Citations

Background: Rearranged during transfection ( RET ) alterations are targetable oncogenic drivers in thyroid cancer. Primary data from the open-label, phase 1/2 ARROW study demonstrated clinical activity and manageable safety with pralsetinib, a selective RET inhibitor, in patients with advanced/metastatic RET -altered thyroid cancer. We present an updated analysis with more patients and longer follow-up. Methods: Adult patients with advanced/metastatic RET -mutant medullary thyroid cancer (MTC) or RET fusion-positive thyroid cancer who initiated oral pralsetinib at 400 mg once daily were included. Primary endpoints were overall response rate (ORR) by blinded independent central review (per RECIST v1.1) and safety. Secondary endpoints included duration of response (DoR), progression-free survival (PFS), and overall survival. Responses were assessed in three cohorts of patients with baseline measurable disease: patients with RET -mutant MTC who had received prior cabozantinib and/or vandetanib (C/V), treatment-naïve patients with RET -mutant MTC, and patients with previously treated RET fusion-positive thyroid cancer. Patient-reported outcomes (PROs) were an exploratory endpoint. Results: As of October 18, 2021, the measurable disease population comprised of 61 patients with RET -mutant MTC and prior C/V, 62 treatment-naïve patients with RET -mutant MTC, and 22 patients with RET fusion-positive thyroid cancer who had received prior systemic therapy, including radioactive iodine. The ORR was 55.7% [confidence interval; 95% CI: 42.4–68.5] in patients with RET -mutant MTC and prior C/V, 77.4% [95% CI: 65.0–87.1] in treatment-naïve patients with RET -mutant MTC, and 90.9% [95% CI: 70.8–98.9] in patients with previously treated RET fusion-positive thyroid cancer. Median DoR and median PFS were both 25.8 months in patients with RET -mutant MTC and prior C/V, not reached in treatment-naïve patients with RET -mutant MTC, and 23.6 and 25.4 months, respectively, in patients with previously treated RET fusion-positive thyroid cancer. In the RET -altered thyroid cancer safety population ( N = 175), 97.1% of patients reported a treatment-related adverse event (TRAE); these led to discontinuation in 5.7% and dose reduction in 52.6% of patients. There was one death (0.6%) due to a TRAE. PROs improved or remained stable after pralsetinib treatment. Conclusions: In this updated analysis of the ARROW study, pralsetinib continued to show deep and durable clinical activity and a manageable safety profile in patients with advanced/metastatic RET- altered thyroid cancer. Clinical Trial Registration: NCT03037385

Light chain (LC) amyloidosis (AL) is a systemic amyloidosis associated with significant morbidity and mortality. Current treatment involves depletion of LC producing cells but patients with advanced disease are usually intolerant of chemotherapy and autologous stem cell transplantation, with no treatment available to directly address the tissue pathology of LC. Microvascular injury may be important in it spathobiology. Our overall objective is to discover strategies to ameliorate LC‐mediated injury at the level of endothelial cell death and dysfunction. We tested the hypothesis that nanoliposomes containing monosialoganglioside, phosphatidylcholine and cholesterol (NLGM1) are capable of protecting human endothelial cells from LC‐mediated injury by hormetic induction of the nuclear factor (erythroid‐derived 2)‐like (Nrf2) oxidative defense pathway. Human umbilical vein endothelial cells (HUVEC) were exposed to LC (purified from the urine of 2 AL patients with cardiac involvement, 20μg/ml) for 18–20 hours either with or without co‐treatment with NLGM1 (given at 1:10 mass ratio to LC). Using a fluorescent probe for peroxynitrite, we found that LC caused a 51.2% (p<0.01) increase in peroxynitrite production with a coincident 33% (p<0.01) decrease in cell viability as measured by Calcein‐AM fluorescence. However, in samples that were given NLGM1 and LC, peroxynitrite was diminished and cell viability was preserved to near control levels. Interestingly, in HUVEC that were treated with NLGM1 alone, we noticed a4‐fold increase in Nrf2 gene expression (p<0.05) and in antioxidant enzymes such as heme oxygenase 1 (HO‐1) and NAD(P)H quinone oxioreductase 1 (NQO1)(119‐fold, p<0.05; and 5‐fold, p<0.01 respectively). Similarly, we found significant upregulation of Nrf2, HO‐1 and NQO1 proteins in response to NLGM1 treatment. To determine if induction of HO‐1 and NQO1 by NLGM1 were dependent on Nrf2 activation, we knocked down Nrf2 using specific siRNAs. We were able to reduce Nrf2 gene expression in HUVEC by 41%. HUVEC treated with control siRNA demonstrated a 9.4‐fold increase in HO‐1 (p<0.05)and a 3.8‐fold increase in NQO1 (p<0.05) gene expression, whereas cell streated with Nrf2‐specific siRNAs had blunted NLGM1‐mediated increases to 3.0‐fold and 1.3‐fold for HO‐1 and NQO1 ( p<0.001 and p<0.05 vs. control siRNA ), respectively. Nrf2 knockdown abolished the protective effect of NLGM1 on HUVEC viability; Nrf‐2 siRNA treated cells exposed to LC and NLGM1 demonstrated 30% reduction in viability (p<0.05) similar to the 28% reduction in control siRNA‐treated cells exposed to LC; control siRNA‐treated cells exposed to LC and NLGM1 demonstrated no difference in viability versus those exposed to vehicle. Presently, we show that NLGM1 preserve endothelial cell viability and reduce peroxynitrite production in HUVEC exposed to LC proteins. We further demonstrate that NLGM1 has the potential to modulate Nrf2 antioxidant defense pathway and is likely a relevant protective mechanism against nitrative stress generated by LC exposure. When considered together, these findings hint at a novel potential therapeutic approach to mitigate the pathobiology of AL. Support or Funding Information Funding: Veterans Affairs Merit grant (I01BX007080), National Institutes Health (NIA R21AG044723, GM RO1 071514), Amyloidosis Foundation, American Heart Association 0855683G

Introduction A steadily rising opioid pandemic has left the US suffering significant social, economic, and health crises. Machine learning (ML) domains have been utilized to predict prolonged postoperative opioid (PPO) use. This systematic review aims to compile all up-to-date studies addressing such algorithms’ use in clinical practice. Methods We searched PubMed/MEDLINE, EMBASE, CINAHL, and Web of Science using the keywords “machine learning,” “opioid,” and “prediction.” The results were limited to human studies with full-text availability in English. We included all peer-reviewed journal articles that addressed an ML model to predict PPO use by adult patients. Results Fifteen studies were included with a sample size ranging from 381 to 112898, primarily orthopedic-surgery-related. Most authors define a prolonged misuse of opioids if it extends beyond 90 days postoperatively. Input variables ranged from 9 to 23 and were primarily preoperative. Most studies developed and tested at least two algorithms and then enhanced the best-performing model for use retrospectively on electronic medical records. The best-performing models were decision-tree-based boosting algorithms in 5 studies with AUC ranging from .81 to .66 and Brier scores ranging from .073 to .13, followed second by logistic regression classifiers in 5 studies. The topmost contributing variable was preoperative opioid use, followed by depression and antidepressant use, age, and use of instrumentation. Conclusions ML algorithms have demonstrated promising potential as a decision-supportive tool in predicting prolonged opioid use in post-surgical patients. Further validation studies would allow for their confident incorporation into daily clinical practice.

Electronic health record (EHR) systems collate patient data, and the integration and standardization of documents through Health Information Exchange (HIE) play a pivotal role in refining patient management. Although the clinical implications of AI in EHR systems have been extensively analyzed, its application in HIE as a crucial source of patient data is less explored. Addressing this gap, our systematic review delves into utilizing AI models in HIE, gauging their predictive prowess and potential limitations. Employing databases such as Scopus, CINAHL, Google Scholar, PubMed/Medline, and Web of Science and adhering to the PRISMA guidelines, we unearthed 1021 publications. Of these, 11 were shortlisted for the final analysis. A noticeable preference for machine learning models in prognosticating clinical results, notably in oncology and cardiac failures, was evident. The metrics displayed AUC values ranging between 61% and 99.91%. Sensitivity metrics spanned from 12% to 96.50%, specificity from 76.30% to 98.80%, positive predictive values varied from 83.70% to 94.10%, and negative predictive values between 94.10% and 99.10%. Despite variations in specific metrics, AI models drawing on HIE data unfailingly showcased commendable predictive proficiency in clinical verdicts, emphasizing the transformative potential of melding AI with HIE. However, variations in sensitivity highlight underlying challenges. As healthcare’s path becomes more enmeshed with AI, a well-rounded, enlightened approach is pivotal to guarantee the delivery of trustworthy and effective AI-augmented healthcare solutions.

Abstract Objective To assess the prevalence of bullying in medical residents and its associated factors. Methods In this systematic review and meta-analysis, articles from MEDLINE, EMBASE, Scopus, PsycInfo, Cochrane databases, and Web of Science were searched. Published and unpublished cross-sectional studies were included. Cochrane’s Q test and I2 statistics were used to assess the existence of heterogeneity. Subgroup analysis and sensitivity analysis were performed on evidence of heterogeneity. Egger’s test and funnel plots were performed to investigate publication bias. Results A total of 13 cross-sectional studies with a total of 44 566 study participants from different medical residencies were analyzed. The overall prevalence of bullying was 51% (95% CI 36–66). Furthermore, female residents and residents that belong to a minority group had higher odds of experiencing bullying compared to their peers. Conclusion A high prevalence of bullying in medical residents exists around the world. There is a need for education, dissemination, and more effective interventions among the residents and authorities about bullying to build and promote adequate behaviors and diminish bullying prevalence.

Introduction Decision aids (DAs) are helpful instruments used to support shared decision making (SDM). Patients with atrial fibrillation (AF) face complex decisions regarding stroke prevention strategies. While a few DAs have been made for AF stroke prevention, an encounter DA (EDA) and patient DA (PDA) have not been created to be used in conjunction with each other before. Design Using iterative user-centered design, we developed 2 DAs for anticoagulation choice and stroke prevention in AF. Prototypes were created, and we elicited feedback from patients and experts via observations of encounters, usability testing, and semistructured interviews. Results User testing was done with 33 experts (in AF and SDM) and 51 patients from 6 institutions. The EDA and PDA underwent 1 and 4 major iterations, respectively. Major differences between the DAs included AF pathophysiology and a preparation to meet with the clinician in the PDA as well as different language throughout. Content areas included personalized stroke risk, differences between anticoagulants, and risks of bleeding. Based on user feedback, developers 1) addressed feelings of isolation with AF, 2) improved navigation options, 3) modified content and flow for users new to AF and those experienced with AF, 4) updated stroke risk pictographs, and 5) added structure to the preparation for decision making in the PDA. Limitations These DAs focus only on anticoagulation for stroke prevention and are online, which may limit participation for those less comfortable with technology. Conclusions Designing complementary DAs for use in tandem or separately is a new method to support SDM between patients and clinicians. Extensive user testing is essential to creating high-quality tools that best meet the needs of those using them. Highlights First-time complementary encounter and patient decision aids have been designed to work together or separately. User feedback led to greater structure and different experiences for patients naïve or experienced with anticoagulants in patient decision aids. Online tools allow for easier dissemination, use in telehealth visits, and updating as new evidence comes out.