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Prehabilitation with Impella 5.5 offers hemodynamic support while allowing early mobilization, yet its overall effect on post-transplant recovery remains unclear. The aim was to assess recovery during Impella 5.5–supported prehabilitation and determine its association with early post-heart transplant outcomes, including a potential dose–response relationship with ambulatory activity. We conducted a single-center retrospective cohort study of adults bridged to transplant with Impella 5.5 between April 1, 2022 and September 30, 2024. Daily walking distance, recorded by nursing staff, served as the intervention. Primary outcome was postoperative hospital length of stay (LOS); the secondary outcome was time to extubation. Fine–Gray competing-risk models, adjusted for age, Sequential Organ Failure Assessment (SOFA) score, New York Heart Association (NYHA) class, and days of Impella support, assessed associations. Longitudinal recovery trajectories were analyzed with linear mixed-effects models incorporating restricted cubic splines. Among 82 eligible patients, 65 met the inclusion criteria. Patients who walked farther during Impella 5.5 support in the intensive care unit (ICU) had shorter hospital stays and were taken off the ventilator sooner after transplantation. The overall recovery peaked during the first 2–3 weeks of support, with smaller recovery thereafter, indicating that most functional recovery occurred early during prehabilitation.

Background Ketamine has demonstrated efficacy in treatment-resistant depression, primarily in psychiatric or outpatient populations. Its use in ICU patients remains underexplored, with limited data beyond case reports and small series. This study evaluated the association between subanesthetic ketamine infusions and improvement in depressive symptoms and hemodynamic changes in ICU patients. Methods We conducted a retrospective study of adults admitted to the ICU who received IV ketamine (0.3-0.75 mg/kg over 40 min on three consecutive days) for depressive symptoms. Changes in depressive symptoms were assessed using routine clinical documentation by physicians, nurses, and occupational and physical therapists. Hemodynamic parameters (blood pressure and heart rate) were recorded before and up to 120 min after infusion. The primary outcome was to evaluate depressive symptoms while the secondary outcome was to assess hemodynamic changes post transfusion. Results Thirty-four patients met criteria, including 18 solid organ transplant recipients. Median age was 59 years; 61.8% were male. Ketamine was associated with improvement in apparent sadness (90.0% vs 52.2%, P < .05) and reported sadness (95.0% vs 59.1%, P < .05). In transplant recipients, improvement in apparent sadness remained significant (80.0% vs 41.7%, P < .05). Hemodynamic parameters remained stable; heart rate increased transiently at 15-30 min post-infusion, returning to baseline by 60-90 min. Adverse effects observed were anxiety (12.5%), restlessness and/or agitation (10.4%), and dissociation (8.16%). Conclusion Subanesthetic ketamine improved specific depressive symptoms in critically ill ICU patients without significant hemodynamic instability. These findings support its potential as a rapid-acting antidepressant in the ICU, warranting further prospective trials.

Background: Sepsis survivors experience substantial long-term cardiovascular (CV) morbidity and mortality. The pooled-cohort atherosclerotic cardiovascular disease (ASCVD) score estimates 10-year risk in the general population, but its accuracy soon after sepsis is unknown. Objectives: (1) Describe the absolute five-year incidence of major adverse cardiovascular events (MACE) after sepsis hospitalization. (2) Evaluate the discrimination and calibration of the 10-year ASCVD score for predicting five-year MACE in this setting. Research Questions: The ASCVD score would underpredict five-year MACE in sepsis survivors, reflecting their heightened risk. Methods: We performed a retrospective closed-cohort study using electronic health records from Mayo Clinic hospitals (USA). Adults aged 40–75 years hospitalized for sepsis between 2018 and 2020 were eligible if follow-up was ≥5 years; the age criterion matched the derivation range of the ASCVD score. The primary outcome was five-year MACE—myocardial infarction (MI), ischemic or hemorrhagic stroke, or CV death. Secondly we evaluated the predictive performance of ASCVD score for five-year MACE. Discrimination was assessed with the area under the receiver-operating curve (AUROC); calibration with Calibration-in-the-Large (CITL, ideal = 0) and slope (ideal = 1). Performance metrics were calculated in patients with complete ASCVD variables. Results: A total of 6306 sepsis admissions were screened. 2424 patients were excluded for age outside 40–75 years and 879 for <5 year follow-up, leaving 2718 patients for incidence analyses. Median age was 65 years (IQR 56–70); 55 % were male, 38 % required ICU care, and 91 % were White. Median body-mass index was 28.2 kg/m2 (IQR 23.7-34.7). During follow-up, 125 patients (4.6 %) had MI, 219 (8.1 %) experienced stroke, and 303 (11.1 %) died of CV causes; overall, 1573 patients (57.9 %) met the composite MACE outcome. Complete ASCVD data were available for 409 patients. In this subset, discrimination was moderate (AUROC 0.65; 95 % CI 0.59–0.71). Calibration showed CITL +0.34 (95 % CI 0.09–0.59) and slope 0.50 (95 % CI 0.28–0.72), indicating systematic infra-prediction and over-fitting, respectively. Conclusion: Five-year MACE incidence after sepsis is high. The 10-year ASCVD score moderately discriminates but underestimates five-year CV risk in this population, highlighting the need for sepsis-specific predictive models or recalibration to guide targeted post-sepsis cardiovascular prevention.

Background Safety-net hospitals (SNH) provide care to a significant proportion of low-income, uninsured, and underserved populations disproportionately affected by the COVID-19 pandemic. We evaluated the impact of safety-net burden on outcomes of COVID-19 hospitalizations. Methods We identified patients aged ≥ 18 years hospitalized with COVID-19 from the nationwide inpatient database using the International Classification of Diseases, Tenth Edition (ICD-10-CM) code U07.1. The primary study outcomes were inpatient mortality, respiratory failure requiring mechanical ventilation, and tracheostomy use. Secondary outcomes were total hospitalization costs (THC) and length of hospital stay (LOS). Multivariable logistic regression was used to compare the primary and secondary outcomes in HBH and medium safety-net burden (MBH) with lower-burden hospitals (LBH) (reference). Results An estimated 2,608,232 COVID-19 hospitalizations met the inclusion criteria, with 68.7% occurring in HBH, 22.8% in MBH, and 8.5% in LBH. On adjusted multivariable analysis, HBH was associated with increased odds of inpatient mortality (aOR 1.19, 95% CI 1.09-1.28) compared with LBH. Both HBH (aOR 1.30, 95% CI 1.21-1.40) and MBH (aOR 1.14, 95% CI 1.06-1.24) demonstrated increased odds of mechanical ventilation compared with LBH. THC was lower in HBH and MBH compared with LBH. The LOS was similar across safety net burden categories. Conclusion After adjusting for patient- and hospital-level factors, COVID-19 hospitalizations at HBH had inferior inpatient outcomes compared with LBH. Targeted policy reforms are needed to address the underlying causes of these disparities and increase our health systems’ readiness for future crises, particularly in underserved communities.

Background Artificial intelligence (AI) and large language models (LLMs), are potential tools for enhancing healthcare delivery and clinical research. Presently, there is a scarcity of research regarding the viewpoints of medical students toward LLMs. This study aims to explore the perceptions of LLMs and their applications among this demographic. Methods A cross-sectional study was done using an online survey. It was designed using Google Forms and circulated from July 2023 to August 2023. The target population included medical students from Ecuador, Ethiopia, India, Mauritius, Pakistan, United Arab Emirates (UAE), and the United States (USA). Results A total of 1180 responses were collected from 10 medical colleges across 7 countries. The UAE had the largest number of responses (31.7%), followed by Ecuador (17.6%), and Mauritius (13.7%). And 77.4% respondents were already aware about LLMs before attempting the survey, most popularly, ChatGPT. Participants were most familiar with the specific use of LLMs in research (46%), and the lowest familiarity was seen in the use of LLMs in healthcare (37%). More than half of the participants (52%) support the use of LLMs healthcare, yet a significant number of them remained neutral (37%) or disagreed (31%) on LLMs being safe in this context. Conclusion While most of the medical students are aware of LLMs and their applications, the multinational survey demonstrated hesitancy among medical students in fully adopting LLMs into healthcare practice and clinical research. This study prompts further exploration of medical students’ attitudes and acceptance concerning the integration of LLMs for research methodologies, ethical considerations, and healthcare practice.