Applying Machine Learning Techniques to Implementation Science.

Machine learning (ML) approaches could expand the usefulness and application of implementation science methods in clinical medicine and public health settings. The aim of this viewpoint is to introduce a roadmap for applying ML techniques to address implementation science questions, such as predicting what will work best, for whom, under what circumstances, and with what predicted level of support, and what and when adaptation or deimplementation are needed. We describe how ML approaches could be used and discuss challenges that implementation scientists and methodologists will need to consider when using ML throughout the stages of implementation.

Real-world evaluation of deep learning algorithms to classify functional pathogenic germline variants.

Deep learning models for variant pathogenicity prediction can recapitulate expert-curated annotations, but their performance remains unexplored on actual disease phenotypes in a real-world setting. Here, we apply three state-of-the-art pathogenicity prediction models to classify hereditary breast cancer gene variants in the UK Biobank. Predicted pathogenic variants in BRCA1, BRCA2 and PALB2, but not ATM and CHEK2, were associated with increased breast cancer risk. We explored gene-specific score thresholds for variant pathogenicity, finding that they could improve model performance. However, when specifically tasked with classifying variants of uncertain significance, the deep learning models were generally of limited clinical utility.

Racial Differences in Germline Genetic Testing Completion Among Males With Pancreatic, Breast, or Metastatic Prostate Cancers.

BACKGROUND: Germline genetic testing is a vital component of guideline-recommended cancer care for males with pancreatic, breast, or metastatic prostate cancers. We sought to determine whether there were racial disparities in germline genetic testing completion in this population. PATIENTS AND METHODS: This retrospective cohort study included non-Hispanic White and Black males with incident pancreatic, breast, or metastatic prostate cancers between January 1, 2019, and September 30, 2021. Two nationwide cohorts were examined: (1) commercially insured individuals in an administrative claims database, and (2) Veterans receiving care in the Veterans Health Administration. One-year germline genetic testing rates were estimated by using Kaplan-Meier methods. Cox proportional hazards regression was used to test the association between race and genetic testing completion. Causal mediation analyses were performed to investigate whether socioeconomic variables contributed to associations between race and germline testing. RESULTS: Our cohort consisted of 7,894 males (5,142 commercially insured; 2,752 Veterans). One-year testing rates were 18.0% (95% CI, 16.8%-19.2%) in commercially insured individuals and 14.2% (95% CI, 11.5%-15.0%) in Veterans. Black race was associated with a lower hazard of testing among commercially insured individuals (adjusted hazard ratio [aHR], 0.73; 95% CI, 0.58-0.91; P=.005) but not among Veterans (aHR, 0.99; 95% CI, 0.75-1.32; P=.960). In commercially insured individuals, income (aHR, 0.90; 95% CI, 0.86-0.96) and net worth (aHR, 0.92; 95% CI, 0.86-0.98) mediated racial disparities, whereas education (aHR, 0.98; 95% CI, 0.94-1.01) did not. CONCLUSIONS: Overall rates of guideline-recommended genetic testing are low in males with pancreatic, breast, or metastatic prostate cancers. Racial disparities in genetic testing among males exist in a commercially insured population, mediated by net worth and household income; these disparities are not seen in the equal-access Veterans Health Administration. Alleviating financial and access barriers may mitigate racial disparities in genetic testing.

Impact of GLP-1 Agonists and SGLT-2 Inhibitors on Diabetic Retinopathy Progression: An Aggregated Electronic Health Record Data Study.

PURPOSE: To examine the effects of GLP-1 agonists compared to SGLT-2 inhibitors on diabetic retinopathy. DESIGN: Retrospective clinical cohort study using TriNetX (Cambridge, MA, USA), a federated electronic health records network comprising multiple healthcare organizations. METHODS: Patients with an International Classification of Diseases, Tenth Revision (ICD-10) code of non-proliferative diabetic retinopathy and monotherapy treatment, excluding insulin, with GLP-1 agonists or SGLT-2 inhibitors. Patients with history of proliferative diabetic retinopathy prior to initiation of treatment were excluded. Rate of progression to proliferative diabetic retinopathy and rate of development of diabetic macular edema were compared between patients on GLP-1 agonists compared to those on SGLT-2 inhibitors. The groups were propensity score matched for age, gender, ethnicity, race, type of diabetes, and severity of non-proliferative diabetic retinopathy. Main outcomes included rate and relative risk of progression to proliferative diabetic retinopathy and risk of diabetic macular edema in the GLP-1 agonist group versus the SGLT-2 inhibitor group. RESULTS: A total of 6481 patients were identified in the GLP-1 cohort and the SGLT-2 inhibitor cohort after propensity score matching. At 1 year and 3 years after initiation of therapy, a higher rate of progression of proliferative diabetic retinopathy was noted (RR: 1.26, CI 1.04-1.51, p=0.017 at 1 year, RR: 1.284, CI 1.1-1.499, p=0.002 at 3 years) in the GLP-1 agonist cohort compared to the SGLT-2 inhibitor cohort. There was a higher rate of diabetic macular edema noted at 3 months (RR: 1.192, CI 1.059-1.276, p=0.002), 6 months (RR: 1.22, CI 1.13-1.32, p<0.001), 1 year (RR: 1.24, CI 1.15-1.33, p<0.001), and at 3 years (RR: 1.29, CI 1.21-1.38, p<0.001) in the GLP-1 agonist cohort compared to the SGLT-2 inhibitor cohort. CONCLUSIONS: A higher rate of progression of proliferative diabetic retinopathy and risk of new-onset diabetic macular edema was observed in patients on monotherapy with GLP-1 agonists compared to those on SGLT-2 inhibitors. It is important for clinicians to be aware of these potential effects and to consider the current retinopathy status when initiating treatment with newer hypoglycemic agents to ensure these patients are appropriately monitored for developing potential vision threatening complications.

Trends in low-value cancer care during the COVID-19 pandemic.

OBJECTIVE: To assess the association between the onset of the COVID-19 pandemic and change in low-value cancer services. STUDY DESIGN: In this retrospective cohort study, we used administrative claims from the HealthCore Integrated Research Environment, a repository of medical and pharmacy data from US health plans representing more than 80 million members, between January 1, 2016, and March 31, 2021. METHODS: We used linear probability models to investigate the relation between the onset of the COVID-19 pandemic and 4 guideline-based metrics of low-value cancer care: (1) conventional fractionation radiotherapy instead of hypofractionated radiotherapy for early-stage breast cancer; (2) non-guideline-based antiemetic use for minimal-, low-, or moderate- to high-risk chemotherapies; (3) off-pathway systemic therapy; and (4) aggressive end-of-life care. We identified patients with new diagnoses of breast, colorectal, and/or lung cancer. We excluded members who did not have at least 6 months of continuous insurance coverage and members with prevalent cancers. RESULTS: Among 117,116 members (median [IQR] age, 60 [53-69] years; 72.4% women), 59,729 (51.0%) had breast cancer, 25,751 (22.0%) had colorectal cancer, and 31,862 (27.2%) had lung cancer. The payer mix was 18.7% Medicare Advantage or Medicare supplemental and 81.2% commercial non-Medicare. Rates of low-value cancer services exhibited minimal changes during the pandemic, as adjusted percentage-point differences were 3.93 (95% CI, 1.50-6.36) for conventional radiotherapy, 0.82 (95% CI, -0.62 to 2.25) for off-pathway systemic therapy, -3.62 (95% CI, -4.97 to -2.27) for non-guideline-based antiemetics, and 2.71 (95% CI, -0.59 to 6.02) for aggressive end-of-life care. CONCLUSIONS: Low-value cancer care remained prevalent throughout the pandemic. Policy makers should consider changes to payment and incentive design to turn the tide against low-value cancer care.

US Medicare Hospice and Palliative Medicine Physician Workforce and Service Delivery in 2008-2020.

CONTEXT: Despite clinical benefits of early palliative care, little is known about Medicare physician workforce specialized in Hospice and Palliative Medicine (HPM) and their service delivery settings. OBJECTIVES: To examine changes in Medicare HPM physician workforce and their service delivery settings in 2008-2020. METHODS: Using the Medicare Data on Provider Practice and Specialty from 2008 to 2020, we identified 2375 unique Medicare Fee-For-Service (FFS) physicians (15,565 physician-year observations) with self-reported specialty in "Palliative Care and Hospice". We examined changes in the annual number of HPM physicians, average number of Medicare services overall and by care setting, total number of Medicare FFS beneficiaries, and total Medicare allowed charges billed by the physician. RESULTS: The number of Medicare HPM physicians increased 2.32 times from 771 in 2008 to 1790 in 2020. The percent of HPM physicians practicing in metropolitan areas increased from 90% to 96% in 2008-2020. Faster growth was also observed in female physicians (52.4% to 60.1%). Between 2008 and 2020, we observed decreased average annual Medicare FFS beneficiaries (170 to 123), number of FFS services (467 to 335), and Medicare allowed charges billed by the physician ($47,230 to $37,323). The share of palliative care delivered in inpatient settings increased from 47% to 68% in 2008-2020; whereas the share of services delivered in outpatient settings decreased from 37% to 19%. CONCLUSION: Despite growth in Medicare HPM physician workforce, access is disproportionately concentrated in metropolitan and inpatient settings. This may limit receipt of early outpatient specialized palliative care, especially in nonmetropolitan areas.

Investigating the Utility of Near-Infrared Reflectance Imaging for Diabetic Retinopathy Screening.

BACKGROUND AND OBJECTIVE: We investigated the reliability of near-infrared reflectance (NIR) imaging as a method of assessing severity of diabetic retinopathy (DR). PATIENTS AND METHODS: One hundred ninety-five NIR images were reviewed by two graders for the number of hyporeflective foci, presence or absence of vascular abnormalities, and presumptive DR stage; these were correlated to fundus photography-defined DR stage. Interrater reliability was confirmed via one-way random effects model of intraclass correlation coefficients. Analysis of variance was used in subgroup analysis, receiver operating characteristic (ROC) curves were created to validate reliability of the model, and logistic regression was used to model foci and vascular abnormalities as predictors for moderate or worse disease. RESULTS: A statistically significant difference in mean number of hyporeflective foci was found between no DR and moderate non-proliferative DR (NPDR; P < 0.0001), no DR and severe NPDR (P < 0.001), no DR and proliferative DR (PDR; P < 0.0001), mild and moderate NPDR (P = 0.008), mild and severe NPDR (P < 0.001), and mild NPDR and PDR (P < 0.001). The area under the ROC curve was 0.849 (CI: 0.792 to 0.905). The threshold for detection of moderate NPDR or worse was 4.75 foci, with a sensitivity of 79.0% and a false positive rate of 20.0%. Multivariate logistic regression model incorporating hyporeflective foci with vascular abnormalities (odds ratio [OR] = 1.592, 95% CI: 1.381 to 1.835; P < 0.001) was able to accurately predict moderate disease or worse, just moderate disease (OR = 1.045, 95% CI: 1.003 to 1.089; P = 0.035), severe disease (OR = 1.050, 95% CI: 1.006 to 1.096; P = 0.027), and proliferative disease (OR = 1.050, 95% CI: 1.008 to 1.095; P = 0.018). CONCLUSIONS: NIR imaging may be an adjunct tool in screening for DR. [Ophthalmic Surg Lasers Imaging Retina 2024;55:XX-XX.].

Uses and limitations of artificial intelligence for oncology.

Modern artificial intelligence (AI) tools built on high-dimensional patient data are reshaping oncology care, helping to improve goal-concordant care, decrease cancer mortality rates, and increase workflow efficiency and scope of care. However, data-related concerns and human biases that seep into algorithms during development and post-deployment phases affect performance in real-world settings, limiting the utility and safety of AI technology in oncology clinics. To this end, the authors review the current potential and limitations of predictive AI for cancer diagnosis and prognostication as well as of generative AI, specifically modern chatbots, which interfaces with patients and clinicians. They conclude the review with a discussion on ongoing challenges and regulatory opportunities in the field.

Real-world use, dose intensity, and adherence to enfortumab vedotin in locally advanced or metastatic urothelial cancer.

BACKGROUND: Enfortumab vedotin (EV) monotherapy is approved for the treatment of advanced urothelial cancer as later-line therapy (post-immunotherapy and -platinum-chemotherapy) and as earlier-line therapy (cisplatin-ineligible, at least 1 prior therapy). We examined real-world EV monotherapy use, dose intensity and adherence across 280 US cancer clinics. METHODS: This postmarketing study used data from a nationwide (United States) deidentified patient-level electronic health record-derived database. Included were patients with advanced urothelial cancer initiating EV on or after December 19, 2019 (date of accelerated approval). We summarized characteristics of EV users using descriptive statistics and computed metrics of EV use, EV dose intensity, and EV treatment adherence. RESULTS: We identified 416 advanced urothelial cancer patients initiating EV monotherapy. More than half of patients (55.3%) received EV as later-line therapy (3L+), and nearly half (44.7%) received EV as earlier line therapy (1 or 2L). Dosing frequency (mean [SD] 2.4 [0.5] treatments per 28 day cycle) and dose (1.1 [0.2] mg/kg) were lower than label indication guidelines (1.25 mg/kg, Day 1, 8, 15 of a 28 day cycle). Only 58.8% of patients received an average of >2 treatments per 28-day cycle. CONCLUSIONS: Among patients with advanced urothelial cancer treated with EV monotherapy in contemporary practice, EV dosing frequency, and dosage was lower in clinical practice than recommended in the product labeling. Further research is required to understand clinical factors and outcomes associated with the differences observed.

Performance Assessment of an Artificial Intelligence Chatbot in Clinical Vitreoretinal Scenarios.

PURPOSE: To determine how often ChatGPT is able to provide accurate and comprehensive information regarding clinical vitreoretinal scenarios. To assess the types of sources ChatGPT primarily utilizes and to determine if they are hallucinated. METHODS: A retrospective cross-sectional study. We designed 40 open-ended clinical scenarios across 4 main topics in vitreoretinal disease. Responses were graded on correctness and comprehensiveness by two blinded retina specialists. The primary outcome was the number of clinical scenarios that ChatGPT answered correctly and comprehensively. Secondary outcomes included: theoretical harm to patients, the distribution of the type of references utilized by the chatbot, and the frequency of hallucinated references. RESULTS: In June 2023, ChatGPT answered 83% (33/40) of clinical scenarios correctly but provided a comprehensive answer in only 52.5% (21/40) of cases. Subgroup analysis demonstrated an average correct score of 86.7% in nAMD, 100% in DR, 76.7% in retinal vascular disease and 70% in the surgical domain. There were 6 incorrect responses with 1 (16.7%) case of no harm, 3 (50%) cases of possible harm and 2 (33.3%) cases of definitive harm. CONCLUSION: ChatGPT correctly answered more than 80% of complex open-ended vitreoretinal clinical scenarios, with a reduced capability to provide a comprehensive response.

Clinician Perspectives on Virtual Specialty Palliative Care for Patients With Advanced Illnesses.

Background: Patients with serious illnesses have unmet symptom and psychosocial needs. Specialty palliative care could address many of these needs; however, access varies by geography and health system. Virtual visits and automated referrals could increase access and lead to improved quality of life, health outcomes, and patient-centered care for patients with serious illness. Objectives: We sought to understand referring clinician perspectives on barriers and facilitators to utilizing virtual tools to increase upstream access to palliative care. Design: Participants in this multisite qualitative study included practicing clinicians who commonly place palliative care referrals across multiple specialties, including hematology/oncology, family medicine, cardiology, and geriatrics. All interviews were transcribed and subsequently coded and analyzed by trained research coordinators using Atlas.ti software. Settings/Subjects: This study included 23 clinicians (21 physicians, 2 nonphysicians) across 5 specialties, 4 practice settings, and 7 states in the United States. Results: Respondents felt that community-based specialty palliative services including symptom management, advance care planning, physical therapy, and mental health counseling would benefit their patients. However, they had mixed feelings about automated referrals, with some clinicians feeling hesitant about not being alerted to such referrals. Many respondents were supportive of virtual palliative care, particularly for those who may have difficulty accessing physician offices, but most respondents felt that such care should only be provided after an initial in-person consultation where clinicians can meet face-to-face with patients. Conclusion: Clinicians believe that automated referrals and virtual palliative care could increase access to the benefits of specialty palliative care. However, virtual palliative care models should give attention to iterative communication with primary clinicians and the perceived need for an initial in-person visit.

Ophthalmology Workforce Projections in the United States, 2020 to 2035.

PURPOSE: To analyze ophthalmology workforce supply and demand projections from 2020 to 2035. DESIGN: Observational cohort study using data from the National Center for Health Workforce Analysis (NCHWA). METHODS: Data accessed from the Department of Health and Human Services, Health Resources and Services Administration (HRSA) website were compiled to analyze the workforce supply and demand projections for ophthalmologists from 2020 to 2035. MAIN OUTCOME MEASURES: Projected workforce adequacy over time. RESULTS: From 2020 to 2035, the total ophthalmology supply is projected to decrease by 2650 full-time equivalent (FTE) ophthalmologists (12% decline) and total demand is projected to increase by 5150 FTE ophthalmologists (24% increase), representing a supply and demand mismatch of 30% workforce inadequacy. The level of projected adequacy was markedly different based on rurality by year 2035 with 77% workforce adequacy versus 29% workforce adequacy in metro and nonmetro geographies, respectively. By year 2035, ophthalmology is projected to have the second worst rate of workforce adequacy (70%) of 38 medical and surgical specialties studied. CONCLUSIONS: The HRSA's Health Workforce Simulation Model forecasts a sizeable shortage of ophthalmology supply relative to demand by the year 2035, with substantial geographic disparities. Ophthalmology is one of the medical specialties with the lowest rate of projected workforce adequacy by 2035. Further dedicated workforce supply and demand research for ophthalmology and allied professionals is needed to validate these projections, which may have significant future implications for patients and providers. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

The association between telemedicine, advance care planning, and unplanned hospitalizations among high-risk patients with cancer.

BACKGROUND: Despite the widespread implementation of telemedicine, there are limited data regarding its impact on key components of care for patients with incurable or high-risk cancer. For these patients, high-quality care requires detailed conversations regarding treatment priorities (advance care planning) and clinical care to minimize unnecessary acute care (unplanned hospitalizations). Whether telemedicine affects these outcomes relative to in-person clinic visits was examined among patients with cancer at high risk for 6-month mortality. METHODS: This retrospective cohort study included adult patients with cancer with any tumor type treated at the University of Pennsylvania who were newly identified between April 1 and December 31, 2020, to be at high risk for 6-month mortality via a validated machine learning algorithm. Separate modified Poisson regressions were used to assess the occurrence of advance care planning and unplanned hospitalizations for telemedicine as compared to in-person visits. Additional analyses were done comparing telemedicine type (video or phone) as compared to in-person clinic visits. RESULTS: The occurrence of advance care planning was similar between telemedicine and in-person visits (6.8% vs. 6.0%; adjusted risk ratio [aRR], 1.25; 95% CI, 0.92-1.69). In regard to telemedicine subtype, patients exposed to video encounters were modestly more likely to have documented advance care planning in comparison to those seen in person (7.5% vs. 6.0%; aRR, 1.48; 95% CI, 1.03-2.11). The 3-month risk for unplanned hospitalization was comparable for telemedicine compared to in-person clinic encounters (21% vs. 18%; aRR, 1.06; 95% CI, 0.81-1.38). CONCLUSIONS: In this study, care delivered by telemedicine, compared to in-person clinic visits, produced comparable rates of advance care planning conversations without increasing hospitalizations, which suggests that vulnerable patients can be managed safely by telemedicine.

Patient perspectives on treatment and prevention of recurrent urinary tract infections: a focus group study.

INTRODUCTION AND HYPOTHESIS: We sought to understand factors that are important to patients for the management of recurrent urinary tract infections (UTI) during both an acute episode and for the prevention of future episodes. METHODS: This was a qualitative study with focus groups in women with recurrent UTIs. Participants filled out information about prior recurrent UTI treatment and the Belief about Medicines Questionnaire (BMQ). Each 90-minute focus group was moderated by a nonphysician psychologist. Line-by-line coding of each transcript by three independent physicians was used to develop emergent concepts and themes using Grounded Theory methodology. RESULTS: Twenty-six women participated in six focus groups. The average age of participants was 62 years and 77% were post-menopausal. All women had already tried multiple prevention strategies for their recurrent UTIs. The average BMQ-specific scores indicated a net positive attitude toward medicines specifically prescribed for recurrent UTI prevention. Several themes emerged from the focus groups. First, patients wanted providers to acknowledge the high burden imposed by frequent interactions with the health care system for the management of recurrent UTI. Second, patients wanted earlier access to providers knowledgeable in the management of this condition. Third, patients wanted to self-manage their condition through a structured treatment plan with support from their providers. Finally, patients wanted greater emphasis on education and prevention strategies to reduce their antibiotic intake. CONCLUSIONS: Patients with recurrent UTI want more efficient workflows, a framework that promotes self-management in partnership with their providers, and a greater emphasis on prevention.

Risk of Stroke, Myocardial Infarction, Deep Vein Thrombosis, Pulmonary Embolism, and Death After Retinal Vein Occlusion.

PURPOSE: To examine rates of stroke, myocardial infarction (MI), deep vein thrombosis (DVT), pulmonary embolism (PE), and death in patients after retinal vein occlusion (RVO) compared to controls. DESIGN: Retrospective cohort study. METHODS: An aggregated electronic health records research network, TriNetX, was used to identify patients with diagnosis of RVO and a control group of patients with cataract. Patients were excluded if they had history of stroke, MI, DVT, or PE within 2 years of diagnosis of RVO or cataract. Propensity score matching was performed to control for baseline demographics and medical comorbidities. Main outcomes included relative risk (RR) of death, stroke, MI, DVT, and PE after RVO compared to those in matched controls. RESULTS: A total of 45,304 patients were included in each cohort. There was elevated risk of death in the RVO cohort compared to the control cohort at 1 year (RR = 1.30, P < .01), 5 years (RR = 1.22, P < .01), and 10 years (RR = 1.08, P < .01). There was elevated risk of stroke at 1 year (RR = 1.61, P < .01), 5 years (RR = 1.31, P < .01), and 10 years (RR = 1.18, P < .01). There was elevated risk of MI at 1 year (RR = 1.26, P < .01) and 5 years (RR = 1.13, P < .01), but not at 10 years (RR = 1.06, P = .12). There was mildly elevated risk of DVT at 1 year (RR = 1.65, P < .01) but not at 5 years (RR = 0.94, P = .94) or 10 years (RR = 1.05, P = .37). There was no elevated risk of PE at 1 year (RR = 0.98, P = 0.80), 5 years (RR = 0.95, P = .42), or 10 years (RR = 0.85, P =.40). CONCLUSIONS: There is an increased rate of death, stroke, and MI after RVO compared to those in matched controls. We emphasize the need for long-term systemic evaluation after RVO.

Eliminating Algorithmic Racial Bias in Clinical Decision Support Algorithms: Use Cases from the Veterans Health Administration.

The Veterans Health Administration uses equity- and evidence-based principles to examine, correct, and eliminate use of potentially biased clinical equations and predictive models. We discuss the processes, successes, challenges, and next steps in four examples. We detail elimination of the race modifier for estimated kidney function and discuss steps to achieve more equitable pulmonary function testing measurement. We detail the use of equity lenses in two predictive clinical modeling tools: Stratification Tool for Opioid Risk Mitigation (STORM) and Care Assessment Need (CAN) predictive models. We conclude with consideration of ways to advance racial health equity in clinical decision support algorithms.

Real-world prevalence of homologous recombination repair mutations in advanced prostate cancer: an analysis of two clinico-genomic databases.

BACKGROUND: Homologous recombination repair mutation (HRRm) status may guide risk-stratification and treatment decisions, including polyadenosine diphosphate-ribose polymerase inhibitor use, in advanced prostate cancer. Although HRRm prevalence has been reported in single-institution studies or clinical trials, real-world HRRm prevalence in diverse populations is unknown. We describe HRRm in the clinical setting using two real-world clinicogenomic databases: the Flatiron Health and Foundation Medicine, Inc. Clinico-Genomic Database (CGDB), a national electronic health record-derived database, and the American Association for Cancer Research Project Genomics Evidence Neoplasia Information Exchange (GENIE). METHODS: This cross-sectional analysis included 3757 individuals diagnosed with prostate cancer who had next generation sequencing (NGS) as standard of care. The CGDB included men with advanced/metastatic prostate cancer and genetic data included both germline and somatic pathogenic mutations. The GENIE analysis included men with prostate cancer whose received NGS as standard of care, but the data were filtered to include somatic mutations only. Due to key differences among databases, direct comparisons were not possible. Overall prevalence of HRRm was calculated and stratified by demographic and clinical characteristics. RESULTS: HRRm prevalence (combined germline and somatic) in CGDB (n = 487) was 24.6% (95% CI 20.9-28.7%), with no major differences across demographic and disease characteristic subgroups. HRRm prevalence (somatic) in GENIE (n = 3270) was 11.0% (95% CI 10.0-12.1%), which varied between 9.5% and 18.4% across treatment centers. CONCLUSIONS: Approximately one-quarter of patients with advanced/metastatic prostate cancer in the CGDB had germline and/or somatic HRRm, which is consistent with clinical trials such as the PROfound study that used a similar NGS platform and algorithm to define HRRm. In the GENIE database, HRRm prevalence varied by treatment center or NGS platform. More research is needed to understand real-world HRRm prevalence variations.

Validation of Biomechanical Computed Tomography for Fracture Risk Classification in Metastatic Hormone-sensitive Prostate Cancer.

BACKGROUND: Guidelines recommend dual-energy x-ray absorptiometry (DXA) screening to assess fracture risk and benefit from antiresorptive therapy in men with metastatic hormone-sensitive prostate cancer (mHSPC) on androgen deprivation therapy (ADT). However, <30% of eligible patients undergo DXA screening. Biomechanical computed tomography (BCT) is a radiomic technique that measures bone mineral density (BMD) and bone strength from computed tomography (CT) scans. OBJECTIVE: To evaluate the (1) correlations between BCT- and DXA-assessed BMD, and (2) associations between BCT-assessed metrics and subsequent fracture. DESIGN, SETTING, AND PARTICIPANTS: A multicenter retrospective cohort study was conducted among patients with mHSPC between 2013 and 2020 who received CT abdomen/pelvis or positron emission tomography/CT within 48 wk before ADT initiation and during follow-up (48-96 wk after ADT initiation). OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: We used univariate logistic regression to assess the associations between BCT measurements and the primary outcomes of subsequent pathologic and nonpathologic fractures. RESULTS AND LIMITATIONS: Among 91 eligible patients, the median ([interquartile range) age was 67 yr (62-75), 44 (48.4%) were White, and 41 (45.1%) were Black. During the median follow-up of 82 wk, 17 men (18.6%) developed a pathologic and 15 (16.5%) a nonpathologic fracture. BCT- and DXA-assessed femoral-neck BMD T scores were strongly correlated (R2 = 0.93). On baseline CT, lower BCT-assessed BMD (odds ratio [OR] 1.80, 95% confidence interval or CI [1.10, 3.25], p = 0.03) was associated with an increased risk of a pathologic fracture. Lower femoral strength (OR 1.63, 95% CI [0.99, 2.71], p = 0.06) was marginally associated with an increased risk of a pathologic fracture. Neither BMD (OR 1.52, 95% CI [0.95, 2.63], p = 0.11) nor strength (OR 1.14, 95% CI [0.75, 1.80], p = 0.57) was associated with a nonpathologic fracture. BCT identified nine (9.9%) men eligible for antiresorptive therapy, of whom four (44%) were not treated. Limitations include low fracture numbers resulting in lower power to detect fracture associations. CONCLUSIONS: Among men diagnosed with mHSPC, BCT assessments were strongly correlated with DXA, predicted subsequent pathologic fracture, and identified additional men indicated for antiresorptive therapy. PATIENT SUMMARY: We assess whether biomechanical computer tomography (BCT) from routine computer tomography (CT) scans can identify fracture risk among patients recently diagnosed with metastatic prostate cancer. We find that BCT and dual-energy x-ray absorptiometry-derived bone mineral density are strongly correlated and that BCT accurately identifies the risk for future fracture. BCT may enable broader fracture risk assessment and facilitate timely interventions to reduce fracture risk in metastatic prostate cancer patients.

Remote Patient-Reported Outcomes and Activity Monitoring to Improve Patient-Clinician Communication Regarding Symptoms and Functional Status: A Randomized Controlled Trial.

PURPOSE: Routine collection of patient-generated health data (PGHD) may promote earlier recognition of symptomatic and functional decline. This trial assessed the impact of an intervention integrating remote PGHD collection with patient nudges on symptom and functional status understanding between patients with advanced cancer and their oncology team. METHODS: This three-arm randomized controlled trial was conducted from November 19, 2020, to December 17, 2021, at a large tertiary oncology practice. We enrolled patients with stage IV GI and lung cancers undergoing chemotherapy. Over 6 months, patients in two intervention arms received PROStep-weekly text message-based symptom surveys and passive activity monitoring using a wearable accelerometer. PGHD were summarized in dashboards given to patients' oncology team before appointments. One intervention arm received an additional text-based active choice prompt to discuss worsening symptoms or functional status with their clinician. Control patients did not receive PROStep. The coprimary outcomes patient perceptions of oncology team symptom and functional understanding at 6 months were measured on a 1-5 Likert scale (5 = high understanding). RESULTS: One hundred eight patients enrolled: 55% male, 81% White, and 77% had GI cancers. Patient-reported clinician understanding did not differ between control and intervention arms for symptoms (4.5 v 4.5; P = .87) or functional status (4.5 v 4.3; P = .31). In the intervention arms, combined patient adherence to weekly symptom reports and daily activity monitoring was 64% and 53%, respectively. Intervention patients in the PROStep versus PROStep + active choice arms reported low burden from wearing the accelerometer (mean burden [standard deviation], 2.7 [1.3] v 2.1 [1.3]; P = .15) and completing surveys (2.1 [1.2] v 1.9 [1.3]; P = .44). CONCLUSION: Patients receiving PROStep reported high understanding of symptoms and functional status from their oncology team, although this did not differ from controls.