BACKGROUND:Advance care planning and serious illness conversations can help clinicians understand patients' values and preferences. Data are limited on how to increase the number of these conversations and what their effects are on care patterns. We hypothesized that using a machine learning survival model to select patients for serious illness conversations, along with trained care coaches to conduct the conversations, would increase uptake in patients with cancer at high risk of short-term mortality. METHODS:We conducted a cluster-randomized, stepped-wedge study on the physician level. Oncologists entered the intervention condition in a random order over 6 months. Adult patients with metastatic cancer were included. Patients with a less than 2-year computer-predicted survival and no prognosis documentation were classified as high priority for serious illness conversations. In the intervention condition, clinicians received automated weekly emails highlighting high-priority patients and were asked to document prognoses for them. Care coaches contacted these patients to conduct the remainder of the conversation. The primary endpoint was the proportion of visits with prognosis documentation within 14 days. RESULTS:We included 6372 visits with 1825 patients in the primary analysis. The proportion of visits with prognosis documentation within 14 days was higher in the intervention condition than in the control condition: 2.9% vs 1.1% (adjusted odds ratio = 4.3, P < .001). The proportion of visits with advance care planning documentation was also higher in the intervention condition: 7.7% vs 1.8% (adjusted odds ratio = 14.2, P < .001). For high-priority visits, the advance care planning documentation rate in intervention visits was 24.2% and in control visits was 4.0%. CONCLUSION:The intervention increased documented conversations, with contributions by both clinicians and care coaches.

Abstract Background: Molecular profiling is powerful to match patients’ tumor profile with targeted therapies in oncology. While our understanding of treatments and biomarkers that enhance overall response rates is growing, the level of consistency at which treatment response is assured in biomarker-enriched (BE) cohorts (e.g. immune-checkpoint inhibitors in PD-L1-positive tumors) remains poorly understood. In essence, an optimal biomarker would not only result in high mean survival but also precise, closely clustered response of all patients around this mean. Objectives: The primary objective of this study is to develop a method to measure the variability of survival outcomes from published Kaplan-Meier (KM) curves in trials. The secondary objective is to assess whether novel treatments, when given in the presence of predictive biomarkers, improve not only mean survival, but also its precision. This is clinically relevant as higher precision guarantees patients a treatment outcome closer to the mean, thereby providing safeguards against non-response. Methods: We conducted a criteria-based search in PubMed and EMBASE to identify all phase-II and -III drug trials on breast cancer and non-small cell lung cancer (NSCLC) published between 2018-2022. We developed an algorithm to derive pseudo-individual patient data (p-IPD) from KM curves. We measured variability of survival outcomes in treatment and control groups using ratios of coefficients of variation (CVRs) derived from log-normal models and restricted mean survival time. This novel computational approach enables comparing the consistency of treatment responses in BE subgroups versus total, i.e. intention-to-treat (ITT), populations in each trial. Results: From screening 780 publication records (NSCLC: n = 405; breast cancer: n = 375), we identified 67 biomarker-stratified drug trials (NSCLC: n = 41; breast cancer: n = 26). We successfully constructed p-IPD using KM curves from ITT and BE cohorts for the first 18 trials (NSCLC: n = 10; breast cancer: n = 8) from a total of 11,373 patients (NSCLC: n = 7,547; breast cancer: n = 3,826). The preliminary meta-analysis for overall survival on these first 18 trials shows that BE treatment groups respond more precisely, with a variability reduction from 0.93 [95%-CI: 0.91, 0.96] in the ITT group to 0.86 [0.83, 0.91] in the BE subgroup. This variability reduction varies across treatment, biomarker, and tumor types. Finally, we observe different variability estimates in trials (n = 6) that are stratified by subgroups of different PD-L1 expression. Conclusion: This is the first study that presents a viable approach to analyze the variability of biomarker-treatment pairs in oncology at large scale. We find a reduction of treatment variability in breast and lung cancer trials in BE populations. Our work offers an orthogonal approach to measure biomarker utility with the potential to inform biomarker discovery and trial design. Citation Format: Maximilian Schuessler, Elizaveta Skarga, Pascal Geldsetzer, Ying Lu, Maike Hohberg. Drivers of precision in oncology trials: A landscape analysis of biomarkers and treatments [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2387.

Importance Breast cancer mortality in the US declined between 1975 and 2019. The association of changes in metastatic breast cancer treatment with improved breast cancer mortality is unclear. Objective To simulate the relative associations of breast cancer screening, treatment of stage I to III breast cancer, and treatment of metastatic breast cancer with improved breast cancer mortality. Design, Setting, and Participants Using aggregated observational and clinical trial data on the dissemination and effects of screening and treatment, 4 Cancer Intervention and Surveillance Modeling Network (CISNET) models simulated US breast cancer mortality rates. Death due to breast cancer, overall and by estrogen receptor and ERBB2 (formerly HER2) status, among women aged 30 to 79 years in the US from 1975 to 2019 was simulated. Exposures Screening mammography, treatment of stage I to III breast cancer, and treatment of metastatic breast cancer. Main Outcomes and Measures Model-estimated age-adjusted breast cancer mortality rate associated with screening, stage I to III treatment, and metastatic treatment relative to the absence of these exposures was assessed, as was model-estimated median survival after breast cancer metastatic recurrence. Results The breast cancer mortality rate in the US (age adjusted) was 48/100 000 women in 1975 and 27/100 000 women in 2019. In 2019, the combination of screening, stage I to III treatment, and metastatic treatment was associated with a 58% reduction (model range, 55%-61%) in breast cancer mortality. Of this reduction, 29% (model range, 19%-33%) was associated with treatment of metastatic breast cancer, 47% (model range, 35%-60%) with treatment of stage I to III breast cancer, and 25% (model range, 21%-33%) with mammography screening. Based on simulations, the greatest change in survival after metastatic recurrence occurred between 2000 and 2019, from 1.9 years (model range, 1.0-2.7 years) to 3.2 years (model range, 2.0-4.9 years). Median survival for estrogen receptor (ER)-positive/ERBB2-positive breast cancer improved by 2.5 years (model range, 2.0-3.4 years), whereas median survival for ER-/ERBB2- breast cancer improved by 0.5 years (model range, 0.3-0.8 years). Conclusions and Relevance According to 4 simulation models, breast cancer screening and treatment in 2019 were associated with a 58% reduction in US breast cancer mortality compared with interventions in 1975. Simulations suggested that treatment for stage I to III breast cancer was associated with approximately 47% of the mortality reduction, whereas treatment for metastatic breast cancer was associated with 29% of the reduction and screening with 25% of the reduction.

IMPORTANCE The effects of breast cancer incidence changes and advances in screening and treatment on outcomes of different screening strategies are not well known. OBJECTIVE To estimate outcomes of various mammography screening strategies. DESIGN, SETTING, AND POPULATION Comparison of outcomes using 6 Cancer Intervention and Surveillance Modeling Network (CISNET) models and national data on breast cancer incidence, mammography performance, treatment effects, and other-cause mortality in US women without previous cancer diagnoses. EXPOSURES Thirty-six screening strategies with varying start ages (40, 45, 50 years) and stop ages (74, 79 years) with digital mammography or digital breast tomosynthesis (DBT) annually, biennially, or a combination of intervals. Strategies were evaluated for all women and for Black women, assuming 100% screening adherence and "real-world" treatment. MAIN OUTCOMES AND MEASURES Estimated lifetime benefits (breast cancer deaths averted, percent reduction in breast cancer mortality, life-years gained), harms (false-positive recalls, benign biopsies, over diagnosis), and number of mammograms per 1000 women. RESULTS Biennial screening with DBT starting at age 40, 45, or 50 years until age 74 years averted a median of 8.2, 7.5, or 6.7 breast cancer deaths per 1000 women screened, respectively, vs no screening. Biennial DBT screening at age 40 to 74 years (vs no screening)was associated with a 30.0% breast cancer mortality reduction, 1376 false-positive recalls, and 14 over diagnosed cases per 1000 women screened. Digital mammography screening benefits were similar to those for DBT but had more false-positive recalls. Annual screening increased benefits but resulted in more false-positive recalls and over diagnosed cases. Benefit-to-harm ratios of continuing screening until age 79 years were similar or superior to stopping at age 74. In all strategies, women with higher-than-average breast cancer risk, higher breast density, and lower comorbidity level experienced greater screening benefits than other groups. Annual screening of Black women from age 40 to 49 years with biennial screening thereafter reduced breast cancer mortality disparities while maintaining similar benefit-to-harm trade-offs as for all women. CONCLUSIONS This modeling analysis suggests that biennial mammography screening starting at age 40 years reduces breast cancer mortality and increases life-years gained per mammogram. More intensive screening for women with greater risk of breast cancer diagnosis or death can maintain similar benefit-to-harm trade-offs and reduce mortality disparities.

Importance:The effects of breast cancer incidence changes and advances in screening and treatment on outcomes of different screening strategies are not well known. Objective:To estimate outcomes of various mammography screening strategies. Design, Setting, and Population:Comparison of outcomes using 6 Cancer Intervention and Surveillance Modeling Network (CISNET) models and national data on breast cancer incidence, mammography performance, treatment effects, and other-cause mortality in US women without previous cancer diagnoses. Exposures:Thirty-six screening strategies with varying start ages (40, 45, 50 years) and stop ages (74, 79 years) with digital mammography or digital breast tomosynthesis (DBT) annually, biennially, or a combination of intervals. Strategies were evaluated for all women and for Black women, assuming 100% screening adherence and "real-world" treatment. Main Outcomes and Measures:Estimated lifetime benefits (breast cancer deaths averted, percent reduction in breast cancer mortality, life-years gained), harms (false-positive recalls, benign biopsies, overdiagnosis), and number of mammograms per 1000 women. Results:Biennial screening with DBT starting at age 40, 45, or 50 years until age 74 years averted a median of 8.2, 7.5, or 6.7 breast cancer deaths per 1000 women screened, respectively, vs no screening. Biennial DBT screening at age 40 to 74 years (vs no screening) was associated with a 30.0% breast cancer mortality reduction, 1376 false-positive recalls, and 14 overdiagnosed cases per 1000 women screened. Digital mammography screening benefits were similar to those for DBT but had more false-positive recalls. Annual screening increased benefits but resulted in more false-positive recalls and overdiagnosed cases. Benefit-to-harm ratios of continuing screening until age 79 years were similar or superior to stopping at age 74. In all strategies, women with higher-than-average breast cancer risk, higher breast density, and lower comorbidity level experienced greater screening benefits than other groups. Annual screening of Black women from age 40 to 49 years with biennial screening thereafter reduced breast cancer mortality disparities while maintaining similar benefit-to-harm trade-offs as for all women. Conclusions:This modeling analysis suggests that biennial mammography screening starting at age 40 years reduces breast cancer mortality and increases life-years gained per mammogram. More intensive screening for women with greater risk of breast cancer diagnosis or death can maintain similar benefit-to-harm trade-offs and reduce mortality disparities.

1008 Background: Treatment for metastatic breast cancer has advanced since 2000, but we do not know if those advances have reduced mortality in the general population. Methods: Four Cancer Intervention and Surveillance Network (CISNET) models simulated US breast cancer mortality from 2000 to 2017 using national data on mammography use and performance, efficacy and dissemination of estrogen receptor (ER) and HER2-specific treatments of early-stage (stages I-III) and metastatic (stage IV or distant recurrence) disease, and competing mortality. Models compared overall and ER/HER2-specific breast cancer mortality rates from 2000 to 2017 relative to estimated rates with no screening or treatment, and attributed mortality reductions to screening, early-stage or metastatic treatment. Results of an exemplar model are shown. Results: The mortality reduction attributable to early-stage treatment increased from 35.8% in 2000 to 48.2% in 2017, while the proportion attributable to metastatic treatment decreased slightly from 23.9% to 20.6%. The increasing contribution of early-stage treatment reflects the transition of effective metastatic treatments to early-stage disease: accordingly, ten-year distant recurrence-free survival improved (82.5% in 2000, 87.3% in 2017; for ER+HER2+, 78.2% to 90.9%). Survival time after metastatic diagnosis also increased, doubling from 1.48 years in 2000 to 2.80 years in 2017, with the best survival for women with ER+HER2+ cancers (4.08 years) and worst for ER-HER2- (1.22 years). Conclusions: Advances in metastatic breast cancer treatment are reflected in lower population mortality, both through transition to early-stage treatment and gains for women with metastatic disease. These results may inform patient/physician discussions about breast cancer prognosis and expected benefits of treatment. [Table: see text]

A conference entitled “Evidence-based Traditional Asian Medicine” (ETAM) was held virtually by the Stanford Center for Asian Health Research and Education (CARE) from March 4th to March 6th, 2021. The event sought to answer three key questions regarding the evidence, quality and trust surrounding ETAM practices: Evidence: Are traditional Asian medicine practices effective in promoting health, addressing disease or improving quality of life? Quality: Is there sufficient commitment and consensus on the study components required for high-quality clinical research studies in traditional Asian medicine? Trust: How can traditional Asian medicine make use of modern research build public and clinician trust in its practices? The conference first introduced the evidence-based applications of traditional Chinese medicine (TCM) and traditional Indian medicine (Ayurveda) in the realms of diet and nutrition, pain management, and mental health. It then highlighted the crucial need for common and rigorous guidelines by which to evaluate and compare ETAM practices from around the world. Finally, the conference called for building upon Western precision medicine through ETAM methods and integrating practices from each to build a more robust, global and individually tailored approach to health and health care.

Abdominal aortic aneurysm (AAA) may lead to rupture and death if left untreated. While endovascular or surgical repair is generally recommended for AAA greater than 5–5.5 cm, the vast majority of aneurysms detected by screening modalities are smaller than this threshold. Once discovered, there would be a significant potential benefit in suppressing the growth of these small aneurysms in order to obviate the need for repair and mitigate rupture risk. Patients with diabetes, in particular those taking the oral hypoglycaemic medication metformin, have been shown to have lower incidence, growth rate, and rupture risk of AAA. Metformin therefore represents a widely available, non-toxic, potential inhibitor of AAA growth, but thus far no prospective clinical studies have evaluated this. Here, we present the background, rationale, and design for a randomised, double-blind, placebo-controlled clinical trial of metformin for growth suppression in patients with small AAA.