JAMA

Background: The observational literature on comparative effectiveness is expanding rapidly but remains difficult to synthesize. Discordant findings often stem from structural differences in cohort definitions, inclusion criteria, and follow up windows, leaving stakeholders without a cohesive evidence base. Furthermore, studies typically focus on a narrow subset of outcomes, neglecting the broader needs of diverse healthcare stakeholders 1,2,3,4. Methods We developed a high throughput evidence generation workflow using linked EHR and administrative claims data. The cornerstone is a prespecified measurement architecture applied uniformly across clinical scenarios: six post index windows (acute to two year follow.up); 28 Elixhauser comorbidities; 14 healthcare resource utilization (HCRU) categories; 29 laboratory measures with 52 binary thresholds; and 42 adverse event categories. We generated unadjusted treatment comparisons across ~1,038 outcomes per scenario, including effect-measure modification (EMM) assessments across 130 baseline features. Results Across 40 clinical domains, the workflow produced approximately 32,982,552 outcome evaluations. An evaluation included a treatment comparison outcome population effect estimate with uncertainty bounds and supporting diagnostics. Approximately 5,000 narrative summaries underwent structured clinical and statistical quality control before dissemination. Conclusions Standardized, high throughput workflows can shift evidence generation away from fragmented studies toward comprehensive evidence packages. This shared evidence base supports precision medicine by making treatment effect heterogeneity visible across clinically meaningful subpopulations, reducing the need for redundant, stakeholder-specific studies.

Background: Previous recommendations on screening for prostate cancer relied on ongoing trials of screening with prostate-specific antigen (PSA), which may have lacked sufficient follow-up duration to fully examine effects on mortality and overdiagnosis. Findings which consider absolute effects by age and screening intensity, along with newer guidance for assessing evidence certainty, may lead to different interpretations. Adding magnetic resonance imaging (MRI) to PSA-based screening has been raised as a way to reduce false positives (FPs) and overdiagnosis. Methods: We systematically searched MEDLINE, Embase, and Central from 2014 to January 28, 2026, for randomized controlled trials (RCTs) and prospective observational studies of: (i) screening versus no screening and (ii) sequential screening with MRI for those with a positive PSA test versus PSA alone among men not known to be at high risk for prostate cancer. Studies on screening with PSA or digital rectal examination (DRE) published pre-2014 were identified from existing systematic reviews and reference lists. Studies on FPs and complications from biopsies after PSA screening did not require a control group. Paired reviewers screened titles/abstracts (assisted with artificial intelligence) and full texts, assessed risk of bias, and extracted data, by age when available. We pooled data when suitable using random-effects models, investigated heterogeneity, and assessed the certainty of evidence using GRADE with conclusions of effects based on decision thresholds based on absolute effect sizes. Results: Across both questions, we included 15 RCTs (N=856,000; 8 sites of ERSPC considered separate trials) and 8 observational studies (N=56,122). At 20 years, among 1000 men who underwent repeated PSA-based screening every 2-4 years starting from age 55-69 (mean 62), there is likely a reduction in prostate-cancer mortality ([≥]2 fewer) and metastatic cancer incidence ([≥]6 fewer), at the expense of prostate-cancer overdiagnosis ([≥]24 cases) and FPs ([≥]150 cases) (all moderate certainty). If screening starts at age 50-54 or age 55, the benefits are probably smaller (e.g., 1 vs. 2 fewer prostate-cancer related deaths) with similar harms. Adding DRE or screening with PSA annually does not add benefit. One round of PSA screening or starting screening later at age 70-74 may not offer any important benefit or harm (low to moderate certainty), and any benefit from screening primarily with DRE was not shown. Compared with PSA alone, sequential screening with PSA followed by MRI reduces FPs ([≥]33 fewer) and overdiagnosis (via [≥]10 fewer diagnoses of clinically insignificant [e.g., Gleason 6] cancers without impacting detection of clinically significant cancers) (moderate to high certainty), though findings were limited to one round of screening without long-term follow-up or measurement of mortality. Interpretation: This review provides clinicians and other interest holders with anticipated absolute effects by age, and assessments of certainty across critical and important outcomes and with approximately two decades of follow-up. Findings apply to a general population and may differ for specific groups. Results for most critical outcomes, both benefits and harms, exceeded thresholds for clinically important effect sizes, thereby demonstrating the complexity of guideline developers' and patients' decision-making regarding screening trade-offs. Findings about adding MRI for those with a positive PSA test were limited and would require additional consideration of costs, infrastructure, expertise, and equity. Protocol registration: PROSPERO - CRD420250651056.

Background As part of Singapore's effort towards precision medicine tailored to Asian diversity, we describe the implementation of a nationwide reproductive carrier screening program. Using a customised 112-gene panel, incorporating population-specific recessive genetic diseases, we outline the overall program design, and initial efforts of community and stakeholder engagement, to inform culturally appropriate implementation. Methods Participants receive culturally tailored online education regarding our reproductive screening program and are provided results with genetic counselling and reproductive options. Community and stakeholder perspectives were assessed through questionnaires and consultations with religious leaders. Results Recruitment is nation-wide, and since initiation of our pilot phase in September 2024, 1,619 couples have registered interest, with 60% uptake of those deemed eligible. Among the 456 couples that have received results to date, four couples (0.9%) were identified to be at increased risk. Community questionnaire responses (n=1002), involving couples who participated in the program as well as the general public, indicated interest is high (59%) across the cohort but awareness, intent to participate and implications for reproductive options differed by sociodemographic factors such as ancestry and religion. Healthcare professional respondents (n=113) acknowledged carrier screening will be routine in medical care, but report limited confidence and resources. Engagement with religious leaders indicated support for the program. Conclusion These early program outcomes and community engagement are guiding the implementation of expanding population-based carrier screening in Singapore, contingent on addressing practical challenges through equitable outreach and professional training.

Introduction Cesarean delivery accounts for nearly one-third of U.S. births and is associated with substantial maternal morbidity and health care costs. Persistent racial disparities have been documented, yet the structural factors contributing to these differences remain incompletely understood. The extent to which insurance coverage shapes racial disparities in cesarean delivery remains unclear. Objective To evaluate the independent and interactive associations of race/ethnicity and insurance coverage with cesarean delivery in the United States. Methods Population-based retrospective cohort study using singleton live births recorded in the United States Vital Statistics Natality files from 2014 to 2024. Multivariable logistic regression was used to estimate the independent effects of race/ethnicity and insurance status on cesarean delivery, including interaction terms to test effect modification, using national birth certificate data. Models were adjusted for maternal demographics, clinical factors, and temporal covariates. Adjusted odds ratios, predicted probabilities, and absolute risk differences were derived from post-estimation marginal effects. The main outcome measure was cesarean delivery (yes vs no). Results Among 41,543,568 deliveries from 2014 to 2024, 13,312,221 (32.0%) were cesarean deliveries. After adjustment, both race and ethnicity and insurance status were independently associated with cesarean delivery. Compared with non-Hispanic White women, non-Hispanic Black women had higher odds of cesarean delivery (odds ratio [OR], 1.22; 95% CI, 1.22-1.23). Relative to uninsured women, those with private insurance had 59% higher odds of cesarean delivery (OR, 1.59; 95% CI, 1.58-1.60). Significant interaction effects were observed, indicating that insurance coverage modified racial and ethnic differences in cesarean delivery. Non-Hispanic Black women had the highest predicted probabilities across all insurance categories, with the largest absolute disparities observed among uninsured women. Conclusion Racial and ethnic differences in cesarean delivery persist in the United States and are modified by insurance coverage, suggesting that coverage-related differences may contribute to inequities in obstetric care.

BackgroundArtificial intelligence and machine learning (AI/ML) are among the fastest-growing domains in NIH research funding, but whether children have shared equitably in this expansion is unknown. We characterized pediatric representation in NIH AI/ML funding from fiscal years (FY) 2020 to 2024. MethodsNIH grant data were obtained from Research Portfolio Online Reporting Tools Expenditures and Results bulk files for FY2020 to FY2024. AI/ML grants were identified using the NIH Research, Condition, and Disease Categorization "Machine Learning and Artificial Intelligence" category, and pediatric grants using the "Pediatric" category. Subprojects were excluded. Grants were deduplicated within each fiscal year by core project number for trend analyses and across all years retaining the most recent fiscal year for cross-sectional totals. Disease areas were identified by keyword searches of titles and abstracts. ResultsAcross FY2020 to FY2024, 5,624 unique NIH AI/ML grants totaling $3,371 million were identified. Of these, 836 grants (14.9%) were classified as pediatric, representing $401 million (11.9%) of total NIH AI/ML funding. Although this share was consistent with the historically reported overall NIH pediatric funding baseline of approximately 10% to 12%, it remained substantially below the US pediatric population share of approximately 22%. The pediatric share of NIH AI/ML funding declined from 12.3% in FY2020 to 10.8% in FY2024, despite growth in absolute pediatric funding. Indexed to FY2020, pediatric AI/ML funding grew approximately 2.6-fold compared with 3.0-fold growth in the total portfolio. Across disease areas, unadjusted adult/general-to-pediatric funding ratios ranged from 2.0-fold in mental health to 9.8-fold in cancer. ConclusionsPediatric representation in NIH AI/ML funding remained low and declined over time as the overall portfolio expanded. These findings suggest that growth in NIH AI/ML investment has not been matched by proportional gains for pediatric research.

Background: Artificial intelligence chatbots (AICs), as a form of generative artificial intelligence (AI), are increasingly being considered for use in scholarly peer review to assist with tasks such as identifying methodological issues, verifying references, and improving language clarity. Despite these potential benefits, concerns remain regarding their reliability, ethical implications, and transparency. Evidence on how medical journal peer reviewers perceive the role and impact of AICs is limited. This study explored reviewers' familiarity with AICs, perceived benefits and challenges, ethical concerns, and anticipated future roles in peer review. Methods: We conducted a cross-sectional online survey of medical journal peer reviewers. Corresponding author information was extracted from MEDLINE-indexed articles added to PubMed within a two-month period using an R-based approach. A total of 72,851 authors were invited via email to participate; those who self-identified as peer reviewers were eligible. The 29-item survey assessed familiarity with AICs and perceptions of their benefits and limitations in peer review. The survey was administered via SurveyMonkey from April 28 to June 16, 2025, with two reminder emails sent during the data collection period. Results: A total of 1,260 respondents completed the survey. Most participants were familiar with AICs (86.2%) and had used tools such as ChatGPT for general purposes (87.7%), but the majority had not used AICs for peer review (70.3%). Most respondents reported that their institutions do not provide training on AIC use in peer review (69.5%), although many expressed interest in such training (60.7%). Perceptions of AIC benefits were mixed, while concerns were widely shared, particularly regarding potential algorithmic bias (80.3%) and issues related to trust and user acceptance (73.3%). Conclusions: While familiarity with AICs is high among medical journal peer reviewers, their use in peer review remains limited. There is clear interest in training and guidance, however, concerns related to ethics, data privacy, and research integrity persist and should be addressed before broader implementation.

Background: Cardiac rehabilitation (CR) is a cost-effective, evidence-based intervention that improves outcomes for patients with heart failure (HF), yet access remains inequitable, particularly among Medicaid enrollees. This study evaluates the state-by-state variability in Medicaid coverage for CR services and examines the implications for health equity in vulnerable populations. Methods: We conducted a cross-sectional policy analysis of all 50 U.S. states to assess Medicaid coverage for outpatient CR services billed under CPT codes 93797 (without ECG monitoring) and 93798 (with ECG monitoring). Publicly available Medicaid documents were reviewed and supplemented with direct communication with state Medicaid agencies. States were categorized into full, partial/inconclusive, or no coverage. Geographic trends were visualized through heat maps and contextualized using state-level Medicaid enrollment data. Results: Marked disparities in CR coverage were identified. Only 41 states reimbursed for CPT 93797, and 43 for CPT 93798. Eight states lacked coverage for either code, predominantly in the South and Mountain West, including Arkansas, Georgia, Louisiana, Mississippi, Nevada, and Utah. States with the highest Medicaid enrollment (e.g., Louisiana, Arkansas) often provided no CR coverage, compounding access barriers for high-risk, low-income populations. Conclusions: The absence of standardized Medicaid coverage for CR contributes to systemic inequities in cardiovascular care, disproportionately impacting disadvantaged communities. Aligning Medicaid policies to ensure universal CR access--particularly through tele-rehabilitation and value-based care models--could reduce hospitalizations, improve survival, and promote health equity across the U.S.

Abstract Background Between 2021 and 2022, primary care obesity management was entering the early diffusion phase of newer anti obesity pharmacotherapy, as GLP1 based treatments began reshaping expectations. However, it was unclear whether primary care clinicians and practice environments were prepared to deliver comprehensive obesity care. (1,2) Methods In 2021 to 2022, we surveyed 276 clinicians from three cohorts: an opt-in national physician panel (Cohort A), clinicians from an integrated health system (Cohort B), and clinicians from a rural accountable care organization (Cohort C). The survey, informed by formative patient and physician focus groups conducted in 2021, assessed current and desired competence, attitudes, confidence, perceived forces for change, and barriers to obesity care. Analyses were descriptive (means and standard deviations). Results Across cohorts, desired competence exceeded current competence. The largest gaps involved recommending behavioral interventions, developing comprehensive care plans, and providing ongoing obesity management support. Attitudes toward obesity care were generally favorable, while confidence that current practices reflected best practice was only moderate. Professional and personal forces for change were moderate, patient driven motivators were moderate to high, whereas social (peer/organizational) reinforcement was weak. Reported barriers extended beyond knowledge deficits to include patient engagement, competing demands, cost, and practical constraints. Conclusions At the threshold of the GLP1 era, primary care clinicians were motivated to improve obesity care but lacked consistent support to deliver comprehensive management. The relative absence of peer and organizational reinforcement suggests that readiness for change reflected not only individual knowledge and attitudes, but also the degree of peer and organizational reinforcement that supports comprehensive obesity care in routine practice.

Background: Generative artificial intelligence (GenAI) chatbots have shown utility in assisting with various research tasks. Traditional, complementary, and integrative medicine (TCIM) is a patient-centric approach that emphasizes holistic well-being. The integration of TCIM and GenAI presents numerous key opportunities. However, TCIM researchers' attitudes toward GenAI tools remain less understood. This large-scale, international cross-sectional survey aimed to elucidate the attitudes and perceptions of TCIM researchers regarding the use of GenAI chatbots in the scientific process. Methods: A search strategy in Ovid MEDLINE identified corresponding authors who were TCIM researchers. Eligible authors were invited to complete an anonymous online survey administered via SurveyMonkey. The survey included questions on socio-demographic characteristics, familiarity with GenAI chatbots, and perceived benefits and challenges of using GenAI chatbots. Results were analysed using descriptive statistics and thematic content analysis. Results: The survey received 716 responses. Most respondents reported familiarity with GenAI chatbots (58.08%) and viewed them as very important to the future of scientific research (54.37%). The most acknowledged benefits included workload reduction (74.07%) and increased efficiency in data analysis/experimentation (71.14%). The most frequently reported challenges involved bias, errors, and limitations. More than half of the respondents (57.02%) expressed a need for training to use GenAI chatbots in the scientific process, alongside an interest in receiving training (72.07%). However, 43.67% indicated that their institutions did not offer these programs. Discussion: By developing a deeper understanding of TCIM researchers' perspectives, future AI applications in this field can be more informed, and guide future policies and collaboration among researchers.

Study questionWhat are the characteristics and treatment outcomes of women who undertook planned egg freezing (PEF) in Australia and New Zealand between 2009 and 2023? Summary answerThere has been an average yearly increase in the uptake of PEF of 35%, with most women undergoing a single PEF procedure in their mid-thirties. Given ten years follow-up a little over one in four women return, with nearly half of those using donor sperm and one-third achieving a live birth. What is known alreadyPEF, where women freeze their eggs as a strategy to preserve fertility, has increased dramatically in high income countries in the last decade. Despite the rapid uptake of PEF, there remains limited information to guide women, clinicians and policy makers regarding the characteristics of women undertaking this procedure and treatment outcomes. Study design, size, durationA retrospective population-based cohort study of all women who undertook PEF in Australia and New Zealand between 2009 and 2023, including their subsequent return to thaw their eggs and treatment outcomes. Where women returned to utilise their eggs, all subsequent embryo transfer procedures were linked enabling calculation of live birth rates per woman. Participants/materials, setting, methods20,209 women who undertook PEF in Australia and New Zealand between 2009 and 2023 including 1,657 women who returned to thaw their eggs. Main results and the role of chanceThere has been a huge increase in uptake of PEF, from 55 women in 2009 to 4,919 in 2023. Women who freeze their eggs are typically aged 34-38 years (interquartile range) and nulliparous (98.6%). For women with at least 10 years follow-up (i.e. undertook PEF in 2009-13; N=514), 27.9% returned and thawed their frozen eggs (average time to return: 4.9 years). This reduced to 22.1% in those with at least 5 years follow-up (i.e. undertook PEF in 2009-2018; N=4,288). Of those who used their frozen eggs, 47% used donor sperm. After at least two years follow up, 33.9% had a live birth, rising over time to 37.8% for eggs thawed between 2019-2021. Limitations, reasons for cautionIn the timeframe 2009-2019 we did not have information on whether egg freezing occurred because of a cancer diagnosis, a cohort we wished to exclude from the study. As a result, for this timeframe we weighted observations by the probability that egg freezing occurred due to cancer, with the prediction model developed on the years 2020-2023. Wider implications of the findingsThis study provides recent and comprehensive data on PEF to guide prospective patients and clinicians and inform policy. The exponential growth in PEF in Australia and New Zealand mirrors trends in other high-income countries, suggesting a doubling time of 2-3 years. Study findings highlight the need for setting realistic expectations about the likelihood of returning to use frozen eggs and live birth rates. Study funding/competing interest(s)2020-2025 MRFF Emerging Priorities and Consumer Driven Research initiative: EPCD000014

Background and ObjectivesPatient portals have become essential infrastructure for healthcare delivery following the 21st Century Cures Act, yet adoption remains inequitable. Understanding demographic and geographic determinants of portal activation is critical for addressing digital health disparities, particularly among neurology patients who face unique access barriers. We examined the demographic, geographic, and neighborhood-level factors associated with patient portal activation among neurology patients at multiple geographic scales in the Washington, DC metropolitan area. MethodsWe conducted a retrospective cohort study of 72,417 adult neurology patients seen at two academic medical centers sharing an electronic health record in Washington, DC (February 2021-February 2026). We examined portal activation using multivariable logistic regression and geographic analysis at four nested scales: the metropolitan catchment area, DCs eight wards, individual census tracts (via geocoded patient addresses), and individual DC residents. ResultsPortal activation was 64.7% overall. Activation varied by race/ethnicity (Non-Hispanic White 76.1%, Non-Hispanic Black 57.0%, Non-Hispanic Asian 57.6%, Hispanic 55.0%) and geography (DC Ward 2: 82.0% vs. Ward 7: 48.0%). Ward-level educational attainment (r = 0.948), broadband access (r = 0.889), and income (r = 0.811) were strongly correlated with activation. Within individual wards, Non-Hispanic White patients activated at 84-91% while Non-Hispanic Black patients activated at 48-64%, demonstrating that neighborhood resources alone do not explain disparities. DiscussionPatient portal activation is shaped by demographic, socioeconomic, and geographic factors operating at multiple levels. Persistent within-ward racial disparities indicate that geographically targeted interventions must be paired with culturally tailored approaches to achieve digital health equity.

Background: Clinical documentation and information retrieval consume over half of physicians working hours, contributing to cognitive overload and burnout. While artificial intelligence offers a potential solution, concerns over hallucinations and source reliability have limited adoption at the point of care. Objective: To evaluate clinician-reported time savings, decision-making support, and satisfaction with DR. INFO, an agentic AI clinical assistant, in routine clinical practice. Methods: In this prospective, single-arm pilot study, 29 clinicians across multiple specialties in Portuguese healthcare institutions used DR. INFO v1.0 over five working days within a two-week period. Outcomes were assessed via daily Likert-scale evaluations and a final Net Promoter Score. Non-parametric methods were used throughout. Results: Clinicians reported high perceived time saving (mean 4.27/5; 95% CI: 3.97-4.57) and decision support (4.16/5; 95% CI: 3.86-4.45), with ratings stable across all study days and no evidence of attrition bias. The NPS was 81.2, with no detractors. Conclusions: Clinicians across specialties and career stages reported sustained satisfaction with DR. INFO for both time efficiency and clinical decision support. Validation in larger, controlled studies with objective outcome measures is warranted. Keywords: Medical AI assistant, LLMs in healthcare, Agentic AI, Clinical decision support, Point of care AI

We aimed to evaluate disparities in perinatal ICU admissions at an urban medical center and to contextualize these findings relative to national U.S. data provided by the Centers for Disease Control and Prevention (CDC). To do so, we performed a retrospective review of all pregnant and < 6-week postpartum patients admitted to the ICU between October 2023 and June 2025. The cohort included 58 patients: 81% were non-Hispanic Black, and 91% were publicly insured. These local data can be compared to national data, which demonstrate higher rates of severe maternal morbidity (SMM) and ICU admission among Black patients and those insured by Medicaid. In 2023, the U.S. maternal mortality rate was 18.6 per 100,000 live births, down from 22.3 in 2022. However, significant disparities persist, with mortality rates of 50.3 per 100,000 among Black women compared with 14.5 per 100,000 among White women. The most frequently reported indications for obstetric ICU admission include hypertensive disorders of pregnancy, obstetric hemorrhage, and severe underlying medical comorbidities.

Background: Potassium is involved in multiple physiological processes in the body, and hyperkalemia is a common, potentially life-threatening condition. Objective: The aim of our study was to examine the association between plasma potassium levels, and 30-day mortality in patients presenting to an emergency department with normo- or hyperkalemia. Design: Retrospective Cohort study. Setting: Emergency Departments in the Capital region of Denmark Participants: Persons attending Emergency Departments in the Capital Region of Denmark from 2017--2021 with a plasma potassium level of at least 3.5 mM measured within 4 hours after arrival. Measurements: The study was based on data from Danish National Registries and electronic patient records. We performed Kaplan-Meier survival analyses and unadjusted and adjusted cox regression analyses utilizing plasma [K+] 3.5--4.4 mM as the reference group for 30-day mortality hazard ratios (HRs). Results: A total of 248,453 patients were included with a median age of 60 years (Q1;Q3 42;75), and 6,959 (2.8%) died within 30 days. Mortality was 2.2% for potassium level 3.5--4.4 mM, 6.9% for 4.5--4.9 mM, 17.1% for 5.0--5.9 mM, and 26.9% for [&ge;] 6.0 mM. Unadjusted 30-day HRs were 3.2 (95%CI: 3.0--3.4) for [K+] 4.5--4.9 mM, 8.6 (95%CI: 7.9--9.3) for [K+] 5.0--5.9 mM, and 14.7 (95%CI: 12.5--17.0) for [K+] [&ge;]6.0 mM. Adjusted HRs were 1.4 (1.3--1.5), 2.10 (1.9--2.3), and 2.4 (2.0--2.8), respectively. Limitations: Risk of residual confounding. Missing data. No access to data regarding in-hospital treatment. Conclusion: Plasma potassium levels above 4.4 mM were associated with increased 30-day mortality among patients presenting to emergency departments. Primary funding source: Department of Emergency Medicine, Copenhagen University hospital, Bispebjerg and Frederiksberg Hospital.

Prenatal depression is a substantial contributor to maternal morbidity, and screening is an entry point to psychiatric assessment and treatment during pregnancy. Following updated guidelines and quality metrics for prenatal depression screening, we evaluated whether screening uptake differed by preferred language within a large U.S. healthcare system. We used electronic health record data to identify a retrospective cohort of deliveries at or beyond 20 weeks gestation in 2019-2024. We used logistic regression with a language-year interaction to estimate the adjusted marginal probabilities of screening by language preference. Among 99,526 pregnancies (82,632 individuals), screening increased substantially over time but increases differed across language groups (p<0.001). In 2019, screening probabilities were similar (English 0.50; Spanish 0.48; Another Language 0.50). By 2024, probabilities diverged (English 0.81; Spanish 0.66; Another Language 0.71). Unequal screening uptake can systematically under-identify prenatal depression among patients with non-English language preference, with implications for equitable access to psychiatric care.

Background: Mainstreaming genetic testing has emerged as a strategy to improve access and reduce wait times for patients who may benefit from genetic testing. Ensuring patients fully grasp the implications of testing when formal genetic counselling is not provided, remains a focus for ongoing research. Methods: Patients diagnosed with hypertrophic or dilated cardiomyopathy were offered genetic testing between September 2024 and September 2025 through either the mainstreaming model conducted in cardiology clinics or a referral to Medical Genetics where patients attended an online webinar or a one-on-one genetic counselling appointment. Uptake of testing, time to testing, informed choice and patient satisfaction were evaluated. Results: Among patients offered genetic testing, uptake was higher in the mainstreaming pathway (82%) compared with a referral to Medical Genetics (69%). The difference in access was predominately due to patients not following through with their Genetics referral. Mainstreaming reduced wait times where patients referred to Genetics waited a median of 94-185 additional days to be offered genetic testing. Despite improved access, only 62% of mainstreamed patients were considered informed, compared to 91% of patients that attended a patient webinar through Medical Genetics (p < 0.01). Satisfaction with decision-making was high across both pathways. Conclusion: Integrating genetic testing into cardiology practices increased access and reduced wait times; however, patients demonstrated significantly lower rates of informed decision making compared to those who attended a patient webinar offered through Medical Genetics. These findings highlight the importance of structured education to support informed decision making within mainstreaming pathways.

"Dry Hitting" is a unique phenomenon of e-cigarette use that has been shown to produce toxic chemical degradants and byproducts. Although it is widely understood that nicotine exposure during adolescence impacts neurobiological and behavioral function, little is known about how dry hitting may impact users. We hypothesized that subjects repeatedly exposed to nicotine dry hit vapor would exhibit distinct behavioral responses compared with saturated nicotine vapor and would differentially alter the expression of perineuronal nets (PNNs) in the rodent brain. Using a customized system of e-cigarette vapor inhalation, adolescent male Wistar rats (PND 31-40) received vaporized nicotine (30 or 60 mg/mL; [~]2.5-3 mL/cage), nicotine with dry hits (60 mg/mL; 1.75-2 mL/cage), or propylene glycol (PG) vehicle for 30 minutes over 7 daily sessions. Locomotor activity, antinociception, and elevated plus maze testing were used to assess behavioral response to drug intoxication and tolerance. Immunohistochemistry was used to identify Wisteria Floribunda Agglutinin (WFA)-positive PNN structures in the amygdala and insular cortex. Rats exposed to dry hits exhibited behavioral responses (locomotor sensitization, antinociception) similar to those of rats exposed to saturated nicotine vapor, but spent more time in the open arms of the elevated plus maze. Immunohistochemical analyses confirmed significantly greater WFA intensity in the central nucleus of the amygdala, but not the basolateral amygdala or insular cortex, of rats exposed to dry hits. Overall, these data confirm the impact of dry hit vapor on behavioral responses and perineuronal net expression in rats during adolescence.

Objective: To externally validate, at the national level, a cumulative risk score for vaginal birth after cesarean (VBAC) success and neonatal morbidity derived from single center data. Methods: We conducted a population based cohort study of all trial of labor after cesarean (TOLAC) attempts among term, singleton deliveries recorded in the Centers for Disease Control and Prevention natality files, 2020 to 2024 (N=477,693). The cumulative risk score (range - 1 to 7 points) incorporated body mass index (BMI) 30 or greater (+1), BMI 40 or greater (+1), induction of labor (IOL; +1), diabetes mellitus (+1), hypertensive disorder (+1), maternal age 40 years or older (+1), gestational age 41 weeks or greater (+1), and prior vaginal delivery (-1). VBAC success rates and neonatal intensive care unit (NICU) admission rates were evaluated across risk strata. Results: The overall VBAC rate was 73.3% (350,340/477,693). The cumulative risk score demonstrated a monotonic relationship with VBAC success: score -1, 90.5%; score 0, 76.4%; score 1, 69.4%; score 2, 62.2%; score 3, 55%; and score 4 or higher, 44.8%. NICU admission rates increased concordantly from 43.8 to 111.1 per 1,000 across strata. Prior vaginal delivery was the strongest individual predictor (VBAC 86.4% vs 62.5%). VBAC rates and TOLAC volume were stable across 2020 to 2024. Conclusion: The cumulative risk score derived from single center data was externally validated in a national cohort of 477,693 TOLAC attempts. The monotonic dose-response relationship between risk score and both VBAC success and NICU admission was confirmed, supporting the use of this score for individualized TOLAC counseling.

Introduction: Smoking cessation after acute coronary syndrome (ACS) is a Class I recommendation, yet prescription pharmacotherapy use remains low and its real-world cardiovascular effectiveness when added to nicotine replacement therapy (NRT) is poorly characterized. Methods: We conducted a retrospective cohort study using the TriNetX US Collaborative Network (67 healthcare organizations). Adults hospitalized with ACS who received NRT within one month, serving as a proxy for active smoking status, were identified. Two co-primary propensity-matched (1:1, 50 covariates, caliper 0.10 SD) comparisons evaluated bupropion + NRT and varenicline + NRT individually versus NRT alone; a supportive analysis evaluated combined pharmacotherapy versus NRT alone. All-cause mortality was the primary endpoint. Secondary outcomes included MACE, heart failure exacerbations, major bleeding, TIA/stroke, emergency rehospitalizations, and cardiac rehabilitation utilization, assessed at 6 months and 1 year via Kaplan-Meier analysis. Hazard ratios (HRs) greater than 1.0 indicate higher hazard in the NRT-only group. Results: After matching, the combined analysis comprised 8,574 pairs, the bupropion analysis 4,654 pairs, and the varenicline analysis 2,126 pairs. At 1 year, the combined pharmacotherapy group had significantly lower all-cause mortality (HR 1.26, 95% CI 1.16-1.37), MACE (HR 1.16, 95% CI 1.12-1.21), heart failure exacerbations (HR 1.16, 95% CI 1.08-1.25), major bleeding (HR 1.18, 95% CI 1.08-1.28), and greater cardiac rehabilitation utilization (HR 0.82, 95% CI 0.74-0.92; all p < 0.001). TIA/stroke did not differ significantly. Six-month results were consistent. Both varenicline and bupropion individually showed lower mortality and MACE. A urinary tract infection falsification endpoint showed no between-group differences, supporting matching validity. The pharmacotherapy group had higher rates of new-onset depression, driven predominantly by bupropion recipients. Conclusions: In this propensity-matched real-world analysis, adding prescription smoking cessation pharmacotherapy to NRT after ACS was associated with lower mortality and fewer adverse cardiovascular events, supporting broader integration into post-ACS care pathways.

BackgroundDiabetic kidney disease (DKD) is a leading cause of kidney failure in individuals with type 2 diabetes (T2D), yet risk identification in routine clinical practice remains incomplete. A critical and often overlooked barrier is risk observability: how much of a patients underlying risk is actually captured in their clinical record at the time of screening. Existing prediction models evaluate performance using model-specific thresholds, making it difficult to understand how additional data sources alter real-world screening behavior or which individuals benefit when models are expanded. MethodsWe developed a series of five nested machine learning models evaluated at a one-year landmark following T2D diagnosis using data from the All of Us Research Program (N = 39,431; cases = 16,193). Each successive model added a distinct information layer -- intrinsic risk, laboratory snapshots, medication exposure, longitudinal care trajectories, and social determinants of health (SDOH) -- while retaining all prior features. All models were evaluated under a fixed screening policy targeting 90% specificity, so that the false positive rate remained constant as the information available to the model grew. External validation was conducted in the BioMe Biobank (N = 9,818) without retraining. ResultsDiscrimination improved consistently across layers, from AUROC 0.673 (M1) to 0.797 (M5). Under the fixed screening policy, sensitivity nearly doubled from 0.27 to 0.49, with a cumulative recovery of 30.4% of cases missed by the base model. Gains were driven by distinct subgroups at each transition: laboratory features identified biologically high-risk individuals; medication features captured those with high treatment intensity reflecting advanced cardiometabolic burden; longitudinal care trajectory features rescued cases with biological instability observable only through repeated measurements; and SDOH features recovered individuals with limited clinical observability, with rescue probability highest among those with the fewest recorded monitoring domains. Sparse data in the clinical record indicated low observability, not low risk. Social and genetic features each contributed most when downstream physiologic signal was limited, supporting a contextual rather than universal role for each. In BioMe, discrimination was attenuated (M4 AUROC 0.659), but the relative ordering of information layers was fully preserved, and a systematic upward shift in predicted probability distributions underscored the need for recalibration before deployment in a new setting. ConclusionsDKD risk detection in T2D is substantially improved by integrating complementary information layers under a fixed clinical screening policy, with gains arising from distinct domains that identify at-risk individuals in different clinical contexts. The layered landmark framework introduced here reveals how risk observability -- shaped by monitoring intensity, healthcare engagement, and access -- determines what a screening model can detect, and provides a foundation for context-aware EHR-based screening that accounts for data availability at the time of risk assessment. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/26351384v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@1cc7f4borg.highwire.dtl.DTLVardef@b92956org.highwire.dtl.DTLVardef@48ffbcorg.highwire.dtl.DTLVardef@8dc627_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstract.C_FLOATNO Study design and layered DKD screening framework The top row defines the cohort timeline, in which predictors are derived from clinical data collected between T2D diagnosis and the 1-year landmark, and incident DKD is ascertained after the landmark. The second row depicts the nested model architecture, in which five successive models sequentially incorporate intrinsic risk, laboratory snapshot features, medication exposure, longitudinal care trajectories, and social determinants of health, while retaining all features from prior layers. The third row summarizes model development in the All of Us Research Program (N = 39,431) and external validation in the BioMe Biobank (N = 9,818), where the same trained models and risk thresholds were applied without retraining. The bottom row highlights the three evaluation domains: predictive performance, fixed-policy screening, and missed-case recovery context. DKD, diabetic kidney disease; T2D, type 2 diabetes; PRS, polygenic risk scores; AUROC, area under the receiver operating characteristic curve; AUPRC, area under the precision-recall curve; PPV, positive predictive value; SHAP, SHapley Additive exPlanations. C_FIG