Faculty Bibliography

BACKGROUND/UNASSIGNED:Effective management of alcohol withdrawal syndrome during hospitalization is paramount to patient safety and quality care. NYC Health + Hospitals initiated a quality improvement project to pilot an electronic health record (EHR) integrated, nurse-driven CIWA-Ar symptom-triggered protocol, including recommendations for medications for alcohol use disorder (MAUD), in medical and surgical units at 3 public hospitals. OBJECTIVE/UNASSIGNED:To describe implementation processes and to report related implementation outcomes (appropriateness, feasibility, and adoption) of the updated CIWA-Ar protocol in a safety net hospital setting. METHODS/UNASSIGNED:NYC Health + Hospitals implemented a standardized CIWA-Ar symptom-triggered, nurse-driven EHR protocol on March 15, 2022. The protocol included order sets, practice advisories, task lists, and reminders for assessments and orders. We measured nursing perspectives on feasibility and appropriateness at 6 months via a survey. We measured provider adoption as the proportion of admissions with a CIWA-Ar protocol ordered among admissions that triggered a recommendation, and MAUD use as the proportion of admissions with a MAUD order during hospitalization among all patients with a protocol ordered. RESULTS/UNASSIGNED:= .249). CONCLUSIONS/UNASSIGNED:The CIWA-Ar protocol was appropriate, feasible, and adopted at NYC public hospitals. Quality improvements to ensure protocol fidelity with benzodiazepine dosing and MAUD prescribing are needed.

BACKGROUND AND AIM/UNASSIGNED:Extended-release injectable naltrexone (XR-Naltrexone) is an effective treatment for opioid use disorder (OUD); however, initiation can be challenging as it requires an opioid-free period. This exploratory analysis examines patient characteristics associated with successful initiation of XR-Naltrexone in the National Drug Abuse Treatment Clinical Trials Network (CTN-0051) Extended-Release Naltrexone versus Buprenorphine for Opioid Treatment (X:BOT) trial. METHODS/UNASSIGNED:Patient demographics and clinical variables associated with successful XR-Naltrexone initiation were examined among 283 participants with OUD randomized to XR-Naltrexone in the X:BOT trial. Variables included severity of opioid use, characteristics of opioid and other substance use, treatment history, psychiatric history, baseline depression, and pain. Logistic regression models were used to estimate the effect of variables on the odds of induction success. RESULTS/UNASSIGNED:204 (72%) of 283 participants randomized to receive XR-Naltrexone completed successful induction. Housing status and pain were significantly associated with XR-Naltrexone induction status. Reported homelessness was significantly associated with higher odds of successful XR-Naltrexone induction (OR: 2.31; 95% CI: 1.12, 4.76). Individuals that reported moderate or extreme pain on the EuroQoL had half the odds of successful induction compared to those without pain (OR: 0.49; 95% CI: 0.27, 0.89). CONCLUSIONS/UNASSIGNED:Among patients with OUD initiating treatment on inpatient units, homelessness was associated with greater likelihood of successfully initiating XR-Naltrexone, while chronic pain was associated with lower likelihood of XR-Naltrexone initiation. Future research on XR-Naltrexone initiation should consider tailoring treatment based on housing status and other social determinants, and evaluation and management of pain.

High-grade serous ovarian carcinoma (HGSC) remains an incompletely understood, highly lethal disease. Historically, a lack of fidelitous in vitro and in vivo models representing HGSC biology and therapy response has been a major barrier to progress. As we discuss below, multiple (if not most) early studies used-and some investigators continue to use-human "ovarian cancer cell lines" that lack key genomic/genetic features of HGSC, rendering their conclusions questionable. The frequently deployed ID8 syngeneic mouse model is similarly suspect, as it derives from ovarian surface epithelium (OSE) and is Trp53 wild-type. In contrast, most, if not all, HGSC arises in fallopian tube epithelium (FTE), and bona fide HGSC is universally TP53 mutant or silenced. Over the past 10 years, attempts have been made to rectify these historical deficiencies, including careful assessment of the genetic composition of standard ovarian cancer cell lines and the development of mouse and human organoids, genetically engineered mouse models (GEMMs), and patient-derived xenografts (PDXs). In this review, we discuss these advances, exploring their differences, strengths, and weaknesses. We also describe "next-generation" approaches to more faithfully model HGSC cells in the context of a more realistic tumor microenvironment.

INTRODUCTION/BACKGROUND:Linking electronic health record (EHR) use to care quality may offer insights into potential interventions improving guideline adherence and closing care gaps. We examine how EHR metadata can measure cognitive load in primary care providers during statin prescribing and identify cognitive load points in EHR workflows associated with guideline-concordant statin initiation. METHODS:We retrospectively extracted 2024 data from EHR primary care encounters from a large academic health system. We identified adult patients who met the criteria for statin initiation and calculated their atherosclerotic cardiovascular disease (ASCVD) risk scores. Cognitive load metrics were derived from EHR metadata. Logistic regressions evaluate associations between cognitive load and statin initiation, adjusting for patient covariates and provider fixed effects. Gradient-boosted forests and Shapley Additive explanations (SHAP) values were used to identify key EHR events and cognitive load patterns associated with statin initiation. RESULTS:Longer encounter duration was associated with increased likelihood of statin initiation, whereas more time spent per EHR event was associated with a decreased likelihood. Nonlinear associations were observed for loop count and distinct event count: predicted initiation probability decreased with increasing loop count to 93.9 loops, then increased beyond this threshold. For distinct events, initiation probability increased up to approximately 18 events and declined at higher counts. In a gradient-boosted decision tree model, average event time was the strongest predictor (72.2% relative contribution). Additional positive predictors included time spent reviewing lab results and on suggested medication order sets. Order list modification and looping back to it were negatively associated with statin initiation. DISCUSSION/CONCLUSIONS:EHR metadata can associate cognitive load with appropriate clinical behavior, revealing nonlinear associations between cognitive load and statin initiation rates. This work suggests opportunities to optimize EHR systems to reduce cognitive burden and support clinical decision-making. Connecting cognitive load to prescribing behavior generates hypotheses about how workflow adjustments and enhanced decision support might improve guideline adherence and patient care through prospective evaluation.

Lung cancer in never smokers (LCINS) disproportionately affects younger women and East Asian populations and is characterized by distinct genetic susceptibility, environmental exposures, and molecular alterations. However, never smokers remain excluded from current screening guidelines despite rising incidence and identifiable high-risk subgroups. Family history confers substantial risk, with affected first-degree relatives showing 1.7-fold higher incidence, while genome-wide association studies and polygenic risk scores further refine genetic susceptibility. Female sex and Asian ethnicity are primary independent risk factors, exceeding family history in multivariable analysis. Asian never smokers have a 2.3-fold higher baseline incidence of lung cancer than non-Asian never smokers, and Asian female never smokers exhibit lung cancer detection rates comparable to Asian male ever smokers. Screening trials in never smokers demonstrated detection rates comparable to smoker-based trials, suggesting certain demographic subgroups may reach risk thresholds where screening could be beneficial. Environmental PM_2.5 exposure increases LCINS incidence and mortality in East Asian populations where both PM_2.5 exposure and EGFR mutation rates are elevated. Recent evidence implicates mitochondrial DNA mutations in LCINS susceptibility among Asian never smokers, suggesting integration of environmental exposures and molecular biomarkers may improve risk stratification for screening. The high prevalence of actionable oncogenic drivers in LCINS underscores the importance of early detection, as these tumors benefit from targeted therapies whereas immune checkpoint inhibitors responses are often lower. This review summarizes LCINS epidemiology, genetic susceptibility, molecular alterations, environmental risk factors, and tumor immunology, highlighting the need for greater focus on this underrecognized and growing patient population.

This article provides a focused update to the clinical practice guideline on the treatment and management of patients with COVID-19, developed by the Infectious Diseases Society of America. The guideline panel presents a new recommendation on the use of baricitinib vs. tocilizumab in hospitalized adults with severe or critical COVID-19. The panel has previously issued recommendations on baricitinib vs. no baricitinib and tocilizumab vs. no tocilizumab, but this new recommendation compares baricitinib to tocilizumab when the decision has been made to give one or the other. The new recommendation does not address combinations of multiple immunomodulatory agents (ie, baricitinib, tocilizumab, abatacept, infliximab). The recommendation is based on evidence derived from a systematic literature review and adheres to a standardized methodology for rating the certainty of evidence and strength of recommendation according to the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach.

BACKGROUND AND OBJECTIVES/OBJECTIVE:Bone metastases, affecting more than 4.8% of patients with cancer annually, and particularly spinal metastases require urgent intervention to prevent neurological complications. However, the current process of manually reviewing radiological reports leads to potential delays in specialist referrals. We hypothesized that natural language processing (NLP) review of routine radiology reports could automate the referral process for timely multidisciplinary care of spinal metastases. METHODS:We assessed 3 NLP models-a rule-based regular expression (RegEx) model, GPT-4, and a specialized Bidirectional Encoder Representations from Transformers (BERT) model (NYUTron)-for automated detection and referral of bone metastases. Study inclusion criteria targeted patients with active cancer diagnoses who underwent advanced imaging (computed tomography, MRI, or positron emission tomography) without previous specialist referral. We defined 2 separate tasks: task of identifying clinically significant bone metastatic terms (lexical detection), and identifying cases needing a specialist follow-up (clinical referral). Models were developed using 3754 hand-labeled advanced imaging studies in 2 phases: phase 1 focused on spine metastases, and phase 2 generalized to bone metastases. Standard McRae's line performance metrics were evaluated and compared across all stages and tasks. RESULTS:In the lexical detection, a simple RegEx achieved the highest performance (sensitivity 98.4%, specificity 97.6%, F1 = 0.965), followed by NYUTron (sensitivity 96.8%, specificity 89.9%, and F1 = 0.787). For the clinical referral task, RegEx also demonstrated superior performance (sensitivity 92.3%, specificity 87.5%, and F1 = 0.936), followed by a fine-tuned NYUTron model (sensitivity 90.0%, specificity 66.7%, and F1 = 0.750). CONCLUSION/CONCLUSIONS:An NLP-based automated referral system can accurately identify patients with bone metastases requiring specialist evaluation. A simple RegEx model excels in syntax-based identification and expert-informed rule generation for efficient referral patient recommendation in comparison with advanced NLP models. This system could significantly reduce missed follow-ups and enhance timely intervention for patients with bone metastases.

This article provides a focused update to the clinical practice guideline on the treatment and management of people with COVID-19, developed by the Infectious Diseases Society of America. The guideline panel presents 9 updated recommendations on the use of nirmatrelvir/ritonavir, remdesivir, and molnupiravir, in adults with mild to moderate COVID-19. The recommendations are based on evidence derived from a systematic literature review and adhere to a standardized methodology for rating the certainty of evidence and strength of recommendation according to the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach. The panel also provides a section on how to apply these recommendations, including an algorithm on the selection of antivirals.

IMPORTANCE/UNASSIGNED:Artificial intelligence (AI)-enabled scribes have been proposed to reduce electronic health record (EHR) burden and improve clinician satisfaction. There is limited evidence about their associated results across multiple sites and relative benefits for different clinician groups. OBJECTIVE/UNASSIGNED:To assess the association of AI scribe adoption with changes in EHR time expenditure and visit volume and how associations vary by clinician characteristics. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:Multisite, longitudinal cohort study of AI scribe adoption conducted at 5 US academic health care institutions that introduced AI scribes to their clinicians between June 2023 and August 2025. Participants were ambulatory clinicians. EXPOSURES/UNASSIGNED:AI scribe adoption, defined as receiving access to an AI scribe. This was determined by opt-in decisions by eligible physicians at 4 of the 5 sites. MAIN OUTCOME AND MEASURES/UNASSIGNED:Total time spent on the EHR, time spent on documentation, and time spent on the EHR outside scheduled hours or on unscheduled days, all normalized to 8 scheduled patient hours; weekly visit volume. RESULTS/UNASSIGNED:The sample comprised 8581 clinicians, including 1809 AI scribe adopters. Participants were 57.1% female and were split between primary care (24.4%), medical (62.4%), and surgical (13.2%) specialties. Most (74.1%) were attending physicians, with 18.1% advanced practice clinicians and 7.8% resident physicians. In a difference-in-differences analysis, AI scribe adoption was associated with 13.4 (95% CI, 9.1-17.7) fewer minutes of EHR time, 16.0 (95% CI, 13.7-18.3) fewer minutes of documentation time, and 0.49 (95% CI, 0.17-0.81) additional weekly visits delivered. Electronic health record time outside work hours did not change significantly. Changes associated with AI scribe adoption were greatest for primary care specialists, advanced practice clinicians, female clinicians, and clinicians who used AI scribes in 50% or more of visits. CONCLUSIONS AND RELEVANCE/UNASSIGNED:AI scribe adoption was associated with modest decreases in total EHR time and documentation time and with a modest increase in weekly visit volume.