Citations

OBJECTIVE: To review currently available devices on pressure injury (PI) early detection, summarize challenges and opportunities to clinical implementation, and propose evaluation standards for device categories. DATA SOURCES: PubMed and US Food and Drug Administration (FDA) databases. STUDY SELECTION: Published in English from peer-reviewed journals with full text available. Excluded if opinion statements, lack of empirical data, or unrelated to project’s objective. DATA EXTRACTION: For both clinical device and research equipment: measurement mechanisms, measurement types, outcome/output, FDA classification, and indications for use. Addition data were extracted for clinical devices: instruction for use, end user, order requirement, and billable code. DATA SYNTHESIS: The 4 clinical devices are ultrasound, long-wave infrared thermography, subepidermal moisture assessment, and nearinfrared spectroscopy. The 3 research devices are laser Doppler flowmetry, laser speckle contrast imaging, and colorimetry. CONCLUSIONS: The measurement mechanisms of all devices are unique and different from each other. One commonality is that they could measure the nonvisual signs of PI (eg, inflammation, edema, ischemia, and hypoxia) except colorimeter. Some clinical devices are promising to assist with early identification of PIs, especially in individuals with dark skin tones. Currently, there is no reimbursement available for early detection of PI. Current evidence did not support replacing the standard skin assessment of visual inspection and palpation with the devices reviewed, rather using validated devices to augment the current practice standard. This is especially recommended for individuals identified as high risk for a PI on admission to a facility.

Abstract eSource – particularly EHR-to-EDC – is an emerging paradigm in clinical research that enables automated transfer of electronic health record (EHR) data into electronic data capture (EDC) systems, with the potential to reduce site burden, improve data quality and accelerate oncology clinical trial workflows. However, widespread implementation remains limited due to technical, regulatory and operational barriers. To address these challenges, the European Institute for Innovation through Health Data (i~HD) launched the eSource Scale-Up Task Force in 2024. This multi-stakeholder initiative brings together leading oncology centres and pharmaceutical sponsors to establish a consensus-driven roadmap for eSource adoption. Central to this effort are three foundational resources: readiness criteria for early adopters, a performance indicator framework for monitoring success and an operational playbook to guide implementation. This article provides a structured overview of the Task Force’s objectives, collaborative model and outputs, with specific attention to its focus on interoperability, regulatory alignment and real-world validation. While initially developed for oncology, the Task Force’s framework is applicable across therapeutic areas characterized by data-intensive workflows.

Importance Accumulation of amyloid plaque drives pathogenesis of Alzheimer disease (AD). Reduction of amyloid via amyloid-targeting therapies may result in clinical benefit. Objective To assess the correlation of posttreatment amyloid levels with clinical outcomes and biomarkers in AD. Design, Setting, and Participants This was a post hoc exploratory analysis from the randomized, placebo-controlled phase 3 trial, TRAILBLAZER-ALZ 2, conducted June 2020 through April 2023 at 277 medical research centers/hospitals in 8 countries. A total of 8240 participants aged 60 to 85 years with early symptomatic AD with amyloid and tau pathology based on positron emission tomography (PET) imaging were assessed for eligibility. Of these, 6504 participants were excluded predominantly due to inadequate amyloid or tau pathology. The current analysis included 1582 participants (766 in the donanemab group and 816 in the placebo group) with baseline and at least 1 posttreatment assessment. Data analysis took place from July 2024 to March 2025. Interventions Participants were randomized 1:1 to receive donanemab (700 mg for the first 3 doses and 1400 mg thereafter) or placebo intravenously every 4 weeks for up to 72 weeks, with outcomes assessed through 76 weeks. Main Outcomes and Measures Participants were categorized into 1 of 10 groups (deciles) based on their lowest amyloid value observed posttreatment. Clinical progression was assessed via changes in integrated Alzheimer’s Disease Rating Scale (iADRS) and Clinical Dementia Rating–Sum of Boxes (CDR-SB) scores. Plasma biomarkers measured included phosphorylated tau 217 (p-tau217), p-tau181, glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL). Correlations between the median amyloid level in each decile were assessed with 76-week least-squares mean changes in each outcome and biomarker. Results Analyses included 1582 participants, including 766 treated with donanemab and 816 with placebo. The mean (SD) age was 72.9 (6.2) years, and 900 participants (56.9%) were female. Participants who received donanemab had lower posttreatment amyloid values than those in the placebo group. Across the trial population, lower posttreatment amyloid levels were correlated with slower clinical progression as measured by iADRS score ( R 2 , 0.73 [95% CI, 0.37-0.97]) and CDR-SB score ( R 2 , 0.87 [95% CI, 0.70-0.97]) and with decreases in p-tau217 ( R 2 , 0.86 [95% CI, 0.73-0.97]), p-tau181 ( R 2 , 0.88 [95% CI, 0.77-0.97]), and GFAP ( R 2 , 0.87 [95% CI, 0.76-0.97]). There was no correlation between posttreatment amyloid value and NfL ( R 2 , 0.03 [95% CI, 0.00-0.54]). Conclusions and Relevance The findings in this secondary analysis of a randomized clinical trial demonstrating a correlation between posttreatment amyloid plaque level and clinical benefit support amyloid plaque removal as the mechanism of action for donanemab treatment and the level of amyloid plaque as a potential surrogate biomarker in amyloid-targeting therapies. Trial Registration ClinicalTrials.gov Identifier: NCT04437511

Abstract Clinical and translational research is important for health care and the growth of medical specialties. Physical medicine and rehabilitation offers many opportunities for research, but research growth in physical medicine and rehabilitation is lacking, and research resources and productivity vary across academic physical medicine and rehabilitation departments across North America. The Physiatric Research Consulting Program was developed by the Association of Academic Physiatrists to provide customized recommendations to enhance research capacity and productivity in physical medicine and rehabilitation departments. This report outlines the three components of the Physiatric Research Consulting Program, including a previsit needs assessment, an in-person visit, and a postvisit follow-up final report. The report also provides a qualitative assessment of the impact of the Physiatric Research Consulting Program, with general themes of feedback, site visit evaluations, and final report evaluations. The Physiatric Research Consulting Program was found to be valuable in identifying gaps and needs for physical medicine and rehabilitation departments, providing outside perspectives, and energizing faculty toward research growth in their departments.

Importance Inherited pathogenic variants (PVs) in known predisposition genes can greatly increase breast cancer risk, but the combined impact of PV status, family history, and other factors on breast cancer risk in the general US population has not been well described. Objective To evaluate population-based breast cancer risk estimates for those with established PVs overall and stratified by first-degree family history of breast cancer and other factors. Design, Setting, and Participants This study used pooled data from 13 US-based breast cancer case-control studies participating in the Cancer Risk Estimates Related to Susceptibility (CARRIERS) consortium. Enrollment for individual studies occurred between 1976 and 2013, and results are based on data released March 2023, with analyses conducted from June 2022 to July 2025. Exposures PVs, breast cancer family history, self-reported race and ethnicity, and established risk factors. Main Outcomes and Measures Breast cancer rate ratios for PVs in 7 genes were estimated from the CARRIERS consortium. PV status and incidence and mortality statistics were combined using the Individualized Coherent Absolute Risk Estimation (iCARE) model to estimate conditional cumulative breast cancer risks and 95% CIs, stratified by family history and standardized to the US population. Models that incorporated population-based data and published estimates for established epidemiologic risk factors were also evaluated. Results A total of 67 692 women were studied, including 33 841 who were diagnosed with breast cancer. PVs in ATM , BRCA1 , BRCA2 , CHEK2 , and PALB2 were strongly associated with breast cancer risk, with BRCA1 and PALB2 PVs showing evidence of heterogeneity by family history. In models considering PVs, family history, and established risk factors, the estimated cumulative risks of breast cancer by age 50 years ranged from 2.4% (95% CI, 2.4-2.4) in women with no PVs and no family history to 35.5% (95% CI, 21.6-55.1) in PALB2 PV carriers with a family history. Among women who have not been diagnosed with breast cancer by age 50 years, the cumulative risk of breast cancer by age 80 years ranged from 11.1% (95% CI, 11.0-11.2) in noncarriers with no family history to 70.5% (95% CI, 52.8-83.5) for PALB2 carriers with a family history. PV-specific cumulative risk estimates varied across subgroups defined by race and ethnicity and potentially modifiable epidemiologic risk factors. Conclusions and Relevance In this study, population-based estimates of cumulative breast cancer risk for established PVs, as informed by the CARRIERS case-control sample, varied by family history and potentially modifiable risk factors. These estimates provide guidance for identifying individuals who will most benefit from enhanced screening and prevention strategies.

ABSTRACT Importance Despite the current enthusiasm for anti‐obesity medications, there is a critical need for effective lifestyle interventions that can be broadly implemented. Objective To assess the effectiveness of REWIND, an obesity and diabetes treatment program. Design Prospective cohort study. Setting Virtual, community‐based weight management program. Participants Two‐hundred and nineteen participants, BMI ≥ 30 kg/m 2 , with and without type 2 diabetes (T2D). Intervention Three phases: “induction” with meal replacement diet to promote ≥ 15% weight loss; “transition” to everyday foods; and “maintenance” to prevent weight regain. REWIND leveraged teams including community leaders, media personalities, nationally recognized athletes and participants, to disseminate information, and foster belonging, commitment, and resilience. Main Outcomes The co‐primary outcomes were the change in weight at 6‐, 12‐, and 18 months. Secondary outcomes were the proportion of patients achieving ≥ 5%, 10%, 15%, and 20% weight loss at 6, 12, and 18 months, and remission rates in T2D at 6‐, 12‐, and 18‐month, respectively. Results Two hundred and nineteen participants (mean age 49.1 [SD 10.5] years), 37% men, 12% Black, 26% with T2D. Weight decreased from: 114.4 (20.8) kg to 96.1 (18.4) kg at 6 months, to 98.5 (18.1) kg at 12 months, and to 101.1 (20.5) kg at 18 months. Weight loss percentages at 6, 12, and 18 months were ≥ 5%: 95%, 91%, 84%; ≥ 10%: 79%, 72%, 67%; ≥ 15%: 58%, 46%, 47%; and ≥ 20%: 24%, 25%, 22%. Diabetes remission was achieved by 52% and 43% of participants with T2D at 12‐ and 18‐month, respectively. Limitations Single geographic region. Weight was self‐reported. Conclusion A virtual community‐based program for individuals with obesity and T2D facilitated substantial weight loss and T2D remission. This model has the potential for wide‐scale implementation in diverse settings.

Abstract Chimeric antigen receptor (CAR) T-cell (CAR-T) treatment for B-cell acute lymphoblastic leukemia (ALL) induces high initial response rates, but most patients relapse. Low disease burden (often defined as <5% blasts in the bone marrow) is associated with better outcomes. CAR HEMATOTOX (HT) is a score using prelymphodepletion hematologic and inflammatory parameters to predict outcomes in lymphoma. Here, we assess its prognostic utility in a large multicenter adult B-cell ALL cohort. Patients who received brexucabtagene autoleucel across 33 centers in North America were included as part of the Real-World Outcomes Collaborative of CAR-T in Adult ALL (ROCCA) consortium. An independent cohort of 61 patients with ALL treated with an investigational CD19 CAR-T therapy at 1 center was also described. Among 199 ROCCA consortium patients, 43 (22%) patients with HTlow scores had lower rates of delayed neutrophil recovery than those with HThigh scores (26% vs 52%, P = .002) and fewer severe infections (2.5% vs 18.8%, P = .011). They also had higher response rates, overall survival (OS), and event-free survival (EFS), as well as lower nonrelapse mortality and cumulative incidence of relapse. The survival differences remained significant after multivariable adjustment for disease burden and other covariates. In the investigational cohort of 61 patients, patients with HTlow scores had improved OS and EFS, as well as higher peak CAR-T expansion. In summary, CAR HT score is a prognostic factor independent of disease burden in adult ALL. HTlow score is associated with superior outcomes after CD19 CAR-Ts and higher CAR-T expansion in a single-center cohort. These trials were registered at www.clinicaltrials.gov as #NCT01044069 and #NCT01860937.

Abstract Aims To assess the potential for artificial intelligence-enabled electrocardiogram (AI-ECG) to serve as a long-term cardiac surveillance tool and predict left ventricular systolic dysfunction in childhood cancer patients. Methods and results We assessed performance of our previously established AI-ECG model to predict left ventricular ejection fraction (LVEF) ≤50% and ≤40% in patients with childhood cancer during internal testing (Boston Children’s Hospital) and external validation (Children’s Hospital of Philadelphia). The internal test cohort comprised 447 patients [57% male; age at cancer diagnosis 11.2 (5.4–15.7) years; 1553 ECG–echo pairs at median age 13.5 (IQR 7.7–17.9) years; 6.4% with LVEF ≤50%; 1.3% with LVEF ≤40%], 28% with leukaemia, 16% with lymphoma, 8% with neuroblastoma, 8% with sarcoma, 2% with gastrointestinal cancers, 3% with genitourinary cancers, 6% with central nervous system cancers, 11% with other/unspecified cancers, and 18% with missing/unknown cancer labels. Treatment strategies included anthracyclines (35%), bone marrow transplant (7%), and radiation (1%). The external test cohort comprised 2964 patients [55.4% male; 7054 ECG–echo pairs at median age 11.6 (IQR 6.8–15.1) years; 2.5% with LVEF ≤50%; 0.9% with LVEF ≤40%]. Similar AUROCs (0.80–0.85), sensitivities (0.75–0.82), NPVs (0.986–0.996), and percent predicted negative (51–65%) were obtained across institutions to predict LVEF ≤50%, outperforming a biomarker-based model benchmark. Patients with high AI-ECG risk scores for LVEF ≤50% had higher rates of mortality [hazard ratio 3.1 (95% CI 1.8–5.3), P < 0.001] compared to patients with low AI-ECG risk scores. Conclusion AI-ECG shows promise as a digital biomarker for cardiac surveillance in the vulnerable childhood cancer survivor population.