Citations

Abstract Background One in five adults aged 40 will develop heart failure (HF) during their lifetime. Risk factors (e.g., hypertension, diabetes mellitus, coronary artery disease, etc.) lead to structural and functional changes in cardiomyocytes, resulting in HF. At the cellular level, these changes consist of mis/over‐expression of genes that regulate cardiac identity (e.g., CamK2δ, PKC, Stat3, etc.). The current paradigm for treating HF is pharmacological or device‐based intervention; however, with few exceptions, the condition worsens with time. We are proposing to implement a change in HF treatment, shifting from a drug‐centric system to a cardiac target‐specific molecular approach that would reverse hypertrophy and adverse remodelling of affected cardiomyocytes. Methods A cardiomyocyte targeting peptide (CTP) was reversibly linked to miRNA106a for delivery to a mouse model of HF. Reversal of morphological, signalling, and physiological HF parameters was measured. Additionally, CTP‐miRNA106a was introduced into a human cardiomyocyte cell line to identify mechanism(s) at play for reversing HF characteristics (e.g., hypertrophy). Results Bio‐distribution studies showed that intravenously injected CTP‐miRNA106 delivered its cargo specifically to the heart within 30 min, followed by clearance of CTP from the heart to the kidneys, and to a lesser extent, the liver by 35 h with persistence of miRNA106a in cardiomyocytes until day 7 (the latest tested time‐point). CTP‐miRNA106a reversed angiotensin2/isoproterenol‐induced hypertrophy in 90% of the treated mice. We also identified two potential HF intracellular signalling pathways/mechanisms (PLCβ1/PKC/IP3 and NF‐κB) targeted by CTP‐miRNA106a that could benefit many pathophysiologies underlying HF, including inflammation. Conclusions CTP‐miRNA106a, a first‐of‐a‐kind cardiac‐specific drug, downregulates genes involved in cardiac hypertrophy and inflammation through the PLCβ1 and CamKIIδ kinase pathways. CTP delivery of miRNA106a cargo is specific to cardiomyocytes both in vitro and in vivo, and once delivered, many HF parameters, including hypertrophy, are reversed. Key points Cardiac Targeting Peptide (CTP) delivers, specifically to the heart, reversibly linked miRNA106a. MiRNA106a targets genes in various pathways known to cause heart failure when they are over/hyper activated returning their expression to normal levels. In vivo analyses using a mouse heart failure model resulted in reversal of heart failure parameters in 19/20 mice.

Background Glucagon‐like peptide‐1 receptor agonists (GLP‐1RAs) and sodium–glucose cotransporter‐2 inhibitors (SGLT2is) have favorable cardiovascular outcomes compared with dipeptidyl peptidase‐4 inhibitors (DPP4is) and sulfonylureas in adults with type 2 diabetes and high cardiovascular risk. How these benefits vary across lower levels of cardiovascular risk is unknown. Methods We used nationwide claims data to emulate a comparative effectiveness trial and examine the heterogeneity of treatment effects of GLP‐1RAs, SGLT2is, DPP4is, and sulfonylureas on major adverse cardiovascular events (MACEs) among adults with type 2 diabetes and moderate cardiovascular risk (annualized MACE risk 1%–5%, estimated using the annualized claims‐based MACE estimator). Results Among 386 276 included adults with type 2 diabetes, 25.2% had baseline ACME–predicted MACE risk >1% to ≤2% (lower‐risk patients) and 13.3% had ACME–predicted risk >4% to ≤5% (higher‐risk patients). By year 3 of treatment, higher‐risk patients derived greater absolute benefit than lower‐risk patients when treated with GLP‐1RAs versus sulfonylureas (absolute reduction in the estimated rate of MACE of 3.1% in higher‐risk patients and 1.6% in lower‐risk patients), SGLT2is versus sulfonylureas (absolute reduction, 3.9% in higher‐risk patients and 1.3% in lower‐risk patients), and GLP‐1RAs versus DPP4is (absolute reduction, 1.6% in higher‐risk patients and 0.5% in lower‐risk patients). The relative benefits for MACE were also greater in higher‐risk than lower‐risk patients with SGLT2is versus DPP4is (hazard ratio [HR], 0.78 [95% CI, 0.70–0.87] in higher‐risk patients; HR, 0.99 [95% CI, 0.88–1.12] in lower‐risk patients). Conversely, the relative benefits of DPP4is and GLP‐1RAs versus sulfonylureas were greater in lower‐risk patients: HR 0.76 (95% CI, 0.71–0.81) in lower‐risk and HR 0.91 (95% CI, 0.97–0.96) in higher‐risk patients for DPP4is versus sulfonylureas; HR 0.67 (95% CI, 0.58–0.78) in lower‐risk and HR 0.80 (95% CI, 0.70–0.93) in higher‐risk patients for GLP‐1RAs versus sulfonylurea. Benefits of SGLT2is and GLP‐1RAs were comparable across all risk levels. Conclusions Cardiovascular benefits of SGLT2is and GLP‐1RAs exist across all levels of moderate cardiovascular risk, reinforcing the importance of choosing glucose‐lowering therapies that can prevent MACE in all people with type 2 diabetes.

Machine-learning (ML) models have the potential to transform health care by enabling more personalized and data-driven clinical decision making. However, their successful implementation in clinical practice requires careful consideration of factors beyond predictive accuracy. We provide an overview of essential considerations for developing clinically applicable ML models, including methods for assessing and improving calibration, selecting appropriate decision thresholds, enhancing model explainability, identifying and mitigating bias, as well as methods for robust validation. We also discuss strategies for improving accessibility to ML models and performing real-world testing. Highlights This tutorial provides clinicians with a comprehensive guide to implementing machine-learning classification models in clinical practice. Key areas covered include model calibration, threshold selection, explainability, bias mitigation, validation, and real-world testing, all of which are essential for the clinical deployment of machine-learning models. Following these guidance can help clinicians bridge the gap between machine-learning model development and real-world application and enhance patient care outcomes.

9009 Background: International Medical Graduates (IMGs) are a vital component of the US oncology workforce, representing nearly half of practicing medical oncologists. However, systemic barriers may influence their career trajectories differently. Understanding these challenges is essential for addressing workforce disparities and optimizing cancer care delivery across diverse geographic regions. Methods: We conducted a cross-sectional study of 13,497 medical oncologists using the 2024 Dec CMS Provider Dataset linked with the 2023 Rural-Urban Continuum Codes (RUCC). Medical oncologists were identified by their self-reported primary specialties, including Hematology/Oncology, Medical Oncology, and Hematopoietic Cell Transplant. Physicians were categorized by graduation origin (based on their self-reported medical school), career stage (early: 0–10 years, mid: 11–30 years, late: >30 years post-medical school graduation). The primary outcomes included practice at Main Campus of NCI Designated Cancer Centers (as an indicator of academic practice) and the geographical location of their practice (Metro, Non-Metro, Mixed). We employed chi-squared tests for statistical comparisons and performed stratified analyses by career stage to evaluate transition patterns over time. Results: 6920 IMGs constituted 48.7% of the oncology workforce, with similar gender distributions between IMG and non-IMG oncologists (37.9% vs 36.5% female, p=0.068). Notably, early career oncologists were less likely to be IMGs (IMG vs non-IMGs: 8.2% vs 12.5%, p<0.001). Compared to Non-IMGs, IMG oncologists are less likely to practice at main campuses of NCI Center (19.9% vs. 27.7%, p<0.001), especially as their career advances (all p<0.01). (Table). IMGs had higher representation in non-metropolitan areas than non-IMGs (11.4% vs 9.1%, p<0.001), particularly in late-career stages (14.7% vs 12.3%, p=0.033), indicating their unique role in serving underserved regions. (Table). Conclusions: Our findings reveal that, IMGs are less likely to practice at main campuses of NCI-Cancer Center than their non-IMG colleagues. Meanwhile, IMG oncologists are disproportionately serving rural communities. Supporting IMG pathways in oncology is critical to ensure a balanced cancer care delivery across the United States. Practice setting distribution and academic affiliation among international medical graduates (IMGs) and US graduates (USGs) by career stage. IMG (N=6,577) Non-IMG (N=6,920) IMG (N=6,577) Non-IMG (N=6,920) NCI Center Other NCI Center Other Metro Non-Metro/Mixed Metro Non-Metro/Mixed Total 19.9% 80.1% 27.7% 72.3% 88.6% 11.4% 90.9% 9.1% Career Stage Early (0-10y) 29.1% 70.9% 36.1% 63.9% 91.1% 8.9% 95.4% 4.6% Mid (11-30y) 21.3% 78.7% 28.8% 71.2% 90.1% 9.9% 91.6% 8.4% Senior (30+y) 15.0% 85.0% 22.0% 78.0% 85.3% 14.7% 87.7% 12.3%

Purpose: To develop and validate an explainable machine learning (ML) tool to help clinicians predict the risk of propofol-associated hypertriglyceridemia in critically ill patients receiving propofol sedation. Methods: Patients from 11 intensive care units (ICUs) across five Mayo Clinic hospitals were included if they met the following criteria: a) ≥ 18 years of age, b) received propofol infusion while on invasive mechanical ventilation for ≥24 h, and c) had a triglyceride level measured. The primary outcome was hypertriglyceridemia (triglyceride >400 mg/dL) onset within 10 days of propofol initiation. Both COVID-inclusive and COVID-independent modeling pipelines were developed to ensure applicability post-pandemic. Decision thresholds were chosen to maintain model sensitivity >80%. Nested leave-one-site-out cross-validation (LOSO-CV) was used to externally evaluate pipeline performance. Model explainability was assessed using permutation importance and SHapley Additive exPlanations (SHAP). Results: Among 3922 included patients, 769 (19.6%) developed propofol-associated hypertriglyceridemia, and 879 (22.4%) had COVID-19 at ICU admission. During nested LOSO-CV, the COVID-inclusive pipeline achieved an average AUC-ROC of 0.71 (95% confidence interval [CI] 0.70–0.72), while the COVID-independent pipeline achieved an average AUC-ROC of 0.69 (95% CI 0.68–0.70). Age, initial propofol dose, and BMI were the top three most important features in both models. Conclusion: We developed an explainable ML-based tool with acceptable predictive performance for assessing the risk of propofol-associated hypertriglyceridemia in ICU patients. This tool can aid clinicians in identifying at-risk patients to guide triglyceride monitoring and optimize sedative selection.

Objective Specific sedative hypnotic agents, administered to facilitate endotracheal intubation (ETI) in critically ill adults, may lead to adverse outcomes such as peri-intubation cardiovascular collapse. However, little is known from systematic investigations of the impact these individual agents have on cardiovascular function or other clinical outcomes. Data sources MEDLINE, Embase, CENTRAL, ClinicalTrials.gov, Scopus and Web of science databases. Study selection We conducted a systematic search for randomized and non-randomized studies that evaluated adult (≥18 years) critically ill patients who were sedated to facilitate ETI with ketamine, propofol, ketamine/propofol, etomidate, or a benzodiazepine and who had data on peri-intubation hemodynamics and at least one other outcome involving acute kidney injury, delirium, opioid use, intubation difficulty, sequential organ failure assessment, length of stay, or mortality. Eighty-five studies were identified for eligibility assessment with 23 included in the analysis. Data extraction Two reviewers independently screened articles, extracted data from selected articles, and assessed risk of bias using ROBINS-I for observational studies and revised Cochrane Risk of Bias tool for randomized controlled trials. Data synthesis Acute cardiovascular dysfunction (peri-intubation hemodynamic instability and/or cardiac arrest) was similar between etomidate and ketamine with more events seen when propofol versus non-propofol sedation was administered. However, exploratory meta-analysis demonstrated no difference between etomidate and ketamine (OR 1.05 [95%CI 0.60-1.84]) or between etomidate and propofol (OR 0.91 [95%CI 0.33-2.46]). Compared to ketamine, etomidate demonstrated lower survival to hospital discharge in the included studies in exploratory meta-analysis OR 0.76 (95%CI 0.62-0.92). Limited data existed for other outcomes with no discernible differences between sedative agents. Conclusions Acute cardiovascular dysfunction was more common when propofol, as compared to non-propofol sedation, was administered, although not statistically significant in exploratory meta-analysis. In addition, etomidate conferred lower survival to hospital discharge versus non-etomidate sedation, which was confirmed in exploratory meta-analysis of etomidate versus ketamine.

Abstract Rationale: Propofol can increase the risk of hypertriglyceridemia and subsequent pancreatitis in critically ill patients. It is unclear whether modifying or continuing sedation regimens in patients experiencing hypertriglyceridemia while on propofol infusion influences the development of adverse events such as acute pancreatitis. This study compared the clinical outcomes of patients receiving invasive mechanical ventilation (IMV) who had their propofol regimen modified versus continued following the detection of hypertriglyceridemia. Methods: A targeted trial emulation was conducted using retrospective data from 5 Mayo Clinic hospitals between 05/05/2018 and 06/30/2023. Inclusion criteria included: 1) receiving IMV and continuous propofol infusion for &gt;24 hours, 2) experiencing triglyceride level &gt;400 mg/dL while on propofol, and 3) still requiring IMV at 24-hours after hypertriglyceridemia onset. Patients were then categorized based on whether they had their propofol regimen modified (i.e., dose reduction by ≥50% or any reduction with alternative sedatives started) versus continued (no reduction or &lt;50% reduction without alternative sedative initiation). Outcomes included 30-day ventilator-free days, successful extubation, acute pancreatitis (determined by chart review, in-duplicate), ICU and hospital length of stay, 30- and 90-days alive and at home, as well as all-cause mortality. Analyses were conducted using stabilized inverse probability of treatment weights with adjustments for covariates selected a priori. Sensitivity analyses, where only benzodiazepines or benzodiazepines and ketamine were considered to be eligible alternative sedatives, were done. Results: During the study period, 728 patients were included, of whom 422 had their propofol regimen modified (Image). The two groups had similar demographics, comorbidities, and illness severity. We identified 14 cases of acute pancreatitis possibly attributable to propofol, with 9 cases in the modified and 5 cases in the continued regimen group (2.1% versus 1.6%; P=0.63). Following adjustment for covariates, there were no significant differences in any outcomes of interest. After censoring for mortality, there was no significant difference in the 30-day cumulative incidence of successful extubation when comparing those with their regimen modified versus continued (Hazard ratio [HR] 0.95; 95% confidence intervals [CI] 0.77 to 1.18; P=0.70). There was also no significant difference in the 90-day survival rate between the two groups (HR 0.89; 95% CI 0.71 to 1.11; P=0.34). These findings remained consistent upon sensitivity analyses. Conclusion: There were no differences in outcomes among patients who had their propofol regimens modified versus continued following the detection of hypertriglyceridemia. Further prospective investigations are needed to identify the optimal strategy for managing sedative regimens in this patient population.

Abstract RATIONALE Propofol is the first-line sedative for critically-ill, mechanically-ventilated patients, but it can increase serum triglyceride levels. Machine-learning (ML) offers a promising approach for individualized risk stratification of propofol-associated hypertriglyceridemia, enabling proactive triglyceride monitoring and sedation optimization. This study aims to develop and validate ML models to predict hypertriglyceridemia risks in critically-ill patients receiving propofol. METHODS We analyzed retrospective data from adult ICU patients on mechanical ventilation receiving continuous propofol infusion for ≥24 hours across four Mayo Clinic sites from May 2018 to June 2023. The primary outcome was hypertriglyceridemia, defined as serum triglycerides &gt;400 mg/dL within 10 days of starting propofol. Ten-fold cross-validation and leave-one-site-out nested cross-validation (LOSO-CV) were used for model tuning and external validation of our modeling process, respectively. Feature selection was conducted through a union of Boruta and least absolute shrinkage and selection operator (LASSO). While COVID status was a strong predictor of hypertriglyceridemia, we concurrently developed non-COVID models to ensure future relevance in non-pandemic settings. Six ML algorithms (logistic regression, support vector machines, random forests, LightGBM, XGBoost, and multilayer perceptrons) were tuned using Bayesian optimization to minimize cross-entropy loss. Classification thresholds were set to target sensitivity ≥80%. Model performance was evaluated using AUC-ROC, Brier score, sensitivity, and specificity. Models with the highest Youden's Index were designated the best-performing model. Model explainability was assessed using permutation importance and SHapley Additive exPlanations (SHAP). RESULTS We included 3922 eligible patients with triglyceride measurements. Of these, 3042 patients were COVID-negative and used for non-COVID modeling. The best-performing non-COVID models achieved an average AUC-ROC of 0.69 (95% confidence interval [CI] 0.68-0.70), Brier score of 0.12 (95% CI 0.09-0.14), sensitivity of 0.79 (95% CI 0.75-0.83), and specificity of 0.48 (95% CI 0.44-0.52) during LOSO-CV. The best-performing models that included COVID status achieved an average AUC-ROC of 0.71 (95% CI 0.70-0.72), Brier score of 0.13 (95% CI 0.10-0.16), sensitivity of 0.78 (95% CI 0.71-0.84), and specificity of 0.51 (95% CI 0.45 to 0.56) during LOSO-CV. The permutation importance results of the final non-COVID model are shown in the figure. CONCLUSION We have developed sensitive ML models for predicting hypertriglyceridemia risks in critically-ill patients receiving propofol. Although developed during the COVID-19 pandemic, the non-COVID models are more likely to have future relevance, and they have similar performance as models including COVID status. These models will be integrated into a web-based clinical calculator and undergo further validation in prospective studies.

ABSTRACTIntroductionThe diagnosis of antibody‐mediated rejection (AMR) requires input from several transplant professionals. Bringing clinical and laboratory experts together may help standardize care. Yet, little is known about current global practices of multidisciplinary meetings for AMR management.MethodsThe Banff Antibody‐Mediated Injury Working Group approached professional societies worldwide to distribute a survey on the availability, content, participants, perceived value, and barriers to the implementation of multidisciplinary meetings.ResultsFour hundred two transplant professionals from six continents caring for kidney (90.55%), liver (21.14%), pancreas (20.65%), heart (15.17%), and lung (14.18%) transplant recipients participated in the survey, and 302 (75.12%) reported attending multidisciplinary meetings. Multidisciplinary meetings were more prevalent in academic centers, in high‐ versus low‐to‐middle‐income regions (81.03% and 65.99%, respectively; p < 0.001), and in mid‐to‐large size transplant programs compared to smaller programs. Perceived value included continued professional development (97.68%) and trainee education (95.70%). AMR was reported to be discussed at these meetings by 217 respondents with case presentations reviewing patient characteristics, histology, and HLA antibody data. A third of the respondents reviewed non‐HLA/pathogenic autoantibodies and/or molecular diagnostics, with the latter being more frequently applied in high‐ versus low‐to‐middle‐income regions (46.71% and 12.31%, respectively; p < 0.001). AMR case presentations allowed diagnosis revision, actionable management plans and were perceived as improving care. The primary barrier to the implementation of multidisciplinary meetings (63.27%) was the unavailability of transplant professionals (e.g., transplant immunologists).ConclusionFacilitating multidisciplinary meetings through the remote participation of pertinent experts and incentivizing participation through remuneration, protected time, or continued medical education may help standardize AMR diagnosis and harmonize its management.