Time to First Onset of Chest Binding-Related Symptoms in Transgender Youth.

BACKGROUND AND OBJECTIVES: Most transgender individuals assigned female at birth use chest binding (ie, wearing a tight garment to flatten chest tissue for the purpose of gender expression), often beginning in adolescence, to explore their gender identity. Although binding is often critical for mental health, negative physical side effects, ranging from chronic pain to rib fractures, are common. Time to first onset of symptoms is unknown. METHODS: A community-engaged, online, cross-sectional survey ("The Binding Health Project") enrolled 1800 assigned female at birth or intersex individuals who had ever used chest binding. Lifetime prevalence of 27 pain, musculoskeletal, neurologic, gastrointestinal, generalized, respiratory, and skin or soft tissue symptoms related to binding was assessed. Nonparametric likelihood estimation methods were used to estimate survival curves. RESULTS: More than one-half (56%) of participants had begun binding by age 21, and 30% had begun by age 18. In 18 of 27 symptoms, the majority of people who go on to experience the event will do so within the first binding-year, but several skin-related and rare but serious outcomes (eg, rib fracture) took longer to occur. Pain presents rapidly but continues to rise in intensity over time, peaking at >5 years of binding. CONCLUSIONS: Although many symptoms emerge quickly, others can take years to develop. Individuals and their clinicians can use this information to make informed decisions on how to structure binding practices and top surgery timing while meeting goals related to gender expression and mental health. Access to puberty blockers may delay initiation of binding, preventing binding-related symptoms in youth.

Clinical benefit, toxicity and cost of metastatic breast cancer therapies: systematic review and meta-analysis.

PURPOSE: Oncologists, clinical trialists, and guideline developers need tools that enable them to efficiently review the settings and results of previous studies testing metastatic breast cancer (MBC) drug therapies. METHODS: We searched the literature to identify clinical trials testing MBC drug therapies. Key eligibility criteria included at least 90% of patients enrolled in the trial having MBC, therapeutic clinical trials, and Phase II-III studies. Studies were stratified based on patients' tumor receptor statuses and prior exposure to therapy. Survival and toxicity of each drug therapy were estimated from randomized controlled trials using network meta-analysis and from all studies using meta-analysis. These results, along with estimated drug costs, are presented in a web-based visualization tool. RESULTS: We included 1865 studies containing 2676 treatment arms and 184,563 patients in the tool ( http://www.cancertrials.info ). Meta-analysis-based efficacy and toxicity estimates are available for 85 HER-2-directed therapies, 84 hormonal therapies, and 442 undirected therapies. Network meta-analysis-based estimates are available for 16 HER-2-directed therapies, 26 hormonal therapies, and 131 undirected therapies. CONCLUSIONS: In this era of increasing choices of MBC therapeutic agents and no superior approach to choosing a treatment regimen, the ability to compare multiple therapies based on survival, toxicity and cost would enable treating physicians to optimize therapeutic choices for patients. For investigators, it can point them in research directions that were previously non-obvious and for guideline designers, enable them to efficiently review the MBC clinical trial literature and visualize how regimens compare in the key dimensions of clinical benefit, toxicity, and cost.

Optimal healthcare decision making under multiple mathematical models: application in prostate cancer screening.

Important decisions related to human health, such as screening strategies for cancer, need to be made without a satisfactory understanding of the underlying biological and other processes. Rather, they are often informed by mathematical models that approximate reality. Often multiple models have been made to study the same phenomenon, which may lead to conflicting decisions. It is natural to seek a decision making process that identifies decisions that all models find to be effective, and we propose such a framework in this work. We apply the framework in prostate cancer screening to identify prostate-specific antigen (PSA)-based strategies that perform well under all considered models. We use heuristic search to identify strategies that trade off between optimizing the average across all models' assessments and being "conservative" by optimizing the most pessimistic model assessment. We identified three recently published mathematical models that can estimate quality-adjusted life expectancy (QALE) of PSA-based screening strategies and identified 64 strategies that trade off between maximizing the average and the most pessimistic model assessments. All prescribe PSA thresholds that increase with age, and 57 involve biennial screening. Strategies with higher assessments with the pessimistic model start screening later, stop screening earlier, and use higher PSA thresholds at earlier ages. The 64 strategies outperform 22 previously published expert-generated strategies. The 41 most "conservative" ones remained better than no screening with all models in extensive sensitivity analyses. We augment current comparative modeling approaches by identifying strategies that perform well under all models, for various degrees of decision makers' conservativeness.

Applied Informatics Decision Support Tool for Mortality Predictions in Patients With Cancer.

PURPOSE: With rapidly evolving treatment options in cancer, the complexity in the clinical decision-making process for oncologists represents a growing challenge magnified by oncologists' disposition of intuition-based assessment of treatment risks and overall mortality. Given the unmet need for accurate prognostication with meaningful clinical rationale, we developed a highly interpretable prediction tool to identify patients with high mortality risk before the start of treatment regimens. METHODS: We obtained electronic health record data between 2004 and 2014 from a large national cancer center and extracted 401 predictors, including demographics, diagnosis, gene mutations, treatment history, comorbidities, resource utilization, vital signs, and laboratory test results. We built an actionable tool using novel developments in modern machine learning to predict 60-, 90- and 180-day mortality from the start of an anticancer regimen. The model was validated in unseen data against benchmark models. RESULTS: We identified 23,983 patients who initiated 46,646 anticancer treatment lines, with a median survival of 514 days. Our proposed prediction models achieved significantly higher estimation quality in unseen data (area under the curve, 0.83 to 0.86) compared with benchmark models. We identified key predictors of mortality, such as change in weight and albumin levels. The results are presented in an interactive and interpretable tool ( www.oncomortality.com ). CONCLUSION: Our fully transparent prediction model was able to distinguish with high precision between highest- and lowest-risk patients. Given the rich data available in electronic health records and advances in machine learning methods, this tool can have significant implications for value-based shared decision making at the point of care and personalized goals-of-care management to catalyze practice reforms.