Nucleobase adducts bind MR1 and stimulate MR1-restricted T cells.

MR1T cells are a recently found class of T cells that recognize antigens presented by the major histocompatibility complex-I-related molecule MR1 in the absence of microbial infection. The nature of the self-antigens that stimulate MR1T cells remains unclear, hampering our understanding of their physiological role and therapeutic potential. By combining genetic, pharmacological, and biochemical approaches, we found that carbonyl stress and changes in nucleobase metabolism in target cells promote MR1T cell activation. Stimulatory compounds formed by carbonyl adducts of nucleobases were detected within MR1 molecules produced by tumor cells, and their abundance and antigenicity were enhanced by drugs that induce carbonyl accumulation. Our data reveal carbonyl-nucleobase adducts as MR1T cell antigens. Recognizing cells under carbonyl stress allows MR1T cells to monitor cellular metabolic changes with physiological and therapeutic implications.

Artificial Intelligence-Generated Draft Replies to Patient Inbox Messages.

Importance: The emergence and promise of generative artificial intelligence (AI) represent a turning point for health care. Rigorous evaluation of generative AI deployment in clinical practice is needed to inform strategic decision-making. Objective: To evaluate the implementation of a large language model used to draft responses to patient messages in the electronic inbox. Design, Setting, and Participants: A 5-week, prospective, single-group quality improvement study was conducted from July 10 through August 13, 2023, at a single academic medical center (Stanford Health Care). All attending physicians, advanced practice practitioners, clinic nurses, and clinical pharmacists from the Divisions of Primary Care and Gastroenterology and Hepatology were enrolled in the pilot. Intervention: Draft replies to patient portal messages generated by a Health Insurance Portability and Accountability Act-compliant electronic health record-integrated large language model. Main Outcomes and Measures: The primary outcome was AI-generated draft reply utilization as a percentage of total patient message replies. Secondary outcomes included changes in time measures and clinician experience as assessed by survey. Results: A total of 197 clinicians were enrolled in the pilot; 35 clinicians who were prepilot beta users, out of office, or not tied to a specific ambulatory clinic were excluded, leaving 162 clinicians included in the analysis. The survey analysis cohort consisted of 73 participants (45.1%) who completed both the presurvey and postsurvey. In gastroenterology and hepatology, there were 58 physicians and APPs and 10 nurses. In primary care, there were 83 physicians and APPs, 4 nurses, and 8 clinical pharmacists. The mean AI-generated draft response utilization rate across clinicians was 20%. There was no change in reply action time, write time, or read time between the prepilot and pilot periods. There were statistically significant reductions in the 4-item physician task load score derivative (mean [SD], 61.31 [17.23] presurvey vs 47.26 [17.11] postsurvey; paired difference, -13.87; 95% CI, -17.38 to -9.50; P < .001) and work exhaustion scores (mean [SD], 1.95 [0.79] presurvey vs 1.62 [0.68] postsurvey; paired difference, -0.33; 95% CI, -0.50 to -0.17; P < .001). Conclusions and Relevance: In this quality improvement study of an early implementation of generative AI, there was notable adoption, usability, and improvement in assessments of burden and burnout. There was no improvement in time. Further code-to-bedside testing is needed to guide future development and organizational strategy.

Comparison of bleeding and thrombotic outcomes in veno-venous extracorporeal membrane oxygenation: Heparin versus bivalirudin.

OBJECTIVES: We aimed to evaluate thrombotic and hemorrhagic complications with heparin versus bivalirudin use in veno-venous extracorporeal membrane oxygenation (V-V ECMO). METHODS: We performed a retrospective cohort study of adult patients placed on V-V ECMO with intravenous anticoagulation with either heparin or bivalirudin. Time to thrombotic event and major bleed were analyzed in addition to related outcomes. RESULTS: We identified 95 patients placed on V-V ECMO: 61 receiving heparin, 34 bivalirudin. The bivalirudin group had a higher rate of severe COVID-19, higher BMI, and longer ECMO duration. Despite this, bivalirudin was associated with reduced risk of thrombotic event (HR 0.14, 95% CI 0.06-0.32, p < .001) and increased average lifespan of the circuit membrane lung (16 vs. 10 days, p = 0.004). While there was no difference in major bleeding, the bivalirudin group required fewer transfusions of packed red blood cells and platelets per 100 ECMO days (means of 13 vs. 39, p = 0.004; 5 vs. 19, p = .014, respectively). Lastly, the bivalirudin group had improved survival to ECMO decannulation in univariate analysis (median OS 53 vs. 26 days, p = .015). CONCLUSIONS: In this real-world analysis of bivalirudin versus heparin, bivalirudin is a viable option for V-V ECMO and associated with lower risk of thrombotic complications and fewer transfusion requirements.

Factor XI Inhibition for the Prevention of Catheter-Associated Thrombosis in Patients With Cancer Undergoing Central Line Placement: A Phase 2 Clinical Trial.

BACKGROUND: Despite the ubiquitous utilization of central venous catheters in clinical practice, their use commonly provokes thromboembolism. No prophylactic strategy has shown sufficient efficacy to justify routine use. Coagulation factors FXI (factor XI) and FXII (factor XII) represent novel targets for device-associated thrombosis, which may mitigate bleeding risk. Our objective was to evaluate the safety and efficacy of an anti-FXI mAb (monoclonal antibody), gruticibart (AB023), in a prospective, single-arm study of patients with cancer receiving central line placement. METHODS: We enrolled ambulatory cancer patients undergoing central line placement to receive a single dose of gruticibart (2 mg/kg) administered through the venous catheter within 24 hours of placement and a follow-up surveillance ultrasound at day 14 for evaluation of catheter thrombosis. A parallel, noninterventional study was used as a comparator. RESULTS: In total, 22 subjects (n=11 per study) were enrolled. The overall incidence of catheter-associated thrombosis was 12.5% in the interventional study and 40.0% in the control study. The anti-FXI mAb, gruticibart, significantly prolonged the activated partial thromboplastin time in all subjects on day 14 compared with baseline (P<0.001). Gruticibart was well tolerated and without infusion reactions, drug-related adverse events, or clinically relevant bleeding. Platelet flow cytometry demonstrated no difference in platelet activation following administration of gruticibart. T (thrombin)-AT (antithrombin) and activated FXI-AT complexes increased following central line placement in the control study, which was not demonstrated in our intervention study. CRP (C-reactive protein) did not significantly increase on day 14 in those who received gruticibart, but it did significantly increase in the noninterventional study. CONCLUSIONS: FXI inhibition with gruticibart was well tolerated without any significant adverse or bleeding-related events and resulted in a lower incidence of catheter-associated thrombosis on surveillance ultrasound compared with the published literature and our internal control study. These findings suggest that targeting FXI could represent a safe intervention to prevent catheter thrombosis. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT04465760.

Giving the Green Light to Photochemical Uncaging of Large Biomolecules in High Vacuum.

The isolation of biomolecules in a high vacuum enables experiments on fragile species in the absence of a perturbing environment. Since many molecular properties are influenced by local electric fields, here we seek to gain control over the number of charges on a biopolymer by photochemical uncaging. We present the design, modeling, and synthesis of photoactive molecular tags, their labeling to peptides and proteins as well as their photochemical validation in solution and in the gas phase. The tailored tags can be selectively cleaved off at a well-defined time and without the need for any external charge-transferring agents. The energy of a single or two green photons can already trigger the process, and it is soft enough to ensure the integrity of the released biomolecular cargo. We exploit differences in the cleavage pathways in solution and in vacuum and observe a surprising robustness in upscaling the approach from a model system to genuine proteins. The interaction wavelength of 532 nm is compatible with various biomolecular entities, such as oligonucleotides or oligosaccharides.

Evaluating the predictive ability of natural language processing in identifying tertiary/quaternary cases in prioritization workflows for interhospital transfer.

Objectives: Tertiary and quaternary (TQ) care refers to complex cases requiring highly specialized health services. Our study aimed to compare the ability of a natural language processing (NLP) model to an existing human workflow in predictively identifying TQ cases for transfer requests to an academic health center. Materials and methods: Data on interhospital transfers were queried from the electronic health record for the 6-month period from July 1, 2020 to December 31, 2020. The NLP model was allowed to generate predictions on the same cases as the human predictive workflow during the study period. These predictions were then retrospectively compared to the true TQ outcomes. Results: There were 1895 transfer cases labeled by both the human predictive workflow and the NLP model, all of which had retrospective confirmation of the true TQ label. The NLP model receiver operating characteristic curve had an area under the curve of 0.91. Using a model probability threshold of ≥0.3 to be considered TQ positive, accuracy was 81.5% for the NLP model versus 80.3% for the human predictions (P = .198) while sensitivity was 83.6% versus 67.7% (P<.001). Discussion: The NLP model was as accurate as the human workflow but significantly more sensitive. This translated to 15.9% more TQ cases identified by the NLP model. Conclusion: Integrating an NLP model into existing workflows as automated decision support could translate to more TQ cases identified at the onset of the transfer process.

The Stanford Medicine data science ecosystem for clinical and translational research.

Objective: To describe the infrastructure, tools, and services developed at Stanford Medicine to maintain its data science ecosystem and research patient data repository for clinical and translational research. Materials and Methods: The data science ecosystem, dubbed the Stanford Data Science Resources (SDSR), includes infrastructure and tools to create, search, retrieve, and analyze patient data, as well as services for data deidentification, linkage, and processing to extract high-value information from healthcare IT systems. Data are made available via self-service and concierge access, on HIPAA compliant secure computing infrastructure supported by in-depth user training. Results: The Stanford Medicine Research Data Repository (STARR) functions as the SDSR data integration point, and includes electronic medical records, clinical images, text, bedside monitoring data and HL7 messages. SDSR tools include tools for electronic phenotyping, cohort building, and a search engine for patient timelines. The SDSR supports patient data collection, reproducible research, and teaching using healthcare data, and facilitates industry collaborations and large-scale observational studies. Discussion: Research patient data repositories and their underlying data science infrastructure are essential to realizing a learning health system and advancing the mission of academic medical centers. Challenges to maintaining the SDSR include ensuring sufficient financial support while providing researchers and clinicians with maximal access to data and digital infrastructure, balancing tool development with user training, and supporting the diverse needs of users. Conclusion: Our experience maintaining the SDSR offers a case study for academic medical centers developing data science and research informatics infrastructure.

Creation and Adoption of Large Language Models in Medicine.

Importance: There is increased interest in and potential benefits from using large language models (LLMs) in medicine. However, by simply wondering how the LLMs and the applications powered by them will reshape medicine instead of getting actively involved, the agency in shaping how these tools can be used in medicine is lost. Observations: Applications powered by LLMs are increasingly used to perform medical tasks without the underlying language model being trained on medical records and without verifying their purported benefit in performing those tasks. Conclusions and Relevance: The creation and use of LLMs in medicine need to be actively shaped by provisioning relevant training data, specifying the desired benefits, and evaluating the benefits via testing in real-world deployments.

The shaky foundations of large language models and foundation models for electronic health records.

The success of foundation models such as ChatGPT and AlphaFold has spurred significant interest in building similar models for electronic medical records (EMRs) to improve patient care and hospital operations. However, recent hype has obscured critical gaps in our understanding of these models' capabilities. In this narrative review, we examine 84 foundation models trained on non-imaging EMR data (i.e., clinical text and/or structured data) and create a taxonomy delineating their architectures, training data, and potential use cases. We find that most models are trained on small, narrowly-scoped clinical datasets (e.g., MIMIC-III) or broad, public biomedical corpora (e.g., PubMed) and are evaluated on tasks that do not provide meaningful insights on their usefulness to health systems. Considering these findings, we propose an improved evaluation framework for measuring the benefits of clinical foundation models that is more closely grounded to metrics that matter in healthcare.

Psychological toxicity in classical hematology.

Although considered "benign," mild blood count abnormalities, genetic factors imparting inconsequential thrombotic risk, and low-risk premalignant blood disorders can have significant psychological and financial impact on our patients. Several studies have demonstrated that patients with noncancerous conditions have increased levels of anxiety with distress similar to those with malignancy. Additionally, referral to a classical hematologist can be a daunting process for many patients due to uncertainties surrounding the reason for referral or misconstrued beliefs in a cancer diagnosis ascribed to the pairing of oncology and hematology in medical practice. If not properly triaged, incidental laboratory abnormalities can trigger extensive and costly evaluation. These challenges are compounded by a lack of consensus guidance and generalizability of modern reference ranges that do not adequately account for common influencing factors. Although often benign, incidental hematologic findings can lead to emotional suffering and careful consideration of the potential psychological and financial duress imparted to an individual must be considered. In this article, we will review the current literature describing the psychological effect of some commonly known hematologic conditions, identify benign causes for variations in hematologic laboratory values, and provide recommendations to reduce psychological toxicity as it pertains to hematologic testing.

Oral ENT-01 Targets Enteric Neurons to Treat Constipation in Parkinson Disease : A Randomized Controlled Trial.

BACKGROUND: Parkinson disease (PD) is associated with α-synuclein (αS) aggregation within enteric neurons. ENT-01 inhibits the formation of αS aggregates and improved constipation in an open-label study in patients with PD. OBJECTIVE: To evaluate the safety and efficacy of oral ENT-01 for constipation and neurologic symptoms in patients with PD and constipation. DESIGN: Randomized, placebo-controlled phase 2b study. (ClinicalTrials.gov: NCT03781791). SETTING: Outpatient. PATIENTS: 150 patients with PD and constipation. INTERVENTION: ENT-01 or placebo daily for up to 25 days. After baseline assessment of constipation severity, daily dosing was escalated to the prokinetic dose, the maximum dose (250 mg), or the tolerability limit, followed by a washout period. MEASUREMENTS: The primary efficacy end point was the number of complete spontaneous bowel movements (CSBMs) per week. Neurologic end points included dementia (assessed using the Mini-Mental State Examination [MMSE]) and psychosis (assessed using the Scale for the Assessment of Positive Symptoms adapted for PD [SAPS-PD]). RESULTS: The weekly CSBM rate increased from 0.7 to 3.2 in the ENT-01 group versus 0.7 to 1.2 in the placebo group (P < 0.001). Improvement in secondary end points included SBMs (P = 0.002), stool consistency (P < 0.001), ease of passage (P = 0.006), and laxative use (P = 0.041). In patients with dementia, MMSE scores improved by 3.4 points 6 weeks after treatment in the ENT-01 group (n = 14) versus 2.0 points in the placebo group (n = 14). Among patients with psychosis, SAPS-PD scores improved from 6.5 to 1.7 six weeks after treatment in the ENT-01 group (n = 5) and from 6.3 to 4.4 in the placebo group (n = 6). ENT-01 was well tolerated, with no deaths or drug-related serious adverse events. Adverse events were predominantly gastrointestinal, including nausea (34.4% [ENT-01] vs. 5.3% [placebo]; P < 0.001) and diarrhea (19.4% [ENT-01] vs. 5.3% [placebo]; P = 0.016). LIMITATION: Longer treatment periods need to be investigated in future studies. CONCLUSION: ENT-01 was safe and significantly improved constipation. PRIMARY FUNDING SOURCE: Enterin, Inc.

Considerations in the reliability and fairness audits of predictive models for advance care planning.

Multiple reporting guidelines for artificial intelligence (AI) models in healthcare recommend that models be audited for reliability and fairness. However, there is a gap of operational guidance for performing reliability and fairness audits in practice. Following guideline recommendations, we conducted a reliability audit of two models based on model performance and calibration as well as a fairness audit based on summary statistics, subgroup performance and subgroup calibration. We assessed the Epic End-of-Life (EOL) Index model and an internally developed Stanford Hospital Medicine (HM) Advance Care Planning (ACP) model in 3 practice settings: Primary Care, Inpatient Oncology and Hospital Medicine, using clinicians' answers to the surprise question ("Would you be surprised if [patient X] passed away in [Y years]?") as a surrogate outcome. For performance, the models had positive predictive value (PPV) at or above 0.76 in all settings. In Hospital Medicine and Inpatient Oncology, the Stanford HM ACP model had higher sensitivity (0.69, 0.89 respectively) than the EOL model (0.20, 0.27), and better calibration (O/E 1.5, 1.7) than the EOL model (O/E 2.5, 3.0). The Epic EOL model flagged fewer patients (11%, 21% respectively) than the Stanford HM ACP model (38%, 75%). There were no differences in performance and calibration by sex. Both models had lower sensitivity in Hispanic/Latino male patients with Race listed as "Other." 10 clinicians were surveyed after a presentation summarizing the audit. 10/10 reported that summary statistics, overall performance, and subgroup performance would affect their decision to use the model to guide care; 9/10 said the same for overall and subgroup calibration. The most commonly identified barriers for routinely conducting such reliability and fairness audits were poor demographic data quality and lack of data access. This audit required 115 person-hours across 8-10 months. Our recommendations for performing reliability and fairness audits include verifying data validity, analyzing model performance on intersectional subgroups, and collecting clinician-patient linkages as necessary for label generation by clinicians. Those responsible for AI models should require such audits before model deployment and mediate between model auditors and impacted stakeholders.

Not to Rush-Laboratory Parameters and Procedural Complications in Patients Undergoing Left Atrial Appendage Closure.

Background: As a preventive procedure, minimizing periprocedural risk is crucially important during left atrial appendage closure (LAAC). Methods: We included consecutive patients receiving LAAC at nine centres and assessed the relationship between baseline characteristics and the acute procedural outcome. Major procedural complications were defined as all complications requiring immediate invasive intervention or causing irreversible damage. Logistic regression was performed and included age and left-ventricular function. Furthermore, the association between acute complications and long-term outcomes was evaluated. Results: A total of 405 consecutive patients with a median age of 75 years (37% female) were included. 47% had a history of stroke. Median CHA2DS2-VASc score was 4 (interquartile range, 3−5) and the median HAS-BLED score was 3 (2−4). Major procedural complications occurred in 7% of cases. Low haemoglobin (OR 0.8, 95% CI 0.65−0.99 per g/dL, p = 0.040) and end-stage kidney disease (OR 13.0, CI 2.5−68.5, p = 0.002) remained significant in multivariate analysis. Anaemia (haemoglobin < 12 and < 13 g/dL in female and male patients) increased the risk of complications 2.2-fold. Conclusions: The major complication rate was low in this high-risk patient population undergoing LAAC. End-stage kidney disease and low baseline haemoglobin were independently associated with a higher major complication rate.

Assessment of Adherence to Reporting Guidelines by Commonly Used Clinical Prediction Models From a Single Vendor: A Systematic Review.

Importance: Various model reporting guidelines have been proposed to ensure clinical prediction models are reliable and fair. However, no consensus exists about which model details are essential to report, and commonalities and differences among reporting guidelines have not been characterized. Furthermore, how well documentation of deployed models adheres to these guidelines has not been studied. Objectives: To assess information requested by model reporting guidelines and whether the documentation for commonly used machine learning models developed by a single vendor provides the information requested. Evidence Review: MEDLINE was queried using machine learning model card and reporting machine learning from November 4 to December 6, 2020. References were reviewed to find additional publications, and publications without specific reporting recommendations were excluded. Similar elements requested for reporting were merged into representative items. Four independent reviewers and 1 adjudicator assessed how often documentation for the most commonly used models developed by a single vendor reported the items. Findings: From 15 model reporting guidelines, 220 unique items were identified that represented the collective reporting requirements. Although 12 items were commonly requested (requested by 10 or more guidelines), 77 items were requested by just 1 guideline. Documentation for 12 commonly used models from a single vendor reported a median of 39% (IQR, 37%-43%; range, 31%-47%) of items from the collective reporting requirements. Many of the commonly requested items had 100% reporting rates, including items concerning outcome definition, area under the receiver operating characteristics curve, internal validation, and intended clinical use. Several items reported half the time or less related to reliability, such as external validation, uncertainty measures, and strategy for handling missing data. Other frequently unreported items related to fairness (summary statistics and subgroup analyses, including for race and ethnicity or sex). Conclusions and Relevance: These findings suggest that consistent reporting recommendations for clinical predictive models are needed for model developers to share necessary information for model deployment. The many published guidelines would, collectively, require reporting more than 200 items. Model documentation from 1 vendor reported the most commonly requested items from model reporting guidelines. However, areas for improvement were identified in reporting items related to model reliability and fairness. This analysis led to feedback to the vendor, which motivated updates to the documentation for future users.

Evaluation of 1H-NMR Spectroscopy-Based Quantification Methods of the Supramolecular Aggregation of a Molecular Photosensitizer†.

The supramolecular dimerization of a ruthenium polypyridyl precursor of a well-developed family of hydrogen-evolving photocatalysts via π-π interactions of the polyheteroaromatic bridging ligand was quantified with concentration-dependent 1 H-NMR spectroscopy. The data sets were analyzed with different calculation and fit methods. A comparison between the results of direct calculation and linear and nonlinear approaches showed that the application of a global nonlinear fit procedure yields the best results. The presented methods are also applicable for dimerization processes in the solution of other molecular moieties.

Impact of Contact Force-Sensing Catheters on Fluoroscopy Time in Interventional Electrophysiology: A European Survey.

This multicenter European survey systematically evaluated the impact of using contact force-sensing catheters (CFSCs) on fluoroscopy and procedure time in interventional electrophysiology. Data from 25 participating centers were collected and analyzed, also considering important confounders. With the use of CFSCs, fluoroscopy time was reduced for right- and left-sided atrial ablations (median −6.4 to −9.6 min, p < 0.001 for both groups), whereas no such effect could be found for ventricular ablations. Moreover, the use of CFSCs was associated with an increase in procedure time for right-sided atrial and ventricular ablations (median +26.0 and +44.0 min, respectively, p < 0.001 for both groups), but not for left-sided atrial ablations. These findings were confirmed independent of career level and operator volume, except for very highly experienced electrophysiologists, in whom the effect was blunted. In the subset of pulmonary vein isolations (PVIs), CFSCs were shown to reduce both fluoroscopy and procedure time. In conclusion, the use of CFSCs was associated with a reduced fluoroscopy time for atrial ablations and an increased procedure time for right atrial and ventricular ablations. These effects were virtually independent of the operator experience and caseload. When considering only PVIs as an important subset, CFSCs were shown to reduce both fluoroscopy and procedure time.

Active repair of a dinuclear photocatalyst for visible-light-driven hydrogen production.

The molecular apparatus behind biological photosynthesis retains its long-term functionality through enzymatic repair. However, bioinspired molecular devices designed for artificial photosynthesis, consisting of a photocentre, a bridging ligand and a catalytic centre, can become unstable and break down when their individual modules are structurally compromised, halting their overall functionality and operation. Here we report the active repair of such an artificial photosynthetic molecular device, leading to complete recovery of catalytic activity. We have identified the hydrogenation of the bridging ligand, which inhibits the light-driven electron transfer between the photocentre and catalytic centre, as the deactivation mechanism. As a means of repair, we used the light-driven generation of singlet oxygen, catalysed by the photocentre, to enable the oxidative dehydrogenation of the bridging unit, which leads to the restoration of photocatalytic hydrogen formation.

Triple Antithrombotic Therapy in Patients With Left Ventricular Assist Devices.

Data on the efficacy and safety of the combination of warfarin and dual-antiplatelet therapy compared with warfarin and mono-antiplatelet therapy (MAPT) in patients with left ventricular assist devices (LVAD) remains scarce. Single-center study of 130 consecutive patients with durable LVAD. Baseline demographics, antithrombotic and antiplatelet regimen, and outcomes were compared between patients receiving warfarin plus dual-antiplatelet therapy (Group 1) and warfarin plus MAPT (Group 2). Antiplatelet therapy was assessed at hospital discharge post-LVAD implant and included aspirin, clopidogrel and dipyridamole. Outcomes at 1-year were assessed in each group. All patients were on aspirin and warfarin. No significant differences with regards to age, gender or ethnicity were noted at baseline between the two groups. Group 1 was more likely to have higher lactate dehydrogenase LDH levels at discharge and a history of stroke. No significant differences in international normalized ratio INR, hemoglobin or hematocrit were noted at discharge. During the study period, 48 patients had gastrointestinal bleeding events: 28 of 68 (41.2%) in Group 1 vs 20 of 62 (32.2%) in Group 2 (P = 0.293). At 1year, no statistically significant differences were noted in gastrointestinal bleeding (Group 1=27.90% vs Group 2 = 25.80, P = 0.784), ischemic stroke (Group 1 = 8.8% vs group 2 = 6.5%, P = 0.612), hemorrhagic stroke (Group 1 = 4.4% vs group 2 = 3.2%, P = 0.725) or mortality (Group 1 = 5.9% vs Group 2 = 1.6%, P = 0.206). Rates of pump thrombosis however were lower in Group 1 (Group 1 = 0% vs Group 2 = 6.5%, P = 0.033). Our study showed a high prevalence of triple-therapy antithrombotic use in LVAD patients with no significant differences in bleeding, stroke or survival. However, the risk for pump thrombosis was lower at 1-year when compared to patient receiving MAPT.

Neuropsychological, neuropsychiatric, and quality-of-life assessments in Alzheimer's disease patients treated with plasma exchange with albumin replacement from the randomized AMBAR study.

INTRODUCTION: We report the effects of plasma exchange (PE) with albumin replacement on neuropsychological, neuropsychiatric, and quality-of-life (QoL) outcomes in mild-to-moderate Alzheimer's disease (AD) patients in a phase 2b/3 trial (Alzheimer's Management by Albumin Replacement [AMBAR] study). METHODS: Three hundred forty-seven patients were randomized into placebo (sham-PE) and three PE-treatment arms with low/high doses of albumin, with/without intravenous immunoglobulin (IVIG). Specific test measurements were performed at baseline; month 2 (weekly conventional PE); months 6, 9, and 12 (monthly low-volume PE [LVPE]); and month 14. RESULTS: The PE-treated mild-AD cohort improved their language fluency and processing speed versus placebo at month 14 (effect sizes: >100%; P-values: .03 to .001). The moderate-AD cohort significantly improved short-term verbal memory (effect sizes: 94% to >100%; P-values: .02 to .003). The progression of the neuropsychiatric symptoms of PE-treated was similar to placebo. Mild-AD patients showed improved QoL (P-values: .04 to .008). DISCUSSION: PE-treated AD patients showed improvement in memory, language abilities, processing speed, and QoL-AD. No worsening of their psychoaffective status was observed.