Citations

Normothermic machine perfusion (NMP) enables preservation and functional assessment of liver allografts, improving outcomes, particularly in marginal organs. However, the TransMedics Organ Care System (OCS) permits only single hepatic artery (HA) cannulation, limiting its use in grafts with variant arterial anatomy. Arterial reconstruction before NMP may overcome this limitation and expand the pool of grafts eligible for perfusion. We conducted a retrospective single-center cohort study of adult liver transplants utilizing OCS from January 2022 to September 2024. Allografts with variant hepatic artery anatomy requiring arterial reconstruction before NMP initiation were identified. Postoperative outcomes, including vascular and biliary complications, were compared between reconstructed and non-reconstructed grafts. Among 541 OCS-preserved grafts, 74 (13.7%) underwent arterial reconstruction. Compared with non-reconstructed grafts, reconstructed grafts had longer cross-clamp to cannulation time (2.8 vs. 2.4 h, p <0.001), but similar rates of early allograft dysfunction (23.0% vs. 20.8%, p =0.65), ischemic cholangiopathy (4.1% vs. 4.1%, p =1.00), and primary nonfunction (0.0% vs. 0.4%, p =1.00). Early (2.7% vs. 0.2%, p =0.05) and late hepatic artery thrombosis (HAT) (4.1% vs. 2.1%, p =0.40) were infrequent and not significantly different between non-reconstructed and reconstructed groups, respectively. Hepatic artery stenosis occurred more often in reconstructed grafts (12.2% vs. 4.1%, p =0.008), though most cases were managed with interventional radiology or surgical revision. Only one reconstructed graft required retransplantation due to vascular complications. Arterial reconstruction before NMP is safe and feasible, enabling successful perfusion and transplantation of liver allografts with variant HA anatomy. These findings support the broader application of NMP in anatomically complex donor livers.

Abstract Introduction The rapidly ageing population and multimorbidity are associated with increased emergency department (ED) visits by older adults. In the ED, older adults have higher risk of hospitalization, functional and cognitive decline, and mortality. Boarding, holding admitted patients in the ED awaiting a hospital bed, exacerbates these negative outcomes, which disproportionately affect older adults. Methods We conducted a cross-sectional analysis to investigate US boarding trends by age using clinical administrative data from 5 health systems and publicly available NHAMCS data from 2018 to 2024. Results Boarding ≥3 h in the ED has increased across diverse hospital types, with oldest adults (85+) facing the greatest risk (System 4: IRR [95% CI] = 1.18 [1.15-1.20], System 5: IRR [95% CI] = 1.20 [1.17-1.23], System 3 [Community Hospital]: IRR [95% CI] = 1.25 [1.19-1.33]). These results were recapitulated at the national level in NHAMCS (IRR [95% CI] = 1.30 [1.05-1.61]). Discussion The trend of increased boarding has serious implications for patients, caregivers, and health systems. The 2025 CMS Age-Friendly Hospital Measure offers opportunities to improve processes and procedures to mitigate the negative effects of hospital boarding on older patients. We highlight opportunities to address this challenge, including ongoing quality improvement initiatives, bed prioritization algorithms, and alternate admission pathways.

Abstract Background Delirium is often underdiagnosed in clinical practice and is not routinely coded for billing. While manual chart review can identify delirium, it is labor-intensive and impractical for large-scale studies. Natural language processing (NLP) can analyze unstructured text in electronic health records (EHRs) to extract meaningful clinical information. Methods To support national integration of NLP for EHR-based delirium identification across different institutions, we launched the Delirium Interest Group within the national Evolve to Next-Gen Accrual to Clinical Trials (ENACT) NLP Working Group. This paper outlines our initial efforts to standardize, evaluate, and translate an NLP-based delirium detection model into the i2b2/ENACT platform. Results Multisite contextual inquiry identified several key challenges, including variations in local screening practices (eg, tools used, documentation frequency, and quality control), the need for harmonized definitions in the context of EHRs, and the complexity of modeling temporal logic. Multisite NLP evaluation revealed variable performance degradation driven by differences in delirium screening practices, clinical documentation patterns and semantics, and note syntactic structures. Conclusion Our work represents an important first step toward enabling scalable and standardized NLP-based delirium detection across institutions. By engaging diverse institutions through the ENACT NLP Working Group, we identified shared challenges and site-specific variations that impact model implementation and performance. Our collaborative approach enabled the development of a more robust framework for delirium identification across heterogeneous EHR systems. Future efforts will build on this foundation to enhance the validity, usability, and translational impact of delirium detection.