Citations

Background: Hepatocellular carcinoma (HCC) has a poor long-term prognosis due to high recurrence and cirrhosis-related mortality, even after potentially curative treatments such as liver transplantation (LT), surgical resection, or ablation. This study aimed to identify factors associated with ≥10-year survival in HCC patients. Methods: A retrospective cohort study was conducted among HCC patients diagnosed between 2004 and 2022 using the National Cancer Database. Multivariable Cox regression was used to identify predictors of overall survival, and logistic regression was used to identify predictors of ≥10-year survival. Results: Among 249,600 HCC patients, 177,585 (71.2%) died within 5 years, 8613 (3.5%) died at 5–10 years, 54,988 (22.0%) were alive with <10 years of follow-up, and 8219 (3.3%) survived ≥10 years. LT, resection, and ablation were performed in 6.6%, 9.3%, and 11% of patients, respectively. Compared with ablation as the reference group, LT [adjusted odds ratio (aOR) 11.96, 95% confidence interval (CI): 11.27–13.29] and resection (aOR: 2.83, 95% CI: 2.57–3.08) increased the odds of ≥10-year survival, while non-curative treatments reduced the odds compared with ablation (aOR: 0.50, 95% CI: 0.47–0.55). Cox regression results were consistent with the logistic model, confirming the association. Decision tree analysis confirmed LT as the dominant determinant of long-term survival. Black individuals were associated with lower odds of ≥10-year survival (aOR: 0.88, 95% CI: 0.820–0.96) and decreased likelihood of receiving LT (aOR: 0.73, 95% CI: 0.55–0.96). Conclusions: LT offers the best chance of ≥10-year survival in HCC. Ensuring equitable access is essential, especially for Black patients who have lower transplant rates and worse outcomes.

Importance Seizure-induced brain injury is central to the treatment urgency of new-onset refractory status epilepticus (NORSE). Identifying biomarkers that reflect ongoing neuronal damage could inform therapeutic timing and improve outcomes. Objective To quantify acute brain injury in patients with cryptogenic NORSE (cNORSE), etiology-defined status epilepticus (eSE), and chronic epilepsy. Design, Setting, and Participants This was an international cross-sectional study conducted between 2013 and 2025. Patients were enrolled at 36 hospitals in the US, 2 in Canada, and 1 in Italy, France, and Belgium. Patients with cNORSE and eSE for which biological samples were obtained during ongoing seizure activity were enrolled in the study. Comparison groups without status epilepticus comprised individuals with chronic epilepsy and healthy participants. None were excluded. Exposures Neurofilament light chain (NfL) and S100-beta (S100B) protein concentrations in serum and cerebrospinal fluid (CSF). Main Outcomes and Measures Degree of neuronal and glial damage, indexed by NfL and S100B levels, and their association with short-term functional outcomes. Results A total of 78 patients with cNORSE (mean [95% CI] age, 37 [30-41] years; 44 female [56%]) and 2 independent cohorts of 211 patients (mean [95% CI] age, 69 [66-71] years; 128 female [61%]) and 73 patients (mean [95% CI] age, 56 [45-65] years; 39 male [53%]) with eSE were included. NfL concentrations were markedly elevated in cNORSE—approximately 10-fold higher in CSF and 4-fold higher in serum—compared with the eSE cohorts (CSF: median [IQR], 6408 [1503-22 963] pg/mL compared with 694 [219-2389] pg/mL; serum: median [IQR], 231 [99-855] pg/mL compared with 55 [20-135] pg/mL; P &amp;lt;.001). Serum NfL levels were nearly 20-fold higher in cNORSE than in the cohort with epilepsy and in healthy controls (median [IQR], 11 [7-19 ] and 7 [5-14 ] pg/mL, respectively). Serum and CSF NfL levels were strongly correlated (Spearman ρ = 0.75; P &amp;lt; .001) and rose sharply between week 1 (median [IQR], 101 [51-137] pg/mL), week 2 (median [IQR], 197 [117-324] pg/mL), and week 3 (median [IQR], 598 [163-1000] pg/mL) after onset ( P &amp;lt; .001). In contrast, S100B concentrations did not differ between groups and showed no consistent temporal pattern. NfL discriminated cNORSE from eSE (area under the receiver operating characteristic curve [AUROC], 0.79; 95% CI, 0.68-0.90) and from cohorts without status epilepticus (AUROC, 0.99; 95% CI, 0.78-1.00). Higher serum NfL was independently associated with poor functional outcome at discharge (Glasgow Outcome Scale extended score, 1-4; odds ratio, 1.01; 95% CI, 1.00-1.03; P = .03). Conclusions and Relevance Results of this cross-sectional study suggest that acute neuroaxonal injury, as reflected by elevated NfL levels, was substantially greater in cNORSE than in the cohorts with eSE and in controls without status epilepticus. The rapid early rise in NfL highlights a narrow therapeutic window, emphasizing the need for prompt, effective, and potentially neuroprotective interventions in cNORSE.

INTRODUCTION: Nosocomial infections (NIs) in cirrhosis are associated with high mortality but could be preventable. Logistic regression (LR) models have failed to identify high-risk patients. We aimed to develop machine learning (ML) models to predict NI. METHODS: The CLEARED consortium consists of prospectively enrolled cirrhosis inpatients from >120 centers. Using day-of-admission clinical data, 3 ML approaches (random forest [RF], extreme gradient boosting, and neural networks [NNs]) were used to predict NI. Data were split 80:20 for training and testing stratified by the outcome. Models were compared using area under the receiver operating characteristic curve (AUC). RESULTS: In total, 8,263 patients (55.90 ± 13.34 years; 64.1% men) from 127 centers in 37 countries were included. NI developed in 869 (10.5%), a median of 6 (4–11) days of postadmission. Major NIs were respiratory (29.6%) and urinary tract infection (15.7%), and spontaneous bacterial peritonitis (13.5%). NIs occurred more frequently in patients from low/low-middle income countries and those with severe liver disease, alcohol etiology, and admission infections. NIs were associated with inpatient mortality (31.9% vs 8.1%, P < 0.001) and liver transplantation (4.8% vs 1.9%, P < 0.001). Although the RF model (AUC 0.69) showed good calibration (Brier score 0.09), outperforming extreme gradient boosting, neural network, and LR models (AUC 0.66 for all; LR comparison P = 0.043), no model achieved AUC ≥0.80 for clinical utility. At 10% predicted probability threshold, the RF model demonstrated only 75.4% sensitivity, 52.9% specificity, and 15.9% positive predictive value (PPV). DISCUSSION: NIs cannot be accurately predicted from day-of-admission data using ML models, even in a large, prospective, global cirrhosis cohort. Every hospitalized patient with cirrhosis should receive protocolized infection control measures.

Abstract Objective We evaluated the quality and adoption of a large language model (LLM)-based summarization tool for ongoing hospital care. Materials and methods An AI summarization tool was created to provide a “Patient Story”, specialty-specific “Recent Notes”, and “Recent Events” over the previous 24 or 72 hours. We conducted a pragmatic mixed-methods quality assessment at three tertiary-care academic hospitals utilizing (1) Provider Documentation Summarization Quality Instrument (PDSQI-9), (2) utilization analytics, and (3) qualitative end user feedback. The PDSQI-9 included whether summaries were accurate, cited, comprehensible, organized, succinct, non-stigmatizing, synthesized, thorough, and useful. 512 users were given access, from whom 52 participants submitted 205 surveys (10.2% response rate). Results 52 respondents submitted an average of 4.3 surveys (range 1–8). Users rated the tool favorably across all PDSQI-9 domains, with a combined average score of 4.68 (range 4.56–4.78, S.D. 0.67) across the eight domains scored on a 5-point modified Likert scale. Utilization metrics demonstrated strong uptake with frequent views. Positive qualitative feedback revealed cognitive offloading for complex patients and effective summarization of medical problems. Critical feedback showed a need to cross-reference narrative notes to current-state data and lack of detailed specialty-specific summarization. Discussion Generative artificial intelligence has emerged as a potentially transformative technology for generating succinct, verifiable summaries of ongoing care. In this pragmatic implementation, end-users indicated high perceived quality across PDSQI-9 domains. Conclusions An LLM-based summarization tool for ongoing hospitalization care was rated of high quality by diverse clinicians in real-world settings, demonstrated a favorable safety profile, and showed sustained utilization.

Abstract Aims Heart failure is prevalent; however, there is no cost-effective screening option. Against formal echocardiography, we assessed the diagnostic performance of focused cardiac ultrasound (FoCUS), performed by novice users guided and interpreted by artificial intelligence (AI) for the assessment of left ventricular ejection fraction (LVEF). Methods and results We prospectively enrolled 496 adults referred for diagnostic echocardiography. Novice operators (without clinical or imaging experience) underwent a 4-h imaging workshop, then performed FoCUS using a point-of-care device (Philips Lumify) utilizing real-time AI-image guidance (UltraSight). Images were assessed by AI-image interpretation software (Mayo Clinic), and two expert blinded echocardiologists. Median age was 67, 39% female, and body mass index range 17–56 kg/m2. Forty-one subjects (8.3%) exhibited moderate or greater LV dilatation and 28 (5.6%) had LVEF &lt;40%. The median scan time was 4 min (IQR 3–5). Adequate views were achieved in 95.0% and 97.4% of subjects for AI interpretation and expert analysis, respectively. A two-step diagnostic screening process for low EF in which only abnormal AI reads or uninterpretable scans were reviewed by experts (15.1%) had a sensitivity 96.2%, specificity 95.4%, PPV 54.3%, and NPV 99.8%. A subsequent prospective validation study of 344 subjects demonstrated similar results (sensitivity 100%, specificity 99.4%). Conclusion In this early feasibility investigation, AI-guidance technology embedded on a handheld device, enabled novice users without clinical or imaging experience to acquire sufficient quality images to accurately assess LVEF with minimal training. The technology evaluated in this study may hold promise for AI-guidance and interpretation to facilitate low-cost screening for cardiac dysfunction.

Abstract Vascular anomalies are disorders of arteries or veins which are further sub-classified as vascular malformations or vascular tumors. Vascular malformations are attributed to disorderly vascular morphogenesis, trauma, or hormonal imbalances. We present a rare case of a patient in their 30s who presented to the outpatient department with a localized post-traumatic soft tissue swelling in the temporalis region, which was soft and non-pulsatile with occurrences in periods of acute emotional stress, followed by spontaneous resolution when the same was negated by the patient. The diagnosis was established on the basis of magnetic resonance imaging and contrast enhanced computed tomography. We add to the existing limited literature on intramuscular vascular malformations of the temporalis muscle and highlight the effect of emotional stress and anxiety on vascular anomalies.

Objectives: Amyloidosis is a group of rare diseases that often manifest in multi-organ symptoms requiring an effective multidisciplinary team of care providers to manage. This study aimed to identify the best practices and key barriers to patient-centric care at US specialized amyloidosis centers (SACs) which have emerged as central hubs for multidisciplinary amyloidosis care. Methods: We conducted and analyzed interviews with 77 stakeholders of amyloidosis diseases, including specialist physicians from 17 SACs across the US, patients, patient advocacy group representatives, and referring physicians. Results: The most commonly mentioned best practices were patient-centric multidisciplinary care, timely intake of new high-risk patients, and responsiveness to patient questions. Expanding local physician education was noted as critical to earlier diagnosis. Fifteen out of 17 SACs are actively involved in clinical trials for amyloidosis treatments. Key barriers to care identified were patient distance to SACs, limited medical records and data sharing, and insurance hurdles. Most SACs keep track of metrics, such as patient volume, patient demographics, time to first appointment, as part of internal or in external registries to measure progress. Conclusion: These findings offer practical insights for optimizing amyloidosis care and highlight the structural and informational gaps that must be addressed to improve access and outcomes.