Cumulative Incidence of All-Cause Knee Injury, Concussion, and Stress Fracture among Transgender Patients on Gender-Affirming Hormone Therapy: An Exploratory Retrospective Cohort Study.

Previous research has shown a discrepancy in incidences of knee injuries, stress fractures, and concussions between cisgender men and women. Little is known regarding the incidence of musculoskeletal injuries among patients on gender-affirming hormone therapy (GAHT). This retrospective cohort study examines cumulative incidence of knee injuries, concussions, and stress fracture injuries among transgender patients on GAHT at one health system from 2011-2020. Using relevant ICD-9 and 10 codes, incidences of knee injury, concussion, and stress fracture were calculated. Cohorts included 1971 transgender and 3964 cisgender patients. Transgender patients had significantly higher incidence of all-cause knee injuries over the study period, 109 (5.5%) versus 175 (4.4%) (p < 0.001; OR: 2.14, 95% CI [1.17-3.92]). Subgroup analysis showed significantly higher incidence of knee injuries among cisgender men (5.6%) versus cisgender women (4.1%) (p = 0.042) and among transgender women (6.6%) versus cisgender women (4.1%) (p = 0.005). There were no significant differences between incidences of concussion and stress fracture between groups. This sample showed that patients on GAHT had increased cumulative incidences of all-cause knee injury compared to controls but similar cumulative incidences of concussion and bone-stress injuries. Transgender women on exogenous estrogen had significantly higher cumulative incidences of all-cause knee injuries compared to cisgender women.

Design of an interface to communicate artificial intelligence-based prognosis for patients with advanced solid tumors: a user-centered approach.

OBJECTIVES: To design an interface to support communication of machine learning (ML)-based prognosis for patients with advanced solid tumors, incorporating oncologists' needs and feedback throughout design. MATERIALS AND METHODS: Using an interdisciplinary user-centered design approach, we performed 5 rounds of iterative design to refine an interface, involving expert review based on usability heuristics, input from a color-blind adult, and 13 individual semi-structured interviews with oncologists. Individual interviews included patient vignettes and a series of interfaces populated with representative patient data and predicted survival for each treatment decision point when a new line of therapy (LoT) was being considered. Ongoing feedback informed design decisions, and directed qualitative content analysis of interview transcripts was used to evaluate usability and identify enhancement requirements. RESULTS: Design processes resulted in an interface with 7 sections, each addressing user-focused questions, supporting oncologists to "tell a story" as they discuss prognosis during a clinical encounter. The iteratively enhanced interface both triggered and reflected design decisions relevant when attempting to communicate ML-based prognosis, and exposed misassumptions. Clinicians requested enhancements that emphasized interpretability over explainability. Qualitative findings confirmed that previously identified issues were resolved and clarified necessary enhancements (eg, use months not days) and concerns about usability and trust (eg, address LoT received elsewhere). Appropriate use should be in the context of a conversation with an oncologist. CONCLUSION: User-centered design, ongoing clinical input, and a visualization to communicate ML-related outcomes are important elements for designing any decision support tool enabled by artificial intelligence, particularly when communicating prognosis risk.

External Validation of a Machine Learning Model to Predict 6-Month Mortality for Patients With Advanced Solid Tumors.

This prognostic study performed external validation of a machine learning model to predict 6-month mortality among patients with advanced solid tumors.

Mental health disorders are more common in patients with Hodgkin lymphoma and may negatively impact overall survival.

BACKGROUND: Long-term mental health outcomes were characterized in patients who were diagnosed with Hodgkin lymphoma (HL), and risk factors for the development of mental health disorders were identified. METHODS: Patients who were diagnosed with HL between 1997 and 2014 were identified in the Utah Cancer Registry. Each patient was matched with up to five individuals from a general population cohort identified within the Utah Population Database, a unique source of linked records that includes patient and demographic data. RESULTS: In total, 795 patients who had HL were matched with 3575 individuals from the general population. Compared with the general population, patients who had HL had a higher risk of any mental health diagnosis (hazard ratio, 1.77; 95% confidence interval, 1.57-2.00). Patients with HL had higher risks of anxiety, depression, substance-related disorders, and suicide and intentional self-inflicted injuries compared with the general population. The main risk factor associated with an increased risk of being diagnosed with mental health disorders was undergoing hematopoietic stem cell transplantation, with a hazard ratio of 2.06 (95% confidence interval, 1.53-2.76). The diagnosis of any mental health disorder among patients with HL was associated with a detrimental impact on overall survival; the 10-year overall survival rate was 70% in patients who had a mental health diagnosis compared with 86% in those patients without a mental health diagnosis (p < .0001). CONCLUSIONS: Patients who had HL had an increased risk of various mental health disorders compared with a matched general population. The current data illustrate the importance of attention to mental health in HL survivorship, particularly for patients who undergo therapy with hematopoietic stem cell transplantation.

Cardiovascular disease risk in long-term breast cancer survivors: A population-based cohort study.

BACKGROUND: Breast cancer survival is increasing, making late effects such as cardiovascular disease (CVD) more relevant. The purpose of this study was to evaluate incident CVD following breast cancer diagnosis among long-term survivors and to investigate possible risk factors for CVD. METHODS: A population-based cohort of 6641 breast cancer survivors diagnosed between 1997 and 2009 who survived at least 10 years was identified within the Utah Cancer Registry. In addition, 36,612 cancer-free women from the general population, matched by birth year and state, were identified within the Utah Population Database. Cox proportional hazards models were used to calculate CVD hazard ratios (HRs) for >10 to 15 and >15 years. RESULTS: Long-term breast cancer survivors had an increased risk of newly diagnosed diseases of the circulatory system (HR, 1.32; 99% confidence interval [CI], 1.00-1.75) from 10 to 15 years following cancer diagnosis compared with the general population. No increased CVD risks were observed after 15 years. Breast cancer survivors with Charlson Comorbidity Index score ≥2 had a significantly higher risk of diseases of the circulatory system (HR, 2.64; 95% CI, 1.08-6.45) beyond 10 years following breast cancer diagnosis. Similarly, older age, obesity, lower education, and family history of CVD and breast cancer were risk factors for heart and circulatory system diseases among long-term breast cancer survivors. CONCLUSION: Risk of CVD compared to the general population was moderate among this cohort of long-term breast cancer survivors between 10 to 15 years since cancer diagnosis. Awareness of CVD risks is important for breast cancer survivors.

Association of Clinical Risk Factors With Treatment Outcomes.

A patient's decision to accept treatment recommended by his dental health care provider will be strongly influenced by the quality of the information he is given. Estimates of prognosis and treatment predictability must be based on the evidence available from the literature and the practitioners' own experience. Thorough, accurate, and relevant clinical and adjunctive diagnostic data will be a major influence in the development of the patient's individualized treatment strategy. Some clinical findings such as severity of disease for age, deepening pockets accompanied by loss of clinical attachment, frequent bleeding on probing, and bone loss can be considered as risk and prognosis factors. "Hard" data implicating specific clinical or diagnostic findings as risk factors or markers are difficult to find because there are few randomized longitudinal trials available. A new approach which attempts to focus on reducing the risk of undesirable outcomes while improving the probability of successful outcomes following treatment has been referred to as the Treatment Predictability Model. A key feature of this approach is the focus on individual patient circumstances and preferences through the use of decision analysis techniques. A large scale, long-term project utilizing a practice-based research network (PBRN) provided some descriptive information about factors that could distinguish between responders and nonresponder patients undergoing treatment for advanced periodontitis. Bacterial colonization, level of post-treatment plaque control, and smoking were major predictive variables in this group of periodontitis patients. The predictive treatment approach may be one way to develop evidence that will improve the predictability of outcomes for individual patients. J Periodontol 1994; 65:489-497.