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Kristin Kostka,1,2 Talita Duarte-Salles,3 Albert Prats-Uribe,4 Anthony G Sena,5,6 Andrea Pistillo,3 Sara Khalid,4 Lana YH Lai,7 Asieh Golozar,8,9 Thamir M Alshammari,10 Dalia M Dawoud,11 Fredrik Nyberg,12 Adam B Wilcox,13,14 Alan Andryc,5 Andrew Williams,15 Anna Ostropolets,16 Carlos Areia,17 Chi Young Jung,18 Christopher A Harle,19 Christian G Reich,1,2 Clair Blacketer,5,6 Daniel R Morales,20 David A Dorr,21 Edward Burn,3,4 Elena Roel,3,22 Eng Hooi Tan,4 Evan Minty,23 Frank DeFalco,5 Gabriel de Maeztu,24 Gigi Lipori,19 Hiba Alghoul,25 Hong Zhu,26 Jason A Thomas,13 Jiang Bian,19 Jimyung Park,27 Jordi Martínez Roldán,28 Jose D Posada,29 Juan M Banda,30 Juan P Horcajada,31 Julianna Kohler,32 Karishma Shah,33 Karthik Natarajan,16,34 Kristine E Lynch,35,36 Li Liu,37 Lisa M Schilling,38 Martina Recalde,3,22 Matthew Spotnitz,14 Mengchun Gong,39 Michael E Matheny,40,41 Neus Valveny,42 Nicole G Weiskopf,21 Nigam Shah,29 Osaid Alser,43 Paula Casajust,42 Rae Woong Park,27,44 Robert Schuff,21 Sarah Seager,1 Scott L DuVall,35,36 Seng Chan You,45 Seokyoung Song,46 Sergio Fernández-Bertolín,3 Stephen Fortin,5 Tanja Magoc,19 Thomas Falconer,16 Vignesh Subbian,47 Vojtech Huser,48 Waheed-Ul-Rahman Ahmed,33,49 William Carter,38 Yin Guan,50 Yankuic Galvan,19 Xing He,19 Peter R Rijnbeek,6 George Hripcsak,16,34 Patrick B Ryan,5,16 Marc A Suchard,51 Daniel Prieto-Alhambra4 1IQVIA, Cambridge, MA, USA; 2OHDSI Center at The Roux Institute, Northeastern University, Portland, ME, USA; 3Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain; 4Centre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, UK; 5Janssen Research & Development, Titusville, NJ, USA; 6Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands; 7School of Medical Sciences, University of Manchester, Manchester, UK; 8Regeneron Pharmaceuticals, Tarrytown, NY, USA; 9Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; 10College of Pharmacy, Riyadh Elm University, Riyadh, Saudi Arabia; 11National Institute for Health and Care Excellence, London, UK; 12School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; 13Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA; 14Unviersity of Washington Medicine, Seattle, WA, USA; 15Tufts Institute for Clinical Research and Health Policy Studies, Boston, MA, USA; 16Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA; 17Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; 18Division of Respiratory and Critical Care Medicine, Department of Internal Medicine, Daegu Catholic University Medical Center, Daegu, South Korea; 19University of Florida Health, Gainesville, FL, USA; 20Division of Population Health and Genomics, University of Dundee, Dundee, UK; 21Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA; 22Universitat Autònoma de Barcelona, Barcelona, Spain; 23O’Brien Institute for Public Health, Faculty of Medicine, University of Calgary, Calgary, Canada; 24IOMED, Barcelona, Spain; 25Faculty of Medicine, Islamic University of Gaza, Gaza, Palestine; 26Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China; 27Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, South Korea; 28Director of Innovation and Digital Transformation, Hospital del Mar, Barcelona, Spain; 29Department of Medicine, School of Medicine, Stanford University, Redwood City, CA, USA; 30Georgia State University, Department of Computer Science, Atlanta, GA, USA; 31Department of Infectious Diseases, Hospital del Mar, Institut Hospital del Mar d’Investigació Mèdica (IMIM), Universitat Autònoma de Barcelona, Universitat Pompeu Fabra, Barcelona, Spain; 32United States Agency for International Development, Washington, DC, USA; 33Botnar Research Centre, NDORMS, University of Oxford, Oxford, UK; 34New York-Presbyterian Hospital, New York, NY, USA; 35VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, USA; 36Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA; 37Biomedical Big Data Center, Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China; 38Data Science to Patient Value Program, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA; 39Institute of Health Management, Southern Medical University, Guangzhou, People’s Republic of China; 40Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA; 41Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA; 42Real-World Evidence, TFS, Barcelona, Spain; 43Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; 44Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, South Korea; 45Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, South Korea; 46Department of Anesthesiology and Pain Medicine, Catholic University of Daegu, School of Medicine, Daegu, South Korea; 47College of Engineering, The University of Arizona, Tucson, AZ, USA; 48National Library of Medicine, National Institutes of Health, Bethesda, MD, USA; 49College of Medicine and Health, University of Exeter, St Luke’s Campus, Exeter, UK; 50DHC Technologies Co. Ltd., Beijing, People’s Republic of China; 51Departments of Biostatistics, Computational Medicine, and Human Genetics, University of California, Los Angeles, CA, USACorrespondence: Daniel Prieto-Alhambra, Botnar Research Centre, Windmill Road, Oxford, OX37LD, UK, Email daniel.prietoalhambra@ndorms.ox.ac.ukPurpose: Routinely collected real world data (RWD) have great utility in aiding the novel coronavirus disease (COVID-19) pandemic response. Here we present the international Observational Health Data Sciences and Informatics (OHDSI) Characterizing Health Associated Risks and Your Baseline Disease In SARS-COV-2 (CHARYBDIS) framework for standardisation and analysis of COVID-19 RWD.Patients and Methods: We conducted a descriptive retrospective database study using a federated network of data partners in the United States, Europe (the Netherlands, Spain, the UK, Germany, France and Italy) and Asia (South Korea and China). The study protocol and analytical package were released on 11th June 2020 and are iteratively updated via GitHub. We identified three non-mutually exclusive cohorts of 4,537,153 individuals with a clinical COVID-19 diagnosis or positive test, 886,193 hospitalized with COVID-19, and 113,627 hospitalized with COVID-19 requiring intensive services.Results: We aggregated over 22,000 unique characteristics describing patients with COVID-19. All comorbidities, symptoms, medications, and outcomes are described by cohort in aggregate counts and are readily available online. Globally, we observed similarities in the USA and Europe: more women diagnosed than men but more men hospitalized than women, most diagnosed cases between 25 and 60 years of age versus most hospitalized cases between 60 and 80 years of age. South Korea differed with more women than men hospitalized. Common comorbidities included type 2 diabetes, hypertension, chronic kidney disease and heart disease. Common presenting symptoms were dyspnea, cough and fever. Symptom data availability was more common in hospitalized cohorts than diagnosed.Conclusion: We constructed a global, multi-centre view to describe trends in COVID-19 progression, management and evolution over time. By characterising baseline variability in patients and geography, our work provides critical context that may otherwise be misconstrued as data quality issues. This is important as we perform studies on adverse events of special interest in COVID-19 vaccine surveillance.Keywords: OHDSI, OMOP CDM, descriptive epidemiology, real world data, real world evidence, open science

Rachel B Weinstein, Martijn J Schuemie, Patrick B Ryan, Paul E Stang Epidemiology, Janssen Research and Development, LLC, Titusville, NJ, USA Background: Presumed seasonal use of acetaminophen-containing products for relief of cold/influenza (“flu”) symptoms suggests that there might also be a corresponding seasonal pattern for acute liver injury (ALI), a known clinical consequence of acetaminophen overdose. Objective: The objective of this study was to determine whether there were any temporal patterns in hospitalizations for ALI that would correspond to assumed acetaminophen use in cold/flu season. Methods: In the period 2002–2010, monthly hospitalization rates for ALI using a variety of case definitions were calculated. Data sources included Truven MarketScan® Commercial Claims and Encounters (CCAE) and Medicare Supplemental and Coordination of Benefits (MDCR) databases. We performed a statistical test for seasonality of diagnoses using the periodic generalized linear model. To validate that the test can distinguish seasonal from nonseasonal patterns, we included two positive controls (ie, diagnoses of the common cold [acute nasopharyngitis] and influenza), believed to change with seasons, and two negative controls (female breast cancer and diabetes), believed to be insensitive to season. Results: A seasonal pattern was observed in monthly rates for common cold and influenza diagnoses, but this pattern was not observed for monthly rates of ALI, with or without comorbidities (cirrhosis or hepatitis), breast cancer, or diabetes. The statistical test for seasonality was significant for positive controls (P

SUMMARYAcute respiratory infections (ARI) are the leading cause of death worldwide in children aged <5 years, and understanding contributing factors to their seasonality is important for targeting and implementing prevention strategies. In tropical climates, ARI typically peak during the pre-rainy and rainy seasons. One hypothesis is that rainfall leads to more time spent indoors, thus increasing exposure to other people and in turn increasing the risk of ARI. A case-crossover study design in 718 Bangladeshi children aged <5 years was used to evaluate this hypothesis. During a 3-month period with variable rainfall, rainfall was associated with ARI [odds ratio (OR) 2·97, 95% confidence interval (CI) 1·87-4·70]; some evidence of an increased strength of association as household crowding increased was found (3 people/room, OR 3·31, 95% CI 2·03-5·38), but there was a lack of association in some of the most crowded households (5 to <6 people/room, OR 1·55, 95% CI 0·54-4·47). These findings suggest that rainfall may be increasing exposure to crowded conditions, thus leading to an increased risk of ARI, but that additional factors not captured by this analysis may also play a role. [ABSTRACT FROM AUTHOR]

Objective: Systematic analysis of observational medical databases for active safety surveillance is hindered by the variation in data models and coding systems. Data analysts often find robust clinical data models difficult to understand and ill suited to support their analytic approaches. Further, some models do not facilitate the computations required for systematic analysis across many interventions and outcomes for large datasets. Translating the data from these idiosyncratic data models to a common data model (CDM) could facilitate both the analysts' understanding and the suitability for large-scale systematic analysis. In addition to facilitating analysis, a suitable CDM has to faithfully represent the source observational database. Before beginning to use the Observational Medical Outcomes Partnership (OMOP) CDM and a related dictionary of standardized terminologies for a study of large-scale systematic active safety surveillance, the authors validated the model's suitability for this use by example.Validation By Example: To validate the OMOP CDM, the model was instantiated into a relational database, data from 10 different observational healthcare databases were loaded into separate instances, a comprehensive array of analytic methods that operate on the data model was created, and these methods were executed against the databases to measure performance.Conclusion: There was acceptable representation of the data from 10 observational databases in the OMOP CDM using the standardized terminologies selected, and a range of analytic methods was developed and executed with sufficient performance to be useful for active safety surveillance. [ABSTRACT FROM AUTHOR]

BACKGROUND: 3-Phenoxybenzoic acid (3PBA), a pyrethroid metabolite, was detected in 75% of urine samples analyzed for pesticides in the U.S. National Health and Nutrition Examination Survey (NHANES) 1999-2002. NHANES also includes 24-hr diet data and information on household pesticide use, activities, occupation, demographics, and other exposure factors. OBJECTIVES: The objective of our study was to explore the relative importance of diet versus nondiet predictors in explaining variability in urinary 3PBA. A secondary objective was to explore whether the NHANES data could be used to identify particular foods driving 3PBA levels. METHODS: We divided subjects into child (6-10 years of age), teen (11-18 years), and adult (> or = 19 years) age groups and restricted our analyses to subjects in the morning sampling session who fasted for > or = 8 hr beforehand. Regression modeling consisted of several model-building steps and a final Tobit regression on the left-censored log 3PBA measurements. We also conducted bootstrap analyses to evaluate the stability of the regression parameters. RESULTS: Reported household pesticide use was not significantly associated with urinary 3PBA in any age group. Diet was significant for all three groups, and certain foods appeared to contribute more than others. Among adults, tobacco use was positively associated with 3PBA (p = 0.0326), and positive associations were suggested with the number of cytochrome p450-inhibiting medications taken (p = 0.0652) and minutes spent gardening (p = 0.0613) in the past month. CONCLUSIONS: Although exploratory, our findings underline the importance of collecting accurate data on household pesticide use and dietary intake when evaluating pyrethroid exposure-biomarker relationships. [ABSTRACT FROM AUTHOR]

To address the lack of research on the pulmonary health effects of ozone and fine particulate matter (

A prospective longitudinal study of a home health agency-based drug regimen review program was completed. A consultant pharmacist working collaboratively with home health nurses and physicians reduced medication utilization and costs to homebound patients. Within a 12 month period, medication utilization in 840 homebound patients was reduced by 27%. Projected total net medication cost saving per annum was $183,379.20 (e.g., $218.31 per patient reviewed). Implementation of similar drug regimen review programs in home-health agencies could improve patient care, prevent adverse drug reactions, and achieve cost-effective medication utilization. [ABSTRACT FROM AUTHOR]

Street-level and rooftop (three-story building) concentrations of particulate matter < or = 2.5 µm in diameter (PM2.5) were measured in downtown Trujillo, Peru, in July and August 2003 to determine the PM2.5 concentration reduction on days with normal traffic conditions (32 days) versus a day when motor vehicles were temporarily banned from the downtown district (8:00 a.m. to 6:00 p.m., July 15) known as El Dia Sin Autos ('The Day Without Cars'). The mean 8:00 a.m. to 6:00 p.m. street-level PM2.5 concentration during the motor vehicle ban (21.4 µg/m3; one day) was 49% lower than when vehicles were not impeded (42.2 ± 7.8 µg/m3 -- mean ± 1 standard deviation; 20 days). The rooftop monitoring station indicated a 20% decrease in PM2.5 concentrations (24.8 ± 2.6 µg/m3 vs. 19.9 ± 6.0 µg/m3) when motor vehicles were not present within historic downtown Trujillo. Temperature, relative humidity, and wind speed during the motor vehicle ban and during normal traffic were not significantly different (p > .05). [ABSTRACT FROM AUTHOR]

Objective: Propose a framework to empirically evaluate and report validity of findings from observational studies using pre-specified objective diagnostics, increasing trust in real-world evidence (RWE)., Materials and Methods: The framework employs objective diagnostic measures to assess the appropriateness of study designs, analytic assumptions, and threats to validity in generating reliable evidence addressing causal questions. Diagnostic evaluations should be interpreted before the unblinding of study results or, alternatively, only unblind results from analyses that pass pre-specified thresholds. We provide a conceptual overview of objective diagnostic measures and demonstrate their impact on the validity of RWE from a large-scale comparative new-user study of various antihypertensive medications. We evaluated expected absolute systematic error (EASE) before and after applying diagnostic thresholds, using a large set of negative control outcomes., Results: Applying objective diagnostics reduces bias and improves evidence reliability in observational studies. Among 11 716 analyses (EASE = 0.38), 13.9% met pre-specified diagnostic thresholds which reduced EASE to zero. Objective diagnostics provide a comprehensive and empirical set of tests that increase confidence when passed and raise doubts when failed., Discussion: The increasing use of real-world data presents a scientific opportunity; however, the complexity of the evidence generation process poses challenges for understanding study validity and trusting RWE. Deploying objective diagnostics is crucial to reducing bias and improving reliability in RWE generation. Under ideal conditions, multiple study designs pass diagnostics and generate consistent results, deepening understanding of causal relationships. Open-source, standardized programs can facilitate implementation of diagnostic analyses., Conclusion: Objective diagnostics are a valuable addition to the RWE generation process., (© The Author(s) 2025. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Propensity score adjustment addresses confounding by balancing covariates in subject treatment groups through matching, stratification, or weighting. Diagnostics test the success of adjustment. For example, if the standardized mean difference (SMD) for a relevant covariate exceeds a threshold like 0.1, the covariate is considered imbalanced and the study may be invalid. Unfortunately, for studies with small or moderate numbers of subjects, the probability of falsely rejecting the validity of a study because of chance imbalance-the probability of asserting imbalance by using a cutoff for SMD when no underlying imbalance exists-can be grossly larger than a given nominal level like 0.05. In this paper, we illustrate that chance imbalance is operative in real-world settings even for moderate sample sizes of 2000. We identify a previously unrecognized challenge that as meta-analyses increase the precision of an effect estimate, the diagnostics must also undergo meta-analysis for a corresponding increase in precision. We propose an alternative diagnostic that checks whether the standardized mean difference statistically significantly exceeds the threshold. Through simulation and real-world data, we find that this diagnostic achieves a better trade-off of type 1 error rate and power than standard nominal threshold tests and not testing for sample sizes from 250 to 4000 and for 20 to 100,000 covariates. We confirm that in network studies, meta-analysis of effect estimates must be accompanied by meta-analysis of the diagnostics or else systematic confounding may overwhelm the estimated effect. Our procedure supports the review of large numbers of covariates, enabling more rigorous diagnostics.

Importance: Semaglutide, a glucagonlike peptide-1 receptor agonist (GLP-1RA), has recently been implicated in cases of nonarteritic anterior ischemic optic neuropathy (NAION), raising safety concerns in the treatment of type 2 diabetes (T2D)., Objective: To investigate the potential association between semaglutide and NAION in the Observational Health Data Sciences and Informatics (OHDSI) network., Design, Setting, and Participants: This was a retrospective study across 14 databases (6 administrative claims and 8 electronic health records). Included were adults with T2D taking semaglutide, other GLP-1RA (dulaglutide, exenatide), or non-GLP-1RA medications (empagliflozin, sitagliptin, glipizide) from December 1, 2017, to December 31, 2023. The incidence proportion and rate of NAION were calculated. Association between semaglutide and NAION was assessed using 2 approaches: an active-comparator cohort design comparing new users of semaglutide with those taking other GLP-1RAs and non-GLP-1RA drugs, and a self-controlled case-series (SCCS) analysis to compare individuals' risks during exposure and nonexposure periods for each drug. The cohort design used propensity score-adjusted Cox proportional hazards models to estimate hazard ratios (HRs). The SCCS used conditional Poisson regression models to estimate incidence rate ratios (IRRs). Network-wide HR and IRR estimates were generated using a random-effects meta-analysis model., Exposures: GLP-1RA and non-GLP-1RAs., Main Outcomes and Measures: NAION under 2 alternative definitions based on diagnosis codes: one more inclusive and sensitive, the other more restrictive and specific., Results: The study included 37.1 million individuals with T2D, including 810 390 new semaglutide users. Of the 43 620 new users of semaglutide in the Optum's deidentified Clinformatics Data Mart Database, 24 473 (56%) were aged 50 to 69 years, and 26 699 (61%) were female. The incidence rate of NAION was 14.5 per 100 000 person-years among semaglutide users. The HR for NAION among new users of semaglutide was not different compared with that of the non-GLP-1RAs using the sensitive NAION definition-empagliflozin (HR, 1.44; 95% CI, 0.78-2.68; P = .12), sitagliptin (HR, 1.30; 95% CI, 0.56-3.01; P = .27), and glipizide (HR, 1.23; 95% CI, 0.66-2.28; P = .25). The risk was higher only compared with patients taking empagliflozin (HR, 2.27; 95% CI, 1.16-4.46; P = .02) using the specific definition. SCCS analysis of semaglutide exposure showed an increased risk of NAION (meta-analysis IRR, 1.32; 95% CI, 1.14-1.54; P < .001)., Conclusions and Relevance: Results of this study suggest a modest increase in the risk of NAION among individuals with T2D associated with semaglutide use, smaller than that previously reported, and warranting further investigation into the clinical implications of this association.

Background: Prenatal exposure to persistent organic pollutants (POPs) has been linked to adverse pregnancy outcomes. Existing disparities in exposure across populations highlight the need for identifying modifiable risk factors. Here, we investigated the determinants of serum levels of polybrominated diphenyl ethers (PBDEs) and organochlorine pesticides (OCPs) in pregnant African American women (N = 54)., Methods: Concentrations of 2,2'4,4'-tetrabromodiphenyl ether (BDE-47), 2,2',4,4',5-pentabromodiphenyl ethe (BDE-99), 2,2',4,4',6-pentabromodiphenyl ether (BDE-100), hexachlorobenzene (HCB), and p,p'-dichlorodiphenyldichloroethylene (p,p'-DDE) were measured in first trimester maternal serum from a subset of women enrolled in the Atlanta African American Maternal Child Cohort. Home characteristics, cleaning behaviors, diet, and demographic information were collected via survey questionnaires. Associations between individual determinants and individual PBDE and OCP concentrations were assessed using linear regression., Results: Pregnant women who frequently ate red meat, cheese, milk, or yogurt had elevated levels of HCB and p,p'-DDE compared to those who ate these products less frequently (e.g., red meat: β=0.21; 95% CI=-0.07, 0.48 for p,p'-DDE), while pregnant women who reported living in apartments, homes ≥10 years old, or had couches ≥5 years old, had increased levels of PBDEs compared to reference groups. Pregnant women who vacuumed and cleaned their home more frequently showed decreased levels of PBDEs relative to reference groups (e.g., vacuuming: BDE-100 β= -0.81, 95% CI= -1.55, -0.06)., Conclusions: Our results illustrate modifiable determinants of PBDE and OCP exposures in pregnant women and emphasize the potential to reduce the body burden of these POPs both directly, by changing diet, residence, or home furnishings, and indirectly through cleaning methods., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2025 Elsevier Inc. All rights reserved.)

Background: Fluoroquinolones (FQs) are commonly used to treat urinary tract infections (UTIs), but some studies have suggested they may increase the risk of aortic aneurysm or dissection (AA/AD). However, no large-scale international study has thoroughly assessed this risk., Methods: A retrospective cohort study was conducted using a large, distributed network analysis across 14 databases from 5 countries (United States, South Korea, Japan, Taiwan, and Australia). The study included 13,588,837 patients aged 35 or older who initiated systemic fluoroquinolones (FQs) or comparable antibiotics (trimethoprim with or without sulfamethoxazole [TMP] or cephalosporins [CPHs]) for UTI treatment in the outpatient setting between JAN 01, 2010 and DEC 31, 2019. Patients were included if at the index date they had at least 365 days of prior observation and were not hospitalised for any reason on or within 7 days prior to the index date. The primary outcome was AA/AD occurrence within 60 days of exposure, with secondary outcomes examining AA and AD separately. Cox proportional hazards models with 1:1 propensity score (PS) matching were used to estimate the risk, with results calibrated using negative control outcomes. Analyses were subjected to pre-defined study diagnostics, and only those passing all diagnostics were reported. Hazard ratios (HRs) were pooled using Bayesian random-effects meta-analysis., Findings: Among analyses that passed diagnostics there were 1,954,798 and 1,195,962 propensity-matched pairs for the FQ versus TMP and FQ versus CPH comparisons respectively. For the 60-day follow-up there was no difference in risk of AA/AD between FQ and TMP (absolute rate difference [ARD], 0.21 per 1000 person-year; calibrated HR, 0.91 [95% CI 0.73-1.10]). There was no significant difference in risk for FQ versus CPH (ARD, 0.11 per 1000 person-year; calibrated HR, 1.01 [95% CI 0.82-1.25])., Interpretation: This large-scale study used a rigorous design with objective diagnostics to address bias and confounding. There was no increased risk of AA/AD associated with FQ compared to TMP or CPH in patients treated for UTI in the outpatient setting. As we only examined FQ used to treat UTIs in the outpatient setting, the results may not be generalisable to other indications with different severity., Funding: Yonsei University College of Medicine, Government-wide R&D Fund project for infectious disease research (GFID), Republic of Korea, National Health and Medical Research Council (NHMRC) Australian Government. Department of Veterans Affairs (VA) Informatics and Computing Infrastructure (VINCI), Department of Veterans Affairs, the United States Government., Competing Interests: All authors have completed the International Committee of Medical Journal Editors disclosure of interest form and declare the following disclosures. EM discloses previous consulting relationships with Janssen and Orpyx Medical Technologies, in areas unrelated to this work. DRM was supported by a Wellcome Trust Clinical Research Fellowship. RK received support from the National Heart, Lung, and Blood Institute of the National Institutes of Health (under award K23HL153775) and the Doris Duke Charitable Foundation (under award, 2022060). He also receives research support, through Yale, from BridgeBio, Bristol-Myers Squibb, and Novo Nordisk. He is a coinventor of U.S. Provisional Patent Applications 63/562,355, 63/177,117, 63/428,569, 63/346,610, 63/484,426, 63/508,315, 63/606,203, and 18/813,882, unrelated to current work. He is also a founder of Evidence2Health, a precision health platform to improve evidence-based cardiovascular care. SLD reports grants from Alnylam Pharmaceuticals, Inc., AstraZeneca Pharmaceuticals LP, Biodesix, Inc, Celgene Corporation, Cerner Enviza, GSK PLC, IQVIA Janssen Pharmaceuticals, Inc., Novartis International AG, Parexel International Corporation through the University of Utah or Western Institute for Veteran Research outside the submitted work. KKCM reported receiving grants from the Hong Kong Research Grant Council, the CW Maplethorpe Fellowship, UK, National Institute for Health and Care Research, European Commission Framework Horizon 2020, Innovation and Technology Commission of the Government of the Hong Kong Special Administrative Region, and personal fees from IQVIA Ltd outside the submitted work. SS and MVZ are employees of IQVIA. JPG, PBR and MJS are employees and shareholders of Johnson & Johnson. MAS receives grants and contracts from the US National Institutes of Health, US Food & Drug Administration and Johnson & Johnson outside the scope of this work. MEM received grants from NIH NHLBI and VA ORD and has a patent pending for an AI algorithm related to surveillance. KKCM received grants from the CW Maplethorpe Fellowship, IQVIA, the European Union Horizon 2020, the UK National Institute of Health Research, the Hong Kong Research Grant Council, and the Hong Kong Innovation and Technology Commission. NP received a grant from NHMRC (GNT 1196900). SCY reports being a chief technology officer of PHI Digital Healthcare; and grants from Daiichi Sankyo., (© 2025 The Author(s).)

Objective: This paper introduces a novel framework for evaluating phenotype algorithms (PAs) using the open-source tool, Cohort Diagnostics., Materials and Methods: The method is based on several diagnostic criteria to evaluate a patient cohort returned by a PA. Diagnostics include estimates of incidence rate, index date entry code breakdown, and prevalence of all observed clinical events prior to, on, and after index date. We test our framework by evaluating one PA for systemic lupus erythematosus (SLE) and two PAs for Alzheimer's disease (AD) across 10 different observational data sources., Results: By utilizing CohortDiagnostics, we found that the population-level characteristics of individuals in the cohort of SLE closely matched the disease's anticipated clinical profile. Specifically, the incidence rate of SLE was consistently higher in occurrence among females. Moreover, expected clinical events like laboratory tests, treatments, and repeated diagnoses were also observed. For AD, although one PA identified considerably fewer patients, absence of notable differences in clinical characteristics between the two cohorts suggested similar specificity., Discussion: We provide a practical and data-driven approach to evaluate PAs, using two clinical diseases as examples, across a network of OMOP data sources. Cohort Diagnostics can ensure the subjects identified by a specific PA align with those intended for inclusion in a research study., Conclusion: Diagnostics based on large-scale population-level characterization can offer insights into the misclassification errors of PAs., Competing Interests: All authors were employees of Janssen Research & Development, LLC, and shareholders of Johnson & Johnson (J&J) stock at the time of manuscript conceptualization, drafting, editing, and initial approval for submission. All authors except AM continue to be employees of J&J. Author AM changed affiliation after the initial submission and is currently affiliated with VNS Health. This competing interest does not alter our adherence to PLOS ONE policies on sharing data and materials., (Copyright: © 2025 Rao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Prognostic models help aid medical decision-making. Various prognostic models are available via websites such as MDCalc, but these models typically predict one outcome, for example, stroke risk. Each model requires individual predictors, for example, age, lab results and comorbidities. There is no clinical tool available to predict multiple outcomes from a list of common medical predictors., Objective: Identify a constrained set of outcome-agnostic predictors., Methods: We proposed a novel technique aggregating the standardised mean difference across hundreds of outcomes to learn a constrained set of predictors that appear to be predictive of many outcomes. Model performance was evaluated using the constrained set of predictors across eight prediction tasks. We compared against existing models, models using only age/sex predictors and models without any predictor constraints., Results: We identified 67 predictors in our constrained set, plus age/sex. Our predictors included illnesses in the following categories: cardiovascular, kidney/liver, mental health, gastrointestinal, infectious and oncologic. Models developed using the constrained set of predictors achieved comparable discrimination compared with models using hundreds or thousands of predictors for five of the eight prediction tasks and slightly lower discrimination for three of the eight tasks. The constrained predictor models performed as good or better than all existing clinical models., Conclusions: It is possible to develop models for hundreds or thousands of outcomes that use the same small set of predictors. This makes it feasible to implement many prediction models via a single website form. Our set of predictors can also be used for future models and prognostic model research., Competing Interests: Competing interests: JMR and PBR are employees and shareholders of Johnson & Johnson. EAF, LHJ and RDW work for a research group that in the past 3 years has received/received unconditional research grants from Chiesi, UCB, Amgen, Johnson and Johnson, Innovative Medicines Initiative and the European Medicines Agency. None of these grants result in a conflict of interest to the content of this paper. CK has received consultation fees from IQVIA., (© Author(s) (or their employer(s)) 2025. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ Group.)

Using administrative claims and electronic health records for observational studies is common but challenging due to data limitations. Researchers rely on phenotype algorithms, requiring labor-intensive chart reviews for validation. This study investigates whether case adjudication using the previously introduced Knowledge-Enhanced Electronic Profile Review (KEEPER) system with large language models (LLMs) is feasible and could serve as a viable alternative to manual chart review. The task involves adjudicating cases identified by a phenotype algorithm, with KEEPER extracting predefined findings such as symptoms, comorbidities, and treatments from structured data. LLMs then evaluate KEEPER outputs to determine whether a patient truly qualifies as a case. We tested four LLMs including GPT-4, hosted locally to ensure privacy. Using zero-shot prompting and iterative prompt optimization, we found LLM performance, across ten diseases, varied by prompt and model, with sensitivities from 78 to 98% and specificities from 48 to 98%, indicating promise for automating phenotype evaluation., Competing Interests: Competing interests: The authors declare no competing interests. MAS receives contracts and grants from the US National Institutes of Health, US Food & Drug Administration, the US Department of Veterans Affairs and Janssen Research & Development, all outside the scope of this work., (© 2025. The Author(s).)

Introduction: The aim of this study is to use observational methods to evaluate reliability of evidence generated by a study of the effect of glucagon-like peptide 1 receptor agonists (GLP-1RA) on chronic lower respiratory disease (CLRD) outcomes among Type-2 diabetes mellitus (T2DM) patients., Research Design and Methods: We independently reproduced a study comparing effects of GLP-1RA versus dipeptidyl peptidase-4 inhibitors (DPP4-i) on CLRD outcomes among patients with T2DM and prior CLRD. We reproduced inputs and outputs using the original study data (national administrative claims) and evaluated the robustness of results in comparison to alternate design/analysis decisions. To evaluate generalizability, we applied an analysis protocol and conducted a meta-analysis across a research network that includes a diverse array of populations and data sources. We also produced additional analyses evaluating individual drugs within the GLP-1RA class and CLRD outcomes., Results: We confirmed alignment of study inputs and outputs and closely reproduced effect estimates and sensitivity analyses. Adjusted effect estimates were robust to empirical calibration. Network meta-analysis confirmed original findings but indicated weaker effects than originally published. Meta-analysis of drugs within the GLP-1RA class against DPP4-i provided evidence that effects vary within the GLP-1RA class, indicating stronger effects for exenatide and weaker effects of dulaglutide., Conclusions: This study supports and establishes the reliability of the original study by (1) producing consistent findings in a range of alternate databases and populations, (2) demonstrating effects for multiple drugs within the GLP-1RA class, and (3) independently confirming the reproducibility of the original study and its findings. This reliability evaluation provides the beginnings of a broader framework for using standardized tools and distributed data networks to systematically interrogate the reliability of findings generated using observational data., (© 2025 The Author(s). Pharmacoepidemiology and Drug Safety published by John Wiley & Sons Ltd.)

Background: Longitudinal trends in per- and polyfluoroalkyl substances (PFAS) serum concentrations across pregnancy have not been thoroughly examined, despite evidence linking prenatal PFAS exposures with adverse birth outcomes., Objectives: We sought to characterize longitudinal PFAS concentrations across pregnancy and to examine the maternal-fetal transfer ratio among participants in a study of risk and protective factors for adverse birth outcomes among African Americans., Methods: In the Atlanta African American Maternal-Child cohort (2014-2020), we quantified serum concentrations of four PFAS in 376 participants and an additional eight PFAS in a subset of 301 participants during early (8-14 weeks gestation) and late pregnancy (24-30 weeks gestation). Among these, PFAS concentrations were also measured among 199 newborns with available dried blood spot (DBS) samples. We characterized the patterns, variability, and associations in PFAS concentrations at different time points across pregnancy using intraclass correlation coefficients (ICCs), maternal-newborn pairs transfer ratios, linear mixed effect models, and multivariable linear regression, adjusting for socioeconomic and prenatal predictors., Results: Perfluorohexane sulfonic acid (PFHxS), perfluorooctane sulfonic acid (PFOS), perfluorooctanoic acid (PFOA), and perfluorononanoic acid (PFNA) were detected in > 95 % of maternal samples, with PFHxS and PFOS having the highest median concentrations. We observed high variability in PFAS concentrations across pregnancy time points ( ICC = 0.03 - 0.59 ). All median PFAS concentrations increased from early to late pregnancy, except for PFOA and N-methyl perfluorooctane sulfonamido acetic acid (NMFOSAA), which decreased [paired t -test for all PFAS p < 0.05 except for PFOA and perfluorobutane sulfonic acid (PFBS)]. Prenatal serum PFAS were weakly to moderately correlated with newborn DBS PFAS ( - 0.05 < rho < 0.49 ). The median maternal-fetal PFAS transfer ratio was lower for PFAS with longer carbon chains. After adjusting for socioeconomic and prenatal predictors, in linear mixed effect models, the adjusted mean PFAS concentrations significantly increased during pregnancy, except for PFOA. In multivariable linear regression, PFAS concentrations in early pregnancy significantly predicted the PFAS concentrations in late pregnancy and in newborns., Discussion: We found that the concentrations of most PFAS increased during pregnancy, and the magnitude of variability differed by individual PFAS. Future studies are needed to understand the influence of within-person PFAS variability during and after pregnancy on birth outcomes. https://doi.org/10.1289/EHP14334.

Background: In mechanistic and preliminary human studies, prenatal exposure to per- and polyfluoroalkyl substances (PFAS) is associated with oxidative stress, a potential contributor to maternal liver disease. Bilirubin is an endogenous antioxidant abundant in the liver that may serve as a physiological modulator of oxidative stress in pregnant people. Hence, our objective was to estimate the association between repeated measures of PFAS and bilirubin during pregnancy., Methods: The study population included 332 participants in the Atlanta African American Maternal-Child Cohort between 2014 and 2020. Serum samples were collected up to two times (early pregnancy: 6-18 gestational weeks; late pregnancy: 21-36 gestational weeks) for the measurement of perfluorohexane sulfonate (PFHxS), perfluorooctane sulfonate (PFOS), perfluorooctanoic acid (PFOA), perfluorononanoic acid (PFNA), and total bilirubin. We analyzed single PFAS with linear mixed effect regression and a mixture of the four PFAS with quantile g-computation. Models were repeated with a multiplicative interaction term to explore effect modification by study visit., Results: Overall, PFHxS was positively associated with bilirubin (β = 0.08, 95 % CI = 0.01, 0.15). We also found during late pregnancy, there was a positive association of PFHxS and the PFAS mixture with bilirubin (β = 0.12, 95 % CI = 0.02, 0.22; ψ = 0.19, 95 % CI = 0.03, 0.34, respectively). Finally, study visit modified the PFOA-bilirubin association (interaction p-value = 0.09), which was greater during early pregnancy (β = 0.08, 95 % CI = 0.01, 0.15)., Conclusion: In a prospective cohort of pregnant African Americans, an increase in PFOA, PFHxS, and the PFAS mixture was associated with an increase in bilirubin. Our results suggest that, depending on pregnancy stage, prenatal PFAS exposure disrupts the maternal liver antioxidant capacity., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

Background: Sodium-glucose cotransporter 2 inhibitors (SGLT2is) and glucagon-like peptide-1 receptor agonists (GLP-1 RAs) reduce the risk of major adverse cardiovascular events (MACE) in patients with type 2 diabetes mellitus (T2DM). However, their effectiveness relative to each other and other second-line antihyperglycemic agents is unknown, without any major ongoing head-to-head clinical trials., Objectives: The aim of this study was to compare the cardiovascular effectiveness of SGLT2is, GLP-1 RAs, dipeptidyl peptidase-4 inhibitors (DPP4is), and clinical sulfonylureas (SUs) as second-line antihyperglycemic agents in T2DM., Methods: Across the LEGEND-T2DM (Large-Scale Evidence Generation and Evaluation Across a Network of Databases for Type 2 Diabetes Mellitus) network, 10 federated international data sources were included, spanning 1992 to 2021. In total, 1,492,855 patients with T2DM and cardiovascular disease (CVD) on metformin monotherapy were identified who initiated 1 of 4 second-line agents (SGLT2is, GLP-1 RAs, DPP4is, or SUs). Large-scale propensity score models were used to conduct an active-comparator target trial emulation for pairwise comparisons. After evaluating empirical equipoise and population generalizability, on-treatment Cox proportional hazards models were fit for 3-point MACE (myocardial infarction, stroke, and death) and 4-point MACE (3-point MACE plus heart failure hospitalization) risk and HR estimates were combined using random-effects meta-analysis., Results: Over 5.2 million patient-years of follow-up and 489 million patient-days of time at risk, patients experienced 25,982 3-point MACE and 41,447 4-point MACE. SGLT2is and GLP-1 RAs were associated with lower 3-point MACE risk than DPP4is (HR: 0.89 [95% CI: 0.79-1.00] and 0.83 [95% CI: 0.70-0.98]) and SUs (HR: 0.76 [95% CI: 0.65-0.89] and 0.72 [95% CI: 0.58-0.88]). DPP4is were associated with lower 3-point MACE risk than SUs (HR: 0.87; 95% CI: 0.79-0.95). The pattern for 3-point MACE was also observed for the 4-point MACE outcome. There were no significant differences between SGLT2is and GLP-1 RAs for 3-point or 4-point MACE (HR: 1.06 [95% CI: 0.96-1.17] and 1.05 [95% CI: 0.97-1.13])., Conclusions: In patients with T2DM and CVD, comparable cardiovascular risk reduction was found with SGLT2is and GLP-1 RAs, with both agents more effective than DPP4is, which in turn were more effective than SUs. These findings suggest that the use of SGLT2is and GLP-1 RAs should be prioritized as second-line agents in those with established CVD., Competing Interests: Funding Support and Author Disclosures This study was partially funded through National Institutes of Health grants R01 HL167858, K23 HL153775, R01 LM006910, R01 HG006139, and R01 HL169954 and the U.S. Department of Veterans Affairs under the research priority to “Put VA Data to Work for Veterans” (VA ORD 22-D4V). The funders had no role in the design and conduct of the protocol; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Dr Khera is an associate editor at JAMA; and has received support from the National Heart, Lung, and Blood Institute of the National Institutes of Health (awards R01 HL167858 and K23 HL153775) and the Doris Duke Charitable Foundation (award 2022060); has received research support, through Yale University, from Bristol Myers Squibb, Novo Nordisk, and BridgeBio; is a coinventor of U.S. provisional patent applications WO2023230345A1, US20220336048A1, 63/346,610, 63/428,569, 63/484,426, 63/508,315, 63/580,137, 63/606,203, 63/619,241, and 63/562,335, unrelated to the present work; and is a cofounder of Evidence2Health and Ensight-AI, precision health platforms to improve evidence-based cardiovascular care and cardiovascular diagnostics. Dr DuVall has received grants from Alnylam Pharmaceuticals, AstraZeneca Pharmaceuticals, Biodesix, Celgene, Cerner Enviza, GlaxoSmithKline, Janssen Pharmaceuticals, Novartis International, and Parexel International through the University of Utah or the Western Institute for Veteran Research (outside the submitted work). Dr Man has received support from the C.W. Maplethorpe Fellowship, the National Institute of Health Research, European Commission Framework Horizon 2020, the Hong Kong Research Grant Council, and the Innovation and Technology Commission of the Hong Kong Special Administration Region Government outside the submitted work). Dr Morales is supported by a Wellcome Trust Clinical Research Fellowship (214588/Z/18/Z). In the past 3 years, Dr Krumholz has received expenses and/or personal fees from UnitedHealth, Element Science, Aetna, Reality Labs, Tesseract/4Catalyst, F-Prime, the Siegfried and Jensen Law Firm, the Arnold and Porter Law Firm, and the Martin/Baughman Law Firm; is a cofounder of Refactor Health, HugoHealth, and Ensight-AI; and is associated with contracts, through Yale New Haven Hospital, from the Centers for Medicare and Medicaid Services and, through Yale University, from Johnson & Johnson. Dr You is chief technology officer of PHI Digital Healthcare. Dr Ross has received research support through Yale University from Johnson & Johnson to develop methods of clinical trial data sharing, from the Food and Drug Administration for the Yale-Mayo Clinic Center for Excellence in Regulatory Science and Innovation program (grant U01 FD005938), from the Agency for Healthcare Research and Quality (grant R01 HS022882), and from Arnold Ventures; has received research support from the Medical Device Innovation Consortium as part of the National Evaluation System for Health Technology and from the National Heart, Lung, and Blood Institute of the National Institutes of Health (grants R01 HS025164 and R01 HL144644); and was an expert witness at the request of the relator’s attorneys, the Greene Law Firm, in a qui tam suit alleging violations of the False Claims Act and Anti-Kickback Statute against Biogen that was settled September 2022. Dr Thangaraj is a coinventor on provisional patent 63/606,203, unrelated to the present work, and is funded by grant 5T32 HL155000-03. Ms Blacketer, Dr Ostropolets, and Dr Ryan are employees of Johnson & Johnson. Dr Schuemie is an employee and shareholder of Johnson & Johnson. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2024 American College of Cardiology Foundation. All rights reserved.)

Background: Prenatal exposure to phthalates, a group of synthetic chemicals widely used in consumer products, has previously been associated with adverse infant and child development. Studies also suggest that maternal depression and anxiety, may amplify the harmful effects of phthalates on infant and child neurodevelopment., Study Design: Our analysis included a subset of dyads enrolled in the Atlanta African American Maternal-Child Cohort (N = 81). We measured eight phthalate metabolites in first and second trimester (8-14 weeks and 24-32 weeks gestation) maternal urine samples to estimate prenatal exposures. Phthalate metabolite concentrations were averaged across visits and natural log-transformed for analysis. Maternal symptoms of depression and anxiety were assessed using validated questionnaires (Edinberg Postnatal Depression Scale and State Trait Anxiety Inventory, respectively) and the total score on each scale was averaged across study visits. The NICU Network Neurobehavioral Scale (NNNS) was administered at two weeks of age. Our primary outcomes included two composite NNNS scores reflecting newborn attention and arousal. Linear regression was used to estimate associations between individual phthalate exposures and newborn attention and arousal. We assessed effect modification by maternal depression and anxiety., Results: Higher levels of urinary phthalate metabolites were not associated with higher levels of infant attention and arousal, but true associations may still exist given the limited power of this analysis. In models examining effect modification by maternal depression, we observed that an interquartile range increase in mono (2-ethlyhexyl) phthalate (MEHP), mono (2-ethyl-5-oxohexyl) phthalate (MEOHP), and mono (2-ethyl-5-hydroxyhexyl) phthalate (MEHHP) was associated with a significant increase in newborn arousal only among those with high depressive symptoms (MEHP: β = 0.71, 95% confidence interval [CI] = 0.10, 1.32 for high, β = -0.30, 95% CI = -0.73, 0.12 for low; MEOHP: β = 0.60, 95% CI = -0.03, 1.23 for high, β = -0.12, 95% CI = -0.58, 0.33 for low; MEHHP: β = 0.54, 95% CI = -0.04, 1.11 for high, β = -0.11, 95% CI = -0.54, 0.32 for low). Similar patterns were observed in models stratified by maternal anxiety, although CIs were wide., Conclusion: Our results suggest maternal anxiety and depression symptoms may exacerbate the effect of phthalates on infant neurodevelopment. Future studies are needed to determine the optimal levels of attention and arousal in early infancy., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Purpose: To characterize the incidence of kidney failure associated with intravitreal anti-VEGF exposure; and compare the risk of kidney failure in patients treated with ranibizumab, aflibercept, or bevacizumab., Design: Retrospective cohort study across 12 databases in the Observational Health Data Sciences and Informatics (OHDSI) network., Subjects: Subjects aged ≥ 18 years with ≥ 3 monthly intravitreal anti-VEGF medications for a blinding disease (diabetic retinopathy, diabetic macular edema, exudative age-related macular degeneration, or retinal vein occlusion)., Methods: The standardized incidence proportions and rates of kidney failure while on treatment with anti-VEGF were calculated. For each comparison (e.g., aflibercept versus ranibizumab), patients from each group were matched 1:1 using propensity scores. Cox proportional hazards models were used to estimate the risk of kidney failure while on treatment. A random effects meta-analysis was performed to combine each database's hazard ratio (HR) estimate into a single network-wide estimate., Main Outcome Measures: Incidence of kidney failure while on anti-VEGF treatment, and time from cohort entry to kidney failure., Results: Of the 6.1 million patients with blinding diseases, 37 189 who received ranibizumab, 39 447 aflibercept, and 163 611 bevacizumab were included; the total treatment exposure time was 161 724 person-years. The average standardized incidence proportion of kidney failure was 678 per 100 000 persons (range, 0-2389), and incidence rate 742 per 100 000 person-years (range, 0-2661). The meta-analysis HR of kidney failure comparing aflibercept with ranibizumab was 1.01 (95% confidence interval [CI], 0.70-1.47; P = 0.45), ranibizumab with bevacizumab 0.95 (95% CI, 0.68-1.32; P = 0.62), and aflibercept with bevacizumab 0.95 (95% CI, 0.65-1.39; P = 0.60)., Conclusions: There was no substantially different relative risk of kidney failure between those who received ranibizumab, bevacizumab, or aflibercept. Practicing ophthalmologists and nephrologists should be aware of the risk of kidney failure among patients receiving intravitreal anti-VEGF medications and that there is little empirical evidence to preferentially choose among the specific intravitreal anti-VEGF agents., Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article., (Copyright © 2024 American Academy of Ophthalmology. All rights reserved.)

Background: A prediction model can be a useful tool to quantify the risk of a patient developing dementia in the next years and take risk-factor-targeted intervention. Numerous dementia prediction models have been developed, but few have been externally validated, likely limiting their clinical uptake. In our previous work, we had limited success in externally validating some of these existing models due to inadequate reporting. As a result, we are compelled to develop and externally validate novel models to predict dementia in the general population across a network of observational databases. We assess regularization methods to obtain parsimonious models that are of lower complexity and easier to implement., Methods: Logistic regression models were developed across a network of five observational databases with electronic health records (EHRs) and claims data to predict 5-year dementia risk in persons aged 55-84. The regularization methods L1 and Broken Adaptive Ridge (BAR) as well as three candidate predictor sets to optimize prediction performance were assessed. The predictor sets include a baseline set using only age and sex, a full set including all available candidate predictors, and a phenotype set which includes a limited number of clinically relevant predictors., Results: BAR can be used for variable selection, outperforming L1 when a parsimonious model is desired. Adding candidate predictors for disease diagnosis and drug exposure generally improves the performance of baseline models using only age and sex. While a model trained on German EHR data saw an increase in AUROC from 0.74 to 0.83 with additional predictors, a model trained on US EHR data showed only minimal improvement from 0.79 to 0.81 AUROC. Nevertheless, the latter model developed using BAR regularization on the clinically relevant predictor set was ultimately chosen as best performing model as it demonstrated more consistent external validation performance and improved calibration., Conclusions: We developed and externally validated patient-level models to predict dementia. Our results show that although dementia prediction is highly driven by demographic age, adding predictors based on condition diagnoses and drug exposures further improves prediction performance. BAR regularization outperforms L1 regularization to yield the most parsimonious yet still well-performing prediction model for dementia., (© 2024. The Author(s).)

Per- and polyfluoroalkyl substances (PFAS) are fluorinated organic compounds used in a variety of consumer products and industrial applications that persist in the environment, bioaccumulate in biological tissues, and can have adverse effects on human health, especially in vulnerable populations. In this study, we focused on PFAS exposures in residents of senior care facilities. To investigate relationships between indoor, personal, and internal PFAS exposures, we analyzed 19 PFAS in matched samples of dust collected from the residents' bedrooms, and wristbands and serum collected from the residents. The median ∑PFAS concentrations (the sum of all PFAS detected in the samples) measured in dust, wristbands, and serum were 120 ng/g, 0.05 ng/g, and 4.0 ng/mL, respectively. The most abundant compounds in serum were linear- and branched-perfluorooctane sulfonic acid (L-PFOS and B-PFOS, respectively) at medians of 1.7 ng/mL and 0.83 ng/mL, respectively, followed by the linear perfluorooctanoic acid (L-PFOA) found at a median concentration of 0.59 ng/mL. Overall, these three PFAS comprised 80 % of the serum ∑PFAS concentrations. A similar pattern was observed in dust with L-PFOS and L-PFOA found as the most abundant PFAS (median concentrations of 13 and 7.8 ng/g, respectively), with the overall contribution of 50 % to the ∑PFAS concentration. Only L-PFOA was found in wristbands at a median concentration of 0.02 ng/g. Significant correlations were found between the concentrations of several PFAS in dust and serum, and in dust and wristbands, suggesting that the indoor environment could be a significant contributor to the personal and internal PFAS exposures in seniors. Our findings demonstrate that residents of assisted living facilities are widely exposed to PFAS, with several PFAS found in blood of each study participant and in the assisted living environment., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: Per- and polyfluoroalkyl substances (PFAS) are man-made chemicals that are slow to break down in the environment and widely detected in humans. Epidemiological evidence suggests that prenatal exposure to perfluorooctanoic acid (PFOA), a legacy PFAS, is linked to gestational hypertension and preeclampsia. However, the relationship between other PFAS, which are structurally similar, and these outcomes remains largely understudied, despite biologic plausibility. Here, we examined associations between serum PFAS mixtures in relation to hypertensive disorders of pregnancy within a birth cohort of African Americans., Methods: Participants in the present study were enrolled in the Atlanta African American Maternal-Child cohort between 2014 and 2020 (n = 513). Serum samples collected between 8 and 14 weeks gestation were analyzed for four PFAS. Logistic regression was used to assess associations between individual natural log transformed PFAS and specific hypertensive disorders of pregnancy (preeclampsia, gestational hypertension), while quantile g-computation was used to estimate mixture effects. Preeclampsia and gestational hypertension were treated as separate outcomes in individual models. All models were adjusted for maternal education, maternal age, early pregnancy body mass index, parity, and any alcohol, tobacco, or marijuana use., Results: The geometric mean of PFOS and PFHxS was slightly lower among those with preeclampsia relative to those without a hypertensive disorder (e.g., geometric mean for PFOS was 1.89 and 1.94, respectively). Serum concentrations of PFAS were not strongly associated with gestational hypertension or preeclampsia in single pollutant or mixture models. For example, using quantile g-computation, a simultaneous one quartile increase in all PFAS was not associated with odds of gestational hypertension (odds ratio = 0.86, 95% CI = 0.60, 1.23), relative to those without a hypertensive disorder of pregnancy., Conclusions: In this birth cohort of African Americans, there was no association between serum PFAS measured in early pregnancy and hypertensive disorders of pregnancy, which may be reflective of the fairly low PFAS levels in our study population., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoint resources and abstraction algorithms), and benchmarks (e.g., prebuilt KGs). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 different large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability., (© 2024. The Author(s).)

Background: The immune system undergoes unique adaptations during pregnancy and is particularly sensitive to environmental chemicals, such as phthalates, which are associated with acute and chronic inflammatory medical conditions. However, current knowledge of how phthalate exposures are associated with systemic inflammation in pregnant people is limited by cross-sectional study designs and single chemical models. Our objective was to estimate the association between repeated measures of prenatal phthalate exposures, examined individually and collectively, and a panel of clinical inflammatory biomarkers., Methods: In the Atlanta African American Maternal-Child Cohort, biospecimens were collected at mean 11 and 26 weeks gestation (N = 126). Concentrations of eight urinary phthalate metabolites and five serum inflammatory biomarkers, including CRP, IFN-γ, IL-6, IL-10, and TNF-α, were measured. Linear mixed effect regression and quantile g-computation models were used to estimate the associations for single phthalates and their exposure mixture, respectively., Results: Participants who self-reported any use of alcohol, tobacco, or marijuana in the month prior to pregnancy had increased MEP, MBP, MiBP, and CRP, relative to those with no substance use. IFN-γ was elevated in response to MECPP (% change = 17.35, 95 % confidence interval [CI] = 0.32, 32.27), MEHHP (% change = 12.75, 95 % CI = 2.22, 24.36), MEOHP (% change = 11.63, 95 % CI = 1.21, 23.12), and their parent phthalate, ΣDEHP (% change = 15.03, 95 % CI = 0.28, 31.94). The phthalate mixture was also associated with an increase in IFN-γ (% change = 15.03, 95 % CI = 6.18, 24.61)., Conclusions: Our findings suggest DEHP metabolites induce systemic inflammation during pregnancy. The pro-inflammatory cytokine IFN-γ may play an important role in the relationship between prenatal phthalate exposures and adverse pregnancy outcomes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: SGLT2 inhibitors (SGLT2is) and GLP-1 receptor agonists (GLP1-RAs) reduce major adverse cardiovascular events (MACE) in patients with type 2 diabetes mellitus (T2DM). However, their effectiveness relative to each other and other second-line antihyperglycemic agents is unknown, without any major ongoing head-to-head trials., Methods: Across the LEGEND-T2DM network, we included ten federated international data sources, spanning 1992-2021. We identified 1,492,855 patients with T2DM and established cardiovascular disease (CVD) on metformin monotherapy who initiated one of four second-line agents (SGLT2is, GLP1-RAs, dipeptidyl peptidase 4 inhibitor [DPP4is], sulfonylureas [SUs]). We used large-scale propensity score models to conduct an active comparator, target trial emulation for pairwise comparisons. After evaluating empirical equipoise and population generalizability, we fit on-treatment Cox proportional hazard models for 3-point MACE (myocardial infarction, stroke, death) and 4-point MACE (3-point MACE + heart failure hospitalization) risk, and combined hazard ratio (HR) estimates in a random-effects meta-analysis., Findings: Across cohorts, 16·4%, 8·3%, 27·7%, and 47·6% of individuals with T2DM initiated SGLT2is, GLP1-RAs, DPP4is, and SUs, respectively. Over 5·2 million patient-years of follow-up and 489 million patient-days of time at-risk, there were 25,982 3-point MACE and 41,447 4-point MACE events. SGLT2is and GLP1-RAs were associated with a lower risk for 3-point MACE compared with DPP4is (HR 0·89 [95% CI, 0·79-1·00] and 0·83 [0·70-0·98]), and SUs (HR 0·76 [0·65-0·89] and 0·71 [0·59-0·86]). DPP4is were associated with a lower 3-point MACE risk versus SUs (HR 0·87 [0·79-0·95]). The pattern was consistent for 4-point MACE for the comparisons above. There were no significant differences between SGLT2is and GLP1-RAs for 3-point or 4-point MACE (HR 1·06 [0·96-1·17] and 1·05 [0·97-1·13])., Interpretation: In patients with T2DM and established CVD, we found comparable cardiovascular risk reduction with SGLT2is and GLP1-RAs, with both agents more effective than DPP4is, which in turn were more effective than SUs. These findings suggest that the use of GLP1-RAs and SGLT2is should be prioritized as second-line agents in those with established CVD., Funding: National Institutes of Health, United States Department of Veterans Affairs.