Master of Science, S.U.N.Y. State University at Buffalo (2012)
Bachelor of Science, University Of Patras (2009)
Doctor of Philosophy, S.U.N.Y. State University at Buffalo (2015)
Sylvia Plevritis, Postdoctoral Faculty Sponsor
Disparities of national lung cancer screening guidelines in the U.S. population.
Journal of the National Cancer Institute
BACKGROUND: Current U.S. Preventive Services Task Force (USPSTF) lung cancer screening guidelines are based on smoking history and age (55-80 y). These guidelines may miss those at higher risk, even at lower exposures of smoking or younger ages, due to other risk factors such as race, family history or comorbidity. In this study, we characterized the demographic and clinical profiles of those selected by risk-based screening criteria but missed by USPSTF guidelines in younger (50-54 y) and older (71-80 y) age groups.METHODS: We used data from the National Health Interview Survey, the CISNET Smoking History Generator, and results of logistic prediction models to simulate life-time lung cancer risk-factor data for 100,000 individuals in the 1950-1960 birth cohorts. We calculated age-specific 6-year lung cancer risk for each individual from ages 50-90 y using the PLCOm2012 model, and evaluated age-specific screening eligibility by USPSTF guidelines and by risk-based criteria (varying thresholds between 1.3%-2.5%).RESULTS: In the 1950 birth cohort, 5.4% would have been ineligible for screening by USPSTF criteria in their younger ages, but eligible based on risk-based criteria. Similarly, 10.4% of the cohort would be ineligible for screening by USPSTF in older ages. Notably, high proportions of Blacks were ineligible for screening by USPSTF criteria at younger (15.6%) and older (14.2%) ages, which were statistically significantly greater than those of Whites (4.8% and 10.8%, respectively, P<0.001). Similar results were observed with other risk thresholds and for the 1960 cohort.CONCLUSIONS: Further consideration is needed to incorporate comprehensive risk factors, including race/ethnicity, into lung cancer screening to reduce potential racial disparities.
View details for DOI 10.1093/jnci/djaa013
View details for PubMedID 32040195
- Cost-Effectiveness Analysis of Lung Cancer Screening Accounting for the Effect of Indeterminate Findings JNCI CANCER SPECTRUM 2019; 3 (3)
Development and Validation of a Multivariable Lung Cancer Risk Prediction Model That Includes Low-Dose Computed Tomography Screening Results: A Secondary Analysis of Data From the National Lung Screening Trial.
JAMA network open
2019; 2 (3): e190204
Importance: Low-dose computed tomography lung cancer screening is most effective when applied to high-risk individuals.Objectives: To develop and validate a risk prediction model that incorporates low-dose computed tomography screening results.Design, Setting, and Participants: A logistic regression risk model was developed in National Lung Screening Trial (NLST) Lung Screening Study (LSS) data and was validated in NLST American College of Radiology Imaging Network (ACRIN) data. The NLST was a randomized clinical trial that recruited participants between August 2002 and April 2004, with follow-up to December 31, 2009. This secondary analysis of data from the NLST took place between August 10, 2013, and November 1, 2018. Included were LSS (n=14 576) and ACRIN (n=7653) participants who had 3 screens, adequate follow-up, and complete predictor information.Main Outcomes and Measures: Incident lung cancers occurring 1 to 4 years after the third screen (202 LSS and 96 ACRIN). Predictors included scores from the validated PLCOm2012 risk model and Lung CT Screening Reporting & Data System (Lung-RADS) screening results.Results: Overall, the mean (SD) age of 22 229 participants was 61.3 (5.0) years, 59.3% were male, and 90.9% were of non-Hispanic white race/ethnicity. During follow-up, 298 lung cancers were diagnosed in 22 229 individuals (1.3%). Eight result combinations were pooled into 4 groups based on similar associations. Adjusted for PLCOm2012 risks, compared with participants with 3 negative screens, participants with 1 positive screen and last negative had an odds ratio (OR) of 1.93 (95% CI, 1.34-2.76), and participants with 2 positive screens with last negative or 2 negative screens with last positive had an OR of 2.66 (95% CI, 1.60-4.43); when 2 or more screens were positive with last positive, the OR was 8.97 (95% CI, 5.76-13.97). In ACRIN validation data, the model that included PLCOm2012 scores and screening results (PLCO2012results) demonstrated significantly greater discrimination (area under the curve, 0.761; 95% CI, 0.716-0.799) than when screening results were excluded (PLCOm2012) (area under the curve, 0.687; 95% CI, 0.645-0.728) (P<.001). In ACRIN validation data, PLCO2012results demonstrated good calibration. Individuals who had initial negative scans but elevated PLCOm2012 six-year risks of at least 2.6% did not have risks decline below the 1.5% screening eligibility criterion when subsequent screens were negative.Conclusions and Relevance: According to this analysis, some individuals with elevated risk scores who have negative initial screens remain at elevated risks, warranting annual screening. Positive screens seem to increase baseline risk scores and may identify high-risk individuals for continued screening and enrollment into clinical trials.Trial Registration: ClinicalTrials.gov Identifier: NCT00047385.
View details for PubMedID 30821827
- Development and Validation of a Multivariable Lung Cancer Risk Prediction Model That Includes Low-Dose Computed Tomography Screening Results A Secondary Analysis of Data From the National Lung Screening Trial JAMA NETWORK OPEN 2019; 2 (3)
Cost-Effectiveness Analysis of Lung Cancer Screening in the United States: A Comparative Modeling Study.
Annals of internal medicine
Recommendations vary regarding the maximum age at which to stop lung cancer screening: 80 years according to the U.S. Preventive Services Task Force (USPSTF), 77 years according to the Centers for Medicare & Medicaid Services (CMS), and 74 years according to the National Lung Screening Trial (NLST).To compare the cost-effectiveness of different stopping ages for lung cancer screening.By using shared inputs for smoking behavior, costs, and quality of life, 4 independently developed microsimulation models evaluated the health and cost outcomes of annual lung cancer screening with low-dose computed tomography (LDCT).The NLST; Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial; SEER (Surveillance, Epidemiology, and End Results) program; Nurses' Health Study and Health Professionals Follow-up Study; and U.S. Smoking History Generator.Current, former, and never-smokers aged 45 years from the 1960 U.S. birth cohort.45 years.Health care sector.Annual LDCT according to NLST, CMS, and USPSTF criteria.Incremental cost-effectiveness ratios (ICERs) with a willingness-to-pay threshold of $100 000 per quality-adjusted life-year (QALY).The 4 models showed that the NLST, CMS, and USPSTF screening strategies were cost-effective, with ICERs averaging $49 200, $68 600, and $96 700 per QALY, respectively. Increasing the age at which to stop screening resulted in a greater reduction in mortality but also led to higher costs and overdiagnosis rates.Probabilistic sensitivity analysis showed that the NLST and CMS strategies had higher probabilities of being cost-effective (98% and 77%, respectively) than the USPSTF strategy (52%).Scenarios assumed 100% screening adherence, and models extrapolated beyond clinical trial data.All 3 sets of lung cancer screening criteria represent cost-effective programs. Despite underlying uncertainty, the NLST and CMS screening strategies have high probabilities of being cost-effective.CISNET (Cancer Intervention and Surveillance Modeling Network) Lung Group, National Cancer Institute.
View details for DOI 10.7326/M19-0322
View details for PubMedID 31683314
Cost-Effectiveness Analysis of Lung Cancer Screening Accounting for the Effect of Indeterminate Findings.
JNCI cancer spectrum
2019; 3 (3): pkz035
Numerous health policy organizations recommend lung cancer screening, but no consensus exists on the optimal policy. Moreover, the impact of the Lung CT screening reporting and data system guidelines to manage small pulmonary nodules of unknown significance (a.k.a. indeterminate nodules) on the cost-effectiveness of lung cancer screening is not well established.We assess the cost-effectiveness of 199 screening strategies that vary in terms of age and smoking eligibility criteria, using a microsimulation model. We simulate lung cancer-related events throughout the lifetime of US-representative current and former smokers. We conduct sensitivity analyses to test key model inputs and assumptions.The cost-effectiveness efficiency frontier consists of both annual and biennial screening strategies. Current guidelines are not on the frontier. Assuming 4% disutility associated with indeterminate findings, biennial screening for smokers aged 50-70 years with at least 40 pack-years and less than 10 years since smoking cessation is the cost-effective strategy using $100 000 willingness-to-pay threshold yielding the highest health benefit. Among all health utilities, the cost-effectiveness of screening is most sensitive to changes in the disutility of indeterminate findings. As the disutility of indeterminate findings decreases, screening eligibility criteria become less stringent and eventually annual screening for smokers aged 50-70 years with at least 30 pack-years and less than 10 years since smoking cessation is the cost-effective strategy yielding the highest health benefit.The disutility associated with indeterminate findings impacts the cost-effectiveness of lung cancer screening. Efforts to quantify and better understand the impact of indeterminate findings on the effectiveness and cost-effectiveness of lung cancer screening are warranted.
View details for DOI 10.1093/jncics/pkz035
View details for PubMedID 31942534
View details for PubMedCentralID PMC6947892
A comparative modeling analysis of risk-based lung cancer screening strategies.
Journal of the National Cancer Institute
Risk-prediction models have been proposed to select individuals for lung cancer screening. However, their long-term effects are uncertain. This study evaluates long-term benefits and harms of risk-based screening compared to current United States Preventive Services Task Force (USPSTF) recommendations.Four independent natural-history models performed a comparative modeling study evaluating long-term benefits and harms of selecting individuals for lung cancer screening through risk-prediction models. 363 risk-based screening strategies varying by screening starting and stopping age, risk-prediction model used for eligibility (Bach, PLCOm2012, LCDRAT), and risk-threshold were evaluated for a 1950 U.S. birth-cohort. Among the evaluated outcomes were percentage of individuals ever screened, screens required, lung cancer deaths averted, life-years gained and overdiagnosis.Risk-based screening strategies requiring similar screens among individuals aged 55-80 as the USPSTF-criteria (corresponding risk-thresholds: Bach: 2.8%, PLCOm2012: 1.7%, LCDRAT: 1.7%) averted considerably more lung cancer deaths (Bach: 693, PLCOm2012: 698, LCDRAT: 696, USPSTF: 613). However, life-years gained were only modestly higher (Bach: 8,660, PLCOm2012: 8,862, LCDRAT, 8,631,USPSTF: 8,590) and risk-based strategies had more overdiagnosis (Bach: 149, PLCOm2012: 147, LCDRAT: 150, USPSTF: 115). Sensitivity analyses suggests excluding individuals with limited life-expectancies (<5 years) from screening retains the life-years gained by risk-based screening, while reducing overdiagnosis by > 65.3%.Risk-based lung cancer screening strategies prevent considerably more lung cancer deaths than current recommendations. However, they yield modest additional life-years and increased overdiagnosis due to predominantly selecting older individuals. Efficient implementation of risk-based lung cancer screening requires careful consideration of life-expectancy for determining optimal individual stopping ages.
View details for DOI 10.1093/jnci/djz164
View details for PubMedID 31566216
Evaluating the impact of varied compliance to lung cancer screening recommendations using a microsimulation model
CANCER CAUSES & CONTROL
2017; 28 (9): 947–58
The US preventive services task force (USPSTF) recently recommended that individuals aged 55-80 with heavy smoking history be annually screened by low-dose computed tomography (LDCT), thereby extending the stopping age from 74 to 80 compared to the national lung screening trial (NLST) entry criterion. This decision was made partly with model-based analyses from cancer intervention and surveillance modeling network (CISNET), which assumed perfect compliance to screening.As part of CISNET, we developed a microsimulation model for lung cancer (LC) screening and calibrated and validated it using data from NLST and the prostate, lung, colorectal, and ovarian cancer screening trial (PLCO), respectively. We evaluated population-level outcomes of the lifetime screening program recommended by the USPSTF by varying screening compliance levels.Validation using PLCO shows that our model reproduces observed PLCO outcomes, predicting 884 LC cases [Expected(E)/Observed(O) = 0.99; CI 0.92-1.06] and 563 LC deaths (E/O = 0.94 CI 0.87-1.03) in the screening arm that has an average compliance rate of 87.9% over four annual screening rounds. We predict that perfect compliance to the USPSTF recommendation saves 501 LC deaths per 100,000 persons in the 1950 U.S. birth cohort; however, assuming that compliance behaviors extrapolated and varied from PLCO reduces the number of LC deaths avoided to 258, 230, and 175 as the average compliance rate over 26 annual screening rounds changes from 100 to 46, 39, and 29%, respectively.The implementation of the USPSTF recommendation is expected to contribute to a reduction in LC deaths, but the magnitude of the reduction will likely be heavily influenced by screening compliance.
View details for PubMedID 28702814
View details for PubMedCentralID PMC5880208
Comparative Effectiveness of Up To Three Lines of Chemotherapy Treatment Plans for Metastatic Colorectal Cancer.
MDM policy & practice
2017; 2 (2): 2381468317729650
Modern chemotherapy agents transformed standard care for metastatic colorectal cancer (mCRC) but raised concerns about the financial burden of the disease. We studied comparative effectiveness of treatment plans that involve up to three lines of therapies and impact of treatment sequencing on health and cost outcomes. We employed a Markov model to represent the dynamically changing health status of mCRC patients and used Monte-Carlo simulation to evaluate various treatment plans consistent with existing guidelines. We calibrated our model by a meta-analysis of published data from an extensive list of clinical trials and measured the effectiveness of each plan in terms of cost per quality-adjusted life year. We examined the sensitivity of our model and results with respect to key parameters in two scenarios serving as base case and worst case for patients' overall and progression-free survivals. The derived efficient frontiers included seven and five treatment plans in base case and worst case, respectively. The incremental cost-effectiveness ratio (ICER) ranged between $26,260 and $152,530 when the treatment plans on the efficient frontiers were compared against the least costly efficient plan in the base case, and between $21,256 and $60,040 in the worst case. All efficient plans were expected to lead to fewer than 2.5 adverse effects and on average successive adverse effects were spaced more than 9 weeks apart from each other in the base case. Based on ICER, all efficient treatment plans exhibit at least 87% chance of being efficient. Sensitivity analyses show that the ICERs were most dependent on drug acquisition cost, distributions of progression-free and overall survivals, and health utilities. We conclude that improvements in health outcomes may come at high incremental costs and are highly dependent in the order treatments are administered.
View details for PubMedID 30288431
View details for PubMedCentralID PMC6124942
- Worst-Case Conditional Value-at-Risk Minimization for Hazardous Materials Transportation TRANSPORTATION SCIENCE 2016; 50 (4): 1174-1187
- Routing hazardous materials on time-dependent networks using conditional value-at-risk TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2013; 37: 73-92