JNCI J Natl Cancer Inst (2015) 107(4): djv005 doi:10.1093/jnci/djv005 First published online March 20, 2015 Article
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Comparative Effectiveness of Screening Strategies for Lynch Syndrome Affiliations of authors: Department of Medicine, Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA (AB, SS); Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH (MWK); Cleveland Clinic Lerner College of Medicine, Cleveland, OH (MWK); Department of Epidemiology and Biostatistics (MWK) and Department of Medicine (NJM), School of Medicine, and Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, OH; Department of Medicine, University Hospitals Case Medical Center, Cleveland, OH (NJM). *Authors contributed equally to this work. † Principal Investigator. Correspondence to: Sarmad Sadeghi, MD, PhD, Department of Medicine, Norris Comprehensive Cancer Center, University of Southern California, 1441 Eastlake Ave, Suite 3440, Los Angeles, CA 90033 (e-mail: [email protected]).
Abstract Background: Colorectal cancer is the second leading cause of cancer death in the United States. Approximately 3% of colorectal cancers are associated with Lynch Syndrome. Controversy exists regarding the optimal screening strategy for Lynch Syndrome. Methods: Using an individual level microsimulation of a population affected by Lynch syndrome over several years, effectiveness and cost-effectiveness of 21 screening strategies were compared. Modeling assumptions were based upon published literature, and sensitivity analyses were performed for key assumptions. In a two-step process, the number of Lynch syndrome diagnoses (Step 1) and life-years gained as a result of foreknowledge of Lynch syndrome in otherwise healthy carriers (Step 2) were measured. article
Results: The optimal strategy was sequential screening for probands starting with a predictive model, then immunohistochemistry for mismatch repair protein expression (IHC), followed by germline mutation testing (incremental cost-effectiveness ratio [ICER] of $35 143 per life-year gained). The strategies of IHC + BRAF, germline testing and universal germline testing of colon cancer probands had ICERs of $144 117 and $996 878, respectively. Conclusions: This analysis suggests that the initial step in screening for Lynch Syndrome should be the use of predictive models in probands. Universal tumor testing and general population screening strategies are not costeffective. When family history is unavailable, alternate strategies are appropriate. Documentation of family history and screening for Lynch Syndrome using a predictive model may be considered a quality-of-care measure for patients with colorectal cancer.
Colorectal cancer (CRC) is the second leading cause of cancer death in the United States, with an incidence of over 142 820 new cases and 50 830 deaths per year (1). Up to six percent of these cancers are hereditary and potentially preventable. Lynch Syndrome (LS) is the most common hereditary colorectal cancer syndrome (2), accounting for approximately 3% of all colorectal cancers. Detection of Lynch Syndrome allows for
personalization of medical care for the affected individual and provides an opportunity for preventive cancer care in family members. In the case of Lynch Syndrome, this is particularly important given that there is increased risk for a variety of cancers (3–7). Recently, two distinct approaches to screening new CRC patients (probands) and screening the general population for
Received: April 21, 2014; Revised: July 28, 2014; Accepted: December 19, 2014 © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: [email protected].
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Afsaneh Barzi*, Sarmad Sadeghi*†, Michael W. Kattan, Neal J. Meropol
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Study Design and Setting The modeling paradigm for comparative effectiveness analysis of LS was built around a cost-effectiveness endpoint. With respect to the assessment of cost, this study took a societal perspective and included two steps: Step 1: The process by which healthy individuals with LS were identified. These were either first-degree relatives of probands, or members of the general population who were identified as a result of primary testing. Step 2: The healthy individuals affected by LS who were followed over time with lifetime screening to detect colon cancer at earlier stages and prophylactic interventions for gynecologic cancers to reduce mortality and morbidity from LS. Step 1 included 20 strategies: four strategies that started with clinical criteria (Amsterdam [15–18] or revised Bethesda [15,19]) and six strategies that started with a predictive model (PREMM1,2,6 [13], MMRpro [17], or MMRpredict [20,21]), all of
Cost-effectiveness The benefits (effects) were measured in LYG accrued as a result of the foreknowledge of LS diagnosis and reduced overall mortality. Incremental cost-effectiveness ratios (ICERs, ie, the ratio of the increase in cost to the increase in the effect) using LYG as effect were the primary measure of cost-effectiveness. If a strategy is not cost-effective compared with the next more effective strategy, it is designated as dominated. Absolute dominance (AD) indicates that the next strategy is more effective and less costly, and extended dominance (ED) indicates that the next strategy is more effective and more costly, but has a lower ICER. All of the strategies were modeled using TreeAge Pro 2012 by TreeAge Software, Inc. Costs structures used in previous studies were updated where appropriate based on the literature. Sensitivity and specificity for various tests were obtained from the validation studies, and stage distribution and disease incidence were obtained from SEER database (24) (see the Supplementary Materials, available online).
Study Models The Step 1 model was a decision tree that represented all strategies using a cohort (n = 100 000) of CRC probands or healthy general population and calculated the number of LS diagnoses using each strategy (primary diagnoses). Using the assumptions about the first-degree relatives, this number was translated to a number for healthy individuals affected with LS (secondary diagnoses) who could be offered preventive measures described in Step 2. Similarly, the number of missed but detectable LS diagnoses by other strategies were identified and were used to divide the LS population in the Step 2 model into aware (detectable and diagnosed) and not aware (detectable but missed) cases of LS. The Step 2 model was a Markov model that represented the natural history of CRC and other LS-related cancers in the population affected with LS. Colonoscopy every year between ages 20
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Methods
which were followed by either IHC then germline testing or directly followed by germline testing. As with clinical criteria, predictive models used a subject’s medical history to calculate a predicted risk for LS. A cutoff value of 5% or higher was used to identify subjects in whom further testing is warranted (13,20,22,23). An additional five strategies started with tumor testing (MSI, IHC, BRAF testing, or combination) followed by germline testing. Four strategies started with PREMM1,2,6 in the general population (GP), followed by germline testing. It is important to note that there is no rationale for the use of any of the predictive models or clinical criteria for screening the general population. These models were designed and validated in specific populations with personal or family history of colorectal cancer. Therefore, MMRpredict, MMRpro, Amsterdam Criteria, and revised Bethesda Criteria were excluded from the general population screening setting. However, given the published data by Dinh et al. we chose to include PREMM1,2,6-based general population screening strategies in the model to allow for a head-tohead comparison with proband screening strategies. Lastly, universal germline testing for all newly diagnosed CRCs was also modeled. All twenty strategies were compared with a referent strategy of no additional screening to diagnose LS in the proband setting.
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LS have been recommended based on separate cost-effectiveness analyses (8–12). These approaches are based on the recognition that LS is caused by mutations in one of several DNA mismatch repair genes, leading to loss of expression of the specific protein product and the phenotype of microsatellite instability (MSI). In 2010, Mvundura et al. found that it was cost-effective with an Incremental Cost Effectiveness Ratio (ICER) of less than $45 000 per life-years gained (LYG) to perform immunohistochemistry (IHC) studies for mismatch repair (MMR) protein expression in all newly diagnosed CRC cases followed by genotyping in patients with loss of MMR protein expression by IHC (8). All strategies in this study started with laboratory testing of the pathologic specimen. Dinh et al. subsequently concluded that screening of the general population for LS was also cost-effective (10). Various strategies using PREMM1,2,6—a predictive model for assessment of risk for LS based on history—assigned risk levels to subjects in the general population. A risk of 5% and age cutoff of 25 to 35 years were found to be the most cost-effective strategies. However, PREMM1,2,6 was not intended for use in the general population and has not been validated in this population. When applied to the general population setting even using the sensitivity and specificity found by the validation studies in high-risk populations, PREMM1,2,6 will result in a low positive predictive value and a substantial number of false positives (13,14). Furthermore, the costs of using PREMM1,2,6 were not included in the model (14). In 2011, Ladabaum et al. published a study of proband screening for LS and included strategies based on patient history before initiating laboratory tumor-based testing. The authors concluded that IHC + BRAF was the most cost effective strategy, when strategies based on history were excluded from analysis (12). Notably, the number of relatives for each proband was high (8), introducing a potential bias in favor of more expensive screening strategies. A comparison of proband vs general population screening has not been done. In an effort to clarify the optimal approach to screening for LS, we conducted a comparison of all published algorithms and compared their effectiveness and cost-effectiveness.
0 0 493 641 594 772 1546 2010 1863 2422 1994 2593 2403 3124 2017 2622 2430 3159 1838 2389 2214 2878 2241 2913 2241 2913 2295 2984 2430 3159 2430 3159 2700 3510 138 179 129 168 120 156 110 144
$0 $0 $11 618 $11 202 $22 284 $19 406 $5365 $6391 $22 699 $19 725 $4924 $6053 $24 783 $21 328 $7298 $7878 $47 309 $38 656 $6495 $7261 $37 702 $31 266 $17 145 $15 453 $14 796 $13 646 $38 261 $31 696 $36 045 $29 991 $36 181 $30 096 $117 333 $92 521 $597 283 $461 713 $597 283 $461 713 $597 283 $461 713 $597 283 $461 713 AD AD AD
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$668 535
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$127 603
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0.69
0.57
0.22
0.18
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Cost per diagnosis Incremental of LS costs per primary Rank and Primary (P) diagnosis of LS status of the Probability of Secondary (S) (ICER) strategy being aware Sensitivity† 33 357 19 245 31 374 17 698 30 932 17 388 27 069 14 338 25 810 13 353 25 226 12 940 23 564 11 706 25 157 12 882 23 455 11 605 25 904 13 423 24 367 12 272 24 247 12 185 24 247 12 185 24 056 12 048 23 455 11 605 23 455 11 605 22 328 10 760 22 328 10 760 23 086 11 328 23 841 11 893 24 594 12 432
Colorectal cancer Cases Deaths
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12 815 8464 11 834 7775 11 621 7643 9683 6440 9018 5997 8724 5805 7802 5226 8679 5780 7751 5187 9059 6029 8197 5480 8141 5442 8141 5442 8033 5380 7751 5187 7751 5187 7166 4803 7166 4803 7554 5055 7941 5309 8325 5551
Endometrial and ovarian cancer Cases Deaths 7590 6496 7683 6565 7695 6580 7777 6627 7834 6644 7874 6672 7991 6770 7872 6673 7999 6779 7840 6648 7914 6707 7934 6725 7934 6725 7971 6757 7999 6779 7999 6779 8024 6817 8024 6817 8008 6788 8000 6780 7912 6707
28 224 6 28 955 42 29 127 48 30 597 101 31 060 115 31 318 131 32 002 158 31 359 131 32 042 161 31 037 113 31 670 142 31 749 144 31 749 144 31 803 148 32 042 161 32 042 161 32 493 177 32 493 177 32 205 164 31 891 152 31 562 137
Other cancer Death because of Cases Natural causes Deaths Colonoscopy
* The first six columns summarize the performance results of each strategy in Step 1. The last four columns provide frequencies of cancer diagnoses and mortalities because of cancer and noncancer causes. ICER = incremental cost-effectiveness ratio; IHC = immunohistochemistry; LS = Lynch Syndrome; MSI = microsatellite instability. † Specificity of all strategies other than the referent strategy is considered to be 1, as all strategies include germline testing as a final step.
PREMM GP screening 1, germline age > 20 y PREMM GP screening 2, germline age > 25 y PREMM GP screening 3, germline age > 30 y PREMM GP screening 4, germline age > 35 y
Up-front germline
MSI + IHC + BRAF, germline
MSI + IHC, germline
MSI, germline
IHC + BRAF, germline
IHC, germline
Bethesda, germline
Bethesda, IHC, germline
PREMM, germline
PREMM, IHC, germline
MMRpro, germline
MMRpro, IHC, germline
MMRpredict, germline
MMRpredict, IHC, germline
Amsterdam, germline
No screening (referent strategy) Amsterdam, IHC, germline
Strategy
Diagnoses of LS Primary (P) Secondary (S)
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Table 1. Summary of the results*
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Effectiveness of the Screening Strategies, Simulation Scenarios
Sensitivity Analysis One-way sensitivity analyses using the range for cost of germline testing ($100-$880 per gene, varied in five equal steps), prevalence of LS in proband population (0.01–0.06 in six equal steps), and the number of first-degree relatives per primary diagnosis of LS (one to six in six equal steps) were performed and are reported here. One-way sensitivity analysis was also performed for the test parameters of the predictive models. In this analysis, the sensitivity values for the predictive models were decreased by a factor of 50% at increments of 10%. Further sensitivity analyses involving improved test parameters for predictive models are reported in the Supplementary Materials (available online).
Results The results demonstrated an absolute risk reduction for CRC and CRC deaths by 2% to 11% and 1.5% to 8.5%, respectively. These numbers for gynecological cancers and cancer deaths were 1% to 5.6% and 0.7% to 3.7%, respectively. The numbers for other cancers did not change, as there was no standard screening or prophylactic treatment offered to prevent them. As expected, the most effective results were achieved by germline testing of all probands (Table 1). In contrast, the most cost-effective strategy was a three-step approach beginning with a predictive model: MMRpro, IHC, germline, with an ICER of $35 143 per LYG. The remaining undominated strategies were IHC + BRAF, germline with an ICER of $144 117, MMRpro, germline with an ICER of $223 988, and universal germline testing with an ICER of $996 878. All other strategies including all general population screening strategies were dominated (Figure 1). Using a threshold of $50 000 per life-year gained, only MMRpro, IHC, and germline testing met the cost effectiveness threshold (see Table 2).
Sensitivity Analysis Decreasing the prevalence of LS among the CRC patient population to 1% or increasing it to 6% did not affect the ranking of cost-effective strategies, although the ICER did change (Table 3). As expected, the results were most sensitive to the costs of germline testing. Decreasing the cost of germline testing to $685 per gene resulted in domination of IHC + BRAF, germline by MMRpro, germline with an ICER of $136 482. Decreasing the cost of germline testing to $295 per gene resulted in MMRpro, germline becoming marginally cost-effective with an ICER of $60 953 per LYG. Decreasing the cost of germline testing further improved the ICER for MMRpro, germline until it reached $33 195 when the cost of germline testing was set at $100 per gene, dominating MMRpro, IHC, germline testing. Interestingly, the ICER for universal germline testing decreased only to $116 355, still notably higher than using MMRpro, germline testing (Table 3). Decreasing the number of first-degree relatives to one while increasing the ICERs did not change the status or ranking of strategies. Increasing this number to six resulted in improved ICERs while maintaining the status and ranking of cost-effective strategies (Table 4). Reducing the sensitivity of the predictive models simultaneously to 0.6 and 0.5 of the original values resulted in MMRpro, IHC, germline being dominated by IHC + BRAF, germline with an ICER of $48 339 and $49 976, respectively. The results are summarized in Table 4.
Discussion Our results suggest that screening algorithms that begin with predictive models are the most cost-effective strategies for screening for LS. This evidence suggests that universal tumor testing for all colon cancer patients for LS is not cost-effective. In the event that a CRC patient was adopted or the patient does not have knowledge of these cancers in his/her family and clinical suspicion exists, then proceeding to tumor-based testing would be appropriate. Given that routine screening of all colon cancers with IHC has been recently endorsed (26,27), our analysis suggests that using a predictive model first has comparable sensitivity (effectiveness) and can spare considerable societal resources. General population screening starting with PREMM1,2,6
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The simulated individuals in the Step 2 model had preferences in terms of compliance with screening recommendations given the status of the foreknowledge of LS (aware or not aware of carrying LS mutations). Since young otherwise healthy adults who carry LS mutations are the ones who benefit most from a screening, an age distribution for the simulated population conforming to a normal distribution with a median age of 30 years and standard deviation of three years was selected, with a male: female ratio of 1:1, similar to a previous study (12). Simulation terminated upon subjects’ death, age of 80 years, or 50 years of follow up, whichever came first. We assumed that the diagnostic strategy used in Step 1 would influence the outcome of LS in the cohort. However, alternatives to this assumption are also discussed and examined in the Supplementary Materials (available online). We used the sensitivity and specificity values for predictive models that were reported in the validation studies for each model. However, a study by Green et al. demonstrated that performance of predictive models such as MMRpro can be optimized. These optimized values are discussed and examined in the Supplementary Materials (available online).
If the predictive models and clinical criteria were removed from the ICER calculations, IHC + BRAF (germline with an ICER of $46 925), MSI, IHC (germline with an ICER of $400 728), and universal germline testing (ICER of $940 024) emerged as undominated. Of these, only IHC + BRAF and germline met the cost-effectiveness criteria at a threshold of $50 000. The results of Step 1, using the number of diagnoses of LS for each strategy as the “effect” also produced a table of ICERs. The strategies identified as undominated and their rankings matched the results of the simulation in Step 2. The yield of strategies in Step 1 in terms of the number of LS diagnoses is markedly lower for general population strategies. This would have created a bias against these strategies. In order to remove this bias, we used the general population strategy with the highest number of LS diagnoses (including all diagnoses, both primary and secondary) as the gold standard and used this number to determine the aware and not aware breakdown of the population for Step 2. Clinical criteria and general population screening strategies did not emerge as cost-effective strategies for screening for LS (Table 2).
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and 80 years was used as the screening modality (25). The study population represented the same characteristics of the LS individuals in Step 1 in the much larger cohort of 100 000 subjects.
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$350 000 No screening (1) MMRpro, IHC, germline (2) IHC + BRAF, germline (3)
$300 000
MMRpro, germline (4) Up-front germline (5) Amsterdam, IHC, germline (ED)
$250 000
MMRpredict, IHC, germline (ED) PREMM, IHC, germline (ED) MSI + IHC, germline (ED)
$200 000
Bethesda, IHC, germline (AD)
Cost
Amsterdam, germline (AD) IHC, germline (AD) MMRpredict, germline (AD)
$150 000
MSI + IHC + BRAF, germline (AD) MSI, germline (AD)
$100 000
PREMM, germline (AD) PREMM GP screening 4, germline age > 30 y (AD) PREMM GP screening 3, germline age > 30 y (AD)
$50 000
PREMM GP screening 2, germline age > > 25 y (AD) PREMM GP screening 1, germline age > 20 y (AD) Undominated
$0 21.5
21.6
21.7
21.8
21.9
22
22.1
22.2
22.3
22.4
Life-years gained Figure 1. Cost-effectiveness of 21 screening strategies. The line connects the undominated strategies in the analysis. Undominated strategies progressively become more expensive, and the slope of the line between any two undominated strategies is the incremental cost-effectiveness ratio (ICER) for the more expensive strategy. The four outlier strategies in the top right corner represent the general population screening strategies. Numbers in parentheses indicate the rank of the strategy with respect to the ICERs. AD = absolute dominance; ED = extended dominance; IHC = immunohistochemistry; MSI = microsatellite instability.
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failed to result in sufficient numbers of LS diagnoses to make it a contender compared with proband screening. The prevalence of LS is much lower in the general population compared with colon cancer population, and the costs of identifying LS subjects are much higher compared with proband screening strategies. Given the similarities in performance characteristics of other predictive models, if they were to be evaluated in the general population setting, their performances would suffer from similar shortcomings as PREMM1,2,6 in this context. Our results remained stable over a host of sensitivity analyses—some reported here and some listed in the Supplementary Materials (available online). While there is no predefined threshold for willingness to pay to aid the decision-making based on the ICERs in Step 1, it can be inferred from our results that the strategies that cost less per diagnosis of LS will perform better overall. As demonstrated in this analysis, the sensitivity of MMRpro had to be decreased by 40% for MMRpro, IHC, germline to be dominated by tumor-based testing. Furthermore, even if the cost of universal germline testing decreased to $100 per gene, MMRpro remained more cost-effective than universal germline testing, which dominated all other tumor based testing. Up to 6% of CRC is hereditary, with LS accounting for only 50% of these. This fact points out that even if universal tumor testing is adopted as the strategy of choice for LS, there are still 50% of hereditary cases that require family history and investigation for a substantial number of CRC patients. Thus, a careful family history should be the first step, not only in LS diagnosis
but also for identification of other syndromes that might be otherwise missed by universal tumor testing for LS. MMRPro is data intensive and requires information about the affected and unaffected family members, which may lead to incomplete input in routine practice (23). MMRPredict is modeled based on a cohort of colon cancer patients who underwent evaluation for the identification of MLH1, MSH2, and MSH6. Therefore, it is best suited for patients with colon cancer and has limitations when used in families with less common Lynchrelated cancers (20). PREMM1,2,6 is easy to use and accounts for many Lynch-associated tumors beyond colorectal and endometrial cancers. It is modeled based on the data from a large population of probands with known mismatch repair mutation (13). As expected, performance of these models is directly associated with the quality of information fed into them. With high-quality information, a negative screening result (as defined by a risk for Lynch Syndrome of 20 y PREMM GP screening 2, germline age > 25 y PREMM GP screening 3, germline age > 30 y PREMM GP screening 4, germline age > 35 y
Strategy
Discounted life-years per LS diagnosis
Table 2. Summary of the results of the microsimulation for Step 2*
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$0 $0 ED AD ED ED ED ED ED ED ED ED $5850 $33 195 AD ED ED ED AD AD AD AD AD AD AD AD AD AD AD AD AD AD $66 499 $116 355 AD AD AD AD AD AD AD AD
$100 $0 $0 ED ED ED AD ED ED AD AD $6053 $35 143 $27 621 $60 953 ED ED ED ED AD AD AD AD AD AD AD AD AD AD AD AD AD AD $217 008 $336 486 AD AD AD AD AD AD AD AD
$295 $0 $0 ED ED ED AD ED ED AD AD $6053 $35 143 $50 384 $98 717 ED ED ED ED AD AD AD AD AD AD ED ED AD AD AD AD AD AD $367 517 $556 616 AD AD AD AD AD AD AD AD
$490
Germline testing costs Step 1 ICERs Step 2 ICERs
$0 $0 ED ED AD AD ED ED AD AD $6053 $35 143 $74 703 $136 482 ED ED AD AD AD AD AD AD AD AD $72 124 ED AD AD ED ED AD AD $518 026 $776 747 AD AD AD AD AD AD AD AD
$685 $0 $0 ED ED AD AD ED ED AD AD $6053 $35 143 $127 603 $223 988 ED ED AD AD AD AD AD AD AD AD $75 082 $144 117 AD AD ED ED AD AD $668 535 $996 878 AD AD AD AD AD AD AD AD
$880 $0 $0 ED AD AD AD ED ED AD AD $13 149 $48 624 $329 540 $538 757 ED ED AD AD AD AD AD AD AD AD $212 732 $369 166 AD AD ED ED AD AD $2 037 507 $3 001 459 AD AD AD AD AD AD AD AD
0.01
0.03 $0 $0 ED ED AD AD ED ED AD AD $6053 $35 143 $127 603 $223 988 ED ED AD AD AD AD AD AD AD AD $75 082 $144 117 AD AD ED ED AD AD $668 535 $996 878 AD AD AD AD AD AD AD AD
$0 $0 ED AD AD AD ED ED AD AD $7827 $38 513 $178 087 $302 680 ED ED AD AD AD AD AD AD AD AD $109 494 $200 379 AD AD ED ED AD AD $1 010 778 $1 498 023 AD AD AD AD AD AD AD AD
0.04 $0 $0 ED ED AD AD ED ED AD AD $5166 $33 458 $102 360 $184 642 ED ED AD AD AD ED AD AD AD AD $57 875 $115 986 AD AD ED ED AD AD $497 414 $746 305 AD AD AD AD AD AD AD AD
LS prevalence in CRC Step 1 ICERs Step 2 ICERs 0.02
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$0 $0 ED ED AD AD ED ED AD AD $4633 $32 447 $87 215 $161 034 ED ED AD AD AD ED AD AD AD AD $47 552 $99 108 AD AD ED ED AD AD $394 741 $595 961 AD AD AD AD AD AD AD AD
0.05
$0 $0 ED ED AD AD ED ED AD AD $4278 $31 773 $77 118 $145 295 ED ED AD AD AD ED AD AD AD AD $40 669 $87 855 AD AD ED ED AD AD $326 292 $495 732 AD AD AD AD AD AD AD AD
0.06
* Prevalence of Lynch Syndrome with a range of 0.01 to 0.06. Cost of Germline Testing with a range of $100 to $880 per gene with five equally spaced values. AD = absolute dominance; CRC = colorectal cancer; ED = extended dominance; ICER = incremental cost-effectiveness ratio; IHC = immunohistochemistry; LS = Lynch Syndrome; MSI = microsatellite instability.
PREMM GP screening 4, germline age > 35 y
PREMM GP screening 3, germline age > 30 y
PREMM GP screening 2, germline age > 25 y
PREMM GP screening 1, germline age > 20 y
Up-front germline
MSI + IHC + BRAF, germline
MSI + IHC, germline
MSI, germline
IHC + BRAF, germline
IHC, germline
Bethesda, germline
Bethesda, IHC, germline
PREMM, germline
PREMM, IHC, germline
MMRpro, germline
MMRpro, IHC, germline
MMRpredict, germline
MMRpredict, IHC, germline
Amsterdam, germline
Amsterdam, IHC, germline
No screening
Strategy
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Table 3. The results of sensitivity analysis*
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$0 $0 ED AD AD AD ED ED AD AD $21 204 $63 749 $628 954 $908 321 ED ED AD AD AD AD AD AD AD AD $366 365 $688 261 AD AD ED ED AD AD $3 333 617 $4 263 540 AD AD AD AD AD AD AD AD
1 $0 $0 ED AD AD AD ED ED AD AD $11 735 $46 549 $315 609 $512 973 ED ED AD AD AD AD AD AD AD AD $184 300 $327 204 AD AD ED ED AD AD $1 667 941 $2 321 786 AD AD AD AD AD AD AD AD
2 $0 $0 ED AD AD AD ED ED AD AD $8578 $39 441 $211 161 $303 489 ED ED AD AD AD AD AD AD AD AD $123 625 $213 951 AD AD ED ED AD AD $1 112 715 $1 625 661 AD AD AD AD AD AD AD AD
3 $0 $0 ED ED AD AD ED ED AD AD $7000 $35 230 $158 937 $281 703 ED ED AD AD AD AD AD AD AD AD $93 286 $204 997 AD AD ED ED AD AD $835 103 $1 112 200 AD AD AD AD AD AD AD AD
4 $0 $0 ED ED AD AD ED ED AD AD $6053 $35 143 $127 603 $223 988 ED ED AD AD AD AD AD AD AD AD $75 082 $144 117 AD AD ED ED AD AD $668 535 $996 878 AD AD AD AD AD AD AD AD
5
Number of family members per primary LS diagnosis Step 1 ICERs Step 2 ICERs
$0 $0 ED ED AD AD ED ED AD AD $5421 $35 466 $106 713 $181 282 ED ED AD AD AD AD AD AD AD AD $62 945 $114 427 AD AD ED ED AD AD $557 490 $913 274 AD AD AD AD AD AD AD AD
6 $0 $0 ED ED AD AD ED ED AD AD $9552 ED AD ED ED ED AD AD AD AD AD AD AD AD $16 928 $49 976 ED ED $223 804 $470 700 AD AD $655 287 $959 041 AD AD AD AD AD AD AD AD
0.5
0.7 $0 $0 ED ED AD AD ED ED AD AD $6928 $41 739 AD AD ED ED AD AD AD AD AD AD AD AD $30 255 $51 365 ED AD $223 804 $394 976 AD AD $655 287 $914 742 AD AD AD AD AD AD AD AD
0.6 $0 $0 ED ED AD AD ED ED AD AD $8386 ED AD AD ED ED AD AD AD AD AD AD AD AD $19 673 $48 339 ED AD $223 804 $342 546 AD AD $655 287 $984 980 AD AD AD AD AD AD AD AD
$0 $0 ED ED AD AD ED ED AD AD $6928 $41 896 AD AD ED ED AD AD AD AD AD AD AD AD $30 255 $61 906 ED AD $223 804 $345 684 AD AD $655 287 $954 918 AD AD AD AD AD AD AD AD
0.8 $0 $0 ED ED AD AD ED ED AD AD $6441 $38 897 AD AD ED ED AD AD AD AD AD AD AD AD $42 644 $82 590 ED AD $223 804 $342 390 AD AD $655 287 $877 994 AD AD AD AD AD AD AD AD
0.9
Adjustment to the sensitivity of predictive models Step 1 ICERs Step 2 ICERs
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$0 $0 ED ED AD AD ED ED AD AD $6053 $35 143 $127 603 $223 988 ED ED ED AD AD AD AD AD AD AD $75 082 $144 117 AD AD ED ED AD AD $668 535 $996 878 AD AD AD AD AD AD AD AD
1.0
* The number of relatives was varied from one to six per diagnosis of Lynch Syndrome. The sensitivities of all predictive models were subjected to an adjustment factor to determine when tumor testing could dominate the predictive models. AD = absolute dominance; ED = extended dominance; ICER = incremental cost-effectiveness ratio; IHC = immunohistochemistry; LS = Lynch Syndrome; MSI = microsatellite instability.
PREMM GP screening 4, germline age > 35 y
PREMM GP screening 3, germline age > 30 y
PREMM GP screening 2, germline age > 25 y
PREMM GP screening 1, germline age > 20 y
Up-front germline
MSI + IHC + BRAF, germline
MSI + IHC, germline
MSI, germline
IHC + BRAF, germline
IHC, germline
Bethesda, germline
Bethesda, IHC, germline
PREMM, germline
PREMM, IHC, germline
MMRpro, germline
MMRpro, IHC, germline
MMRpredict, germline
MMRpredict, IHC, germline
Amsterdam, germline
Amsterdam, IHC, germline
No screening
Strategy
Table 4. The results of sensitivity analysis*
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Funding This study was funded in part by an institutional grant from the Cleveland Clinic Foundation (RPC-2011-1002) and Cancer Center Support Grant National Cancer Institute P30 CA43703.
Notes The authors do not have any financial disclosures to make. All authors have had access to the data and certify the integrity of the analysis.
References
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1. Siegel R, Naishadham D, Jemal A. Cancer statistics, 2013. CA Cancer J Clin. 2013;63(1):11–30. 2. Cunningham D, Atkin W, Lenz HJ, et al. Colorectal cancer. Lancet. 2010;375(9719):1030–1047. 3. Aarnio M, Mecklin JP, Aaltonen LA, Nystrom-Lahti M, Jarvinen HJ. Life-time risk of different cancers in hereditary non-polyposis colorectal cancer (HNPCC) syndrome. Int J Cancer. 1995;64(6):430–433. 4. Stoffel E, Mukherjee B, Raymond VM, et al. Calculation of risk of colorectal and endometrial cancer among patients with Lynch syndrome. Gastroenterology. 2009;137(5):1621–1627. 5. Engel C, Loeffler M, Steinke V, et al. Risks of less common cancers in proven mutation carriers with lynch syndrome. J Clin Oncol. 2012;30(35):4409–4415. 6. Win AK, Lindor NM, Winship I, et al. Risks of colorectal and other cancers after endometrial cancer for women with Lynch syndrome. J Natl Cancer Inst. 2013;105(4):274–279. 7. Watson P, Vasen HF, Mecklin JP, et al. The risk of extra-colonic, extra-endometrial cancer in the Lynch syndrome. Int J Cancer. 2008;123(2):444–449.
8. Mvundura M, Grosse SD, Hampel H, Palomaki GE. The cost-effectiveness of genetic testing strategies for Lynch syndrome among newly diagnosed patients with colorectal cancer. Genet Med. 2010;12(2):93–104. 9. Hampel H. Point: justification for Lynch syndrome screening among all patients with newly diagnosed colorectal cancer. J Natl Compr Canc Netw. 2010;8(5):597–601. 10. Dinh TA, Rosner BI, Atwood JC, et al. Health Benefits and Cost-Effectiveness of Primary Genetic Screening for Lynch Syndrome in the General Population. Cancer Prev Res (Phila). 2011;4(1):9–22. 11. Palomaki GE, McClain MR, Melillo S, Hampel HL, Thibodeau SN. EGAPP Supplementary Materials evidence review: DNA testing strategies aimed at reducing morbidity and mortality from Lynch syndrome. Genet Med. 2009;11(1):42–65. 12. Ladabaum U, Wang G, Terdiman J, et al. Strategies to identify the Lynch syndrome among patients with colorectal cancer: a cost-effectiveness analysis. Ann Intern Med. 2011;155(2):69–79. 13. Kastrinos F, Steyerberg EW, Mercado R, et al. The PREMM(1,2,6) Model Predicts Risk of MLH1, MSH2, and MSH6 Germline Mutations Based on Cancer History. Gastroenterology. 2010;140(1):73–81. 14. Sadeghi S, Barzi A, Kattan MW, Meropol NJ. Screening for Lynch syndrome in the general population-letter. Cancer Prev Res (Phila). 2011;4(3):471; author reply 472. 15. Hampel H, Frankel WL, Martin E, et al. Screening for the Lynch syndrome (hereditary nonpolyposis colorectal cancer). N Engl J Med. 2005;352(18):1851– 1860. 16. Salovaara R, Loukola A, Kristo P, et al. Population-based molecular detection of hereditary nonpolyposis colorectal cancer. J Clin Oncol. 2000;18(11):2193– 2200. 17. Green RC, Parfrey PS, Woods MO, Younghusband HB. Prediction of Lynch syndrome in consecutive patients with colorectal cancer. J Natl Cancer Inst. 2009;101(5):331–340. 18. Sjursen W, Haukanes BI, Grindedal EM, et al. Current clinical criteria for Lynch syndrome are not sensitive enough to identify MSH6 mutation carriers. J Med Genet. 2010;47(9):579–585. 19. Pinol V, Castells A, Andreu M, et al. Accuracy of revised Bethesda guidelines, microsatellite instability, and immunohistochemistry for the identification of patients with hereditary nonpolyposis colorectal cancer. JAMA. 2005;293(16):1986–1994. 20. Barnetson RA, Tenesa A, Farrington SM, et al. Identification and survival of carriers of mutations in DNA mismatch-repair genes in colon cancer. N Engl J Med. 2006;354(26):2751–2763. 21. Balmana J, Balaguer F, Castellvi-Bel S, et al. Comparison of predictive models, clinical criteria and molecular tumour screening for the identification of patients with Lynch syndrome in a population-based cohort of colorectal cancer patients. J Med Genet. 2008;45(9):557–563. 22. Balaguer F, Balmana J, Castellvi-Bel S, et al. Validation and extension of the PREMM1,2 model in a population-based cohort of colorectal cancer patients. Gastroenterology. 2008;134(1):39–46. 23. Chen S, Wang W, Lee S, et al. Prediction of germline mutations and cancer risk in the Lynch syndrome. JAMA. 2006;296(12):1479–1487. 24. Fast Stats: An interactive tool for access to SEER cancer statistics. 2013; http://seer.cancer.gov/faststats. Accessed May 23, 2013. 25. Winawer S, Fletcher R, Rex D, et al. Colorectal cancer screening and surveillance: clinical guidelines and rationale-Update based on new evidence. Gastroenterology. 2003;124(2):544–560. 26. Beamer LC, Grant ML, Espenschied CR, et al. Reflex immunohistochemistry and microsatellite instability testing of colorectal tumors for Lynch syndrome among US cancer programs and follow-up of abnormal results. J Clin Oncol. 2012;30(10):1058–1063. 27. (NCCN) NCCN. Colorectal Cancer Screening. Practice Guidlines 2013 version 1. 2013.
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all eventualities is not feasible and would still remain vulnerable to concurrent changes in the assumptions. Because of the lack of validation data for the use of predictive models in screening the general population for LS, all such models, with the exception of PREMM1,2,6, were excluded from analysis. Unforeseen advances in diagnosis or management of CRC or LS can also alter these findings. Our conclusions also assume that information about family history is available and accurate. In clinical situations where these assumptions are not met or where pedigree size is small, routine screening with IHC is appropriate. These findings highlight the importance of obtaining a careful and accurate family history in the evaluation of all patients with colon cancer. This information remains an effective as well as cost-effective first step in the evaluation of hereditary risk and is necessary for application of predictive models to screening for Lynch Syndrome. We recommend that such assessment using predictive models should be considered as a quality-ofcare measure.
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Comparative Effectiveness of Screening Strategies for Lynch Syndrome Affiliations of authors: Department of Medicine, Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA (AB, SS); Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH (MWK); Cleveland Clinic Lerner College of Medicine, Cleveland, OH (MWK); Department of Epidemiology and Biostatistics (MWK) and Department of Medicine (NJM), School of Medicine, and Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, OH; Department of Medicine, University Hospitals Case Medical Center, Cleveland, OH (NJM). *Authors contributed equally to this work. † Principal Investigator. Correspondence to: Sarmad Sadeghi, MD, PhD, Department of Medicine, Norris Comprehensive Cancer Center, University of Southern California, 1441 Eastlake Ave, Suite 3440, Los Angeles, CA 90033 (e-mail: [email protected]).
Abstract Background: Colorectal cancer is the second leading cause of cancer death in the United States. Approximately 3% of colorectal cancers are associated with Lynch Syndrome. Controversy exists regarding the optimal screening strategy for Lynch Syndrome. Methods: Using an individual level microsimulation of a population affected by Lynch syndrome over several years, effectiveness and cost-effectiveness of 21 screening strategies were compared. Modeling assumptions were based upon published literature, and sensitivity analyses were performed for key assumptions. In a two-step process, the number of Lynch syndrome diagnoses (Step 1) and life-years gained as a result of foreknowledge of Lynch syndrome in otherwise healthy carriers (Step 2) were measured. article
Results: The optimal strategy was sequential screening for probands starting with a predictive model, then immunohistochemistry for mismatch repair protein expression (IHC), followed by germline mutation testing (incremental cost-effectiveness ratio [ICER] of $35 143 per life-year gained). The strategies of IHC + BRAF, germline testing and universal germline testing of colon cancer probands had ICERs of $144 117 and $996 878, respectively. Conclusions: This analysis suggests that the initial step in screening for Lynch Syndrome should be the use of predictive models in probands. Universal tumor testing and general population screening strategies are not costeffective. When family history is unavailable, alternate strategies are appropriate. Documentation of family history and screening for Lynch Syndrome using a predictive model may be considered a quality-of-care measure for patients with colorectal cancer.
Colorectal cancer (CRC) is the second leading cause of cancer death in the United States, with an incidence of over 142 820 new cases and 50 830 deaths per year (1). Up to six percent of these cancers are hereditary and potentially preventable. Lynch Syndrome (LS) is the most common hereditary colorectal cancer syndrome (2), accounting for approximately 3% of all colorectal cancers. Detection of Lynch Syndrome allows for
personalization of medical care for the affected individual and provides an opportunity for preventive cancer care in family members. In the case of Lynch Syndrome, this is particularly important given that there is increased risk for a variety of cancers (3–7). Recently, two distinct approaches to screening new CRC patients (probands) and screening the general population for
Received: April 21, 2014; Revised: July 28, 2014; Accepted: December 19, 2014 © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: [email protected].
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Afsaneh Barzi*, Sarmad Sadeghi*†, Michael W. Kattan, Neal J. Meropol
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Study Design and Setting The modeling paradigm for comparative effectiveness analysis of LS was built around a cost-effectiveness endpoint. With respect to the assessment of cost, this study took a societal perspective and included two steps: Step 1: The process by which healthy individuals with LS were identified. These were either first-degree relatives of probands, or members of the general population who were identified as a result of primary testing. Step 2: The healthy individuals affected by LS who were followed over time with lifetime screening to detect colon cancer at earlier stages and prophylactic interventions for gynecologic cancers to reduce mortality and morbidity from LS. Step 1 included 20 strategies: four strategies that started with clinical criteria (Amsterdam [15–18] or revised Bethesda [15,19]) and six strategies that started with a predictive model (PREMM1,2,6 [13], MMRpro [17], or MMRpredict [20,21]), all of
Cost-effectiveness The benefits (effects) were measured in LYG accrued as a result of the foreknowledge of LS diagnosis and reduced overall mortality. Incremental cost-effectiveness ratios (ICERs, ie, the ratio of the increase in cost to the increase in the effect) using LYG as effect were the primary measure of cost-effectiveness. If a strategy is not cost-effective compared with the next more effective strategy, it is designated as dominated. Absolute dominance (AD) indicates that the next strategy is more effective and less costly, and extended dominance (ED) indicates that the next strategy is more effective and more costly, but has a lower ICER. All of the strategies were modeled using TreeAge Pro 2012 by TreeAge Software, Inc. Costs structures used in previous studies were updated where appropriate based on the literature. Sensitivity and specificity for various tests were obtained from the validation studies, and stage distribution and disease incidence were obtained from SEER database (24) (see the Supplementary Materials, available online).
Study Models The Step 1 model was a decision tree that represented all strategies using a cohort (n = 100 000) of CRC probands or healthy general population and calculated the number of LS diagnoses using each strategy (primary diagnoses). Using the assumptions about the first-degree relatives, this number was translated to a number for healthy individuals affected with LS (secondary diagnoses) who could be offered preventive measures described in Step 2. Similarly, the number of missed but detectable LS diagnoses by other strategies were identified and were used to divide the LS population in the Step 2 model into aware (detectable and diagnosed) and not aware (detectable but missed) cases of LS. The Step 2 model was a Markov model that represented the natural history of CRC and other LS-related cancers in the population affected with LS. Colonoscopy every year between ages 20
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Methods
which were followed by either IHC then germline testing or directly followed by germline testing. As with clinical criteria, predictive models used a subject’s medical history to calculate a predicted risk for LS. A cutoff value of 5% or higher was used to identify subjects in whom further testing is warranted (13,20,22,23). An additional five strategies started with tumor testing (MSI, IHC, BRAF testing, or combination) followed by germline testing. Four strategies started with PREMM1,2,6 in the general population (GP), followed by germline testing. It is important to note that there is no rationale for the use of any of the predictive models or clinical criteria for screening the general population. These models were designed and validated in specific populations with personal or family history of colorectal cancer. Therefore, MMRpredict, MMRpro, Amsterdam Criteria, and revised Bethesda Criteria were excluded from the general population screening setting. However, given the published data by Dinh et al. we chose to include PREMM1,2,6-based general population screening strategies in the model to allow for a head-tohead comparison with proband screening strategies. Lastly, universal germline testing for all newly diagnosed CRCs was also modeled. All twenty strategies were compared with a referent strategy of no additional screening to diagnose LS in the proband setting.
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LS have been recommended based on separate cost-effectiveness analyses (8–12). These approaches are based on the recognition that LS is caused by mutations in one of several DNA mismatch repair genes, leading to loss of expression of the specific protein product and the phenotype of microsatellite instability (MSI). In 2010, Mvundura et al. found that it was cost-effective with an Incremental Cost Effectiveness Ratio (ICER) of less than $45 000 per life-years gained (LYG) to perform immunohistochemistry (IHC) studies for mismatch repair (MMR) protein expression in all newly diagnosed CRC cases followed by genotyping in patients with loss of MMR protein expression by IHC (8). All strategies in this study started with laboratory testing of the pathologic specimen. Dinh et al. subsequently concluded that screening of the general population for LS was also cost-effective (10). Various strategies using PREMM1,2,6—a predictive model for assessment of risk for LS based on history—assigned risk levels to subjects in the general population. A risk of 5% and age cutoff of 25 to 35 years were found to be the most cost-effective strategies. However, PREMM1,2,6 was not intended for use in the general population and has not been validated in this population. When applied to the general population setting even using the sensitivity and specificity found by the validation studies in high-risk populations, PREMM1,2,6 will result in a low positive predictive value and a substantial number of false positives (13,14). Furthermore, the costs of using PREMM1,2,6 were not included in the model (14). In 2011, Ladabaum et al. published a study of proband screening for LS and included strategies based on patient history before initiating laboratory tumor-based testing. The authors concluded that IHC + BRAF was the most cost effective strategy, when strategies based on history were excluded from analysis (12). Notably, the number of relatives for each proband was high (8), introducing a potential bias in favor of more expensive screening strategies. A comparison of proband vs general population screening has not been done. In an effort to clarify the optimal approach to screening for LS, we conducted a comparison of all published algorithms and compared their effectiveness and cost-effectiveness.
0 0 493 641 594 772 1546 2010 1863 2422 1994 2593 2403 3124 2017 2622 2430 3159 1838 2389 2214 2878 2241 2913 2241 2913 2295 2984 2430 3159 2430 3159 2700 3510 138 179 129 168 120 156 110 144
$0 $0 $11 618 $11 202 $22 284 $19 406 $5365 $6391 $22 699 $19 725 $4924 $6053 $24 783 $21 328 $7298 $7878 $47 309 $38 656 $6495 $7261 $37 702 $31 266 $17 145 $15 453 $14 796 $13 646 $38 261 $31 696 $36 045 $29 991 $36 181 $30 096 $117 333 $92 521 $597 283 $461 713 $597 283 $461 713 $597 283 $461 713 $597 283 $461 713 AD AD AD
AD
$$-
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5
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ED
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3
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ED
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ED
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$668 535
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$75 082
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ED
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0.89 0.89
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0.89
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0.84
0.82
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0.8
0.66
0.89
0.73
0.88
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0.67
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0.82
0.68
0.9
0.75
0.89
0.74
0.69
0.57
0.22
0.18
0
Cost per diagnosis Incremental of LS costs per primary Rank and Primary (P) diagnosis of LS status of the Probability of Secondary (S) (ICER) strategy being aware Sensitivity† 33 357 19 245 31 374 17 698 30 932 17 388 27 069 14 338 25 810 13 353 25 226 12 940 23 564 11 706 25 157 12 882 23 455 11 605 25 904 13 423 24 367 12 272 24 247 12 185 24 247 12 185 24 056 12 048 23 455 11 605 23 455 11 605 22 328 10 760 22 328 10 760 23 086 11 328 23 841 11 893 24 594 12 432
Colorectal cancer Cases Deaths
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12 815 8464 11 834 7775 11 621 7643 9683 6440 9018 5997 8724 5805 7802 5226 8679 5780 7751 5187 9059 6029 8197 5480 8141 5442 8141 5442 8033 5380 7751 5187 7751 5187 7166 4803 7166 4803 7554 5055 7941 5309 8325 5551
Endometrial and ovarian cancer Cases Deaths 7590 6496 7683 6565 7695 6580 7777 6627 7834 6644 7874 6672 7991 6770 7872 6673 7999 6779 7840 6648 7914 6707 7934 6725 7934 6725 7971 6757 7999 6779 7999 6779 8024 6817 8024 6817 8008 6788 8000 6780 7912 6707
28 224 6 28 955 42 29 127 48 30 597 101 31 060 115 31 318 131 32 002 158 31 359 131 32 042 161 31 037 113 31 670 142 31 749 144 31 749 144 31 803 148 32 042 161 32 042 161 32 493 177 32 493 177 32 205 164 31 891 152 31 562 137
Other cancer Death because of Cases Natural causes Deaths Colonoscopy
* The first six columns summarize the performance results of each strategy in Step 1. The last four columns provide frequencies of cancer diagnoses and mortalities because of cancer and noncancer causes. ICER = incremental cost-effectiveness ratio; IHC = immunohistochemistry; LS = Lynch Syndrome; MSI = microsatellite instability. † Specificity of all strategies other than the referent strategy is considered to be 1, as all strategies include germline testing as a final step.
PREMM GP screening 1, germline age > 20 y PREMM GP screening 2, germline age > 25 y PREMM GP screening 3, germline age > 30 y PREMM GP screening 4, germline age > 35 y
Up-front germline
MSI + IHC + BRAF, germline
MSI + IHC, germline
MSI, germline
IHC + BRAF, germline
IHC, germline
Bethesda, germline
Bethesda, IHC, germline
PREMM, germline
PREMM, IHC, germline
MMRpro, germline
MMRpro, IHC, germline
MMRpredict, germline
MMRpredict, IHC, germline
Amsterdam, germline
No screening (referent strategy) Amsterdam, IHC, germline
Strategy
Diagnoses of LS Primary (P) Secondary (S)
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Table 1. Summary of the results*
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Effectiveness of the Screening Strategies, Simulation Scenarios
Sensitivity Analysis One-way sensitivity analyses using the range for cost of germline testing ($100-$880 per gene, varied in five equal steps), prevalence of LS in proband population (0.01–0.06 in six equal steps), and the number of first-degree relatives per primary diagnosis of LS (one to six in six equal steps) were performed and are reported here. One-way sensitivity analysis was also performed for the test parameters of the predictive models. In this analysis, the sensitivity values for the predictive models were decreased by a factor of 50% at increments of 10%. Further sensitivity analyses involving improved test parameters for predictive models are reported in the Supplementary Materials (available online).
Results The results demonstrated an absolute risk reduction for CRC and CRC deaths by 2% to 11% and 1.5% to 8.5%, respectively. These numbers for gynecological cancers and cancer deaths were 1% to 5.6% and 0.7% to 3.7%, respectively. The numbers for other cancers did not change, as there was no standard screening or prophylactic treatment offered to prevent them. As expected, the most effective results were achieved by germline testing of all probands (Table 1). In contrast, the most cost-effective strategy was a three-step approach beginning with a predictive model: MMRpro, IHC, germline, with an ICER of $35 143 per LYG. The remaining undominated strategies were IHC + BRAF, germline with an ICER of $144 117, MMRpro, germline with an ICER of $223 988, and universal germline testing with an ICER of $996 878. All other strategies including all general population screening strategies were dominated (Figure 1). Using a threshold of $50 000 per life-year gained, only MMRpro, IHC, and germline testing met the cost effectiveness threshold (see Table 2).
Sensitivity Analysis Decreasing the prevalence of LS among the CRC patient population to 1% or increasing it to 6% did not affect the ranking of cost-effective strategies, although the ICER did change (Table 3). As expected, the results were most sensitive to the costs of germline testing. Decreasing the cost of germline testing to $685 per gene resulted in domination of IHC + BRAF, germline by MMRpro, germline with an ICER of $136 482. Decreasing the cost of germline testing to $295 per gene resulted in MMRpro, germline becoming marginally cost-effective with an ICER of $60 953 per LYG. Decreasing the cost of germline testing further improved the ICER for MMRpro, germline until it reached $33 195 when the cost of germline testing was set at $100 per gene, dominating MMRpro, IHC, germline testing. Interestingly, the ICER for universal germline testing decreased only to $116 355, still notably higher than using MMRpro, germline testing (Table 3). Decreasing the number of first-degree relatives to one while increasing the ICERs did not change the status or ranking of strategies. Increasing this number to six resulted in improved ICERs while maintaining the status and ranking of cost-effective strategies (Table 4). Reducing the sensitivity of the predictive models simultaneously to 0.6 and 0.5 of the original values resulted in MMRpro, IHC, germline being dominated by IHC + BRAF, germline with an ICER of $48 339 and $49 976, respectively. The results are summarized in Table 4.
Discussion Our results suggest that screening algorithms that begin with predictive models are the most cost-effective strategies for screening for LS. This evidence suggests that universal tumor testing for all colon cancer patients for LS is not cost-effective. In the event that a CRC patient was adopted or the patient does not have knowledge of these cancers in his/her family and clinical suspicion exists, then proceeding to tumor-based testing would be appropriate. Given that routine screening of all colon cancers with IHC has been recently endorsed (26,27), our analysis suggests that using a predictive model first has comparable sensitivity (effectiveness) and can spare considerable societal resources. General population screening starting with PREMM1,2,6
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The simulated individuals in the Step 2 model had preferences in terms of compliance with screening recommendations given the status of the foreknowledge of LS (aware or not aware of carrying LS mutations). Since young otherwise healthy adults who carry LS mutations are the ones who benefit most from a screening, an age distribution for the simulated population conforming to a normal distribution with a median age of 30 years and standard deviation of three years was selected, with a male: female ratio of 1:1, similar to a previous study (12). Simulation terminated upon subjects’ death, age of 80 years, or 50 years of follow up, whichever came first. We assumed that the diagnostic strategy used in Step 1 would influence the outcome of LS in the cohort. However, alternatives to this assumption are also discussed and examined in the Supplementary Materials (available online). We used the sensitivity and specificity values for predictive models that were reported in the validation studies for each model. However, a study by Green et al. demonstrated that performance of predictive models such as MMRpro can be optimized. These optimized values are discussed and examined in the Supplementary Materials (available online).
If the predictive models and clinical criteria were removed from the ICER calculations, IHC + BRAF (germline with an ICER of $46 925), MSI, IHC (germline with an ICER of $400 728), and universal germline testing (ICER of $940 024) emerged as undominated. Of these, only IHC + BRAF and germline met the cost-effectiveness criteria at a threshold of $50 000. The results of Step 1, using the number of diagnoses of LS for each strategy as the “effect” also produced a table of ICERs. The strategies identified as undominated and their rankings matched the results of the simulation in Step 2. The yield of strategies in Step 1 in terms of the number of LS diagnoses is markedly lower for general population strategies. This would have created a bias against these strategies. In order to remove this bias, we used the general population strategy with the highest number of LS diagnoses (including all diagnoses, both primary and secondary) as the gold standard and used this number to determine the aware and not aware breakdown of the population for Step 2. Clinical criteria and general population screening strategies did not emerge as cost-effective strategies for screening for LS (Table 2).
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and 80 years was used as the screening modality (25). The study population represented the same characteristics of the LS individuals in Step 1 in the much larger cohort of 100 000 subjects.
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$350 000 No screening (1) MMRpro, IHC, germline (2) IHC + BRAF, germline (3)
$300 000
MMRpro, germline (4) Up-front germline (5) Amsterdam, IHC, germline (ED)
$250 000
MMRpredict, IHC, germline (ED) PREMM, IHC, germline (ED) MSI + IHC, germline (ED)
$200 000
Bethesda, IHC, germline (AD)
Cost
Amsterdam, germline (AD) IHC, germline (AD) MMRpredict, germline (AD)
$150 000
MSI + IHC + BRAF, germline (AD) MSI, germline (AD)
$100 000
PREMM, germline (AD) PREMM GP screening 4, germline age > 30 y (AD) PREMM GP screening 3, germline age > 30 y (AD)
$50 000
PREMM GP screening 2, germline age > > 25 y (AD) PREMM GP screening 1, germline age > 20 y (AD) Undominated
$0 21.5
21.6
21.7
21.8
21.9
22
22.1
22.2
22.3
22.4
Life-years gained Figure 1. Cost-effectiveness of 21 screening strategies. The line connects the undominated strategies in the analysis. Undominated strategies progressively become more expensive, and the slope of the line between any two undominated strategies is the incremental cost-effectiveness ratio (ICER) for the more expensive strategy. The four outlier strategies in the top right corner represent the general population screening strategies. Numbers in parentheses indicate the rank of the strategy with respect to the ICERs. AD = absolute dominance; ED = extended dominance; IHC = immunohistochemistry; MSI = microsatellite instability.
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failed to result in sufficient numbers of LS diagnoses to make it a contender compared with proband screening. The prevalence of LS is much lower in the general population compared with colon cancer population, and the costs of identifying LS subjects are much higher compared with proband screening strategies. Given the similarities in performance characteristics of other predictive models, if they were to be evaluated in the general population setting, their performances would suffer from similar shortcomings as PREMM1,2,6 in this context. Our results remained stable over a host of sensitivity analyses—some reported here and some listed in the Supplementary Materials (available online). While there is no predefined threshold for willingness to pay to aid the decision-making based on the ICERs in Step 1, it can be inferred from our results that the strategies that cost less per diagnosis of LS will perform better overall. As demonstrated in this analysis, the sensitivity of MMRpro had to be decreased by 40% for MMRpro, IHC, germline to be dominated by tumor-based testing. Furthermore, even if the cost of universal germline testing decreased to $100 per gene, MMRpro remained more cost-effective than universal germline testing, which dominated all other tumor based testing. Up to 6% of CRC is hereditary, with LS accounting for only 50% of these. This fact points out that even if universal tumor testing is adopted as the strategy of choice for LS, there are still 50% of hereditary cases that require family history and investigation for a substantial number of CRC patients. Thus, a careful family history should be the first step, not only in LS diagnosis
but also for identification of other syndromes that might be otherwise missed by universal tumor testing for LS. MMRPro is data intensive and requires information about the affected and unaffected family members, which may lead to incomplete input in routine practice (23). MMRPredict is modeled based on a cohort of colon cancer patients who underwent evaluation for the identification of MLH1, MSH2, and MSH6. Therefore, it is best suited for patients with colon cancer and has limitations when used in families with less common Lynchrelated cancers (20). PREMM1,2,6 is easy to use and accounts for many Lynch-associated tumors beyond colorectal and endometrial cancers. It is modeled based on the data from a large population of probands with known mismatch repair mutation (13). As expected, performance of these models is directly associated with the quality of information fed into them. With high-quality information, a negative screening result (as defined by a risk for Lynch Syndrome of 20 y PREMM GP screening 2, germline age > 25 y PREMM GP screening 3, germline age > 30 y PREMM GP screening 4, germline age > 35 y
Strategy
Discounted life-years per LS diagnosis
Table 2. Summary of the results of the microsimulation for Step 2*
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$0 $0 ED AD ED ED ED ED ED ED ED ED $5850 $33 195 AD ED ED ED AD AD AD AD AD AD AD AD AD AD AD AD AD AD $66 499 $116 355 AD AD AD AD AD AD AD AD
$100 $0 $0 ED ED ED AD ED ED AD AD $6053 $35 143 $27 621 $60 953 ED ED ED ED AD AD AD AD AD AD AD AD AD AD AD AD AD AD $217 008 $336 486 AD AD AD AD AD AD AD AD
$295 $0 $0 ED ED ED AD ED ED AD AD $6053 $35 143 $50 384 $98 717 ED ED ED ED AD AD AD AD AD AD ED ED AD AD AD AD AD AD $367 517 $556 616 AD AD AD AD AD AD AD AD
$490
Germline testing costs Step 1 ICERs Step 2 ICERs
$0 $0 ED ED AD AD ED ED AD AD $6053 $35 143 $74 703 $136 482 ED ED AD AD AD AD AD AD AD AD $72 124 ED AD AD ED ED AD AD $518 026 $776 747 AD AD AD AD AD AD AD AD
$685 $0 $0 ED ED AD AD ED ED AD AD $6053 $35 143 $127 603 $223 988 ED ED AD AD AD AD AD AD AD AD $75 082 $144 117 AD AD ED ED AD AD $668 535 $996 878 AD AD AD AD AD AD AD AD
$880 $0 $0 ED AD AD AD ED ED AD AD $13 149 $48 624 $329 540 $538 757 ED ED AD AD AD AD AD AD AD AD $212 732 $369 166 AD AD ED ED AD AD $2 037 507 $3 001 459 AD AD AD AD AD AD AD AD
0.01
0.03 $0 $0 ED ED AD AD ED ED AD AD $6053 $35 143 $127 603 $223 988 ED ED AD AD AD AD AD AD AD AD $75 082 $144 117 AD AD ED ED AD AD $668 535 $996 878 AD AD AD AD AD AD AD AD
$0 $0 ED AD AD AD ED ED AD AD $7827 $38 513 $178 087 $302 680 ED ED AD AD AD AD AD AD AD AD $109 494 $200 379 AD AD ED ED AD AD $1 010 778 $1 498 023 AD AD AD AD AD AD AD AD
0.04 $0 $0 ED ED AD AD ED ED AD AD $5166 $33 458 $102 360 $184 642 ED ED AD AD AD ED AD AD AD AD $57 875 $115 986 AD AD ED ED AD AD $497 414 $746 305 AD AD AD AD AD AD AD AD
LS prevalence in CRC Step 1 ICERs Step 2 ICERs 0.02
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$0 $0 ED ED AD AD ED ED AD AD $4633 $32 447 $87 215 $161 034 ED ED AD AD AD ED AD AD AD AD $47 552 $99 108 AD AD ED ED AD AD $394 741 $595 961 AD AD AD AD AD AD AD AD
0.05
$0 $0 ED ED AD AD ED ED AD AD $4278 $31 773 $77 118 $145 295 ED ED AD AD AD ED AD AD AD AD $40 669 $87 855 AD AD ED ED AD AD $326 292 $495 732 AD AD AD AD AD AD AD AD
0.06
* Prevalence of Lynch Syndrome with a range of 0.01 to 0.06. Cost of Germline Testing with a range of $100 to $880 per gene with five equally spaced values. AD = absolute dominance; CRC = colorectal cancer; ED = extended dominance; ICER = incremental cost-effectiveness ratio; IHC = immunohistochemistry; LS = Lynch Syndrome; MSI = microsatellite instability.
PREMM GP screening 4, germline age > 35 y
PREMM GP screening 3, germline age > 30 y
PREMM GP screening 2, germline age > 25 y
PREMM GP screening 1, germline age > 20 y
Up-front germline
MSI + IHC + BRAF, germline
MSI + IHC, germline
MSI, germline
IHC + BRAF, germline
IHC, germline
Bethesda, germline
Bethesda, IHC, germline
PREMM, germline
PREMM, IHC, germline
MMRpro, germline
MMRpro, IHC, germline
MMRpredict, germline
MMRpredict, IHC, germline
Amsterdam, germline
Amsterdam, IHC, germline
No screening
Strategy
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Table 3. The results of sensitivity analysis*
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$0 $0 ED AD AD AD ED ED AD AD $21 204 $63 749 $628 954 $908 321 ED ED AD AD AD AD AD AD AD AD $366 365 $688 261 AD AD ED ED AD AD $3 333 617 $4 263 540 AD AD AD AD AD AD AD AD
1 $0 $0 ED AD AD AD ED ED AD AD $11 735 $46 549 $315 609 $512 973 ED ED AD AD AD AD AD AD AD AD $184 300 $327 204 AD AD ED ED AD AD $1 667 941 $2 321 786 AD AD AD AD AD AD AD AD
2 $0 $0 ED AD AD AD ED ED AD AD $8578 $39 441 $211 161 $303 489 ED ED AD AD AD AD AD AD AD AD $123 625 $213 951 AD AD ED ED AD AD $1 112 715 $1 625 661 AD AD AD AD AD AD AD AD
3 $0 $0 ED ED AD AD ED ED AD AD $7000 $35 230 $158 937 $281 703 ED ED AD AD AD AD AD AD AD AD $93 286 $204 997 AD AD ED ED AD AD $835 103 $1 112 200 AD AD AD AD AD AD AD AD
4 $0 $0 ED ED AD AD ED ED AD AD $6053 $35 143 $127 603 $223 988 ED ED AD AD AD AD AD AD AD AD $75 082 $144 117 AD AD ED ED AD AD $668 535 $996 878 AD AD AD AD AD AD AD AD
5
Number of family members per primary LS diagnosis Step 1 ICERs Step 2 ICERs
$0 $0 ED ED AD AD ED ED AD AD $5421 $35 466 $106 713 $181 282 ED ED AD AD AD AD AD AD AD AD $62 945 $114 427 AD AD ED ED AD AD $557 490 $913 274 AD AD AD AD AD AD AD AD
6 $0 $0 ED ED AD AD ED ED AD AD $9552 ED AD ED ED ED AD AD AD AD AD AD AD AD $16 928 $49 976 ED ED $223 804 $470 700 AD AD $655 287 $959 041 AD AD AD AD AD AD AD AD
0.5
0.7 $0 $0 ED ED AD AD ED ED AD AD $6928 $41 739 AD AD ED ED AD AD AD AD AD AD AD AD $30 255 $51 365 ED AD $223 804 $394 976 AD AD $655 287 $914 742 AD AD AD AD AD AD AD AD
0.6 $0 $0 ED ED AD AD ED ED AD AD $8386 ED AD AD ED ED AD AD AD AD AD AD AD AD $19 673 $48 339 ED AD $223 804 $342 546 AD AD $655 287 $984 980 AD AD AD AD AD AD AD AD
$0 $0 ED ED AD AD ED ED AD AD $6928 $41 896 AD AD ED ED AD AD AD AD AD AD AD AD $30 255 $61 906 ED AD $223 804 $345 684 AD AD $655 287 $954 918 AD AD AD AD AD AD AD AD
0.8 $0 $0 ED ED AD AD ED ED AD AD $6441 $38 897 AD AD ED ED AD AD AD AD AD AD AD AD $42 644 $82 590 ED AD $223 804 $342 390 AD AD $655 287 $877 994 AD AD AD AD AD AD AD AD
0.9
Adjustment to the sensitivity of predictive models Step 1 ICERs Step 2 ICERs
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$0 $0 ED ED AD AD ED ED AD AD $6053 $35 143 $127 603 $223 988 ED ED ED AD AD AD AD AD AD AD $75 082 $144 117 AD AD ED ED AD AD $668 535 $996 878 AD AD AD AD AD AD AD AD
1.0
* The number of relatives was varied from one to six per diagnosis of Lynch Syndrome. The sensitivities of all predictive models were subjected to an adjustment factor to determine when tumor testing could dominate the predictive models. AD = absolute dominance; ED = extended dominance; ICER = incremental cost-effectiveness ratio; IHC = immunohistochemistry; LS = Lynch Syndrome; MSI = microsatellite instability.
PREMM GP screening 4, germline age > 35 y
PREMM GP screening 3, germline age > 30 y
PREMM GP screening 2, germline age > 25 y
PREMM GP screening 1, germline age > 20 y
Up-front germline
MSI + IHC + BRAF, germline
MSI + IHC, germline
MSI, germline
IHC + BRAF, germline
IHC, germline
Bethesda, germline
Bethesda, IHC, germline
PREMM, germline
PREMM, IHC, germline
MMRpro, germline
MMRpro, IHC, germline
MMRpredict, germline
MMRpredict, IHC, germline
Amsterdam, germline
Amsterdam, IHC, germline
No screening
Strategy
Table 4. The results of sensitivity analysis*
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Funding This study was funded in part by an institutional grant from the Cleveland Clinic Foundation (RPC-2011-1002) and Cancer Center Support Grant National Cancer Institute P30 CA43703.
Notes The authors do not have any financial disclosures to make. All authors have had access to the data and certify the integrity of the analysis.
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all eventualities is not feasible and would still remain vulnerable to concurrent changes in the assumptions. Because of the lack of validation data for the use of predictive models in screening the general population for LS, all such models, with the exception of PREMM1,2,6, were excluded from analysis. Unforeseen advances in diagnosis or management of CRC or LS can also alter these findings. Our conclusions also assume that information about family history is available and accurate. In clinical situations where these assumptions are not met or where pedigree size is small, routine screening with IHC is appropriate. These findings highlight the importance of obtaining a careful and accurate family history in the evaluation of all patients with colon cancer. This information remains an effective as well as cost-effective first step in the evaluation of hereditary risk and is necessary for application of predictive models to screening for Lynch Syndrome. We recommend that such assessment using predictive models should be considered as a quality-ofcare measure.
