المؤلفون: Weberpals, Janick, Shaw, Pamela A., Lin, Kueiyu Joshua, Wyss, Richard, Plasek, Joseph M, Zhou, Li, Ngan, Kerry, DeRamus, Thomas, Raman, Sudha R., Hammill, Bradley G., Lee, Hana, Toh, Sengwee, Connolly, John G., Dandreo, Kimberly J., Tian, Fang, Liu, Wei, Li, Jie, Hernández-Muñoz, José J., Schneeweiss, Sebastian, Desai, Rishi J.

مصطلحات موضوعية: Statistics - Methodology, Computer Science - Artificial Intelligence, Computer Science - Machine Learning

الوصف: Multiple imputation (MI) models can be improved by including auxiliary covariates (AC), but their performance in high-dimensional data is not well understood. We aimed to develop and compare high-dimensional MI (HDMI) approaches using structured and natural language processing (NLP)-derived AC in studies with partially observed confounders. We conducted a plasmode simulation study using data from opioid vs. non-steroidal anti-inflammatory drug (NSAID) initiators (X) with observed serum creatinine labs (Z2) and time-to-acute kidney injury as outcome. We simulated 100 cohorts with a null treatment effect, including X, Z2, atrial fibrillation (U), and 13 other investigator-derived confounders (Z1) in the outcome generation. We then imposed missingness (MZ2) on 50% of Z2 measurements as a function of Z2 and U and created different HDMI candidate AC using structured and NLP-derived features. We mimicked scenarios where U was unobserved by omitting it from all AC candidate sets. Using LASSO, we data-adaptively selected HDMI covariates associated with Z2 and MZ2 for MI, and with U to include in propensity score models. The treatment effect was estimated following propensity score matching in MI datasets and we benchmarked HDMI approaches against a baseline imputation and complete case analysis with Z1 only. HDMI using claims data showed the lowest bias (0.072). Combining claims and sentence embeddings led to an improvement in the efficiency displaying the lowest root-mean-squared-error (0.173) and coverage (94%). NLP-derived AC alone did not perform better than baseline MI. HDMI approaches may decrease bias in studies with partially observed confounders where missingness depends on unobserved factors.

الوصول الحر: http://arxiv.org/abs/2405.10925Test

المؤلفون: Weberpals, Janick, Raman, Sudha R, Shaw, Pamela A, Lee, Hana, Hammill, Bradley G, Toh, Sengwee, Connolly, John G, Dandreo, Kimberly J, Tian, Fang, Liu, Wei, Li, Jie, Hernández-Muñoz, José J, Glynn, Robert J, Desai, Rishi J

المساهمون: US Food and Drug Administration

المصدر: JAMIA Open ; volume 7, issue 1 ; ISSN 2574-2531

مصطلحات موضوعية: Health Informatics

الوصف: Objectives Partially observed confounder data pose a major challenge in statistical analyses aimed to inform causal inference using electronic health records (EHRs). While analytic approaches such as imputation are available, assumptions on underlying missingness patterns and mechanisms must be verified. We aimed to develop a toolkit to streamline missing data diagnostics to guide choice of analytic approaches based on meeting necessary assumptions. Materials and methods We developed the smdi (structural missing data investigations) R package based on results of a previous simulation study which considered structural assumptions of common missing data mechanisms in EHR. Results smdi enables users to run principled missing data investigations on partially observed confounders and implement functions to visualize, describe, and infer potential missingness patterns and mechanisms based on observed data. Conclusions The smdi R package is freely available on CRAN and can provide valuable insights into underlying missingness patterns and mechanisms and thereby help improve the robustness of real-world evidence studies.

الإتاحة: https://doi.org/10.1093/jamiaopen/ooae008Test
https://academic.oup.com/jamiaopen/article-pdf/7/1/ooae008/56534882/ooae008.pdfTest

المؤلفون: Einhorn, Lisa M., Zhao, Congwen, Goldstein, Benjamin A., Raman, Sudha R., Cheng, Jeffrey

المساهمون: National Center for Advancing Translational Sciences, National Institute of General Medical Sciences

المصدر: Laryngoscope Investigative Otolaryngology ; volume 8, issue 3, page 775-785 ; ISSN 2378-8038 2378-8038

الوصف: Objectives Tonsillectomy is a common pediatric surgery, and pain is an important consideration in recovery. Due to the opioid epidemic, individual states, medical societies, and institutions have all taken steps to limit postoperative opioids, yet few studies have examined the effect of these interventions on pediatric otolaryngology practices. The primary aim of this study was to characterize opioid prescribing practices following North Carolina state opioid legislation and targeted institutional changes. Methods This single center retrospective cohort study included 1552 pediatric tonsillectomy patient records from 2014 to 2021. The primary outcome was number of oxycodone doses per prescription. This outcome was assessed over three time periods: (1) Before 2018 North Carolina opioid legislation. (2) Following legislation, before institutional changes. (3) After institutional opioid‐specific protocols. Results The mean (± standard deviation) number of doses per prescription in Periods 1, 2, and 3 was: 58 ± 53, range 4–493; 28 ± 36, range 3–488; and 23 ± 17, range 1–139, respectively. In the adjusted model, Periods 2 and 3 had lower doses by −41% (95% CI −49%, −32%) and −40% (95% CI −55%, −19%) compared to Period 1. After 2018 North Carolina legislation, dosage decreased by −9% (95% CI −13%, −5%) per year. Despite interventions, ongoing variability in prescription regimens remained in all periods. Conclusion Legislative and institution specific opioid interventions was associated with a 40% decrease in oxycodone doses per prescription following pediatric tonsillectomy. While variability in opioid practices decreased post‐interventions, it was not eliminated. Level of evidence 3

الإتاحة: https://doi.org/10.1002/lio2.1074Test

المؤلفون: Raman, Sudha R., Qualls, Laura G., Hammill, Bradley G., Nelson, Adam J., Nilles, Ester Kim, Marsolo, Keith, O’Brien, Emily C.

المساهمون: U.S. Food and Drug Administration

المصدر: Trials ; volume 24, issue 1 ; ISSN 1745-6215

مصطلحات موضوعية: Pharmacology (medical), Medicine (miscellaneous)

الوصف: Background Despite great promise, trials that ascertain patient clinical data from electronic health records (EHR), referred to here as “EHR-sourced” trials, are limited by uncertainty about how existing trial sites and infrastructure can be best used to operationalize study goals. Evidence is needed to support the practical use of EHRs in contemporary clinical trial settings. Main text We describe a demonstration project that used EHR data to complement data collected for a contemporary multi-center pharmaceutical industry outcomes trial, and how a central coordinating center supported participating sites through the technical, governance, and operational aspects of this type of activity. We discuss operational considerations related to site selection, data extraction, site performance, and data transfer and quality review, and we outline challenges and lessons learned. We surveyed potential sites and used their responses to assess feasibility, determine the potential capabilities of sites and choose an appropriate data extraction strategy. We designed a flexible, multimodal approach for data extraction, enabling each site to either leverage an existing data source, create a new research datamart, or send all data to the central coordinating center to produce the requisite data elements. We evaluated site performance, as reflected by the speed of contracting and IRB approval, total patients enrolled, enrollment yield, data quality, and compared performance by data collection strategy. Conclusion While broadening the type of sites able to participate in EHR-sourced trials may lead to greater generalizability and improved enrollment, sites with fewer technical resources may require additional support to participate. Central coordinating center support is essential to facilitate the execution of operational processes. Future work should focus on sharing lessons learned and creating reusable tools to facilitate participation of heterogeneous trial sites.

الإتاحة: https://doi.org/10.1186/s13063-023-07563-yTest

المؤلفون: Raman, Sudha R., Ford, Cassie B., Hammill, Bradley G., Clark, Amy G., Clifton, Dana C., Jackson, George L.

المساهمون: School of Medicine, Duke University

المصدر: Addiction Science & Clinical Practice ; volume 18, issue 1 ; ISSN 1940-0640

مصطلحات موضوعية: General Medicine

الوصف: Background Acute care inpatient admissions outside of psychiatric facilities have been increasingly identified as a critical touchpoint for opioid use disorder (OUD) treatment. We sought to describe non-opioid overdose hospitalizations with documented OUD and examine receipt of post-discharge outpatient buprenorphine. Methods We examined acute care hospitalizations with an OUD diagnosis in any position within US commercially-insured adults age 18–64 years (IBM MarketScan claims, 2013–2017), excluding opioid overdose diagnoses. We included individuals with ≥ 6 months of continuous enrollment prior to the index hospitalization and ≥ 10 days following discharge. We described demographic and hospitalization characteristics, including outpatient buprenorphine receipt within 10 days of discharge. Results Most (87%) hospitalizations with documented OUD did not include opioid overdose. Of 56,717 hospitalizations (49,959 individuals), 56.8% had a primary diagnosis other than OUD, 37.0% had documentation of an alcohol-related diagnosis code, and 5.8% end in a self-directed discharge. Where opioid use disorder was not the primary diagnosis, 36.5% were due to other substance use disorders, and 23.1% were due to psychiatric disorders. Of all non-overdose hospitalizations who had prescription medication insurance coverage and who were discharged to an outpatient setting (n = 49, 237), 8.8% filled an outpatient buprenorphine prescription within 10 days of discharge. Conclusions Non-overdose OUD hospitalizations often occur with substance use disorders and psychiatric disorders, and very few are followed by timely outpatient buprenorphine. Addressing the OUD treatment gap during hospitalization may include implementing medication for OUD for inpatients with a broad range of diagnoses.

الإتاحة: https://doi.org/10.1186/s13722-023-00396-9Test

المؤلفون: Eiffert, Samantha R., Raman, Sudha R.

المصدر: Expert Review of Vaccines ; volume 22, issue 1, page 419-420 ; ISSN 1476-0584 1744-8395

الإتاحة: https://doi.org/10.1080/14760584.2023.2211165Test

المؤلفون: Eiffert, Samantha R., Stürmer, Til, Thorpe, Carolyn T., Traub, Rebecca, Raman, Sudha R., Pate, Virginia, Kinlaw, Alan C.

المساهمون: National Center for Advancing Translational Sciences

المصدر: Vaccine ; volume 41, issue 39, page 5763-5768 ; ISSN 0264-410X

مصطلحات موضوعية: Infectious Diseases, Public Health, Environmental and Occupational Health, General Veterinary, General Immunology and Microbiology, Molecular Medicine

الإتاحة: https://doi.org/10.1016/j.vaccine.2023.08.014Test
https://api.elsevier.com/content/article/PII:S0264410X23009477?httpAccept=text/xmlTest
https://api.elsevier.com/content/article/PII:S0264410X23009477?httpAccept=text/plainTest

المؤلفون: Moorthy, Ganga S., Young, Rebecca R., Raman, Sudha R., Smith, Michael J.

المصدر: Journal of Rural Health; Jun2024, Vol. 40 Issue 3, p585-590, 6p

مصطلحات موضوعية: INAPPROPRIATE prescribing (Medicine), ANTIBIOTICS, RISK assessment, RESEARCH funding, WHITE people, DESCRIPTIVE statistics, RACE, PHYSICIAN practice patterns, MEDICAL records, ACQUISITION of data, RURAL conditions, DRUG prescribing, MEDICAID

مصطلحات جغرافية: NORTH Carolina

مستخلص: Purpose: The majority of pediatric antibiotic prescribing occurs in the outpatient setting and inappropriate use contributes to antimicrobial resistance. There are regional variations in outpatient antibiotic use with the highest rates occurring in the Southern states, including in Appalachia. The purpose of this study was to describe the rates and risk factors for inappropriate antibiotic prescription among pediatric patients enrolled in North Carolina (NC) Medicaid. Methods: We used Medicaid prescription claims data from 2013 to 2019 to describe patterns of pediatric antibiotic prescription in NC. We assessed patient and provider factors to identify variations in prescribing. Findings: Children who were less than 2 years of age, non‐Hispanic White, and living in a rural area had the highest overall rates of antibiotic prescription. Compared to pediatricians, the risk of inappropriate antibiotic prescription was highest among other specialists and general practioners and lowest among nurse practitioners. Rural areas of NC had the highest rates of inappropriate antibiotic prescribing, and the risk for non‐Hispanic Black children compared to children of other races/ethnicities was compounded by rurality. Conclusions: Prescribing practices in NC differ compared to neighboring states with a lower overall risk of inappropriate prescription in Appalachian regions; however, disparities by race and rurality exist. Outpatient stewardship efforts in NC should focus on ensuring health equity by appreciating racial and geographic variations in prescribing patterns and providing education to all health care providers. [ABSTRACT FROM AUTHOR]

: Copyright of Journal of Rural Health is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

المؤلفون: Weberpals, Janick, Raman, Sudha R, Shaw, Pamela A, Lee, Hana, Russo, Massimiliano, Hammill, Bradley G, Toh, Sengwee, Connolly, John G, Dandreo, Kimberly J, Tian, Fang, Liu, Wei, Li, Jie, Hernández-Muñoz, José J, Glynn, Robert J, Desai, Rishi J

المصدر: Clinical Epidemiology; May2024, Vol. 16, p329-343, 15p

مصطلحات موضوعية: ELECTRONIC health records, MULTIPLE imputation (Statistics), RANDOM forest algorithms, MISSING data (Statistics)

مستخلص: Partially observed confounder data pose challenges to the statistical analysis of electronic health records (EHR) and systematic assessments of potentially underlying missingness mechanisms are lacking. We aimed to provide a principled approach to empirically characterize missing data processes and investigate performance of analytic methods. Methods: Three empirical sub-cohorts of diabetic SGLT2 or DPP4-inhibitor initiators with complete information on HbA1c, BMI and smoking as confounders of interest (COI) formed the basis of data simulation under a plasmode framework. A true null treatment effect, including the COI in the outcome generation model, and four missingness mechanisms for the COI were simulated: completely at random (MCAR), at random (MAR), and two not at random (MNAR) mechanisms, where missingness was dependent on an unmeasured confounder and on the value of the COI itself. We evaluated the ability of three groups of diagnostics to differentiate between mechanisms: 1)-differences in characteristics between patients with or without the observed COI (using averaged standardized mean differences [ASMD]), 2)-predictive ability of the missingness indicator based on observed covariates, and 3)-association of the missingness indicator with the outcome. We then compared analytic methods including "complete case", inverse probability weighting, single and multiple imputation in their ability to recover true treatment effects. Results: The diagnostics successfully identified characteristic patterns of simulated missingness mechanisms. For MAR, but not MCAR, the patient characteristics showed substantial differences (median ASMD 0.20 vs 0.05) and consequently, discrimination of the prediction models for missingness was also higher (0.59 vs 0.50). For MNAR, but not MAR or MCAR, missingness was significantly associated with the outcome even in models adjusting for other observed covariates. Comparing analytic methods, multiple imputation using a random forest algorithm resulted in the lowest root-mean-squared-error. Conclusion: Principled diagnostics provided reliable insights into missingness mechanisms. When assumptions allow, multiple imputation with nonparametric models could help reduce bias. [ABSTRACT FROM AUTHOR]

: Copyright of Clinical Epidemiology is the property of Dove Medical Press Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

المؤلفون: Boyer, Matthew J., Carpenter, David J., Gingrich, Jeffrey R., Raman, Sudha R., Sirohi, Deepika, Tabriz, Amir Alishahi, Rompre-Broduer, Alexis, Lunyera, Joseph, Basher, Fahmin, Bitting, Rhonda L., Kosinski, Andrzej, Cantrell, Sarah, Gordon, Adelaide M., Ear, Belinda, Gierisch, Jennifer M., Jacobs, Morgan, Goldstein, Karen M.

المصدر: Prostate Cancer and Prostatic Diseases ; ISSN 1365-7852 1476-5608

مصطلحات موضوعية: Cancer Research, Urology, Oncology

الإتاحة: https://doi.org/10.1038/s41391-023-00766-zTest
https://www.nature.com/articles/s41391-023-00766-z.pdfTest
https://www.nature.com/articles/s41391-023-00766-zTest