Citations

Background The current classification of pulmonary hypertension (PH), based largely on expert opinion, has limitations in prognostication and guiding therapies. We hypothesize that novel PH clusters that predict survival will reveal mechanistic phenotypes associated with biomarkers of vascular health across all PH groups. Methods We first identify novel PH clinical clusters by performing unsupervised clustering analysis on the CC‐PH (Cleveland Clinic PH) registry (N=1529). We develop classification models to predict the new PH clusters and then apply them to the multicenter PVDOMICS (Pulmonary Vascular Diseases Phenomics) cohort (N=853) for validation. We compare transplantation‐free survival across the new PH clusters. We quantify metabolites of the arginine‐nitric oxide pathway and D‐dimer levels and calculate global arginine bioavailability (arginine/[ornithine+citrulline]) to assess endothelial function and activation in the new clusters and link these biomarkers to clinical outcomes. Results Clustering analysis identify 3 clear clusters in CC ‐ PH that are validated in PVDOMICS and outperform conventional classifications in predicting transplantation‐free survival. The phenotype associated with the worst survival is characterized by reduced lung diffusion capacity, decreased arginine bioavailability and nitrate levels, and elevated D‐dimer levels, consistent with loss of pulmonary microcirculation and endothelial dysfunction. Conclusions We identify new informative PH phenotypes associated with mortality and defined by biomarkers of endothelial function and activation. Loss of endothelial health and pronounced pulmonary vascular rarefication contribute more substantially to mortality across the spectrum of PH than right heart function. REGISTRATION: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02980887.

Most genetic variants associated with complex traits are hypothesized to regulate gene expression. To understand the genetics underlying gene expression variability, we characterized 14,324 RNA-sequencing samples from the Trans-Omics for Precision Medicine program and performed expression and splicing quantitative trait locus (e/sQTL) analyses in six tissues and cell types, including whole blood ( n = 6454) and lung ( n = 1291). We detected tens of thousands of secondary cis-e/sQTLs, showing that secondary cis-e/sQTL discovery remains unsaturated. We fine-mapped UK Biobank–derived genome-wide association study (GWAS) signals from 164 traits and identified e/sQTL colocalizations for 10,611 GWAS signals, including 7096 that colocalize with secondary e/sQTLs. Our results suggest that even larger e/sQTL analyses will uncover additional secondary e/sQTLs, further benefiting GWAS interpretation.

Abstract Background Quality assurance (QA) in radiation therapy is a critical component of ensuring accurate and consistent patient treatments, but many tasks are time consuming and rely on human visual acuity, which limits their accuracy. Purpose We aimed to automate monthly mechanical QA tests, improve measurement accuracy and precision, and decrease inter‐user variability using a computer vision‐based quality assurance (CVQA) system. Methods A custom marker board incorporating four ArUco markers was created along with a custom camera holder that mounted onto the gantry head. OpenCV was utilized to automate tests for couch translation, collimator and table angles, collimator and table walkout, optical distance indicator (ODI), and field size detection. A GUI was created to guide users through the tests, determine passing status of measurements based on MPPG8.b criteria, and display image captures to allow users to troubleshoot if needed. Reproducibility tests were taken across four days. The system was tested against manual measurements by graph paper or digital level for each test. Field sizes and ODI were read by 12 different physicists to determine human variability and comparisons for CVQA. Results The CVQA system took 5 min to set up and 7 min to perform all tests. All field sizes, collimator/table angles, collimator/table walkout, and table translations were reproducible within 0.5 mm and 0.5°. ODI measurements were reproducible within 1 mm. Table travel, ODI, and walkout measurements agreed with manual measurements within 0.5 mm and 0.4°, except for vertical table motions that agreed within 0.9 mm due to the lens focus being optimized for the 100 cm SSD plane. The standard deviation between physicists for almost all symmetric and asymmetric jaws was larger than the reproducibility of CVQA. CVQA has been utilized for five years and has demonstrated ability to identify mechanical machine issues. Conclusions CVQA successfully automates mechanical QA tasks, providing an efficient and precise system. CVQA is open source and freely available for academic institutions. Its adoption can improve workflow efficiency and consistency in clinical environments.

Abstract Polygenic risk scores (PRSs), which quantify inherited susceptibility to complex traits and diseases, have emerged as valuable tools for risk stratification and precision medicine. Despite their promise, PRS developed on European cohorts often demonstrate substantially reduced predictive accuracy in non-European populations, due to differences in genetic architecture. The disproportionate representation of European ancestry cohorts in genome-wide association studies (GWAS) leads to inequitable deployment of PRS technologies across diverse populations. Here, we introduce PRANA (Polygenic Risk Adaptation via Neural-network Architecture), a deep learning framework that adapts an existing PRS developed on one population to other ancestries. Unlike methods that require large-scale GWAS in the target population, PRANA leverages pre-trained PRS models derived from European cohorts and adapts them using modestly sized cohorts from the target population. We evaluated PRANA on seven complex traits in South Asian, East Asian and Ashkenazi Jewish populations, as well as in selected smaller East Asian subpopulations where the scarcity of training data poses a particular challenge. PRANA mostly improved predictive performance of the baseline PRS models by 5%-20% in terms of effect size (β) and Nagelkerke’s R², and, in most cases, outperformed existing cross-ancestry multi-PRS approaches. These results highlight PRANA as a scalable and practical strategy to reduce disparities in genomic risk prediction and advance the equitable application of PRS in diverse populations.

Importance Although the Martin-Hopkins equation is widely validated and used to estimate low-density lipoprotein cholesterol (LDL-C) for clinical care, a simplified equation could streamline implementation and enable broader adoption. Objective To develop a simplified machine learning–based alternative to the Martin-Hopkins equation using multivariate adaptive regression splines (LDL-C–MH-MARS) and compare its performance with the Friedewald (LDL-C–F), Sampson–National Institutes of Health (LDL-C–S), Modified Sampson (LDL-C–MS), and Martin-Hopkins (LDL-C–MH) equations. Design, Setting, and Participants The Very Large Database of Lipids is a cross-sectional database of clinical lipid measurements from a population-representative convenience sample of adult and pediatric patients. Lipid measurements were performed by Vertical Auto Profile ultracentrifugation between October 1, 2015, and June 30, 2019. The study included patients with complete lipid panel data who were randomly assigned to a training set and a test set. External validation against preparative ultracentrifugation-based LDL-C measurements was performed in reference laboratory datasets from the Mayo Clinic (broad LDL-C concentrations) and the Further Cardiovascular Outcomes Research With Proprotein Convertase Subtilisin/Kexin Type 9 (PCSK9) Inhibition in Subjects with Elevated Risk (FOURIER) trial (observations from patients with low LDL-C concentrations while taking evolocumab). Study data were analyzed from January to October 2025. Exposures LDL-C–F, LDL-C–S, LDL-C–MS, LDL-C–MH, and LDL-C–MH-MARS equations. Main Outcomes and Measures Accuracy of estimated vs ultracentrifugation-measured LDL-C concentration based on bias, root mean square error (RMSE), and concordance by guideline-based categories. Results The study included 4 939 528 patients (mean [SD] age, 56 [16] years; 2 635 486 female [53%]) with complete lipid panel data who were randomly assigned to a training set (n = 3 292 889) and a test set (n = 1 646 639). LDL-C–MH-MARS equation demonstrated a very low median (IQR) bias of −0.1 (−2.1 to 1.8) mg/dL (to convert to millimoles per liter, multiply by 0.0259), comparable with LDL-C–MH. The median (IQR) difference between the MARS and original Martin-Hopkins equations (ie, LDL-C–MH-MARS – LDL-C–MH) was −0.5 (−1.2 to 0) mg/dL, further suggesting comparability between the 2 methods. The RMSE was smallest for LDL-C–MH-MARS (4.7 mg/dL) and LDL-C–MH (4.9 mg/dL), followed by LDL-C–S (5.8 mg/dL), LDL-C-MS (6.0 mg/dL), and LDL-C–F (7.2 mg/dL). The proportion of patients correctly classified according to clinical categories was nearly identical for LDL-C–MH-MARS (89.7%) and LDL-C–MH (89.6%) but lower for LDL-C–S (86.3%), LDL-C–MS (84.7%), and LDL-C–F (83.1%). The LDL-C–MH-MARS, LDL-C–MH, LDL-C–MS, LDL-C–S, and LDL-C–F equations underestimated LDL-C concentration in 16%, 17%, 28%, 39%, and 60%, respectively, when classifying LDL-C less than 70 mg/dL in patients with triglyceride concentrations of 200 to 399 mg/dL (to convert to millimoles per liter, multiply by 0.0113). The patterns of results were similar in external validation datasets, with the LDL-C–MH-MARS and LDL-C–MH equations demonstrating the highest accuracy. Conclusions and Relevance Study results suggest that the new machine learning–based LDL-C equation provided results comparable with those of the original Martin-Hopkins method while simplifying implementation to a single equation.

Down syndrome (DS), the genetic condition caused by trisomy 21 (T21), is characterized by lifelong immune dysregulation leading to high rates of autoimmune disorders, elevated risk of complications from infections, immune hypersensitivity, and a unique form of immunodeficiency. It is now appreciated that DS shares key hallmarks with interferonopathies, with vast remodeling of all branches of the immune system, hypercytokinemia, and widespread autoantibody production. Here within, we review the existing literature with an emphasis on clinical considerations toward monitoring, management, and therapeutic opportunities. We highlight recent research advances that illuminate diagnostic approaches to evaluate immune dysregulation in DS. We also discuss the evidence supporting specific immunomodulatory strategies that could have multidimensional benefits in this population, including JAK inhibitors, intravenous immunoglobulin, and B cell–depleting agents.

Objectives: To evaluate the association of a templated interhospital transfer (IHT) note on transfer appropriateness and clinical outcomes. Methods: We conducted a retrospective, single-center study of 100 IHTs to the Department of Medicine services between May and June 2023. The exposure was the type of documentation at the time of transfer, categorized as templated note, nontemplated note, or no note. The primary outcome was appropriateness of transfer, determined by 2 independent adjudicators using a standardized framework developed as part of the POINT Study. Documentation type by clinical specialty and reason for transfer were assessed, with statistical analyses of clinical outcomes limited to those with an incidence >5% (ie, escalation/downgrade in level of care within 24 h of transfer and 30 d mortality). Results: Among 100 transfers, 81 (81%) were deemed appropriate and 19 (19%) were deemed inappropriate. Documentation was present in 59%, including templated (35%) and nontemplated notes (24%). IHTs without documentation had the highest appropriateness rate (87.8%, 36/41), compared with templated notes (29/35, 82.9%; P =0.15) and nontemplated notes (66.7%, 16/24; P =0.042). Downgrades were more common in inappropriate transfers with nontemplated notes (50.0%, 4/8) than in appropriate transfers with templated notes (10.3%, 3/29; P =0.012). Conclusions: Structured IHT documentation was inconsistently used and did not predict the appropriateness of transfer. Transfers without documentation were most often appropriate, suggesting straightforward cases may not require formal notes. In contrast, nontemplated notes in inappropriate transfers showed high downgrade rates, implying that unstructured documentation may inflate perceived acuity. Further evaluation of documentation strategies is needed to support appropriate transfer decision-making.

ABSTRACT Objective(s) Fetal pleural effusion is a rare condition that may significantly impact fetal and neonatal outcomes. Currently, no universal standardized approach exists for diagnosing and managing primary fetal pleural effusions. This study aimed to develop structured, expert‐based clinical guidelines for the diagnosis, monitoring, management, and follow‐up care of primary fetal pleural effusion. Methods A Delphi method was employed to achieve consensus among an international panel of experts in fetal medicine. Experts were selected based on their clinical expertise, affiliations, and relevant publications. A three‐round anonymous electronic survey was sent to the full panel. In the initial two rounds, participants rated each statement on a Likert scale (1–5) and provided suggestions for modifications. Statements with a median score of five and no suggested changes were accepted as consensus. Statements with a median score below four were rejected. Statements scoring a median of four were revised according to suggestions and reconsidered in the subsequent round. In the final round, participants indicated their agreement or disagreement with the remaining statements. Consensus was defined as agreement from more than 70% of participants with no suggestions for further changes. Results A total of 101 expert clinicians accepted the invitation and completed the first round with 73 (72%) completing all three rounds. Consensus statements were achieved on aspects of diagnosis, monitoring, intervention, post‐intervention management, and delivery. Key recommendations concerning the diagnosis of primary fetal pleural effusion include the use of ultrasound as the primary modality for identifying fetal pleural effusion and excluding secondary causes through detailed genetic, infectious, and metabolic evaluations. Management strategies emphasized thoracoamniotic shunting as the preferred intervention in hydropic fetuses with primary pleural effusion. Experts reached consensus that shunt placement could be offered as early as 16 weeks. No consensus was reached for isolated effusion without hydrops, as management was considered case‐dependent. The importance of ultrasonographic monitoring of fetal status at least weekly post‐intervention was strongly endorsed. Conclusion(s) The Delphi method facilitated the development of a consensus‐based protocol for diagnosing and managing primary fetal pleural effusion. These standardized guidelines are intended to enhance clinical practice across various settings, improve perinatal outcomes, and serve as a foundation for future research for this high‐risk fetal condition.

ABSTRACT Background Implementation of remote robotic‐assisted surgery (rRAS) has the potential to significantly improve access to high‐quality surgical care for patients worldwide. Advances in robotic systems and telecommunications now enable highly reliable, low latency, and cybersecure connectivity. These technological developments, together with the broader adoption of telemedicine, have driven an increasing interest in the implementation of rRAS. Methods The Clinical Robotic Surgery Association (CRSA) convened a conference to develop international clinical recommendations for rRAS using the Danish Model of Consensus. The society identified five key issue categories and invited a panel of experts to address specific questions for each. Panel members and a jury of 10 unbiased, non‐expert in rRAS professionals participated in a day‐long conference on November 19, 2025 (Los Angeles, USA). During the conference, participants presented, discussed, and reached consensus on specific guidelines relevant to each category. Immediately after the conference, the jury reviewed and approved the recommended guidelines. Results Multiple recommendations were approved for each of the following categories: Operating Models, Roles and Responsibilities (2 recommendations), Clinical Pathway (2 recommendations), Emergency Management (8 recommendations), Technical Considerations (5 recommendations), and Medico‐legal and Ethical Considerations (4 recommendations). Conclusions There was consensus that with recent advances in telecommunication and robotic technologies, rRAS can be offered safely and effectively if the identified safeguards are realized. Adoption of rRAS is expected to continue advancing rapidly.