Faculty Bibliography

BACKGROUND:IPMN consensus guidelines make implicit judgments on what cancer risk level should prompt surgery. We used decision modeling to estimate this cancer risk threshold (CRT) for BD-IPMN patients. METHODS:We created a decision model to compare quality-adjusted life years (QALYs) following surgery or surveillance for BD-IPMNs. We simulated treatment decisions for hypothetical patients, varying age, comorbidities and lesion location (pancreatic head/tail). The base case was a 60-year-old patient with mild comorbidities and pancreatic head IPMN. Probabilities, life expectancies, and utilities were incorporated from literature/public datasets. CRT was defined as the level of cancer risk at which the expected value of QALYs for surgery first exceeded that of surveillance. RESULTS:In the base case, surgery was preferred over surveillance, yielding 21.90 vs. 21.88 QALYs. The optimal CRT for a BD-IPMN patient depended on age, comorbidities, and location. CRT in the base case was 20 % and 3 % for an IPMN in the head and tail of the pancreas, respectively. Other drivers of preferred treatment were age and likelihood of postoperative mortality. CONCLUSION/CONCLUSIONS:For BD-IPMNs, the optimal CRT varies depending on patient age and risk of surgical complications. Personalized risk threshold values could guide treatment decisions and inform future treatment consensus guidelines.

OBJECTIVE:This analysis evaluated the validation results of the Tobacco, Alcohol, Prescription Medication, and Other Substance Use (TAPS) tool for older adults. METHODS:We performed a subgroup analysis of older adults aged ≥65 (n = 184) from the TAPS tool validation study conducted in 5 primary care clinics. We compared the interviewer and self-administered versions of the TAPS tool at a cutoff of ≥1 for identifying problem use with a reference standard measure, the modified World Mental Health Composite International Diagnostic Interview. RESULTS:The mean age was 70.6 ± 5.9 years, 52.7% were female, and 49.5% were non-Hispanic Black. For identifying problem use, the self-administered TAPS tool had sensitivity of 0.91 (95% CI: 0.75-0.98) and specificity of 0.91 (95% CI: 0.85-0.95) for tobacco; sensitivity of 0.68 (95% CI: 0.45-0.86) and specificity of 0.88 (95% CI: 0.82-0.93) for alcohol; and sensitivity 0.86 (95% CI: 0.42-1.00) and specificity 0.94 (95% CI: 0.90-0.97) for cannabis. The interviewer-administered TAPS tool had similar results. We were unable to evaluate its performance for identifying problem use of individual classes of drugs other than cannabis in this population due to small sample sizes. CONCLUSIONS:While the TAPS had excellent sensitivity and specificity for identifying tobacco use among older adults, the results for other substances lack precision, and we were unable to evaluate its performance for prescription medications and individual illicit drugs in this sample. This analysis underlines the critical need to adapt and validate screening tools for unhealthy substance use, specifically for older populations who have unique risks.

Melamine, its analogues, and aromatic amines (AAs) were commonly detected in a previous study of pregnant women in the Environmental influences on Child Health Outcomes (ECHO) Cohort. While these chemicals have identified toxicities, little is known about their influences on fetal development. We measured these chemicals in gestational urine samples in 3 ECHO cohort sites to assess associations with birth outcomes (n = 1,231). We estimated beta coefficients and 95% confidence intervals (CIs) using adjusted linear mixed models with continuous dilution-standardized concentrations (log₂ transformed and scaled by interquartile range, IQR) or binary indicators for detection. As secondary analyses, we repeated analyses using categorical outcomes. Forty-one of 45 analytes were detected in at least one sample, with > 95 % detection of melamine, cyanuric acid, ammelide, and aniline. Higher melamine concentration was associated with longer gestational age (β^ per IQR increase of log₂-transformed: 0.082 [95 % CI: -0.012, 0.177]; 2nd vs 1st tertile: 0.173 [-0.048, 0.394]; 3rd vs 1st tertile: 0.186 [-0.035, 0.407]). Similarly in secondary analyses using categorical outcomes, an IQR increase in log₂(melamine) was associated with 1.22 [0.99, 1.50] higher odds of post-term (>40 & ≤42 weeks) as compared to full-term (≥38 & ≤40 weeks). Several AAs were associated with birthweight and gestational length, with the direction of associations varying by AA. Some stronger associations were observed in females. Our findings suggest melamine and its analogs and AAs may influence gestational length and birthweight.

AIMS/OBJECTIVE:Because one-hour post-load plasma glucose (1h-PG) ≥ 155 mg/dL (8.6 mmol/L) has been proposed as an early marker for future diabetes but lacks sufficient longitudinal confirmation of its risk, we aimed to evaluate the risk of T2D based on 1h-PG and track changes of insulin sensitivity and β-cell function over time by 1h-PG in a longitudinal cohort. METHODS:OGTTs were conducted every 2 years in the 10-year longitudinal Korean Genome Epidemiology study (n = 6144) with three groups characterized at baseline: Low 1h-PG (< 155 mg/dL) with Normal Glucose Tolerance (NGT), High 1h-PG (≥155 mg/dL) with NGT, and prediabetes (PreDM). RESULTS:T2D risk was higher in people with High 1h-PG with NGT and PreDM than those with Low 1h-PG with NGT. Baseline insulin sensitivity in Low 1h-PG as measured by the insulin sensitivity and secretion (ISS) model and Matsuda insulin sensitivity index (ISI) was higher than in High 1h-PG, which was comparable to PreDM. β-cell function as assessed by ISS and the insulinogenic index decreased from Low 1h-PG to High 1h-PG to PreDM. Over time, insulin sensitivity decreased in the three groups. Time from High 1h-PG to T2D was 0.9 years shorter than from Low 1h-PG. All participants passed the 1h-PG threshold for T2D (209 mg/dL, 11.6 mmol/L) first, and 74 % passed the 1h-PG threshold for impaired glucose tolerance (IGT; 155 mg/dL) first. CONCLUSIONS:High 1h-PG NGT is an intermediate risk category between Low 1h-PG NGT and PreDM and may provide an opportunity for early intervention to prese rve ß-cell function.

OBJECTIVE:The goal is to provide an overview of artificial intelligence (AI) and machine learning (ML) methodology and appraisal tailored to clinicians and researchers in the headache field to facilitate interdisciplinary communications and research. BACKGROUND:The application of AI to the study of headache and other healthcare challenges is growing rapidly. It is critical that these findings be accurately interpreted by headache specialists, but this can be difficult for non-AI specialists. METHODS:This paper is a narrative review of the fundamentals required to understand ML/AI headache research. Using guidance from key leaders in the field of headache medicine and AI, important references were reviewed and cited to provide a comprehensive overview of the terminology, methodology, applications, pitfalls, and bias of AI. RESULTS:We review how AI models are created, common model types, methods for evaluation, and examples of their application to headache medicine. We also highlight potential pitfalls relevant when consuming AI research, and discuss ethical issues of bias, privacy and abuse generated by AI. Additionally, we highlight recent related research from across headache-related applications. CONCLUSION/CONCLUSIONS:Many promising current and future applications of ML and AI exist in the field of headache medicine. Understanding the fundamentals of AI will allow readers to understand and critically appraise AI-related research findings in their proper context. This paper will increase the reader's comfort in consuming AI/ML-based research and will prepare them to think critically about related research developments.

Multi-cancer early detection (MCED) tests are already being marketed as noninvasive, convenient opportunities to test for multiple cancer types with a single blood sample. The technology varies-involving detection of circulating tumor DNA, fragments of DNA, RNA, or proteins unique to each targeted cancer. The priorities and tradeoffs of reaching diagnostic resolution in the setting of possible false positives and negatives remain under active study. Given the well-established role of imaging in lesion detection and characterization for most cancers, radiologists have an essential role to play in selecting diagnostic pathways, determining the validity of test results, resolving false-positive MCED test results, and evaluating tradeoffs for clinical policy. Appropriate access to and use of imaging tests will also factor into clinical guidelines. Thus, all clinicians potentially involved with MCED tests for cancer screening will need to weigh the benefits and harms of MCED testing, including consideration of how the tests will be used alongside or in place of other screening options, how diagnostic confirmation tests should be selected, and what the implications are for policy and reimbursement decisions. Further, patients will need regular support to make informed decisions about screening using MCED tests in the context of their personal cancer risks, health-related values, and access to care.

BACKGROUND:In response to the heavy burden of untreated substance use disorders (SUD) in hospital patients, many health systems are implementing addiction consult services staffed by interprofessional teams that diagnose SUD, make recommendations for SUD care in the hospital, and link patients to post-discharge treatment. In 2018, the New York City public hospital system began rolling out the Consult for Addiction Treatment and Care in Hospitals (CATCH) program in six hospitals. CATCH teams are comprised of an addiction-trained medical provider, social worker or addiction counselor, and peer counselor. METHODS:The study conducted qualitative interviews with CATCH staff at all six participating hospitals as part of a pragmatic trial studying the effectiveness and implementation of CATCH. The Consolidated Framework for Implementation Research (CFIR) framework guided interviews conducted from 2018 to 2021 with 26 staff at the start of implementation and with 33 staff 9-12 months post-implementation. The study team created a codebook a priori and further refined it through additional coding of initial interviews. Codes were systematically analyzed using the CFIR. RESULTS:Barriers and facilitators spanned four CFIR domains: inner setting, outer setting, process, and individual characteristics. Barriers identified were primarily related to the outer and inner settings, including patient characteristics and limited resources (e.g. medical comorbidities, homelessness), insurance, CATCH team role confusion, and infrastructure deficits (e.g., availability of physical space). Additional barriers related to process (workload burden), and characteristics of individuals (stigma and lack of comfort treating SUD among medical teams). Facilitators were mostly related to the characteristics of individuals on the CATCH team (advantages and expertise of the CATCH peer counselor, CATCH team communication and cohesiveness) and inner setting (CATCH team relationships with hospital staff, hospital leadership buy-in and support, and infrastructure). Community networks (outer setting) and CATCH training resources (process) were also facilitators of program implementation. CONCLUSION/CONCLUSIONS:Addiction consult services have great potential for improving care for hospital patients with SUD, but as new programs in busy hospital settings they face barriers to implementation that could impact their effectiveness. Barriers may be particularly impactful for programs operating in safety-net hospitals, given limited resources within the health system and the multiple and complex needs of their patients. Understanding the strengths of these programs as well as the barriers to their implementation is critical to utilizing addiction consult services effectively.