Citations

Background: International guidelines recommend targeted screening to identify gestational thyroid dysfunction. However, currently used risk factors have questionable discriminative ability. We quantified the risk for thyroid function test abnormalities for a subset of risk factors currently used in international guidelines. Methods: We included prospective cohort studies with data on gestational maternal thyroid function and potential risk factors (maternal age, body mass index [BMI], parity, smoking status, pregnancy through in vitro fertilization, twin pregnancy, gestational age, maternal education, and thyroid peroxidase antibody [TPOAb] or thyroglobulin antibody [TgAb] positivity). Exclusion criteria were pre-existing thyroid disease and use of thyroid interfering medication. We analyzed individual participant data using mixed-effects regression models. Primary outcomes were overt and subclinical hypothyroidism and a treatment indication (defined as overt hypothyroidism, subclinical hypothyroidism with thyrotropin >10 mU/L, or subclinical hypothyroidism with TPOAb positivity). Results: The study population comprised 65,559 participants in 25 cohorts. The screening rate in cohorts using risk factors currently recommended (age >30 years, parity ≥2, BMI ≥40) was 58%, with a detection rate for overt and subclinical hypothyroidism of 59%. The absolute risk for overt or subclinical hypothyroidism varied <2% over the full range of age and BMI and for any parity. Receiver operating characteristic curves, fitted using maternal age, BMI, smoking status, parity, and gestational age at blood sampling as explanatory variables, yielded areas under the curve ranging from 0.58 to 0.63 for the primary outcomes. TPOAbs/TgAbs positivity was associated with overt hypothyroidism (approximate risk for antibody negativity 0.1%, isolated TgAb positivity 2.4%, isolated TPOAb positivity 3.8%, combined antibody positivity 7.0%; p < 0.001), subclinical hypothyroidism (risk for antibody negativity 2.2%, isolated TgAb positivity 8.1%, isolated TPOAb positivity 14.2%, combined antibody positivity 20.0%; p < 0.001) and a treatment indication (risk for antibody negativity 0.2%, isolated TgAb positivity 2.2%, isolated TPOAb positivity 3.0%, and combined antibody positivity 5.1%; p < 0.001). Twin pregnancy was associated with a higher risk of overt hyperthyroidism (5.6% vs. 0.7%; p < 0.001). Conclusions: The risk factors assessed in this study had poor predictive ability for detecting thyroid function test abnormalities, questioning their clinical usability for targeted screening. As expected, TPOAb positivity (used as a benchmark) was a relevant risk factor for (subclinical) hypothyroidism. These results provide insights into different risk factors for gestational thyroid dysfunction.

Importance Artificial intelligence (AI) tools are rapidly integrating into cancer care. Understanding stakeholder views on ethical issues associated with the implementation of AI in oncology is critical to optimal deployment. Objective To evaluate oncologists’ views on the ethical domains of the use of AI in clinical care, including familiarity, predictions, explainability (the ability to explain how a result was determined), bias, deference, and responsibilities. Design, Setting, and Participants This cross-sectional, population-based survey study was conducted from November 15, 2022, to July 31, 2023, among 204 US-based oncologists identified using the National Plan &amp;amp; Provider Enumeration System. Main Outcomes and Measures The primary outcome was response to a question asking whether participants agreed or disagreed that patients need to provide informed consent for AI model use during cancer treatment decisions. Results Of 387 surveys, 204 were completed (response rate, 52.7%). Participants represented 37 states, 120 (63.7%) identified as male, 128 (62.7%) as non-Hispanic White, and 60 (29.4%) were from academic practices; 95 (46.6%) had received some education on AI use in health care, and 45.3% (92 of 203) reported familiarity with clinical decision models. Most participants (84.8% [173 of 204]) reported that AI-based clinical decision models needed to be explainable by oncologists to be used in the clinic; 23.0% (47 of 204) stated they also needed to be explainable by patients. Patient consent for AI model use during treatment decisions was supported by 81.4% of participants (166 of 204). When presented with a scenario in which an AI decision model selected a different treatment regimen than the oncologist planned to recommend, the most common response was to present both options and let the patient decide (36.8% [75 of 204]); respondents from academic settings were more likely than those from other settings to let the patient decide (OR, 2.56; 95% CI, 1.19-5.51). Most respondents (90.7% [185 of 204]) reported that AI developers were responsible for the medico-legal problems associated with AI use. Some agreed that this responsibility was shared by physicians (47.1% [96 of 204]) or hospitals (43.1% [88 of 204]). Finally, most respondents (76.5% [156 of 204]) agreed that oncologists should protect patients from biased AI tools, but only 27.9% (57 of 204) were confident in their ability to identify poorly representative AI models. Conclusions and Relevance In this cross-sectional survey study, few oncologists reported that patients needed to understand AI models, but most agreed that patients should consent to their use, and many tasked patients with choosing between physician- and AI-recommended treatment regimens. These findings suggest that the implementation of AI in oncology must include rigorous assessments of its effect on care decisions as well as decisional responsibility when problems related to AI use arise.

Abstract Background: Analyzing interactions and the microanatomical distribution of the tumor immune microenvironment (TIME) is essential for a comprehensive understanding of tumor progression. Imaging Mass Cytometry (IMC) is a high-dimensional tissue imaging system that enables the deeper and multiparametric in situ exploration of tumor microenvironments at a single-cell level. This study describes a pipeline for analyzing 39-plex IMC images of human HPV(+) oropharyngeal squamous cell carcinoma (OPSCC) tumor sections, elucidating immune cells' nature, functions, and interactions with tumor cells. The IMC data analysis provides valuable information for clinical studies that could be used for the identification of prognostic biomarkers and mechanisms of resistance to current immunotherapies. Methods: A retrospective study was conducted on (N=20) patients (10 progressors and 10 non-progressors) with HPV(+) OPSCC. The dataset comprises paired hematoxylin and eosin (H&E) images with IMC data from multiple Regions of Interest (ROIs) (N=82), enabling the in-situ profiling of 39 proteins in primary tumor tissue. Cell segmentation was followed by employing original images and their segmentation masks for cell phenotype identification using a probabilistic neural network method to spatially profile cancer cells, macrophages, NK-M cells, B-cells, and distinct T-cell populations. We identified protein expression at single-cell resolution and investigated the impact of multicellular spatial organization on the disease progression. The spatial neighborhood interaction score of tumor cells with various other cells was computed for all the ROIs across the two groups. Results: The markers showed varying expression levels of intensity distribution, likely due to differences in tissue sample composition. Specifically, CD3, CD4, CD8, CD19, CD20, and CD68 showed significantly higher intensity in non-progressors. Interestingly, FoxP3 also had a significantly higher intensity level in non-progressors. Cell phenotyping emphasized that non-progressors generally have increased levels of NK-M cell markers. Spatial analysis revealed a significant increase in interaction between CD3 cells and tumor cells in non-progressors (p-value=0.07, Mann-Whitney U test). Furthermore, a significant rise in the interaction between B-cells and FoxP3+ cells was observed in non-progressors (p-value&lt;0.05, Mann-Whitney U test) suggesting a coordinated suppression of tumor growth. Conclusion: We developed a deep learning-based pipeline to analyze spatial relationships in the TIME of IMC data, demonstrating its clinical relevance in predicting disease progression. Our findings emphasize the importance of spatial relationships in the TME for response and suggest that proximity between either CD3+ T-cells to cancer cells or B-cells to FoxP3+ cells are candidate biomarkers. Citation Format: Sumanth Reddy Nakkireddy, Routman David, Kathleen Bartemes, Daniel Ma, Tae Hyun Hwang, Kathryn Van Abel, Chadi Nimeh Abdel-Halim. AI-enhanced spatial proteomics reveals B-cells & FoxP3+ cells interactions in HPV(+) oropharyngeal squamous cell carcinoma Progression [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5488.

Abstract During the 20th century, inflammatory bowel disease (IBD) was considered a disease of early-industrialized regions in North America, Europe, and Oceania. At the turn of the 21st century, incidence of IBD increased in newly-industrialized and emerging regions in Africa, Asia, and Latin America, while prevalence in early-industrialized regions started to grow steadily. Changes in incidence and prevalence denote evolution of IBD across four epidemiologic stages: Stage 1 (Emergence), characterized by low incidence and prevalence; stage 2 (Acceleration in Incidence), characterized by rapidly rising incidence and low prevalence; and stage 3 (Compounding Prevalence), characterized by decelerating, plateauing, or declining incidence and steadily rising prevalence. A fourth stage (Prevalence Equilibrium) has been proposed where the slope of prevalence plateaus due to shifting demographics of an aging IBD population, but it has not yet been evidenced. To date, these stages have been theoretical without specific incidence/prevalence indicators defining transition points. Using real-world data from 490 population-based studies comprising 80 global regions and spanning over a century (1920–2023), we show spatiotemporal transitions across stages 1–3 and model transition towards stage 4. Understanding the evolution of IBD across epidemiologic stages allows healthcare systems to better prepare for the future worldwide burden of IBD.

Background: Approximately 75% of all head and neck cancer patients are treated with radiotherapy (RT). RT to the oral cavity results in acute and late adverse events which can be severe and detrimental to a patient’s quality of life and function. The purpose of this study was to explore associations between RT dose to a defined oral cavity organ-at-risk (OAR) avoidance structure, provider- and patient-reported outcomes (PROs), opioid use, and hospitalization. Methods: This was a retrospective analysis of prospectively obtained outcomes using multivariable modeling. The study included 196 patients treated with RT involving the oral cavity for a head and neck tumor. A defined oral cavity OAR avoidance structure was used in all patients for RT treatment planning. Validated PROs were collected prospectively. Opioid use and hospitalization were abstracted electronically from medical records. Results: Multivariable modeling revealed the mean dose to the oral cavity OAR was significantly associated with opioid use (p = 0.0082) and hospitalization (p = 0.0356) during and within 30 days of completing RT. Conclusions: The findings of this study may be valuable in RT treatment planning for patients with tumors of the head and neck region to reduce the need for opioid use and hospitalization during treatment.

Importance Transoral robot-assisted surgery (TORS) continues to have a major role in the treatment of oropharyngeal cancer. As new iterations of robotic technology are increasingly utilized, it is important to share learning experiences and clinical outcomes data, to optimize technical efficiency and clinical care. Observations This was a retrospective review of a large academic institution’s initial clinical use of the da Vinci Single Port (SP) compared with the da Vinci Si (Si) system. A total of 205 TORS cases were reviewed: 109 in the SP group (November 22, 2018, through September 30, 2020), and 96 in the Si group (January 1, 2016, through November 12, 2018). Both groups had comparable operative times, rates of postoperative pharyngeal hemorrhage, length of hospital stay, and duration of nasogastric feeding tube use. There was no difference in pathological characteristics, rates of positive margins, or indications for or time to initiation of adjuvant therapy between the groups. The collective experience of 6 faculty members—who have trained 139 TORS surgeons for the SP system rollout—was compiled to provide a summary of learning experiences and technical notes on safe and efficient operation of the SP system. Conclusions and Relevance This Review found that the functional and oncologic outcomes were comparable between TORS cases performed with the Si and SP systems, and they had similar complication rates. Recognized advantages of the SP over the Si system include the availability of bipolar-energized instruments, a usable third surgical arm, and improved camera image quality.

Introduction Artificial Intelligence (AI) is an emerging technology that promises advancements across blood cancer discovery and care delivery. There are already FDA-approved uses of AI in hematology and oncology but few data available about its use or utility. Concerns have been raised over AI's potential biases, “explainability,” and who is responsible for its use. Oncologists' familiarity with AI and perspectives on these issues are not well known and are critical to understanding the ethical deployment of AI in blood cancer care. Methods From January to July 2023, we performed a national cross-sectional survey of practicing oncologists. A draft survey instrument was developed by a team with content and methodological expertise. Iterative refinement occurred through cognitive debriefing with 5 practicing hematologist/oncologists. The final instrument included 24 questions covering domains of AI familiarity, predictions, explainability, and responsibilities. A random sample of US oncologists was identified using the National Plan & Provider Enumeration System. Paper surveys were mailed with $25 incentivizing gift cards. Reminder letters and phone calls were used for non-responders with an electronic survey option provided. Responses were analyzed descriptively. X2 testing assessed bivariate associations between responses and participant practice setting, with odds ratios (OR) reported. Results Of 399 mailed surveys, 12 were undeliverable and 201 were completed for a response rate of 51.9%. Question missingness was &lt;1%. Participants were 63.2% White, 16.9% Indian Asian, 10.0% Eastern Asian or Pacific Islander, and 4.5% Black; 6.0% were Hispanic. They represented 37 states. The majority were male (63.7%), 40-59 years old (54.2%), and had no specific training in AI (52.2%); 68.2% had at least 11 years in practice and 29.9% practiced at a primary academic hospital. Questions assessing overall familiarity with and predictions related to AI are shown in Table 1. Those at primary academic hospitals were more likely to be familiar with radiology models (OR 2.82, p&lt;0.001) but not pathology, prognostic, or clinical decision models (all p&gt;0.05). They were more likely to predict that AI would improve side effect management (OR 2.01, p=0.03) and end-of-life decision making (OR 2.07, p=0.02). High proportions of respondents thought that AI prognostic (67.1%) and clinical decision models (62.1%) needed to be explainable by clinicians and researchers to be used in clinic. When posed with a scenario where an FDA-approved AI model selects a different regimen than the oncologist planned to propose (Figure 1), the largest proportion reported they would present both options and let the patient decide (37.3%). Of those recommending a regimen, respondents from primary academic hospitals more frequently recommend the AI-generated regimen (56.1%), while respondents from elsewhere recommended their original regimen (69.0%; OR 2.85, p=0.007). Acceptability of some direct-to-patient AI model applications is shown in Table 1. A majority of respondents (90.5%) thought that AI developers should be responsible for medico-legal problems caused by AI use; 47.8% thought that physicians using the tool should be responsible. Though most (76.1%) agreed that it was oncologists' responsibility to protect patients from biased AI tools, few (27.3%) were confident in their ability to identify how representative the data used in an AI model was relative to the population with that cancer (including 66.7% of those who agreed it was oncologists' responsibility). More respondents thought that patients should need to consent to the use of AI tools in cancer treatment decisions (81.1%) than cancer diagnosis (55.7%); those from primary academic hospitals were less likely to respond that patients should consent for treatment or diagnosis (OR 0.43, p=0.02 and OR 0.44, p=0.009). Conclusions In this national survey of oncologists' views on AI, there was optimism about its impact on care with notable caveats related to end-of-life care, patient trust, and care disparities. Despite many approving use of AI models too complex for patients to explain, a substantial proportion deferred to patient choice when AI and physician recommendations differed. There was also dissonance between accepting responsibility to protect patients from bias and respondents' self-perceived ability to do so.

Abstract Background Post-discharge culture and susceptibility review has been described in emergency department and urgent care settings, but there is a lack of published literature describing culture review after acute care hospitalizations. Awaiting microbiology results can impede timely patient discharge. However, discharging patients while microbiology results are pending may result in suboptimal therapy or lost opportunity for additional testing/follow up. We implemented an antimicrobial stewardship effort to identify microbiology tests that result post-discharge utilizing a custom report in the electronic medical record (EMR). Methods. This retrospective descriptive study included adult patients ( &gt;18 years of age) at 4 sites within Mayo Clinic from 1/1/2019 to 2/28/2023. Eligible patients had a hospitalization with an ID consult and an abnormal culture that was reviewed by an antimicrobial stewardship pharmacist after discharge. Urine cultures were excluded. The details of pharmacist-initiated antimicrobial stewardship interventions based on the culture review were identified by an EMR based research report. Results A total of 6803 patient encounters with at least 1 microbiology result reviewed post-discharge were identified. Patients were 40% female with a median age of 62 years. Patient race was 89.8% white, 4% black/African American, 1.5% Asian, and &lt; 1% other. Of these patients, 1983 (29%) had at least one pharmacist intervention. A total of 169 (2.5%) of these patients had multiple interventions. Interventions included modification of antimicrobial therapy, addition of susceptibility testing, or additional follow up (labs, imaging, or patient visits). Median time from culture update to pharmacist review was 27.2 hours. Median length of hospital stay was 5 days and discharge disposition was home with self care (65%), home with home health or hospice (22.7%), or discharge to another acute care or skilled nursing facility (12.3%). Conclusion Outpatient review of post-discharge culture updates utilizing EMR reporting led to substantial interventions impacting antimicrobial treatments. Disclosures Christina G. Rivera (O'Connor), Pharm.D, Gilead Sciences: Advisor/Consultant|Gilead Sciences: Board Member|Gilead Sciences: Grant/Research Support|Gilead Sciences: Honoraria

Background Decreases in estrogen are shown to increase body fat in men. However, obese men have high levels of estrogen and the effects of weight gain on estrogen have not been previously investigated. The objective of this study was to investigate the relationship between regional fat depots and estrogen during modest weight in healthy men. Methods We investigated the effects of overfeeding in 25 healthy men (30 ± 7 years, 24.7 ± 3.1 kg/m 2 ). Changes in regional fat depot during weight gain were assessed using dual‐energy X‐ray absorptiometry and abdominal computed tomography scans. Participants gained approximately 5% body weight in 8 weeks. Estrone (E1), 17β‐estradiol (E2) and sex hormone binding globulin (SHBG) were measured at baseline and after weight gain. Results Overfeeding was accompanied by 3.9 ± 1.3 kg weight gain. These increases in weight resulted from increases in total body fat mass (18.3 ± 8.5 vs. 21.7 ± 8.8 kg, p<0.001) while the fat free mass did not change (58.4 ± 8.6 vs. 58.9 ± 8.7 kg, p=0.15). E1 (32.28 ± 14.25 vs. 33.44 ± 12.29 pg/mL, p=0.56) and E2 (25.04 ± 8.3 vs. 26.52 ± 7.8 pg/mL, p=0.28) did not change after weight gain. However, weight gain resulted in decreased SHBG (22.8 ± 10.5 vs. 20.8 ± 11.1 nmol/l, p=0.036) and increased E2/SHBG ratio (1.4 ± 1.0 vs 1.63 ± 1.0, p=0.027). The changes in E2/SHBG ratio, a surrogate marker of bioavailable E2, was positively associated with increases in abdominal fat mass (p=0.048, rho=0.39) and increases in visceral fat mass (p=0.049, rho=0.39). Conclusion Modest weight gain in healthy men does not alter circulating E1 and E2 levels. However weight gain results in decreased SHBG and increased free E2 (E2/SHBG ratio). The increased E2/SHBG ratio suggesting a pro‐estrogen effect is associated with increases in visceral fat mass during weight gain. Our study therefore suggests a complex relationship between abdominal fat gain and free E2 in men which may conceivably predispose to feminizing characteristics in obese men. Support or Funding Information PS is supported by AHA Grant 11SDG7260046 and NIH grant DK81014. NC is supported by AHA grant AHA 13POST16420009. MDJ is supported by NIH grants DK45343 and DK40484. VKS is supported by NIH Grants HL73211, and DK81014. This study was made possible by a grant from NCRR (1UL1 RR024150).

Abstract Although the majority of HPV (+) oropharyngeal squamous cell carcinoma (OPSCC) patients exhibit a favorable prognosis, approximately 20% face recurrent or metastatic disease, posing management challenges. Therefore, the identification of robust prognostic markers for risk stratification is essential. Our study focused on the comprehensive characterization of intratumor heterogeneity within the tumor immune microenvironment (TME) in both primary tumors and metastatic lymph nodes. Utilizing computational approaches, we integrated hematoxylin and eosin (H&E) and 11 adjacent immunohistochemistry (IHC)-stained slides to investigate the cellular composition and functional characteristics across different regions within the TME. Based on these detailed immune characteristics, we classified patients into specific immune subtypes. Our integrative TME analysis indicated that patients with high tumor-infiltrating lymphocytes (TIL), increased CD8 + levels, and reduced CD163 + cell counts within their primary tumors were likely to have a more favorable progression-free survival outcome. The integration of H&E and IHC data enhances the functional characterization of immune phenotypes of the TME with biological interpretability and improves patient stratification in HPV(+) OPSCC.