Citations

Ascites development in cirrhosis reduces 5-year survival from 80% to 30%, yet substantial heterogeneity exists among patients with comparable ascites severity. This multicenter retrospective cohort study included adult liver transplant candidates with ascites at 4 United States centers (2015–2024). Latent class analysis was performed using 7 clinical variables: ascites grade, bilirubin, albumin, platelet count, estimated glomerular filtration rate, systolic blood pressure, and portal vein thrombosis. The derivation cohort (n=625) from the University of California San Francisco and Columbia University was randomly split 80/20 for model development and internal validation. External validation was performed at the University of Southern California (n=59) and Mayo Clinic (n=93). Primary outcomes were acute kidney injury (AKI) and waitlist mortality over 365 days. Three phenotypes emerged: “CKD-Metabolic” (30.4%, lowest eGFR 76.5 mL/min/1.73 m 2 ), “Vasodilatory-Synthetic Dysfunction” (38.9%, highest bilirubin 7.03 mg/dL, lowest blood pressure 117.5 mmHg), and “PVT-Intermediate” (30.7%, severe thrombocytopenia 68.1×10 3 /μL, 24.0% PVT). In validation cohorts (n=305), phenotypes demonstrated robust classification (mean posterior probability 0.823–0.855). Vasodilatory-Synthetic Dysfunction showed increased AKI risk versus CKD-Metabolic in derivation (MELD 3.0-adjusted HR 2.95, 95% CI 2.07–4.22, p <0.001) and validation cohorts (HR 3.39, 95% CI 1.93–5.94, p <0.001). Competing risk analysis revealed no mortality differences, but Vasodilatory-Synthetic Dysfunction was associated with higher transplantation rates (validation: HR 1.63, 95% CI 1.19–2.22, p =0.002). Three reproducible clinical phenotypes of ascites were identified with distinct risk profiles. The Vasodilatory-Synthetic Dysfunction phenotype was associated with a 3-fold increase in the risk of AKI that persisted after adjustment for MELD 3.0. No differences in waitlist mortality were observed, likely reflecting effective MELD-based transplant allocation that preferentially removes the highest-acuity patients from the waitlist.

Cardio-oncology has emerged as a critical discipline in modern medicine due to the growing population of cancer survivors and the increasing recognition of cardiovascular disease as a major cause of morbidity and mortality in this population. While there have been significant advances in chemotherapy such as advances in chemotherapy, targeted therapies, immunotherapies, and radiation therapy, these treatments are linked to a wide range of cardiovascular toxicities such as myocarditis, arrhythmias, and progressive fibrotic remodeling. These toxicities tend to be caused by interrelated and multifactorial cellular processes including endothelial damage, immunological dysregulation and mitochondrial dysfunction. Therefore, early diagnosis of subclinical harm using multimodal surveillance strategies integrating clinical risk assessment, specialized clinical imaging, and circulating biomarkers has replaced reactive care of overt cardiotoxicity in modern cardio-oncology. Echocardiographic techniques such as longitudinal strain, with the help of biomarker-guided surveillance of natriuretic peptides and cardiac troponins, have made the early detection of cardiac dysfunction more effective. However, the current prediction risk models are still constrained and limited by their dependence on static clinical variables and their partial integration of biological mechanisms. This review examines the current methods for risk stratification and surveillance throughout the cancer care continuum, including baseline assessment, monitoring during active therapy, and long-term survivorship surveillance. Future approaches for tailored cardiovascular care are also highlighted, including new advances in precision cardio-oncology such as multi-omics profiling, molecular biomarkers, artificial intelligence, and mechanism-guided preventative strategies. Therefore, advancing biologically informed and risk-adapted monitoring frameworks may enhance early diagnosis, maximize cardioprotective measures, and lower long-term cardiovascular consequences.

ABO incompatibility is frequent in haematopoietic stem cell transplantation (HSCT), yet the clinical impact of non-ABO red blood cell (RBC) alloimmunisation remains less defined. We describe the clinical course of a man in his 50s with chronic eosinophilic leukaemia who underwent a mismatched unrelated donor (MMUD) HSCT. Despite a major ABO mismatch and high-titre pre-existing anti-E antibodies against an E-antigen positive donor, the patient achieved successful trilineage engraftment without haemolysis. Six months after the transplant, he developed severe pancytopenia and a positive direct antiglobulin test (DAT) with anti-E elution. These serologic findings coincided with disease relapse rather than isolated immune-mediated haemolysis. This case illustrates that successful engraftment is possible across RhE-incompatibility and underscores the need to interpret late serologic abnormalities within the broader context of graft function and disease status.

ABSTRACT The integration of Internet of Things devices in medicine has paved the way for novel diagnostic and therapeutic solutions that offer ‘smart’ capabilities, especially for at‐home wound monitoring. Such capabilities include increasing portability, diminished gaps in care through continuous analysis, and real‐time programmable manipulation of device parameters to intervene more rapidly. This scoping review explores the landscape of Internet of Things devices, including smart bandages, wearable and skin‐interfaced sensors, and mobile app‐connected devices, specifically designed for chronic wound monitoring in a remote, at‐home setting. We reviewed literature from PubMed, IEEE Xplore, Scopus, and Web of Science, using keywords related to Internet of Things and chronic wound care. The evaluation criteria focused on biomarkers analysed, Internet of Things connectivity technology, experimental models used for device validation, and unique device strengths or weaknesses. Our review identified 52 unique studies comprising 77 data entries across five biomarker categories: gas and oxygen saturation, pH, metabolic markers, biophysical parameters of the wound microenvironment, and molecular markers of inflammation or infection. These devices span a wide landscape of biomarkers analysed to predict and monitor wound recovery, with the potential to improve patient outcomes through personalised treatment and timely interventions. However, many devices were never validated clinically on humans and faced limitations in power delivery, accuracy, stability, and reliability that challenge their potential usability in humans for wound monitoring. This review provides a comprehensive understanding of the potential and limitations of Internet of Things devices in at‐home wound care, highlighting areas for future research and development.

Health care–related out‐of‐pocket and indirect costs may create financial barriers to initial and/or continued receipt of care among patients with cancer, and subsequent financial toxicity is associated with worse oncologic and quality of life outcomes. Recognition and mitigation of risk factors for financial toxicity are essential to the provision of equitable, high‐quality care.

BACKGROUND: Accurate assessment of left ventricular outflow tract (LVOT) gradients is critical for hypertrophic cardiomyopathy management, yet Doppler-based measurements are technically demanding and require expertise. The objective of this work was to develop a multi-view deep learning model capable of classifying LVOT obstruction (>20 mm Hg) using routine 2-dimensional echocardiographic windows without reliance on Doppler imaging. METHODS: We trained and externally validated a cross-attention-based video-to-video fusion framework that integrated EchoPrime-derived video representations from 3 standard transthoracic echocardiographic views to classify LVOT gradients. RESULTS: Training was performed on a derivation cohort (N=1833) from a tertiary care system in the United States, with model performance evaluated on an internally held-out test set (N=275) and a Korean external validation cohort (N=46). Single-view baselines showed limited discrimination (external area under the receiver operating curves, 0.47–0.70). Conversely, the domain-specific foundational model (EchoPrime) achieved superior single-view performance (area under the receiver operating curves, 0.75–0.80 internal; 0.79–0.83 external), highlighting the importance of echo-specific pretraining and temporal modeling. The proposed multi-view fusion further enhanced predictive performance, with the late fusion model reaching an area under the receiver operating curve of 0.84 on the external cohort with significant population-shift. CONCLUSIONS: These results suggest LVOT physiology is encoded in routine 2-dimensional imaging and can be leveraged for clinically relevant gradient classification without Doppler input. The proposed artificial intelligence-guided strategy demonstrates substantial cost savings compared with the screen-all approach. By integrating complementary spatial-temporal information across multiple views, our approach generalizes robustly across populations and may enable real-time decision support, extend LVOT assessment to portable or resource-limited settings, and complement Doppler-based evaluation for longitudinal hypertrophic cardiomyopathy management.

ABSTRACT Background and Objectives Pancreatic neuroendocrine tumors (pNETs) account for 1%–2% of pancreatic tumors, with an increasing incidence. The safety of concurrent pancreas‐liver resections remains uncertain. This study evaluated the national outcomes of concurrent pancreatectomy and liver‐directed surgery for pNETs. Methods ACS‐NSQIP (2019–2023) was queried for adult patients who underwent pancreatectomy for pNETs. Concurrent liver‐directed procedures were identified by CPT code and classified as hepatectomy or ablation alone; patients were grouped as isolated pancreatectomy, concurrent hepatectomy, or concurrent ablation alone. Pre‐, intra‐, and postoperative characteristics were compared using inferential statistics. Multivariable logistic regression identified predictors of 30‐day morbidity, readmission, reoperation, and mortality, with Firth penalized likelihood used for rare outcomes. Results Of 4794 patients, 4587 (95.7%) underwent isolated pancreatectomy, 172 (3.6%) underwent concurrent hepatectomy, and 35 (0.7%) underwent concurrent ablation alone. Concurrent hepatectomy was parenchymal‐sparing in 163 of 172 patients (94.8%); only nine patients underwent a major hepatectomy. Overall morbidity was similar across groups (38.8%, 45.9%, and 42.9%; p = 0.154), as was serious morbidity (28.7%, 27.9%, 22.9%; p = 0.730) and 30‐day mortality (1.0%, 0.6%, 0%). On multivariable analysis, neither concurrent hepatectomy (OR 1.02, 95% CI 0.71–1.48) nor concurrent ablation (OR 0.94, 95% CI 0.46–1.93) was associated with morbidity. Pancreatoduodenectomy was associated with morbidity (OR 2.38, 95% CI 2.05–2.76), reoperation (OR 2.16, 95% CI 1.52–3.08), and mortality (OR 2.50, 95% CI 1.39–4.50), and a minimally invasive approach was associated with lower morbidity (OR 0.69, 95% CI 0.60–0.80). Conclusions Concurrent liver‐directed surgery was not associated with an increase in 30‐day morbidity or mortality after pancreatectomy for pNETs. These findings support the safety of concurrent hepatic resection in appropriately selected pNET patients.