Randi E. Foraker

Background: Global dementia cases, currently exceeding 55 million, are projected to triple by 2050. In the absence of disease-modifying therapies, identifying modifiable risk factors is critical. Preclinical studies show that herpes simplex virus type-1 (HSV-1) is neurotropic and can drive amyloid-beta accumulation and tau hyperphosphorylation. Epidemiologic findings remain inconsistent, partly because many studies lack standardized designs for real-world data. Objective: To quantify the association between clinically coded HSV-1 diagnosis and incident cognitive impairment or dementia among patients receiving care in a health-system electronic health record (EHR) network. Methods: We assembled a retrospective cohort within an Observational Medical Outcomes Partnership (OMOP)–mapped EHR data lake (2010‐2024), comparing patients with a first HSV-1 diagnosis (n=6274) to those without HSV-1 codes but with parallel baseline criteria (n=379,975). Eligibility required at least 365 days of prior observation and absence of baseline cognitive, HIV, selected neurotropic viral, or recent transplant codes. The outcome combined mild cognitive impairment and Alzheimer disease and related dementias (ADRD). A high-dimensional propensity score (PS) incorporating approximately 17,000 baseline covariates was estimated with regularized logistic regression; 4 strata weights were applied in a Cox model. Sensitivity analyses examined equipoise trimming, doubly adjusted models, fixed 1-, 5-, and 10-year censoring horizons, and a dementia-only outcome subset. Negative-control outcomes (n=271) were prespecified to assess empirical calibration. Results: The HSV-1 cohort contributed 23,186 person-years (PY) and 469 events; the non-HSV-1 cohort contributed 1,519,827 PY and 24,764 events. Crude incidence was 20.23 (95% CI 18.44‐22.14) per 1000 PY in the HSV-1 cohort and 16.29 (95% CI 16.09‐16.50) in the non-HSV-1 cohort, an absolute difference of 3.94 events per 1000 PY. PS-stratified analysis yielded a hazard ratio (HR) of 1.14 (95% CI 1.03‐1.25; P =.007). Estimates were comparable after equipoise trimming (HR 1.12, 95% CI 1.01‐1.25, P =.04), doubly adjusted modeling (HR 1.13, 95% CI 1.02‐1.24, P =.01), and fixed 5-year ( P =.008) and 10-year follow-up ( P =.004) (both HR 1.15). The 1-year censored model showed HR 1.00 (95% CI 0.84‐1.20; P =.96). The dementia-only endpoint mirrored the primary analysis (HR 1.14, 95% CI 1.03‐1.25; P =.008). No postindex events occurred for any negative-control outcome, so empirical calibration could not be performed and all estimates are uncalibrated. Conclusions: Within an OMOP-standardized EHR cohort, an HSV-1 diagnosis was associated with a small elevation in the hazard of subsequent cognitive impairment or dementia compared with individuals with no recorded HSV-1 diagnosis. Given the ubiquity of HSV-1, even a modest elevation in individual hazard could translate to a meaningful population-level increment if the association is causal. Confirmation in external data sets with laboratory viral typing, antiviral treatment records, and mortality linkage is warranted.

Objective: To evaluate antibiotic prescribing knowledge and confidence before versus after deployment of a web-based clinical decision support tool at an academic dental center. Design: Pre and post survey instrument. Setting: Academic dental center in the United States. Patients: Dental students, residents, and faculty. Methods: Participants completed a series of seven clinical vignettes assessing their antibiotic prescribing knowledge and confidence using 5-point Likert scale questions pre (2022) and post (2025) deployment of a web-based educational tool. The survey questions included content about antibiotic prophylaxis and treatment for dental infections. Mean Likert scale responses were compared before and after deployment of the tool. The responses were analyzed individually, pooled, and stratified by training level. Sensitivity analyses were conducted comparing results between tool users and non-users. Results: Two hundred and thirteen participants completed the pretool deployment survey, and 116 participants completed the post survey. Respondents were slightly less likely to prescribe antibiotics after having access to the tool (median pooled 5-point Likert Scale score 2.55 vs 2.67; P = .07). However, antibiotic prescribing confidence did not change significantly (median pooled score 3.37 vs 3.24; P = .21). Stratified results showed substantial variability based on individual vignette. Self-reported users of the tool (n = 37) demonstrated no differences in knowledge versus non-users (n = 97; P = .76) but did demonstrate a trend toward more confidence in their decisions ( P = .07). Conclusions: Uptake of a web-based antibiotic prescribing tool was low overall and was not associated with any change in antibiotic prescribing knowledge and confidence. Additional interventions are needed to improve tool use in dental settings.

BACKGROUND Machine learning can enhance mortality risk prediction within clinical decision support systems (CDSS), but its impact depends on whether clinicians trust and act on model predictions. Across our health system, we implemented a deep‑learning-based CDSS to identify inpatients at high risk of mortality and prompt goals‑of‑care discussions (GOCDs). Hospitalists receiving alerts could indicate agreement or disagreement. OBJECTIVE The objective of our study was to assess what factors were associated with clinician disagreement with the model, and whether incorporating clinician agreement improved 30-day mortality predictions and subsequent clinical action. METHODS We used penalized logistic regression followed by mixed‑effects models to identify features associated with clinician disagreement with the alert. Next, we compared two strategies for predicting 30‑day mortality among eligible encounters: utilizing a higher score threshold (≥50% rather than ≥25%) versus restricting the “high risk” designation to encounters where clinicians agreed with the alert. We then used mixed‑effects and inverse-probability‑weighted models to evaluate the impact of agreement on three post‑alert actions: GOCD documentation, palliative care referral, and code status change. RESULTS Among 1,830 alerts with clinician responses between December of 2020 (the program launch) and December of 2024, 15.7% indicated disagreement. Notable factors associated with disagreement included younger patient age, lower severity of illness, and certain diagnoses (e.g., COVID-19). Both the clinician agreement strategy and the 50% score threshold yielded modest discrimination for 30‑day mortality (AUC-ROC ≈ 0.55-0.57). Compared with the higher threshold strategy, clinician agreement provided higher sensitivity but lower specificity and overall accuracy. Clinician agreement was also associated with substantially higher rates of GOCD documentation, palliative care referral, and code status change. CONCLUSIONS In this high‑risk population, hospitalist judgment did not meaningfully improve mortality prediction and was comparable to increasing the alert score threshold to 50%, but it did identify more patients who later died, which may be preferrable in this context. Moreover, clinician agreement with alerts was critical for prompting subsequent clinical actions. CLINICALTRIAL N/A

BACKGROUND Given the rapid growth of digital healthcare, greater transparency in engaging end-users in developing implementation plans is needed to improve the sustained delivery and effectiveness of digital healthcare. PREVENT is a patient-centered digital health tool designed to engage patients with overweight or obesity in conversations about behavior change to lower their cardiovascular disease (CVD) risk. We are implementing PREVENT in a rural federally qualified health center network with eight clinics. OBJECTIVE Objective: The objective of this paper is to present a multi-method approach used to engage clinic teams, including healthcare team members and administrators, to co-develop an implementation plan for the PREVENT digital health tool. METHODS Methods: Healthcare team members (e.g., clinicians, community health workers) and administrators (e.g., informatics professionals, clinic managers, CEO) were engaged through qualitative interviews, advisory board meetings, and site visits to develop an implementation plan prior to implementation. Qualitative interviews were coded using the Consolidated Framework for Implementation Research to identify potential barriers and facilitators of implementing PREVENT and paired with learnings from previous trials of PREVENT to inform the selection and tailoring of implementation strategies. Direct clinic observations generated clinic workflow maps, roles and responsibilities of team members, clinic resources (e.g., technology, space), and an understanding of the clinic and community context and readiness for implementation. A mixed-methods evaluation plan was developed using validated measures, input from the advisory board, and implementation research logic models. RESULTS Results: Implementation of the PREVENT tool was facilitated by a positive clinic culture, alignment with chronic disease priorities, an easy-to-use and adaptable design with EHR integration, and CHWs’ experience addressing social needs. Key barriers included limited technology infrastructure, variable staffing across clinics, patients’ digital access and literacy challenges, and the need for additional training and support for care team members. An implementation plan that includes thirteen strategies across six implementation strategy clusters was created to leverage facilitators and address barriers. A multi-modal evaluation plan was created to examine implementation and effectiveness. CONCLUSIONS Conclusions: This paper provides an example of how to develop implementation and evaluation plans tailored to the healthcare context that engage end users to increase the impact of a digital health intervention. This work may be replicated to support the successful implementation of other digital health tools, particularly in under-resourced, complex healthcare contexts such as rural clinics where resources are less available.

Climate change poses an escalating threat to cardiovascular and cerebrovascular health in the Global South, where vulnerability is amplified by rapid urbanization, poverty, and weak infrastructure. Air pollution (driven by fossil fuel use, industrial growth, and poor regulation) remains a major contributor to cardiovascular disease and respiratory illness, with regions such as South Asia and Sub‐Saharan Africa experiencing the highest burdens. Extreme heat, floods, and natural disasters further compound cardiovascular risks through direct physiological stress and disruption of health care systems. Urban heat islands intensify the impact of rising temperatures, especially in low‐income and historically marginalized communities with limited access to cooling. Meanwhile, increasingly severe floods, particularly in South and East Asia, demand improved disaster preparedness and urban planning to reduce exposure and health impacts. Many cities in rapidly urbanizing cities in Africa lack basic sanitation and access to clean water, air, and soil. These could have magnified impacts on populations during climate emergencies. To address these interconnected challenges, a global, equity‐centered approach is needed, one that strengthens regulatory frameworks, expands access to clean energy and cooling technologies, and promotes urban resilience. Collaborative efforts in air quality monitoring, disaster risk reduction, and adaptation financing must prioritize the unique needs of the Global South, guided by context‐specific, scalable solutions that also incorporate intergenerational and environmental justice considerations.

Coronary artery calcium (CAC) is a marker of subclinical atherosclerosis that confers increased risk of atherosclerotic cardiovascular disease. Measured by noncontrast cardiac computed tomography, CAC improves risk stratification beyond traditional risk factors and can aid decision-making for allocation of preventive treatments. Although national guidelines recommend consideration of CAC measurement for >17 million individuals in the United States with borderline to intermediate 10-year atherosclerotic cardiovascular disease risk, adoption has been limited. A promising approach to bridge this gap is opportunistic detection of CAC using non–ECG-gated chest computed tomography scans that are performed for a noncardiac indication. Approximately 19 million non–ECG-gated chest computed tomography scans are performed per year, and reporting opportunistic detection of CAC from these scans can enhance atherosclerotic cardiovascular disease risk stratification without additional radiation exposure, cost, or burden. Estimation of risk by traditional risk factor scoring is underused, and reporting of opportunistic detection of CAC has the potential to alert physicians of risk, independent of guideline-recommended risk calculator use. Advancements in artificial intelligence allow integration of automated CAC quantification into clinical practice. Several artificial intelligence algorithms are in use to improve the likelihood of reporting opportunistic detection of CAC and appropriate allocation of preventive therapies. Systematic approaches are needed to ensure appropriate reporting, interpretation, and action while avoiding unnecessary downstream testing. Implementation that includes tailored preventive care and streamlined care pathways involving multidisciplinary clinical teams including radiology, cardiology, and primary care is essential.

BACKGROUND Global dementia cases, currently exceeding 55 million, are projected to triple by 2050. In the absence of disease-modifying therapies, identifying modifiable risk factors is critical to mitigating this impending crisis. Preclinical studies have demonstrated that herpes simplex virus type-1 (HSV-1) exhibits neurotropism and can invade the central nervous system and drive pathological processes such as amyloid-beta accumulation and tau hyperphosphorylation, supporting the hypothesis that HSV-1 contributes to neurodegenerative processes. Epidemiologic findings are inconsistent, partly because many studies lack standardized designs for real-world data (RWD). OBJECTIVE To quantify the association between clinically coded HSV-1 infection and diagnosis of cognitive impairment or dementia among patients receiving care in a health-system electronic health record (EHR) network. METHODS We assembled a retrospective cohort within an OMOP-mapped EHR data lake (2010-2024), comparing patients with a first HSV-1 diagnosis (n = 6,274) to those without HSV-1 codes but with parallel baseline criteria (n = 379,975). Eligibility required ≥365 days of prior observation and absence of baseline cognitive, HIV, selected neurotropic viral, or recent transplant codes. The outcome combined mild cognitive impairment and Alzheimer disease-related dementias. A high-dimensional propensity score incorporating ~17 000 baseline covariates was estimated with regularized logistic regression; four strata weights were applied in a Cox model. Sensitivity analyses examined equipoise trimming, doubly adjusted models, fixed 1, 5, and 10-year censoring horizons, and a dementia-only outcome subset. Negative-control outcomes (n = 271) were analyzed for empirical calibration. RESULTS The HSV-1 cohort contributed 23,186 person-years (PY) and 469 events; the non-HSV-1 cohort contributed 1,519,828 PY and 24,764 events. The crude incidence rate was 20.23 (95% CI 18.44-22.14) per 1,000 person-years in the HSV-1 cohort compared to 16.29 (95% CI 16.09-16.50) in controls, yielding an absolute difference of 3.94 events per 1,000 person-years. Propensity-score-stratified analysis yielded a hazard ratio (HR) of 1.14 (95 % CI 1.03-1.25, P=.01). Estimates were comparable after equipoise trimming (HR 1.12, 95 % CI 1.01-1.25, P=.037), doubly adjusted modeling (HR 1.13, 95 % CI 1.02-1.24, P=.014), and fixed 5 year (P=.008) and 10-year follow-up (P=.004) (both HR 1.15). The 1-year censored model showed HR 1.00 (95 % CI 0.84-1.20, P=1). Results for the dementia-only endpoint mirrored the primary analysis (HR 1.14, 95 % CI 1.03-1.25, P=.008). Negative-control calibration was not performed due to zero events. CONCLUSIONS Within an OMOP-standardized EHR cohort, an HSV-1 diagnosis was associated with a small elevation in the hazard of subsequent cognitive impairment or dementia. Given the ubiquity of HSV-1 infection, even a modest increase in dementia risk may translate to substantial additional cases at the population level. Confirmation in external data sets that include laboratory viral typing, antiviral treatment records, and mortality linkage is warranted.

Violent injury survivors are at risk for revictimization. The St. Louis area hospital-based violence intervention program (HVIP), Life Outside of Violence (LOV), is the first multisystem, region-wide HVIP in the United States. To describe the LOV program during its pilot phase and evaluate violent reinjury 1 year after index injury among LOV participants. Pilot observational cohort study of violently injured patients. Data were queried from the program's multisystem data repository, which contains individual encounter-level data for all violent injury visits from the 2 adult and 2 pediatric LOV-partner level 1 trauma hospitals. Patients eligible for LOV who were violently injured between 15 August 2018 and 31 December 2022 and enrolled in LOV, matched to control participants of nonenrolled LOV-eligible patients selected from the data repository using propensity score matching. Participation in the LOV program. Sociodemographic characteristics, predictors of LOV enrollment, and returning to a LOV partner hospital with a violent reinjury within 1 year of index injury. The probability of violent reinjury and 95% CIs were compared between LOV participants and control participants using Kaplan-Meier estimates. 233 of 3744 eligible patients enrolled in LOV. Of 198 LOV-enrolled participants matched to 388 nonenrolled control participants, Kaplan-Meier estimates for 1-year probability of reinjury were 7.6% (95% CI, 3.8% to 11.2%) among LOV participants and 7.4% (CI, 4.8% to 10.0%) in control participants. This pilot study cannot provide precise estimates of LOV intervention efficacy. Although no informative evidence of differences in reinjury probability for LOV participants was seen, findings suggest that the overwhelming risk in which our patients are immersed cannot be overcome by an approach scaled for individual-level impact. Missouri Foundation for Health.