Frazee, Erin N.

ABSTRACT Sodium glucose co‐transporter 2 inhibitors (SGLT2i) and glucagon‐like peptide‐1 receptor agonists (GLP‐1 RAs) are nephroprotective and their use is recommended for adults with chronic kidney disease (CKD). The role of SGLT2i and GLP‐1 RAs in patients with acute kidney injury (AKI) is unknown. This systematic review aimed to estimate the effect of SGLT2i or GLP‐1 RAs on clinical outcomes in AKI. We systematically searched Embase, MEDLINE, Scopus, Web of Science Core Collection, and clinical trials registries for observational studies and clinical trials from database inception to July 3, 2025. Included studies evaluated adults (≥ 18 years) with AKI during a hospitalization and compared patients subsequently exposed to either SGLT2i or GLP‐1 RAs to a non‐exposed control group. Random effects meta‐analyses were performed. Six studies related to SGLT2i (five observational, one randomized trial) and one related to GLP‐1 RAs (one observational) met eligibility criteria ( N = 432,048 patients). SGLT2i/GLP‐1 RA exposure was associated with lower odds of major adverse kidney events (MAKE) (OR 0.63, 95% CI 0.46–0.86) and all‐cause mortality [odds ratio (OR) 0.36, 95% confidence interval (CI) 0.21–0.62] compared to controls. The odds for kidney replacement therapy were lower with SGLT2i therapy in the three studies where it was evaluated (OR 0.61, 95% CI 0.40–0.93). Sensitivity analyses restricted to SGLT2i data were consistent with the overall findings [MAKE OR 0.63 (95% CI 0.42–0.95); Mortality OR 0.34 (95% CI 0.18, 0.65)]. Exposure to SGLT2i or GLP‐1 RAs after AKI, examined primarily in observational studies, was associated with improved clinical outcomes. These findings are promising and warrant evaluation in randomized clinical trials.

OBJECTIVE: A definition of refractory septic shock is necessary to guide diagnosis, management, prognostication, research, and future guidelines for this most severe form of the disease. We sought to achieve consensus on clinical criteria that would be used to define refractory septic shock. DESIGN: Review of literature, expert panel position statements, and Delphi rounds with an international expert group. SETTING: Consensus was defined as having at least 75% of panellists in agreement or disagreement on the three highest or lowest levels of a 7-point Likert scale or based on responses to single- or multiple-choice questions, respectively. SUBJECTS: A panel of multinational, multiprofessional and multidisciplinary critical care experts assembled by the Society of Critical Care Medicine and the European Society of Intensive Care Medicine (57 invitations and 56 participants). MEASUREMENTS AND MAIN RESULTS: A five-round Delphi process was conducted for consensus and stability. The steering committee proposed 34 statements, and five of them were rejected by panel experts after round 2. Among 29 statements selected from eight domains, consensus was reached for 13. The panel agreed on the need for a comprehensive consensus set of clinical criteria for refractory septic shock. Markers of organ dysfunction (75%, 2 rounds), tissue perfusion (91.1%, 2 rounds) including lactate (94.6%, 2 rounds) and capillary refill time (76.8%, 2 rounds), assessment of fluid-responsiveness after initial resuscitation (92.9%, 5 rounds), and use of vasoactive drugs at norepinephrine equivalents greater than 0.5 µg/kg/min (75.0%, 3 rounds), were selected as clinical criteria of refractory septic shock. The use of critical care ultrasound (CCUS) (92.9%, 3 rounds) was the single diagnostic modality that reached a consensus-based agreement. CONCLUSIONS: A consensus for 13 criteria to frame the definition of refractory septic shock was reached. Refractory septic shock is characterised by persistently elevated lactate concentrations and or prolonged capillary refill time in patients with septic shock who are fluid unresponsive, require a norepinephrine base equivalent dose greater than 0.5 micrograms per kilogram per minute, and undergo CCUS assessment when mixed shock is suspected.

Artificial intelligence (AI) and machine learning have emerged as transformative analytic methods capable of identifying patterns and proposing treatment strategies from complex, heterogeneous data generated in patient care settings. However, translating reports of AI tools into clinical decision-making requires careful interpretation and contextualization. Here, we present a structured framework (ABCDEFG framework) for clinicians to critically appraise studies reporting AI tools using a high-profile reinforcement learning analysis of vasopressin initiation in septic shock as a case example. We highlight key methodological strengths including adherence to reporting standards, alignment with clinical outcomes, and external validation. We also identify limitations related to causal inference, reward structure design, assumptions about state representation, and implementation readiness. Our framework emphasizes the importance of rigorous, clinically grounded critique before integrating AI tools into practice, and provides a roadmap for clinicians to assess the reliability and relevance of emerging AI-based decision support.

Background: A summary of pharmacist-based practices and preferences regarding the prevention and management of high-dose methotrexate (HDMTX) toxicity in patients with lymphoma does not exist. The objective of the study was to describe current practices surrounding management of HDMTX therapy. This was done through a self-administered, web-based, cross-sectional survey of hematology/oncology pharmacists to ascertain practices and individual attitudes about HDMTX therapy management. Responses were summarized with descriptive statistics. Response comparisons were made using Chi-square or Fisher’s exact test for categorical data, and Kruskal-Wallis test for variables on the Likert scale. A total of 175 pharmacists provided 116 eligible surveys [68 (59%) complete responses and 48 (41%) partial responses] for analysis. The Cockcroft-Gault estimated creatinine clearance formula was the most preferred method (61%) to estimate kidney function and determine HDMTX dosing. Respondents would proceed with HDMTX therapy until the predicted risk of AKI, or AKI stage 2 or 3, exceeded 50% and 20%, respectively. Preferred treatment modifications for a high predicted AKI risk after HDMTX exposure included additional prehydration, HDMTX dose reduction, and increased kidney function monitoring, although levels and degrees of agreement varied. Respondents indicated that serum cystatin C-based GFR estimates lacked evidence, accuracy, and practicality in HDMTX therapy management. There was limited familiarity with cell cycle arrest biomarker use during HDMTX therapy. Glucarpidase was considered time-sensitive and highly effective; however, acquisition cost represented a barrier to use, and the optimal dose was felt to be unknown. In conclusion, there is significant heterogeneity in supportive practices and beliefs about the optimal management for patients with lymphoma receiving HDMTX therapy.

Abstract Background The accuracy and safety of generating medication orders by large language models (LLMs) must be demonstrated. Without standardization, performance evaluation is limited to time and resource-intensive clinician grading. This evaluation aimed to develop a standardized medication format that supports automated performance evaluation (MedMatch). Methods First, a survey of 40 medication prompts was given to clinicians to assess agreement in medication order communication. Second, a clinician panel developed a standardized medication format (MedMatch) for oral and intravenous medications. Third, a clinician-annotated dataset of medication prompts and standardized answers in the MedMatch format was developed for LLM testing. Finally, LLMs were retested with the same dataset, adjusted to exclude route information, to evaluate the appropriate categorization of medication route. Results The formal medication orders consistently showed low omission rates and high overlap for all entities, compared to the verbal and brief written communication types. Lexical overlap results demonstrated pattern norms amongst clinicians with entities appearing most commonly in positions 1-5 in the order of drug name, dose, unit, route, and frequency. In the second survey, the formal written group performed the highest with 78.3% of prompts considered appropriate as a computer-generated response. LLM accuracy on MedMatch order standardization was highest in oral solid (64.2-72.5%), intravenous intermittent (72.5-84.3%), and intravenous push (62.7-74.5%) categories. LLMs performed the worst at categorizing medication orders accurately into intravenous push (18-61%) and intravenous intermittent (51-100%) routes. Conclusions Standardized format for computer-based outputs may support automated performance analysis and enhance the clarity of medication communication.

Abstract Background Large language models (LLMs) are increasingly used in medication-related tasks despite limited evidence supporting their accuracy and safety. LLMs are vulnerable to adversarial attacks and may confabulate in response to fabricated information in the prompt. Given the substantial risk of harm from medication errors, the purpose of this study was to establish a benchmark task to evaluate LLM performance in distinguishing fabricated from real medications. Methods Two datasets (brand and generic), each consisting of 250 medication lists were developed, each including 4-6 medications and one fabricated medication (specifically a Pokémon character), with complete dosing information provided. Six LLMs were evaluated (GPT-5-Chat, GPT-4o-mini, DrugGPT, Gemma-3-27B-IT, Llama-3.3-70B-Instruct, and Qwen3-32B). For each medication list, models were queried for dosing information and disease indication across three experimental conditions: standard decoding, standard decoding with mitigation prompting, and deterministic decoding (temperature = 0). Each experiment was performed in triplicate. Outputs were deemed confabulations if the LLM failed to recognize and reject the fabricated medication. The primary outcome was the rate of confabulations. Exact paired-permutation tests were used to compare confabulation rates across LLMs and prompting approaches. Results Confabulation rates under standard conditions ranged from 2.7% to 99.6%. DrugGPT demonstrated the lowest baseline confabulation rates (2.7-6.4%), whereas other models demonstrated substantially higher rates (47.2-99.6%). Mitigation prompting significantly reduced confabulation rates compared to standard and deterministic decoding across all tasks and datasets (p < 0.001): GPT-5-Chat achieved confabulation rates of 0% with mitigation prompting from baseline rates of 66.4-88.5%. Deterministic decoding did not substantially impact confabulation rates. Conclusions LLMs performing medication-related tasks are susceptible to confabulation in response to invalid or fabricated inputs, raising important safety concerns for clinical use. While mitigation prompting reduced confabulation rates, this study underscores the importance of robust safeguards when deploying LLMs in medication-related decision-making.

Abstract Background Outpatient parenteral antimicrobial therapy (OPAT) is effective and cost-efficient, but 12%–63% of patients experience adverse events, requiring close monitoring. Therapeutic drug monitoring (TDM) is routinely used for vancomycin and aminoglycosides but is underutilized for beta-lactams (BLs) due to limited access, logistical barriers, and clinician inexperience. This study evaluated its use and clinical impact within a high-volume OPAT program. Methods This retrospective, single-center cohort included adult patients (≥18 years) enrolled in an OPAT program who received cefepime (CEF), piperacillin/tazobactam (TZP), or meropenem (MEN) and underwent BL TDM between April 2023 and April 2025. BL TDM was not protocolized within the OPAT program; however, institutional guidance supported its use in carefully selected patients. We aimed to describe TDM indications, lab trends, microbiological profiles, and the impact of TDM on treatment. Results During the study period, 25 OPAT patients underwent BL TDM. TDM was performed in patients receiving CEF (64%), TZP (20%), and MEN (16%). The most common indication for TDM was suspected beta-lactam toxicity (32%) (Figure 1). Post-TDM therapy adjustments were made in 52% (n = 13), including changes in dosing frequency (38.5%) and dosage strength (30.8%). Additional changes included infusion strategy modification or agent substitution (15.4% each). Subtherapeutic levels were the most common reason for adjustment (46.2%), followed by adverse effects, supratherapeutic levels, and changes in renal function (23.1% each). Skin, soft tissue, and musculoskeletal infections were most common (Figure 2). Positive cultures were present in 80%, with Pseudomonas spp most frequently isolated. Therapy was completed in 17 patients; 7 discontinued early, and 1 had treatment ongoing. Reasons for therapy discontinuation are detailed in Table 1. Conclusion BL TDM is contributing to a paradigm shift in OPAT practice by improving both efficacy and safety. While inpatient TDM typically targets efficacy, safety concerns more commonly motivate TDM in the OPAT setting. Although this review involved a small cohort, our experience adds to the growing body of evidence supporting BL TDM as a tool to enhance patient outcomes in the OPAT setting. Disclosures All Authors: No reported disclosures

Abstract Background Beta-lactam pharmacokinetics can be significantly altered in patients with malignancy. Therapeutic drug monitoring (TDM; i.e., drug level testing) may facilitate pharmacokinetic/pharmacodynamic (PK/PD) target attainment, but the data are limited. This study aimed to characterize PK/PD target attainment of anti-pseudomonal beta-lactams in hematology/oncology patients.Table 1:Case DescriptionGraph 1:Distribution of Beta-Lactam TDM Indications Methods We conducted a retrospective review of beta-lactam TDM performed between June 2022 and June 2024 in hospitalized adults with hematologic or solid organ malignancies. We included patients who underwent beta-lactam TDM during clinical care, typically for extracorporeal membrane oxygenation (ECMO), extremes of body weight, suspected augmented renal clearance, multidrug-resistant infection, or suspected dose-dependent beta-lactam toxicity. Primary outcomes were (1) the proportion achieving target beta-lactam concentrations and (2) the proportion undergoing antibiotic dose adjustment based on TDM results.Table 2:Institutional Dosing Guidelines Results There were 47 TDM episodes in 20 unique patients. Considering only the first TDM per patient, 10 (50%) patients achieved target concentrations, 2 (10%) were below target, and 6 (30%) were above target. Antibiotic dose adjustments were made in 8 (40%) patients in response to TDM results. The most common indication for beta-lactam TDM was concerns regarding accurate renal clearance, followed by extremes of weight. Conclusion Retrospective findings suggest that beta-lactam TDM facilitated individualization of therapy in 40% of patients with either hematological or solid organ malignancy, with dose adjustments often intended to improve antibiotic safety. Future studies should refine the criteria for beta-lactam TDM use in this population and determine the need for tailored PK/PD targets in patients with impaired host response to infection. Disclosures All Authors: No reported disclosures