Faculty Bibliography

BACKGROUND AND HYPOTHESIS/OBJECTIVE:New equations to estimate GFR based on creatinine (eGFRcr), cystatin C (eGFRcys) or both (eGFRcr-cys) have been developed by the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) and the European Kidney Function Consortium (EKFC). There is a need to evaluate the performance of these equations in diverse European settings to inform implementation decisions, especially among people with key comorbid conditions. METHODS:We performed a cross-sectional study including 6174 adults referred for single-point plasma clearance of iohexol in Stockholm, Sweden, with 9579 concurrent measurements of creatinine and cystatin C. We assessed the performance of the CKD-EPI 2009/2012/2021, EKFC 2021/2023, revised Lund-Malmö (RLM) 2011 and CAPA 2014 equations against measured GFR (mGFR). RESULTS:Mean age was 56 years, mGFR was 62 mL/min/1.73m2 and 40% were female. Comorbid conditions were common: cardiovascular disease (30%), liver disease (28%), diabetes (26%) and cancer (26%). All eGFRcr-cys equations had small bias and P30 close to 90%, and performed better than eGFRcr or eGFRcys equations. Among eGFRcr equations, CKD-EPI 2009 and CKD-EPI 2021 showed larger bias and lower P30 than EKFC 2021 and RLM. There were no meaningful differences in performance across eGFRcys equations. Findings were consistent across comorbid conditions, and eGFRcr-cys equations showed good performance in patients with liver disease, cancer and heart failure. CONCLUSIONS:In conclusion, eGFRcr-cys equations performed best, with minimal variation among equations in this Swedish cohort. The lower performance of CKD-EPI eGFRcr equations compared with EKFC and Lund-Malmö may reflect differences in population characteristics and mGFR methods. Implementing eGFRcr equations will require a trade-off between accuracy and uniformity across regions.

INTRODUCTION/BACKGROUND:While morbidity and mortality related to synthetic opioids such as illicitly manufactured fentanyl (IMF) are monitored in the U.S., there has been a lack of national survey data focusing on use. Survey data are important as self-report can help estimate prevalence of use among living persons. METHODS:Data were examined from the 2022 National Survey on Drug Use and Health, a nationally representative probability sample of noninstitutionalized individuals age ≥12 in the U.S. (N=59,069). Prevalence and correlates of past-year use of IMF were estimated. Data were analyzed in 2024. RESULTS:The estimated prevalence of past-year IMF use was 0.23% (95% confidence interval [CI]: 0.17-0.31). Compared to no past-year use, individuals were at increased odds for IMF use if proxy-diagnosed with use disorder involving use of cannabis (aOR=3.72, 95% CI: 1.34-10.32), cocaine (aOR=11.96, 95% CI: 4.78-29.93), methamphetamine (aOR=5.60, 95% CI: 1.65-19.02), heroin (aOR=20.56, 95% CI: 8.90-47.52), and/or prescription opioids (aOR=10.65, 95% CI: 3.54-32.03). (Mis)use without use disorder was only significant for prescription opioids (aOR=5.77, 95% CI: 2.55-13.06). Those receiving treatment for substance use in the past year were also at increased odds for use (aOR=5.79, 95% CI: 2.58-13.00). CONCLUSIONS:Prevalence of IMF use is rare in the general U.S. POPULATION/METHODS:While past-year (mis)use of other drugs (without use disorder) was not consistently associated with IMF use, cannabis, cocaine, methamphetamine, heroin, and prescription opioid use disorder was associated with higher odds of IMF use, suggesting that more "severe" use of various drugs is more of a risk factor than use.

Medications for opioid use disorder (MOUD) increase retention in care and decrease mortality during active treatment; however, information about the comparative effectiveness of different forms of MOUD is sparse. Observational comparative effectiveness studies are subject to many types of bias; a robust framework to minimize bias would improve the quality of comparative effectiveness evidence. This paper discusses the use of target trial emulation as a framework to conduct comparative effectiveness studies of MOUD with administrative data. Using examples from our planned research project comparing buprenorphine-naloxone and extended-release naltrexone with respect to the rates of MOUD discontinuation, we provide a primer on the challenges and approaches to employing target trial emulation in the study of MOUD.

INTRODUCTION/BACKGROUND:Concurrent electronic nicotine delivery system (ENDS) and cigarette (dual) use is harmful. Identifying longitudinal trajectories of ENDS and cigarette use among dual users can help to determine the public health impact of ENDS and inform tobacco control policies and interventions. OBJECTIVES/OBJECTIVE:(1) To identify independent and joint trajectories of ENDS and cigarette use among wave (W) 1 adult dual users across W1 to W5 of the Population Assessment of Tobacco and Health (PATH) Study; and (2) identify W1 predictors of ENDS and cigarette joint trajectory group membership. METHODS:We used group-based trajectory modelling to estimate independent and joint trajectories of ENDS and cigarette use from wave 1 (W1; 2013-2014) to wave 5 (W5; 2018-2019) among W1 adult established dual users of ENDS and cigarettes (n=545) from the PATH Study. We used multinomial logistic regression to identify W1 predictors of joint trajectories. RESULTS:Two ENDS (early quitters=66.0%, stable users=34.0%) and three cigarette (stable users=55.2%, gradual quitters=27.3%, early quitters=17.5%) trajectories of W1 were identified. In joint trajectory analysis, 41.6% of participants were early ENDS quitters and stable cigarette users; 14.8% early ENDS quitters and gradual cigarette quitters; 14.6% stable ENDS users and stable cigarette users; 11.2% stable ENDS users and gradual cigarette quitters; 10.3% early ENDS quitters and early cigarette quitters; and 7.4% stable ENDS users and early cigarette quitters. Cigarette and ENDS use frequency, nicotine dependence, cannabis use and other non-combusted tobacco product use predicted trajectory group membership (p values <0.05). CONCLUSIONS:Most dual users maintained long-term cigarette smoking or dual use, highlighting the need to address cessation of both products. Continued monitoring of trajectories and their predictors is needed, given ongoing changes to the ENDS marketplace.

People with substance use disorders (SUDs) are increasingly admitted to general hospitals; however, many hospital systems lack both formal structures and skilled staff to provide high-quality care for inpatients with SUDs. Inpatient addiction consult services (ACSs), which are increasingly being implemented around the country, are an evidence-based strategy to add focused care for people with SUDs into the general medical setting. In 2018, New York City Health + Hospitals (H + H) launched an ACS program called Consult for Addiction Care and Treatment in Hospitals in six hospitals, supported by a team of addiction consult experts to deliver teaching and technical assistance (TTA) for the Consult for Addiction Care and Treatment in Hospitals ACSs. This commentary describes the TTA, which included site visits, introductory educational lectures, case conferences, ad hoc support, implementation assistance, and the creation of an addiction care guide. Similar TTA services could be used in the future when hospitals or systems want to launch novel clinical programs.

BACKGROUND:Generative artificial intelligence has the potential to revolutionize health technology product development by improving coding quality, efficiency, documentation, quality assessment and review, and troubleshooting. OBJECTIVE:This paper explores the application of a commercially available generative artificial intelligence tool (ChatGPT) to the development of a digital health behavior change intervention designed to support patient engagement in a commercial digital diabetes prevention program. METHODS:We examined the capacity, advantages, and limitations of ChatGPT to support digital product idea conceptualization, intervention content development, and the software engineering process, including software requirement generation, software design, and code production. In total, 11 evaluators, each with at least 10 years of experience in fields of study ranging from medicine and implementation science to computer science, participated in the output review process (ChatGPT vs human-generated output). All had familiarity or prior exposure to the original personalized automatic messaging system intervention. The evaluators rated the ChatGPT-produced outputs in terms of understandability, usability, novelty, relevance, completeness, and efficiency. RESULTS:Most metrics received positive scores. We identified that ChatGPT can (1) support developers to achieve high-quality products faster and (2) facilitate nontechnical communication and system understanding between technical and nontechnical team members around the development goal of rapid and easy-to-build computational solutions for medical technologies. CONCLUSIONS:ChatGPT can serve as a usable facilitator for researchers engaging in the software development life cycle, from product conceptualization to feature identification and user story development to code generation. TRIAL REGISTRATION/BACKGROUND:ClinicalTrials.gov NCT04049500; https://clinicaltrials.gov/ct2/show/NCT04049500.

IMPORTANCE/UNASSIGNED:Immigrant birthing people have lower rates of preterm birth compared with their US-born counterparts. This advantage and associated racial and ethnic disparities across the gestational age spectrum have not been examined nationally. OBJECTIVE/UNASSIGNED:To examine associations of maternal nativity, ethnicity, and race with preterm birth. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:This cohort study used birth certificates from the National Vital Statistics System to analyze in-hospital liveborn singleton births in the US between January 1, 2009, and December 31, 2018. Data were analyzed from January to June 2023. EXPOSURE/UNASSIGNED:Mutually exclusive nativity, ethnicity, and race subgroups were constructed using nativity (defined as US-born or non-US-born), ethnicity (defined as Hispanic or non-Hispanic), and race (defined as American Indian or Alaska Native, Asian, Black, Native Hawaiian or Other Pacific Islander, White, or other [individuals who selected other race or more than 1 race]). MAIN OUTCOMES AND MEASURES/UNASSIGNED:The primary outcome of interest was preterm birth. Modified Poisson and multinomial logistic regression models quantified relative risk (RR) of preterm birth overall (<37 weeks' gestation) and by gestational category (late preterm: 34-36 weeks' gestation; moderately preterm: 29-33 weeks' gestation; and extremely preterm: <29 weeks' gestation) for each maternal nativity, ethnicity, and race subgroup compared with the largest group, US-born non-Hispanic White (hereafter, White) birthing people. The RR of preterm birth overall and by category was also measured within each racial and ethnic group by nativity. Models were adjusted for maternal demographic and medical covariates, birth year, and birth state. RESULTS/UNASSIGNED:A total of 34 468 901 singleton live births of birthing people were analyzed, with a mean (SD) age at delivery of 28 (6) years. All nativity, ethnicity, and race subgroups had an increased adjusted risk of preterm birth compared with US-born White birthing people except for non-US-born White (adjusted RR, 0.85; 95% CI, 0.84-0.86) and Hispanic (adjusted RR, 0.98; 95% CI, 0.97-0.98) birthing people. All racially and ethnically minoritized groups had increased adjusted risks of extremely preterm birth compared with US-born White birthing people. Non-US-born individuals had a decreased risk of preterm birth within each subgroup except non-Hispanic Native Hawaiian or Other Pacific Islander individuals, in which immigrants had significantly increased risk of overall (adjusted RR, 1.07; 95% CI, 1.01-1.14), moderately (adjusted RR, 1.10; 95% CI, 0.92-1.30), and late (adjusted RR, 1.11; 95% CI, 1.02-1.22) preterm birth than their US-born counterparts. CONCLUSIONS AND RELEVANCE/UNASSIGNED:Results of this cohort study suggest heterogeneity of preterm birth across maternal nativity, ethnicity, and race and gestational age categories. Understanding these patterns could aid the design of targeted preterm birth interventions and policies, especially for birthing people typically underrepresented in research.

The coronavirus disease 2019 (COVID-19) pandemic has caused significant medical, social, and economic impacts globally, both in the short and long term. Although most individuals recover within a few days or weeks from an acute infection, some experience longer lasting effects. Data regarding the postacute sequelae of severe acute respiratory syndrome coronavirus 2 infection (PASC) in children, or long COVID, are only just emerging in the literature. These symptoms and conditions may reflect persistent symptoms from acute infection (eg, cough, headaches, fatigue, and loss of taste and smell), new symptoms like dizziness, or exacerbation of underlying conditions. Children may develop conditions de novo, including postural orthostatic tachycardia syndrome, myalgic encephalomyelitis/chronic fatigue syndrome, autoimmune conditions and multisystem inflammatory syndrome in children. This state-of-the-art narrative review provides a summary of our current knowledge about PASC in children, including prevalence, epidemiology, risk factors, clinical characteristics, underlying mechanisms, and functional outcomes, as well as a conceptual framework for PASC based on the current National Institutes of Health definition. We highlight the pediatric components of the National Institutes of Health-funded Researching COVID to Enhance Recovery Initiative, which seeks to characterize the natural history, mechanisms, and long-term health effects of PASC in children and young adults to inform future treatment and prevention efforts. These initiatives include electronic health record cohorts, which offer rapid assessments at scale with geographical and demographic diversity, as well as longitudinal prospective observational cohorts, to estimate disease burden, illness trajectory, pathobiology, and clinical manifestations and outcomes.