25,000 per life-year saved (compared with no screening) and that screening with immunohistochemistry is substantially more efficient than other options. Our simulation models suggest that not only does including BRAF mutation testing substantially improve efficiency but that adding methylation testing improves it further. We characterized a variety of other metrics that contributed not only to local decisions but to the broader evidence base on this topic.
The current state of evidence at the time of the study period suggests an LS screening program can be both effective in reducing mortality from CRC and cost-effective. However, direct evidence remains limited and multiple factors could threaten success of such a program. We have identified opportunities for optimizing the efficiency of available screening protocols. While there was enough evidence for our system to proceed with an LS screening program, we recognize the threats to program success and will prospectively collect outcome data supporting empirical examination of the program.
(Am J Manag Care. 2011;17(8):e288-e300)
Our study summarizes the evidence on the cost-effectiveness of Lynch syndrome (LS) screening in colorectal cancer (CRC) populations and, through computer simulation models, extended the evidence base to address the specific information needs of decision makers in our integrated healthcare system.
Colorectal cancer (CRC) is one of the most common cancers worldwide, with more than a million cases detected and half a million who die from the disease annually.1,2 Approximately 5% to 10% of CRC is familial, reflecting shared genetic and environmental predisposition.3 However, some CRC is due to mutations in highly penetrant genes such as APC, MYH, and PTEN, as well as the DNA mismatch repair (MMR) genes MLH1, MSH2, MSH6, and PMS2. These are referred to as hereditary CRC to distinguish them as single gene disorders.4 Between 2% and 5% of all CRC is due to genetic mutations in MMR genes, collectively known as Lynch syndrome (LS), previously referred to as hereditary non-polyposis colorectal cancer. 5 Individuals with LS have a lifetime risk of CRC as high as 70% to 80% as well as higher risk of endometrial, ovarian, pancreatic, and urologic cancers.6
Importantly, risk for CRC can be dramatically lowered by intensive surveillance of LS mutation carriers, principally colonoscopy beginning at an early age and at increased frequency.7,8 This can be achieved by identifying MMR mutations in index cases and if a mutation is identified, performing family-specific mutation analysis on at-risk relatives. Relatives who test negative revert to routine population screening for CRC.
The optimal way to identify index cases remains undetermined. Family history was initially the recommended screening approach, but recent evidence has suggested substantial deficiencies in this approach. Emerging evidence suggested that tumor-based testing might be a clinically effective and cost-effective alternative to family history. However, multiple approaches could be used to identify individuals at risk, about which evidence was immature.
The goals of this study were 3-fold. The first goal was to thoroughly understand the current evidence base on the effectiveness and “value” of the different LS screening approaches. Based on the results of the first goal, the second goal was to make decisions about the details required for simulation models and construct appropriate models that would address screening efficiency and other questions from the perspective of our healthcare system. The third goal was to communicate the results to our clinical and administrative leaders to inform decisions on implementation of a screening program.
To address our first goal, we performed a series of narrative or qualitative reviews, which shaped our strategies for addressing the layers of questions important to remaining goals. The qualitative reviews were followed by more focused reviews, including review of unpublished data when available to provide data and summary statistics for the simulation models. Queries of our electronic data warehouse were performed to determine system-specific statistics for the models.
Narrative reviews were chosen for their flexible nature, both to extract the background information needed to understand the subject area and allow for incorporation of new data in a rapidly evolving field.9 The evidence base searched was PubMed, augmented by hand searches, the expertise of our team, contact with Hampel and colleagues of the Ohio State University LS research group, and information garnered from professional meetings.
As the nature and scope of the important questions to be addressed became clear from the narrative reviews, more focused reviews were used to address the questions. The first was the clinical effectiveness of available methods for LS case finding among CRC patients. The second, given our commitment to bringing value to our integrated system, was the costeffectiveness of these different approaches. Finally, the task of identifying numerical parameters to populate the analytic models involved both focused reviews and expert opinion.
The primary study period was mid-2007 to fall 2009, with final updates made up to fall 2010 in order to rerun the model with the most recent data available. All searches and analyses were performed by the first author (JMG); input was provided by all co-authors as well as other sources.
The computer simulation models were theoretic models, thus exempt from institutional review board approval. Our theoretic cohort consisted of new CRC patients expected to be diagnosed and treated within our delivery system. The models were constructed in TreeAge Pro software (Williamstown, Massachusetts) and Excel software (Microsoft; Redmond, Washington) using @Risk software as a decision analysis “addon” (Palisade, Ithaca, New York). Final model constructs were constructed in both platforms for validation purposes.
Model structures were constructed to estimate a combination of budget impact and cost-consequences analyses.10,11 The primary outcomes included the absolute and relative effectiveness and costs of the screening protocols, including total costs of testing, number of cases detected, cost per case detected, and the incremental differences between the different protocols. Secondary outcomes were defined by decision makers as we proceeded through decision making, which we describe in the Results section.
Sensitivity analyses (SAs) were performed to test the models as well as examine their output. The influence of model variables on outcomes was explored via multiple 1-way SAs. Calculation of point estimates and plausible ranges of outcomes was performed by Monte Carlo analysis.
Data Sources for Models
Test performance values, except those of methylation testing, were taken largely from the recent Hampel et al and Palomaki et al studies.5,8,12 Methylation testing performance values, including conditional dependence (CD) with the BRAF test, were extracted from recent studies.13,14 When gaps in published data were identified, we contacted outside investigators to obtain unpublished data. Sensitivity and specificity values were based on clinical validity (ie, the tests’ abilities to detect the presence or absence of LS). See for primary model input.
Costs of tests were obtained from our reference laboratory, based on prices available to our healthcare system (current as of fall 2010), plus a $30 charge by our pathology department when applicable. At the time of completion of this study, there were no other labs in the United States that offered all the tests (with all 4 MMR proteins and genes) considered in the models.
The preponderance of evidence identified by our review suggested that screening unselected CRC patients for LS index cases with systematically applied tumor-based testing protocols could lead to a substantial reduction in CRC mortality in our patient population.7,8 The early modeling studies provided preliminary evidence suggesting that tumor-based LS screening protocols can provide good economic value at the societal level on the order of $5000 to $10,000 per life-year (LY) saved.13,15-22 A recent modeling study by Mvundura et al, using conservative baseline estimates and modeling more contemporary screening protocols, provided further evidence of the value of this screening; it demonstrated a cost-effectiveness ratio of about $22,500 per life-year saved (using an immunohistochemistry-first [IHC-first] approach) compared with no LS screening.23
The review also established that family history approaches have poor sensitivity or specificity for LS case finding under ideal circumstances.24,25 Performance in real-world practice would be far less than ideal because of barriers in the collection and interpretation of family history data.26
Limiting screening of CRC cases to persons under 50 years of age, while decreasing the cost of the screening program, would miss approximately 50% of LS cases; thus this approach was judged not sufficiently sensitive for our purposes.23,27 There is early evidence that using a prediction algorithm combining pathology and other features might efficiently predict a positive microsatellite instability (MSI) test as a marker for the presence of LS.28 This screening method requires validation, as its performance is likely operator dependent.
The tests determined to be candidates for initial screening of tumors and thus represented in our simulation models included IHC staining for the 4 MMR proteins, MSI, BRAF mutation, and MMR gene sequencing/rearrangement (ie, deletion/ duplication) analyses (Seq-Rearr). Reflex testing after a positive screening test included various combinations of these tests, always finishing with confirmatory Seq-Rearr. Seq-Rearr tests are bundled by our reference lab and thus are treated as a single test in our models. In the final months of the study, evidence regarding the utility of an additional test emerged: methylation of the MLH1 promoter (methyl), which was added to the models.
Based on published evidence, several options were eliminated early. Screening by Seq-Rearr was not viable due to poor cost-effectiveness.16,23 Initial screening by BRAF analysis was ruled out given its low prevalence in the CRC population. MSI-first screening was removed from the final models as evidence emerged that it would be substantially less cost-effective than IHC-first screening because of its inability to target subsequent sequencing with equivalent sensitivity.5,7,12,23
With the decision that only an IHC-first screening approach would be used in our final models, it was recognized that multiple options could be applied after IHC testing. Following is a list of the final screening protocols identified by our group as viable given currently available tests and evidence. (See for a schematic of the model structure of the “IHC with BRAF then methyl” protocol.)
• IHC direct to sequencing
• IHC with BRAF
• IHC with methyl
• IHC with BRAF then methyl
• IHC with methyl then BRAF
The BRAF and methyl tests both identify somatic changes in tumors with abnormal IHC for MLH1, which when positive rules out the presence of LS. Their comparative performance has not been well characterized. Combining data from 2 studies identified late during our study period revealed the performance of these tests (),13,14 suggesting why the tests would be expected to make different contributions to overall LS screening/testing performance.
The aforementioned studies performed both tests on cases with and without LS, which enabled us to use the data as the basis for estimation of the correlation or dependence of sensitivity and specificity pairs, referred to as CD. When statistically significant, these relationships alter the sensitivity and/or specificity of the second test when used in sequence.29 The revised test performance parameters were used to adjust for the CD phenomena in 2 of the 5 testing protocols.
Table 3 provides best estimates of the primary outcomes of the 5 protocols, with 95% confidence intervals for total program costs and number of LS cases expected to be identified in a cohort of 100 CRC cases, calculated by Monte Carlo simulation and modeled at baseline conditions defined in this study, with adjustment for CD of BRAF and methyl tests. IHC with methyl is dominated by IHC with BRAF then methyl (ie, it is both less effective at case finding and more expensive). The IHC straight to sequencing protocol was the most effective of the modeled protocols at finding LS cases, but the expected cost to find 1 additional LS case was more than $1.5 million over that of the next most efficient protocol.
Local decision makers were interested in additional metrics to help them determine the impact on clinical resources and budget, including the number of CRC patients treated annually within our delivery system for whom tumor tissue was expected to be available for testing (about 335). Our models allowed us to estimate the percentage of total test costs that would fall under Diagnosis-Related Group payment (about 69%) and the percentage of screened patients who would be eligible for sequencing, thus requiring genetic counseling (5%-6%).
From a separate simulation model we determined the impact of compliance in screened CRC patients eligible for Seq-Rearr testing (assumed to be 100% in our primary models). At 50% compliance, for the protocol currently provided by our reference lab, the total cost to screen/test would decrease by about 11% and the cost per case detected would increase by about 79%.
illustrates the 8 variables with the most influence on screening efficiency, based on a 1-way SA. See the Appendix for more details.
In this study, review and presentation of the evidence led to deliberation by appropriate decision makers within our healthcare system about providing and covering tumor-based LS screening. The review also provided sufficient evidence to rule out multiple screening alternatives, permitting us to focus only on tumor-based protocols that began with IHC testing. Simulation models of the protocols provided point estimates and plausible ranges of various screening efficiency and business impact metrics of interest to decision makers.
From the results presented in Table 3 it is apparent that, with the exception of the IHC with methyl protocol, the more money spent, the more LS cases can be identified. Performing sequencing on all screen-positive IHC cases, while most effective, is substantially more expensive than the alternatives. The dramatic increase in cost to attain a small increase in detection is difficult to justify from a societal perspective. That left only 3 protocols for us to consider for implementation. Our models suggest that IHC with BRAF then methyl, the standard protocol currently offered by our reference lab, is not the most efficient option. Thus, we must reconcile the high cost to detect 1 additional LS case (best estimate of $216,498) with the more efficient IHC with methyl then BRAF protocol (not currently available from any commercial lab).
This story is incomplete, however, without considering the uncertainty in the point estimates, reflected by the ranges reported on the 2 key outcomes in Table 3. One-way SA identified that LS prevalence has the biggest impact on the model. The uncertainty associated with this variable can only be substantially reduced by measuring or (validly) estimating the LS prevalence in the population of interest. In our case, we will estimate the LS prevalence in our CRC population via the screening program, with the recognition that we will only be performing the gold standard testing (ie, Seq-Rearr) on cases that screen positive by IHC, with its imperfect sensitivity, and survive the BRAF and methyl rule-out tests. We are currently collecting these follow-up data (see Appendix).
IHC test performance was the second most influential variable. For our simulation models we used results from the latest study from the Ohio State group8 and expert opinion for plausible ranges, because our reference lab could not provide us with estimates of these figures, reflecting the lack of proficiency testing for this assay during our study period. As LS screening involving the IHC assay continues to disseminate, appropriate proficiency testing may become available, thus reducing uncertainty about test performance. Of course, the true value of these test parameters may be lower or higher than our estimates. Our data tracking system may allow us to improve IHC test performance by addressing preanalytic issues (eg, sample preparation and transport) as well as collecting data on uninformative results—a data element that was not modeled.
Consideration of Patient and Family Outcomes
Identification of a patient with LS can have a profound effect on patients and their families. The importance of genetic counseling for patients and family members who were identified to be at risk prior to initiation of the screening program (either on the basis of family history or tumor screening ordered by the treating physician) was already recognized by the organization. Systems were in place to ensure access to these services. The primary question for this analysis was whether the systematic application of tumor-based screening raised issues different from those in existing care practices.
The 2 issues that were identified were whether the tumor screening represented a “genetic test” and whether informed consent beyond that already obtained for the surgical procedure would be required to perform the screening. A group of internal and external experts including medical ethicists was convened to address these issues. The conclusions were that because the test was a screen that required confirmatory molecular testing, the screening itself did not constitute a genetic test; thus, formal consent for the screening was not required. The group also noted that more and more molecular tests are being applied to tumors, and while most do not provide information about a possible germ-line genetic mutation, they are increasingly used to assess prognosis and treatment. To require patients to consent to each of these molecular diagnostics was thought to be unsustainable. It was recommended that educational materials be prepared and distributed to patients prior to surgery, which was done. A number of other healthcare systems that had already embarked on an LS tumor screening program were contacted regarding the consent issue. While some had decided to formally get consent from patients, the majority were not obtaining consent for the screening, but were providing educational materials and using genetic counselors to follow all screen-positive patients.
In order to ensure that outcomes of a positive screen could be appropriately addressed, the screening program was den signed so the oncology genetic counselor reviewed all screening results. The genetic counselor was given the responsibility of following up with all screen-positive patients so the concerns of the patients and their families would be addressed in a manner consistent with current practice. The systematic application of this intervention was endorsed by oncology care providers, who recognized that depending on referrals to genetic counseling could result in haphazard follow-up and poor clinical and psychological outcomes.
It can be quite challenging for a private healthcare system in the United States to base clinical policy decisions on evidence of cost-effectiveness determined from a national perspective. In most cases published models have been structured or framed differently from local care delivery environments and populated with “average” data that are valid for no specific institution, thereby limiting their relevance. However, existing evidence can be used to customize models to answer local questions, as suggested by Sculpher et al.30
Limitations of Study
Our study has several limitations. Our literature searches were limited to those indexed in PubMed and excluded studies not published in English. However, interaction with groups actively collecting data relevant to screening allowed incorporation of the most recent and relevant data prior to its publication, allowing refinement of the model using the most up-to-date information, an approach endorsed by Gudgeon et al.31
The conclusion that LS case finding by tumor-based screening results in improvement in health outcomes is, according to the recent EGAPP report, “limited but promising.”7 Although the more recent modeling study by Mvundura et al23 provides additional evidence that LS screening is costeffective, as a simulation model it cannot be considered highlevel evidence. Given that LS screening is rapidly emerging into clinical practice, decisions should be based on the best possible evidence, which simulation modeling can augment.
The results of our internal studies will have limited relevance to some healthcare systems or insurers due to population or operational differences. However, the methods described here could be used to develop customized approaches appropriate to local decision needs.
Given that the primary issue is the impact of LS screening on CRC-related morbidity and mortality of family members of LS probands and that the evidence remains “limited but promising,” the focus of research should be on answering questions at this level. Mvundura et al and Hall discuss the barriers to successful implementation of LS screening,23,32 not least of which are issues related to acceptance of and compliance with testing and CRC surveillance, which are currently not well understood.
Given these knowledge gaps, it is important for systems that do implement LS screening to prospectively collect data on important outcomes. Monitoring outcomes can address many issues, including identification of process failures resulting in errors and missed opportunities. Outcome capture is ongoing in our system.
The results of our modeling efforts were useful to internal decision makers and led to implementation of tumor-based screening for LS within our delivery system. This is the first time that a formal decision analysis has been used to guide implementation of clinical services on a systemwide basis within Intermountain Healthcare.
Acknowledgments: We acknowledge Tom Belnap for his many queries of the Intermountain Oncology Clinical Programs database and their interpretation, thereby allowing our team to better understand the patient population in which the LS screening program would be applied; Mary Brieske for her help as a liaison with our pathology group and reference laboratory, and in sorting out the operational issues related to implementation of screening; Scott Grosse for his help with a wide variety of health economic and cost-effectiveness issues, including those specific to LS screening; and Heather Hampel for her help in providing information about nuanced clinical details of LS as well as data from the work of the Ohio State group that we were unable to extract from their publications.
Author Affiliations: From Intermountain Healthcare (JMG, JLW, GLS, MSW), Salt Lake City, UT; Department of Internal Medicine (RWB), University of Utah School of Medicine, Salt Lake City; Huntsman Cancer Institute (RWB), University of Utah, Salt Lake City; Department of Pathology (WSS), University of Utah Health Sciences Center, Salt Lake City.
Funding Source: None reported.
Author Disclosures: Dr Burt reports receiving consultancies or paid advisory boards from Myriad Genetics. The other authors (JMG, JLW, WSS, GLS, MSW) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.
Authorship Information: Concept and design (JMG, JLW, RWB, WSS, GLS, MSW); acquisition of data (JMG, JLW, WSS); analysis and interpretation of data (JMG, JLW, RWB, WSS, MSW); drafting of the manuscript (JMG, RWB, WSS, MSW); critical revision of the manuscript for important intellectual content (JMG, RWB, WSS, GLS, MSW); statistical analysis (JMG, GLS); provision of study materials or patients (JMG, JLW); administrative, technical, or logistic support (JMG, MSW); and supervision (MSW).
Address correspondence to: James M. Gudgeon, MS, MBA, Clinical Genetics Institute, Intermountain Healthcare, 324 10th Ave, Ste 183, Salt Lake City, UT 84103. E-mail: firstname.lastname@example.org.
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