Medical Decision Making最新文献

{"title":"Icon Arrays for Medical Risk Communication: Do Icon Type and Color Influence Cardiovascular Risk Perception and Recall?","authors":"Rebecca Blase, Julia Meis-Harris, Birgitta Weltermann, Simone Dohle","doi":"10.1177/0272989X241263040","DOIUrl":"10.1177/0272989X241263040","url":null,"abstract":"<p><strong>Background: </strong>Icon arrays have been shown to be an effective method for communicating medical risk information. However, in practice, icon arrays used to visualize personal risks often differ in the type and color of the icons. The aim of this study was to examine the influence of icon type and color on the perception and recall of cardiovascular risk, as little is known about how color affects the perception of icon arrays.</p><p><strong>Methods: </strong>A total of 866 participants aged 40 to 90 years representative of the German population in terms of gender and age completed an online experiment. Using a 2 × 2 between-subjects design, participants were randomly assigned to 1 of 4 experimental groups. They received their hypothetical 10-year cardiovascular risk using an icon array that varied by icon type (smiley v. person) and color (black/white v. red/yellow). We measured risk perception, emotional response, intentions of taking action to reduce the risk (e.g., increasing one's physical activity), risk recall, and graph evaluation/trustworthiness, as well as numeracy and graphical literacy.</p><p><strong>Results: </strong>Icon arrays using person icons were evaluated more positively. There was no effect of icons or color on risk perception, emotional response, intentions of taking action to reduce the risk, or trustworthiness of the graph. While more numerate/graphical literate participants were more likely to correctly recall the presented risk estimate, icon type and color did not influence the probability of correct recall.</p><p><strong>Conclusions: </strong>Differences in the perception of the tested icon arrays were rather small, suggesting that they may be equally suitable for communicating medical risks. Further research on the robustness of these results across other colors, icons, and risk domains could add to guidelines on the design of visual aids.</p><p><strong>Highlights: </strong>The use of different icons and colors did not influence the perception and the probability of recalling the 10-year cardiovascular risk, the emotional response, or the intentions to reduce the presented risk.Icon arrays with person icons were evaluated more positively.There was no evidence to suggest that the effectiveness of the studied icon arrays varied based on individuals' levels of numerical or graphical literacy, nor did it differ between people with or without a history of CVD or on medication for an increased CVD risk.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"661-673"},"PeriodicalIF":3.1,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141762108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Perceived Penalties for Sharing Patient Beliefs with Health Care Providers.","authors":"Jessecae K Marsh, Onur Asan, Samantha Kleinberg","doi":"10.1177/0272989X241262241","DOIUrl":"10.1177/0272989X241262241","url":null,"abstract":"<p><strong>Background: </strong>Health care interactions may require patients to share with a physician information they believe but is incorrect. While a key piece of physicians' work is educating their patients, people's concerns of being seen as uninformed or incompetent by physicians may lead them to think that sharing incorrect health beliefs comes with a penalty. We tested people's perceptions of patients who share incorrect information and how these perceptions vary by the reasonableness of the belief and its centrality to the patient's disease.</p><p><strong>Design: </strong>We recruited 399 United States Prolific.co workers (357 retained after exclusions), 200 Prolific.co workers who reported having diabetes (139 after exclusions), and 244 primary care physicians (207 after exclusions). Participants read vignettes describing patients with type 2 diabetes sharing health beliefs that were central or peripheral to the management of diabetes. Beliefs included true and incorrect statements that were reasonable or unreasonable to believe. Participants rated how a doctor would perceive the patient, the patient's ability to manage their disease, and the patient's trust in doctors.</p><p><strong>Results: </strong>Participants rated patients who shared more unreasonable beliefs more negatively. There was an extra penalty for incorrect statements central to the patient's diabetes management (sample 1). These results replicated for participants with type 2 diabetes (sample 2) and physician participants (sample 3).</p><p><strong>Conclusions: </strong>Participants believed that patients who share incorrect information with their physicians will be penalized for their honesty. Physicians need to be educated on patients' concerns so they can help patients disclose what may be most important for education.</p><p><strong>Highlights: </strong>Understanding how people think they will be perceived in a health care setting can help us understand what they may be wary to share with their physicians.People think that patients who share incorrect beliefs will be viewed negatively.Helping patients share incorrect beliefs can improve care.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"617-626"},"PeriodicalIF":3.1,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11346123/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141876477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Making Drug Approval Decisions in the Face of Uncertainty: Cumulative Evidence versus Value of Information.","authors":"Stijntje W Dijk, Eline Krijkamp, Natalia Kunst, Jeremy A Labrecque, Cary P Gross, Aradhana Pandit, Chia-Ping Lu, Loes E Visser, John B Wong, M G Myriam Hunink","doi":"10.1177/0272989X241255047","DOIUrl":"10.1177/0272989X241255047","url":null,"abstract":"<p><strong>Background: </strong>The COVID-19 pandemic underscored the criticality and complexity of decision making for novel treatment approval and further research. Our study aims to assess potential decision-making methodologies, an evaluation vital for refining future public health crisis responses.</p><p><strong>Methods: </strong>We compared 4 decision-making approaches to drug approval and research: the Food and Drug Administration's policy decisions, cumulative meta-analysis, a prospective value-of-information (VOI) approach (using information available at the time of decision), and a reference standard (retrospective VOI analysis using information available in hindsight). Possible decisions were to reject, accept, provide emergency use authorization, or allow access to new therapies only in research settings. We used monoclonal antibodies provided to hospitalized COVID-19 patients as a case study, examining the evidence from September 2020 to December 2021 and focusing on each method's capacity to optimize health outcomes and resource allocation.</p><p><strong>Results: </strong>Our findings indicate a notable discrepancy between policy decisions and the reference standard retrospective VOI approach with expected losses up to $269 billion USD, suggesting suboptimal resource use during the wait for emergency use authorization. Relying solely on cumulative meta-analysis for decision making results in the largest expected loss, while the policy approach showed a loss up to $16 billion and the prospective VOI approach presented the least loss (up to $2 billion).</p><p><strong>Conclusion: </strong>Our research suggests that incorporating VOI analysis may be particularly useful for research prioritization and treatment implementation decisions during pandemics. While the prospective VOI approach was favored in this case study, further studies should validate the ideal decision-making method across various contexts. This study's findings not only enhance our understanding of decision-making strategies during a health crisis but also provide a potential framework for future pandemic responses.</p><p><strong>Highlights: </strong>This study reviews discrepancies between a reference standard (retrospective VOI, using hindsight information) and 3 conceivable real-time approaches to research-treatment decisions during a pandemic, suggesting suboptimal use of resources.Of all prospective decision-making approaches considered, VOI closely mirrored the reference standard, yielding the least expected value loss across our study timeline.This study illustrates the possible benefit of VOI results and the need for evidence accumulation accompanied by modeling in health technology assessment for emerging therapies.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"512-528"},"PeriodicalIF":3.1,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11283736/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141201040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Thinking Fast, Slow, and Forever: Daniel Kahneman Obituary.","authors":"Donald A Redelmeier","doi":"10.1177/0272989X241256121","DOIUrl":"https://doi.org/10.1177/0272989X241256121","url":null,"abstract":"","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":"44 5","pages":"467-469"},"PeriodicalIF":3.1,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141762147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stability of Willingness to Pay: Does Time and Treatment Allocation in a Randomized Controlled Trial Influence Willingness to Pay?","authors":"Marjon van der Pol, Verity Watson, Dwayne Boyers","doi":"10.1177/0272989X241249654","DOIUrl":"10.1177/0272989X241249654","url":null,"abstract":"<p><strong>Background: </strong>Willingness-to-pay (WTP) estimates are useful to policy makers only if they are generalizable beyond the moment when they are collected. To understand the \"shelf life\" of preference estimates, preference stability needs be tested over substantial periods of time.</p><p><strong>Methods: </strong>We tested the stability of WTP for preventative dental care (scale and polish) using a payment-card contingent valuation question administered to 909 randomized controlled trial participants at 4 time points: baseline (prerandomization) and at annual intervals for 3 years. Trial participants were regular attenders at National Health Service dental practices. Participants were randomly offered different frequencies (intensities) of scale polish (no scale and polish, 1 scale and polish per year, 2 scale and polishes per year). We also examined whether treatment allocation to these different treatment intensities influenced the stability of WTP. Interval regression methods were used to test for changes in WTP over time while controlling for changes in 2 determinants of WTP. Individual-level changes were also examined as well as the WTP function over time.</p><p><strong>Results: </strong>We found that at the aggregate level, mean WTP values were stable over time. The results were similar by trial arm. Individuals allocated to the arm with the highest scale and polish intensity (2 per year) had a slight increase in WTP toward the latter part of the trial. There was considerable variation at the individual level. The WTP function was stable over time.</p><p><strong>Conclusions: </strong>The payment-card contingent valuation method can produce stable WTP values in health over time. Future research should explore the generalizability of these results in other populations, for less familiar health care services, and using alternative elicitation methods.</p><p><strong>Highlights: </strong>Stated preferences are commonly used to value health care.Willingness-to-pay (WTP) estimates are useful only if they have a \"shelf life.\"Little is known about the stability of WTP for health care.We test the stability of WTP for dental care over 3 y.Our results show that the contingent valuation method can produce stable WTP values.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"470-480"},"PeriodicalIF":3.1,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11282685/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140913151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cost-effectiveness Analysis of Colorectal Cancer Screening Strategies Using Active Learning and Monte Carlo Simulation.","authors":"Amirhossein Fouladi, Amin Asadi, Eric A Sherer, Mahboubeh Madadi","doi":"10.1177/0272989X241258224","DOIUrl":"10.1177/0272989X241258224","url":null,"abstract":"<p><strong>Introduction: </strong>Detection of colorectal cancer (CRC) in the early stages through available screening tests increases the patient's survival chances. Multimodal screening policies can benefit patients by providing more diverse screening options and balancing the risks and benefits of screening tests. We investigate the cost-effectiveness of a wide variety of multimodal CRC screening policies.</p><p><strong>Methods: </strong>We developed a Monte Carlo simulation framework to model CRC dynamics. We proposed an innovative calibration process using machine learning models to estimate age- and size-specific adenomatous polyps' progression and regression rates. The proposed approach significantly expedites the model parameter space search.</p><p><strong>Results: </strong>Two multimodal proposed policies (i.e., 1] colonoscopy at 50 y and fecal occult blood test annually between 60 and 75 y and 2] colonoscopy at 50 and 60 y and fecal immunochemical test annually between 70 and 75 y) are identified as efficient frontier policies. Both policies are cost-effective at a willingness to pay of $50,000. Sensitivity analyses were performed to assess the sensitivity of results to a change in screening test costs as well as adherence behavior. The sensitivity analysis results suggest that the proposed policies are mostly robust to the considered changes in screening test costs, as there is a significant overlap between the efficient frontier policies of the baseline and the sensitivity analysis cases. However, the efficient frontier policies were more sensitive to changes in adherence behavior.</p><p><strong>Conclusion: </strong>Generally, combining stool-based tests with visual tests will benefit patients with higher life expectancy and a lower expected cost compared with unimodal screening policies. Colonoscopy at younger ages (when the colonoscopy complication risk is lower) and stool-based tests at older ages are shown to be more effective.</p><p><strong>Highlights: </strong>We propose a detailed Markov model to capture the colorectal cancer (CRC) dynamics. The proposed Markov model presents the detailed dynamics of adenomas progression to CRC.We use more than 44,000 colonoscopy reports and available data in the literature to calibrate the proposed Markov model using an innovative approach that leverages machine learning models to expedite the calibration process.We investigate the cost-effectiveness of a wide variety of multimodal CRC screening policies and compare their performances with the current in-practice policies.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"554-571"},"PeriodicalIF":3.1,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11325561/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141441046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Risk-Adapted Breast Screening for Women at Low Predicted Risk of Breast Cancer: An Online Discrete Choice Experiment.","authors":"Charlotte Kelley Jones, Suzanne Scott, Nora Pashayan, Stephen Morris, Yasmina Okan, Jo Waller","doi":"10.1177/0272989X241254828","DOIUrl":"10.1177/0272989X241254828","url":null,"abstract":"<p><strong>Background: </strong>A risk-stratified breast screening program could offer low-risk women less screening than is currently offered by the National Health Service. The acceptability of this approach may be enhanced if it corresponds to UK women's screening preferences and values.</p><p><strong>Objectives: </strong>To elicit and quantify preferences for low-risk screening options.</p><p><strong>Methods: </strong>Women aged 40 to 70 y with no history of breast cancer took part in an online discrete choice experiment. We generated 32 hypothetical low-risk screening programs defined by 5 attributes (start age, end age, screening interval, risk of dying from breast cancer, and risk of overdiagnosis), the levels of which were systematically varied between the programs. Respondents were presented with 8 choice sets and asked to choose between 2 screening alternatives or no screening. Preference data were analyzed using conditional logit regression models. The relative importance of attributes and the mean predicted probability of choosing each program were estimated.</p><p><strong>Results: </strong>Participants (<i>N</i> = 502) preferred all screening programs over no screening. An older starting age of screening, younger end age of screening, longer intervals between screening, and increased risk of dying had a negative impact on support for screening programs (<i>P</i> < 0.01). Although the risk of overdiagnosis was of low relative importance, a decreased risk of this harm had a small positive impact on screening choices. The mean predicted probabilities that risk-adapted screening programs would be supported relative to current guidelines were low (range, 0.18 to 0.52).</p><p><strong>Conclusions: </strong>A deintensified screening pathway for women at low risk of breast cancer, especially one that recommends a later screening start age, would run counter to women's breast screening preferences. Further research is needed to enhance the acceptability of offering less screening to those at low risk of breast cancer.</p><p><strong>Highlights: </strong>Risk-based breast screening may involve the deintensification of screening for women at low risk of breast cancer.Low-risk screening pathways run counter to women's screening preferences and values.Longer screening intervals may be preferable to a later start age.Work is needed to enhance the acceptability of a low-risk screening pathway.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"586-600"},"PeriodicalIF":3.1,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11283735/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141201041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feedback Loop Failure Modes in Medical Diagnosis: How Biases Can Emerge and Be Reinforced.","authors":"Rachael C Aikens, Jonathan H Chen, Michael Baiocchi, Julia F Simard","doi":"10.1177/0272989X241248612","DOIUrl":"10.1177/0272989X241248612","url":null,"abstract":"<p><strong>Background: </strong>Medical diagnosis in practice connects to research through continuous feedback loops: Studies of diagnosed cases shape our understanding of disease, which shapes future diagnostic practice. Without accounting for an imperfect and complex diagnostic process in which some cases are more likely to be diagnosed correctly (or diagnosed at all), the feedback loop can inadvertently exacerbate future diagnostic errors and biases.</p><p><strong>Framework: </strong>A feedback loop failure occurs if misleading evidence about disease etiology encourages systematic errors that self-perpetuate, compromising future diagnoses and patient care. This article defines scenarios for feedback loop failure in medical diagnosis.</p><p><strong>Design: </strong>Through simulated cases, we characterize how disease incidence, presentation, and risk factors can be misunderstood when observational data are summarized naive to biases arising from diagnostic error. A fourth simulation extends to a progressive disease.</p><p><strong>Results: </strong>When severe cases of a disease are diagnosed more readily, less severe cases go undiagnosed, increasingly leading to underestimation of the prevalence and heterogeneity of the disease presentation. Observed differences in incidence and symptoms between demographic groups may be driven by differences in risk, presentation, the diagnostic process itself, or a combination of these. We suggested how perceptions about risk factors and representativeness may drive the likelihood of diagnosis. Differing diagnosis rates between patient groups can feed back to increasingly greater diagnostic errors and disparities in the timing of diagnosis and treatment.</p><p><strong>Conclusions: </strong>A feedback loop between past data and future medical practice may seem obviously beneficial. However, under plausible scenarios, poorly implemented feedback loops can degrade care. Direct summaries from observational data based on diagnosed individuals may be misleading, especially concerning those symptoms and risk factors that influence the diagnostic process itself.</p><p><strong>Highlights: </strong>Current evidence about a disease can (and should) influence the diagnostic process. A feedback loop failure may occur if biased \"evidence\" encourages diagnostic errors, leading to future errors in the evidence base.When diagnostic accuracy varies for mild versus severe cases or between demographic groups, incorrect conclusions about disease prevalence and presentation will result without specifically accounting for such variability.Use of demographic characteristics in the diagnostic process should be done with careful justification, in particular avoiding potential cognitive biases and overcorrection.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"481-496"},"PeriodicalIF":3.1,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11281873/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140913150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Impact of Model Assumptions on Personalized Lung Cancer Screening Recommendations.","authors":"Kevin Ten Haaf, Koen de Nijs, Giulia Simoni, Andres Alban, Pianpian Cao, Zhuolu Sun, Jean Yong, Jihyoun Jeon, Iakovos Toumazis, Summer S Han, G Scott Gazelle, Chung Ying Kong, Sylvia K Plevritis, Rafael Meza, Harry J de Koning","doi":"10.1177/0272989X241249182","DOIUrl":"10.1177/0272989X241249182","url":null,"abstract":"<p><strong>Background: </strong>Recommendations regarding personalized lung cancer screening are being informed by natural-history modeling. Therefore, understanding how differences in model assumptions affect model-based personalized screening recommendations is essential.</p><p><strong>Design: </strong>Five Cancer Intervention and Surveillance Modeling Network (CISNET) models were evaluated. Lung cancer incidence, mortality, and stage distributions were compared across 4 theoretical scenarios to assess model assumptions regarding 1) sojourn times, 2) stage-specific sensitivities, and 3) screening-induced lung cancer mortality reductions. Analyses were stratified by sex and smoking behavior.</p><p><strong>Results: </strong>Most cancers had sojourn times <5 y (model range [MR]; lowest to highest value across models: 83.5%-98.7% of cancers). However, cancer aggressiveness still varied across models, as demonstrated by differences in proportions of cancers with sojourn times <2 y (MR: 42.5%-64.6%) and 2 to 4 y (MR: 28.8%-43.6%). Stage-specific sensitivity varied, particularly for stage I (MR: 31.3%-91.5%). Screening reduced stage IV incidence in most models for 1 y postscreening; increased sensitivity prolonged this period to 2 to 5 y. Screening-induced lung cancer mortality reductions among lung cancers detected at screening ranged widely (MR: 14.6%-48.9%), demonstrating variations in modeled treatment effectiveness of screen-detected cases. All models assumed longer sojourn times and greater screening-induced lung cancer mortality reductions for women. Models assuming differences in cancer epidemiology by smoking behaviors assumed shorter sojourn times and lower screening-induced lung cancer mortality reductions for heavy smokers.</p><p><strong>Conclusions: </strong>Model-based personalized screening recommendations are primarily driven by assumptions regarding sojourn times (favoring longer intervals for groups more likely to develop less aggressive cancers), sensitivity (higher sensitivities favoring longer intervals), and screening-induced mortality reductions (greater reductions favoring shorter intervals).</p><p><strong>Implications: </strong>Models suggest longer screening intervals may be feasible and benefits may be greater for women and light smokers.</p><p><strong>Highlights: </strong>Natural-history models are increasingly used to inform lung cancer screening, but causes for variations between models are difficult to assess.This is the first evaluation of these causes and their impact on personalized screening recommendations through easily interpretable metrics.Models vary regarding sojourn times, stage-specific sensitivities, and screening-induced lung cancer mortality reductions.Model outcomes were similar in predicting greater screening benefits for women and potentially light smokers. Longer screening intervals may be feasible for women and light smokers.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"497-511"},"PeriodicalIF":3.1,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11281869/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140913153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Spillover Effects of Extending Liver Transplantation to Patients with Colorectal Liver Metastases: A Discrete Event Simulation Analysis.","authors":"Hanna Meidell Sjule, Caroline N Vinter, Svein Dueland, Pål-Dag Line, Emily A Burger, Gudrun Marie Waaler Bjørnelv","doi":"10.1177/0272989X241249154","DOIUrl":"10.1177/0272989X241249154","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Liver transplantation is an alternative treatment for patients with nonresectable colorectal cancer liver-only metastases (CRLM); however, the potential effects on wait-list time and life expectancy to other patients on the transplant waiting list have not been considered. We explored the potential effects of expanding liver transplantation eligibility to include patients with CRLM on wait-list time and life expectancy in Norway.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We developed a discrete event simulation model to reflect the Norwegian liver transplantation waiting list process and included 2 groups: 1) patients currently eligible for liver transplantation and 2) CRLM patients. Under 2 alternative CRLM-patient transplant eligibility criteria, we simulated 2 strategies: 1) inclusion of only currently eligible patients (CRLM patients received standard-of-care palliative chemotherapy) and 2) expanding waiting list eligibility to include CRLM patients under 2 eligibility criteria. Model outcomes included median waiting list time, life expectancy, and total life-years.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;For every additional CRLM patient listed per year, the overall median wait-list time, initially 52 d, increased by 8% to 11%. Adding 2 additional CRLM patients under the most restrictive eligibility criteria increased the CRLM patients' average life expectancy by 10.64 y and decreased the average life expectancy for currently eligible patients by 0.05 y. Under these assumptions, there was a net gain of 149.61 life-years over a 10-y programmatic period, which continued to increase under scenarios of adding 10 CRLM patients to the wait-list. Health gains were lower under less restrictive CRLM eligibility criteria. For example, adding 4 additional CRLM patients under the less restrictive eligibility criteria increased the CRLM patients' average life expectancy by 5.64 y and decreased the average life expectancy for currently eligible patients by 0.12 y. Under these assumptions, there was a net gain of 96.36 life-years over a 10-y programmatic period, which continued to increase up to 7 CRLM patients.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Our model-based analysis enabled the consideration of the potential effects of enlisting Norwegian CRLM patients for liver transplantation on wait-list time and life expectancy. Enlisting CRLM patients is expected to increase the total health effects, which supports the implementation of liver transplantation for CRLM patients in Norway.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Highlights: &lt;/strong&gt;Given the Norwegian donor liver availability, adding patients with nonresectable colorectal cancer liver-only metastases (CRLM) to the liver transplantation waiting list had an overall modest, but varying, impact on total waiting list time.Survival gains for selected CRLM patients treated with liver transplantation would likely outweigh the losses incurred to patients listed currently.To improve the total life-years gained in the p","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"529-542"},"PeriodicalIF":3.1,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11283734/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141201046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}