Citations

INTRODUCTION: Cardiovascular disease is a leading cause of maternal mortality. Our aim was to evaluate the feasibility and quality of artificial intelligence (AI)-guided focused cardiac ultrasound (FoCUS) by novice scanners to screen for left ventricular (LV) systolic dysfunction in pregnant individuals. METHODS: Patients were prospectively enrolled during routine obstetric appointments. FoCUS images were acquired by individuals without scanning or clinical experience using AI-guided image acquisition software (UltraSight) on a Philips Lumify device. Standard two-dimensional imaging views were attempted: parasternal long axis (PSLA), parasternal short axis mid-level (PSSA Mid), apical four chamber (A4C), three chamber (A3C), and two chamber (A2C). Two experienced cardiologists reviewed all images and graded diagnostic quality: poor, adequate, good, excellent. RESULTS: A total of 101 pregnant participants were enrolled; median age was 32 (interquartile range [IQR], 29, 35) years. The median gestational age was 23 (IQR, 16, 31) weeks; 16% first trimester, 45% second, and 39% third. The most feasible views were the PSLA and PSSA Mid, 98% and 97% with adequate or greater image quality, respectively. A3C and A2C were least feasible, acquired in 34% and 40% with adequate or greater image quality in 28% and 35%, respectively. Average scan time was 6 minutes, 46 seconds ± 0.2. The LV ejection fraction could be determined for the entire cohort and was normal (50–75%) for all. CONCLUSIONS/IMPLICATIONS: Implementation of AI-guided FoCUS in pregnant individuals is feasible, even in the absence of an experienced sonographer. This approach could allow for low-cost screening and early diagnosis of LV systolic dysfunction during pregnancy.

Abstract Immune checkpoint inhibitors (ICIs) block immunoregulatory receptor-ligand interactions and robustly increase survival of cancer patients but frequently result in immune-related adverse events (irAEs). While rheumatologic toxicities are commonly reported as irAEs, the effect of immune checkpoint blockade on the underlying mechanisms of ICI-induced fractures and bone loss is controversial, with reports of both positive and negative effects on bone mass in preclinical models. However, no previous reports have investigated the impact of ICIs on females or aged mice, or on fracture risk in either sex. We report that global deletion of programmed cell death protein 1 (PD-1) broadly results in bone loss in skeletally mature male and female PD-1−/− mice, with a sexually divergent phenotype in adolescent mice, decreased bone strength in adult males and young females, and expansion of multiple T cell subsets in the bone marrow. In a model of pharmacologic PD-1 blockade, administration of α-PD-1 reduced bone mass, expanded multiple T cell subsets in the bone marrow, and increased osteoclast activity and resorptive capacity. T cell deficient mice were resistant to osteoclast-mediated bone loss following α-PD-1 therapy, suggesting that T cells in the bone marrow are necessary for bone loss in the setting of ICI therapy. These findings may be leveraged to identify patients at greater fracture risk following ICI therapy due to enrichment of effector T cell populations in the bone marrow.

Over the past decade, stress echocardiography has evolved from a test for assessing epicardial artery stenosis to a comprehensive functional test, targeting multiple cardiovascular parameters. The new approach includes several structured steps: (a) evaluating regional wall motion abnormalities to detect epicardial artery stenosis or vasospasm; (b) assessing pulmonary congestion and diastolic function via B-lines with lung ultrasound; (c) gauging preload and contractile reserve with volumetric echocardiography; (d) measuring coronary microvascular reserve using Doppler-based coronary flow velocity in the middistal left anterior descending artery; and (e) determining cardiac sympathetic reserve by tracking heart rate reserve on an ECG. This evolution was supported extensively by the Italian Society of Echocardiography and Cardiovascular Imaging (SIECVI), which played a key role in five areas: (1) developing the initial, curiosity-driven project; (2) disseminating protocols and results at national and international conferences, supporting logistic infrastructure and publication expenses; (3) establishing a digital platform (customized Redcap) for data entry and storage; (4) facilitating patient recruitment across 19 Italian centers; and (5) offering formal endorsement through six presidencies, adding credibility and reach beyond any single institution. The protocol quickly advanced from concept to high-impact publications, earning inclusion in 2024 specialty guidelines. Initially Italian-led, the study now includes 50 centers across 20 countries (e.g. USA and China). Beyond the 50 peer-reviewed papers published in 2016–2024, this study offers a novel, sustainable approach to cardiac stress testing, providing more information at lower costs, with zero radiation and minimal environmental impact. SIECVI’s endorsement was instrumental in amplifying the study’s rigor and outreach.

Abstract Aims To develop a deep learning model that: (i) utilizes transthoracic echocardiography (TTE) clips to detect left ventricular (LV) enlargement without being provided information regarding a patient’s sex and body size; and (ii) can be accurately applied to clips acquired using either standard comprehensive TTE or handheld cardiac ultrasound (HCU). Methods and results Using retrospective TTE data (training: 8722 patients; internal validation: 468 patients), we developed a deep learning model that estimates a patient’s end-diastolic LV volume (indexed to body surface area and normalized across the sexes), and then thresholds this estimate to perform the following classifications: (1) normally sized LV vs. ≥ mild LV enlargement; (2) normal/mildly enlarged LV vs. ≥ moderate LV enlargement. For retrospective datasets, the model showed strong performance in TTE across three geographically distinct locations (Minnesota and Wisconsin: 1082 patients, AUC = 0.925 and 0.953 for classifications 1 and 2, respectively; Arizona: 1475 patients, AUC = 0.935 and 0.969; and Florida: 1481 patients, AUC = 0.934 and 0.970). Additionally, performance was strong for both TTE and HCU clips collected from a prospective cohort of 410 patients who underwent HCU immediately following TTE (TTE: AUC = 0.925 and 0.971; HCU: AUC = 0.874 and 0.902, for classifications 1 and 2, respectively). Conclusion An automated deep learning model applied to TTE or HCU images accurately categorizes LV volumes. These results lay a foundation for future work aimed at optimizing clinical outcomes for heart failure patients by enabling early detection of LV enlargement across various point-of-care settings.

Abstract Purpose/Background Clozapine was approved in the United States (US) using 1989 regulations and knowledge. After 30 years, many sections of the US package insert (PI) are outdated. Methods We comprehensively reviewed the literature to propose PI updates. We present the information in 2 articles. In Part I, we focus on basic pharmacology based on 407 relevant articles. Part II focuses on clinical aspects and pharmacovigilance. Findings/Results Based on more recent expectations of Food and Drug Administration regulations, we reviewed clozapine basic pharmacology including the following: 1) clearance, 2) pharmacokinetics and pharmacodynamics, and 3) monitoring tools. We identified 9 major problems in the basic pharmacological sections of the PI including the following: 1) in vivo studies indicate that clozapine is dependent on CYP1A2 for its metabolism, 2) the minor role of CYP2D6 in clozapine metabolism requires removing the PI recommendation to lower clozapine doses in CYP2D6 poor metabolizers, 3) in nontoxic concentrations CYP3A4 has a minor role in clozapine metabolism and potent CYP3A4 inhibitors lack clinically relevant effects, 4) several drug-drug interactions need to be updated based on recent literature, 5) systemic inflammation may decrease clozapine metabolism and increase the risk of clozapine intoxication, 6) obesity may decrease clozapine metabolism, 7) patients of Asian and Indigenous American ancestry need lower clozapine doses, 8) personalized titration and c-reactive protein monitoring should be considered until prospective studies are available, and 9) the half-life section needs to be modified to acknowledge that single dosing at night is frequent in the US. Implications/Conclusions An improvement in the US clozapine PI may lead to improvement in PIs worldwide.

Abstract Purpose/Background This is the second part of a 2-part article that proposes improving the United States (US) clozapine package insert. Part II focuses on fatal outcomes and the 5 boxed warnings, 4 specifically for clozapine: severe neutropenia, seizure, orthostatic hypotension and myocarditis, and 1 for all antipsychotics (elderly with dementia). Methods US reports to the World Health Organization's global pharmacovigilance database were analyzed from clozapine's introduction to January 15, 2023. Findings/Results The US was the top reporter worldwide for clozapine with 56,003 reports and 9587 associated fatal outcomes. The 4 clozapine boxed warnings were associated with 534 fatal outcomes (218 with severe neutropenia, 131 with seizures, 125 with orthostasis, 36 with myocarditis, 24 with cardiomyopathy, and 0 with mitral valve prolapse). With no boxed warnings, pneumonia was associated with 674 fatal outcomes and increased white blood cell count (a sign of infection) with 596 fatal outcomes. After considering overlaps, pneumonia and increases in white blood cell count explained 900 fatalities, or 9.4% of 9587 fatal outcomes. The Food and Drug Administration continues to focus on severe neutropenia which was associated with only 218 or 2.3% of fatal outcomes, whereas 97.7% of fatal outcomes reported in US clozapine-treated patients had another cause. Implications/Conclusions To help prevent future deaths in clozapine-treated patients, the clozapine package insert should focus on fatal outcomes during infections. Part II offers detailed solutions regarding current boxed warnings and lack of a warning for pneumonia and other infections. The Supplementary Material includes letters of support from 124 non-US clozapine experts from 44 countries/regions who support Parts I and II.

ABSTRACT Presented is a 76-year-old man with metastatic melanoma that was successfully treated with pembrolizumab, an immune checkpoint inhibitor (ICI). He underwent 20 months of ICI treatment without dose limiting side effects. Nearly 18 months after ICI discontinuation, the patient developed intermittent epigastric pain and diarrhea. Owing to mild symptoms, he was not immediately evaluated. Three years after ICI cessation, he was diagnosed with stricturing, jejunal Crohn's disease. He was treated with vedolizumab and displayed clinical and radiographic response to treatment. This case serves as an example of potential gastrointestinal-related, long-term autoimmune implications of ICI therapy, even in patients without acute side effects.

Key Points Pooled discrimination metrics were acceptable (area under the receiver operating characteristic curve >0.70) for all AKI risk classification categories in both internal and external validation. Better performance was observed in most recently published studies and those with a low or unclear risk of bias. Significant heterogeneity in patient populations, definitions, clinical predictors, and methods limit implementation in real-world clinical scenarios. Background Artificial intelligence through machine learning models seems to provide accurate and precise AKI risk classification in some clinical settings, but their performance and implementation in real-world settings has not been established. Methods PubMed, Excerpta Medica (EMBASE) database, Web of Science, and Scopus were searched until August 2023. Articles reporting on externally validated models for prediction of AKI onset, AKI severity, and post-AKI complications in hospitalized adult and pediatric patients were searched using text words related to AKI, artificial intelligence, and machine learning. Two independent reviewers screened article titles, abstracts, and full texts. Areas under the receiver operating characteristic curves (AUCs) were used to compare model discrimination and pooled using a random-effects model. Results Of the 4816 articles initially identified and screened, 95 were included, representing 3.8 million admissions. The Kidney Disease Improving Global Outcomes (KDIGO)-AKI criteria were most frequently used to define AKI (72%). We identified 302 models, with the most common being logistic regression (37%), neural networks (10%), random forest (9%), and eXtreme gradient boosting (9%). The most frequently reported predictors of hospitalized incident AKI were age, sex, diabetes, serum creatinine, and hemoglobin. The pooled AUCs for AKI onset were 0.82 (95% confidence interval, 0.80 to 0.84) and 0.78 (95% confidence interval, 0.76 to 0.80) for internal and external validation, respectively. Pooled AUCs across multiple clinical settings, AKI severities, and post-AKI complications ranged from 0.78 to 0.87 for internal validation and 0.73 to 0.84 for external validation. Although data were limited, results in the pediatric population aligned with those observed in adults. Between-study heterogeneity was high for all outcomes (I 2 >90%), and most studies presented high risk of bias (86%) according to the Prediction Model Risk of Bias Assessment Tool. Conclusions Most externally validated models performed well in predicting AKI onset, AKI severity, and post-AKI complications in hospitalized adult and pediatric populations. However, heterogeneity in clinical settings, study populations, and predictors limits their generalizability and implementation at the bedside.