Citations

Abstract In a real-world analysis of brexucabtagene autoleucel recipients with relapsed/refractory B-cell acute lymphoblastic leukemia (N = 278), lack of response to prior blinatumomab correlates with significantly worse following chimeric antigen receptor T-cell therapy outcomes.

Abstract Purpose While randomized controlled trials remain the gold standard for assessing treatment efficacy, studies using real-world data (RWD) offer valuable insights into treatment effectiveness across broader, more diverse patient populations. This commentary explores the importance of using fit-for-purpose data and emphasizes the need for rigorous evaluation of RWD quality to support valid and actionable evidence generation. Summary The utility of RWD-based studies depends heavily on the fitness-for-purpose of the data source, which requires careful assessment of 5 key quality dimensions: relevance, extensiveness, timeliness, coherence, and reliability. Practical examples from coronavirus disease 2019 (COVID-19) comparative effectiveness research are used to illustrate each data quality domain. Conclusions As the need for RWD increases, especially for post–COVID-19 pandemic decision-making, ensuring high data quality and appropriate study design is critical. Proper evaluation of RWD sources enhances the credibility of findings and supports their use in meta-analyses, clinical guidelines, and healthcare policy.

ABSTRACT Tumorigenesis and metastasis are frequently attributed to the intricate interplay between cancer cells and the tumor microenvironment (TME). Comprehending the mechanisms and key regulators of cancer‐immune crosstalk in the TME is imperative for developing efficacious immunotherapy. Through a series of in vivo CRISPR screens, we identified tumor‐intrinsic ITGB1 as a critical regulator of triple‐negative breast cancer (TNBC) development and deciphered its underlying mechanisms. Tumoral ITGB1 facilitated the establishment of pro‐tumorigenic TME by orchestrating tumor‐associated myeloid populations. Suppressing ITGB1 favored the enrichment of anti‐tumorigenic myeloid cells and enhanced infiltration of CD4 and CD8 T cells, culminating in superior antitumor effects. CRISPR scanning pinpointed a previously unrecognized functional domain essential for ITGB1's pro‐tumorigenic activity. This domain is distinct from all known ligand‐binding sites in ITGB1. An antibody capable of sterically blocking this domain significantly impaired TNBC progression. These findings position tumoral ITGB1 as a promising therapeutic target for reprogramming the TME from a pro‐ to an anti‐tumorigenic state, thereby effectively inhibiting TNBC development. Our study uncovers a novel mechanism of TNBC development and provides a unique therapeutic strategy for targeting ITGB1 in TNBC treatment.

Introduction Despite its demonstrated effectiveness at improving outcomes, pulmonary rehabilitation (PR) for chronic obstructive pulmonary disease (COPD) is underutilized. Sensor-generated data from wearable devices have the potential to mitigate this challenge by generating digital endpoints that provide insights into patient behaviors at home; however, there is no consensus on how to measure home-based PR (HBPR) outcomes with these tools. This review aims to describe (1) the most frequent digital endpoints used in HBPR studies and (2) the devices used to capture these endpoints, summarizing gaps in their applications to HBPR for COPD patients. Methods We completed a scoping review using the PRISMA checklist across databases (Web of Science, Scopus, and OVID) from January 1, 2005 to June 1, 2025. We included peer-reviewed articles on HBPR for COPD, excluding reviews, commentaries/editorials, poster abstracts, and conference proceedings. Eligible articles included cohort studies and clinical trials of adult patients (age ≥ 18 years) with COPD participating in HBPR that include one or more digital endpoints. Results Among eligible articles ( n = 218), 13 (6.0%) met inclusion criteria, the majority of which were published after 2020 (61.5%). Most studies enrolled fewer than 100 COPD patients (76.9%) for an average monitoring period of 12.5 weeks. Activity trackers were the most commonly used device (46.2%) to capture data. The most frequently used digital endpoints were step count (84.6%), time spent active (38.5%), and time spent sedentary (30.8%). Two study designs were used: randomized controlled trial (76.9%) and observational cohort. Study designs were heterogenous with more than one-third (38.5%) presenting a lack of statistically significant results. Discussion Although we identified analogous digital endpoints in some studies, dissimilar methods and study designs remain barriers to synthesizing results generated from HBPR programs for COPD. Wearable devices have the potential to build novel PR models, but more work is needed to translate real-world data into clinically meaningful measures. Future research should elucidate which participants would benefit most from and complete HBPR to build an evidence base for the validation of HBPR-relevant digital endpoints, particularly those derived from less common sources like cardiovascular and sleep measures.

Objective: Perimenopause is the time leading up to a woman’s last menstrual cycle and includes the 12 months afterward. Studies that systematically compare perimenopause symptoms across diverse cultural and geographic settings are lacking. This study, utilizing data from Flo, an international mobile health application, aimed to assess global knowledge and symptom experiences related to perimenopause. Methods: This cross-sectional survey was conducted via the Flo application, offered to users aged 18 years and above. The primary endpoints were knowledge of perimenopause symptoms from all survey participants, and self-reported perimenopause symptoms for survey participants aged 35 years and above. Secondary analyses compared knowledge scores and symptoms across geographic regions. Results: A total of 17,494 women from 158 countries were included. Commonly recognized perimenopause symptoms included hot flashes (71%), sleep problems (68%), and weight gain (65%). Of the participants, 12,681 were aged 35 years or above, with the most common self-reported symptoms being fatigue (83%), physical and mental exhaustion (83%), irritability (80%), depressive mood (77%), sleep problems (76%), digestive issues (76%), and anxiety (75%). This pattern of symptoms was similar among those who self-reported being in perimenopause, though higher than in those not in perimenopause. International variation in perimenopause symptom knowledge and symptoms experienced was noted ( P <0.001). Conclusions: This survey highlights a discordance between perimenopause knowledge and actual symptoms experienced across diverse global populations. While hot flashes were the most widely recognized symptom, respondents aged 35 years or above most commonly reported experiencing fatigue, physical and mental exhaustion, and irritability.

BACKGROUND & OBJECTIVE Sleep-disordered breathing (SDB) may be a remediable contributor to abnormal cognitive outcomes for children with myelomeningocele. We investigated the frequency of SDB among neonates with fetal vs postnatal myelomeningocele repair. METHODS In this nine-center, prospective, observational study, neonates with myelomeningocele born at more than 30 weeks gestational age underwent a 10-hour bedside polysomnogram at at least 35 weeks postmenstrual age, before hospital discharge. The primary outcome was SDB severity, reflected by the apnea-hypopnea index (AHI; number of respiratory events per hour of sleep). Decisions regarding surgical timing (fetal vs postnatal), technique (open fetal vs fetoscopic approach), and SDB treatment were made by the clinical teams. RESULTS Among 173 infants, 92/173 (53%) had SDB (median AHI 22.5, IQR 12.2–44.2). In unadjusted analysis, AHI was higher among the 90 neonates with fetal repair vs 83 with postnatal surgery (median 29, IQR 14–49, vs 19, IQR 10–36, P = .008). In propensity-adjusted multivariable models, prematurity was independently associated with AHI, whereas surgical timing (fetal vs postnatal) was not. Hindbrain herniation, myelomeningocele level, and clinical need for ventriculoperitoneal shunt did not predict AHI in adjusted models. Supplemental oxygen was commonly prescribed, for 44% of fetal vs 39% of postnatal myelomeningocele repair infants (P = .5). CONCLUSIONS SDB affects more than half of neonates with myelomeningocele. The higher AHI after fetal surgery could stem from the elevated incidence of prematurity in this group. As SDB is treatable and has a potential impact on neurodevelopment, routine screening could become an integral part of a strategy to optimize long-term medical and neurodevelopmental outcomes.

Context.— The US Food and Drug Administration (FDA) Emergency Use Authorization (EUA) for SARS-CoV-2 assays encompassed both in vitro diagnostic and laboratory-developed tests. Related testing data and documents from EUAs were published on the FDA website as PDF files, constituting a rich data resource. Objective.— To highlight the value of structured descriptive and validation data and provide recommendations on improving the quality, accessibility, and shareability of the FDA’s digital resources. Design.— SARS-CoV-2 diagnostics EUAs and related files were acquired from the FDA, and data were extracted through an iterative manual or automated process with quality assurance in accordance with a set of data entry definitions to generate a data product (ie, data analysis and presentation of data into tables, graphs, and reports, optionally including different data sets) that could be disseminated. Results.— Four challenges limited the usability of FDA EUA data, including (1) distinguishing EUA tests owing to similar names or inconsistent naming conventions; (2) document location and tracking owing to inconsistent EUA document identifiers, resource locators, and versioning; (3) data location and retrieval owing to heterogeneous structure and data formatting within EUA documentation; and (4) maintenance of existing data owing to lack of clear and traceable communication around EUA amendments. Conclusions.— Converging efforts from disparate groups to extract and synthesize FDA test data reveal an unaddressed need for better organization and communication. Existing FDA data reporting systems must be improved and adapted for the demands of the connected data science era.

Background In pediatric patients presenting after blunt trauma, the incidence of clinically significant cervical spine injury (CSI) is approximately 1%. Although multidetector CT (MDCT) is widely accepted for cervical spine clearance in adults, its utility in children remains uncertain. The purpose of this study is to evaluate the sensitivity of MDCT in identifying clinically significant CSI in pediatric patients, stratified by age groups: adolescents (12 years to 17 years) and children (0 years to 11 years). Methods We designed a national, multicenter, prospective observational study to assess the diagnostic performance of MDCT in detecting clinically significant CSI in pediatric trauma patients. Data are being collected from participating centers on imaging findings, clinical outcomes, and the presence of confirmed CSIs after blunt trauma. The analysis plan includes stratification by age group and calculation of sensitivity, specificity, and predictive values of MDCT for CSI. Results This abstract outlines the background, methodology, and analytical framework of the study. Data collection and analysis are ongoing. This study represents the largest known cohort of pediatric patients undergoing cervical spine imaging after blunt trauma and is the first prospective study of its kind focused exclusively on this population. Conclusions This study will provide critical data to inform guidelines for pediatric cervical spine clearance after blunt trauma. By evaluating the sensitivity of MDCT in detecting clinically significant CSI, the findings aim to support evidence-based imaging strategies and improve the safety and efficiency of pediatric trauma care.

Abstract Objective To develop a comprehensive risk stratification model to identify patients at high risk of cesarean surgical site infection (cSSI) based on information available at the time of hospital discharge. Methods This is a retrospective cohort study based on datasets from two independent level IV academic medical centers to improve generalizability. The first study includes 2336 cesarean deliveries for analysis with 109 instances of cSSI (4.7%). The second study includes 2936 cesarean deliveries with 72 instances of cSSI (2.5%). The first study cohort was utilized for both model training and internal validation, using the 15 shared features present in both datasets. Random search with cross‐validation was used to choose the type of machine learning (ML) model and its hyperparameters, with area under the receiver operating characteristic curve (AUC) as the validation metric. The second study cohort was then used to evaluate testing accuracy (external validation) and generalization capability of this model. The predictive importance of each feature was evaluated using permutation importance applied to the second cohort. Risk stratification cutoff scores were defined based on centiles over the first patient cohort and evaluated on the second cohort. Results The ML model with the highest validation AUC was an XGBoost classifier. This trained model achieved an AUC of 0.78 on the training data and a 0.72 cross‐validated AUC. When tested on data from the second study cohort, it achieved an AUC of 0.69. The features showing the strongest predictive generalization, as measured by permutation importance, were length of hospital stay, primary indication for cesarean delivery, and estimated blood loss. Using the second cohort to test our model, the model shows a 22.7% positive predictive value (PPV) of cSSI among women in the highest risk group, and a 0.8% PPV among women in the bottom risk group, compared with 2.5% cSSI incidence for the study population at large. Conclusion We developed and validated a predictive ML model for cSSI with information available to healthcare professionals at the time of hospital discharge. This model suggests that the most predictive risk factors for cSSI include length of hospital stay, primary indication for cesarean delivery, and estimated blood loss. Additionally, the model provides reasonable risk stratifications for individual patients. This tool may be used for identifying subgroups at higher risk of cSSI and identifying those who may require closer surveillance or clinical intervention.

Abstract Motivation: Epilepsy is a diverse group of neurological disorders affecting over 50 million people worldwide. While common epilepsy types are well studied, rare epilepsies—often severe and genetically complex—pose significant challenges in diagnosis, research, and treatment. Accurate and interoperable etiology and disease classifications are critical for improving data sharing, supporting clinical decision-making, and advancing rare disease research. Results: To enhance the accuracy of epilepsy-related disease concept representation within the Mondo Disease Ontology (Mondo), we conducted a series of expert-driven workshops in collaboration with the team from the Rare Disease Cures Accelerator-Data and Analytics Platform (RDCA-DAP). Specialists in epileptology, genetics, neurodevelopment, biomedical ontology, and patient community advocates systematically reviewed and revised the epilepsy hierarchy in Mondo, aligning it with the International League Against Epilepsy (ILAE) classification system. These updates include reclassification of epilepsy subtypes, including syndromes, age-related epilepsies, and developmental epileptic encephalopathies, resulting in a more granular, standardized, and clinically relevant structure. Mondo now offers an enhanced framework for integrating epilepsy data across resources, enabling improved interoperability and facilitating rare disease research and data curation, with continued efforts underway to further refine and expand this integration.