Frazee, Erin N

ABSTRACT Beta‐lactam antibiotics (beta‐lactams) are first‐line treatments for most major infectious syndromes in acutely ill patients, with in vitro activity against common pathogens, demonstrated efficacy in trials, and a perceived low risk of adverse effects. The current dosing approach, informed by population‐level data, tends to be simplistically reduced to a “one size fits all” dosing method which only accounts for body weight (i.e., in pediatrics) and end organ function to estimate drug clearance (e.g., estimated glomerular filtration rate); this approach results in substantial variability in observed serum concentrations in acutely ill patients, which can compromise real‐world effectiveness and safety. Beta‐lactam dose individualization, therefore, defined as a dosing regimen for one patient informed by measured concentrations from that patient, has been recommended in international clinical guidelines. Comprehensive guidance on best practices for beta‐lactam dose individualization is lacking. To address this knowledge gap and develop guidance, a multidisciplinary panel of international experts was assembled from the disciplines of infectious diseases, critical care, pharmacometrics, and laboratory medicine, representing both adults and pediatrics. The panel systematically evaluated the literature, using the GRADE approach where feasible, to address broad thematic questions pertaining to whether beta‐lactam dose individualization should be pursued, for what indications beta‐lactam dose individualization is warranted, and the most critical considerations and best practices for implementation of beta‐lactam individualization. The resultant consensus recommendations will equip healthcare professionals caring for acutely ill patients treated with beta‐lactam therapy with the evidence and tools necessary to guide optimal use of beta‐lactam dose individualization.

Accurate assessment of renal function is fundamental to dosing renally eliminated drugs. For decades, serum creatinine–based estimating equations have been used to approximate glomerular filtration rate (GFR) for this purpose. While these equations perform reasonably well at the population level, they exhibit substantial individual‐level imprecision and inaccuracy. Simultaneously, recent efforts to remove race from estimating equations addressed important ethical concerns but did not resolve the core limitation of population‐based estimation for individual drug dosing. In this perspective, we argue that personalized dosing requires a shift away from demographic surrogates toward direct measurement of renal clearance and clarify when incremental therapeutic complexity is warranted. We review the limitations of endogenous biomarkers, discuss the role of cystatin C, and propose exogenously measured GFR using iohexol integrated within a Bayesian pharmacometric framework as a clinically actionable approach to individualized drug dosing. Practically, this framework can be implemented by administering a small iohexol dose, collecting sparse timed samples, estimating absolute mGFR and its uncertainty with Bayesian software, and using that posterior estimate to refine renal dose selection.

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.

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: For large language models (LLMs) to reach their potential as information technology tools that make medication use safer, clinically relevant benchmarks capable of automated grading and designed specifically to measure the performance of LLMs for medication tasks are required. The purpose of this study was to design a suite of benchmarking tests reflective of Comprehensive Medication Management (CMM; the standard of care for medication optimization) and quantify the baseline performance of the latest LLMs. Methods: We established six benchmarks representing critical stages of the CMM process: drug formulation matching, drug order (sig) generation, drug route matching, drug-drug interaction identification, renal dose identification, and drug-indication matching. For each benchmark, we curated a clinician-annotated dataset comprising 250 standardized input-output pairs including both inpatient and outpatient medications. We evaluated the clinical knowledge retrieval capabilities of three LLMs: GPT-4o-mini, MedGemma-27B, and LLaMA3-70B. We employed a zero-shot prompting strategy, excluding in-context examples, to assess the models’ internal clinical knowledge rather than their few-shot learning potential. To check reliability, each model was run three times using a temperature of 0.7 (a mid-range value of an LLM setting controlling text generation randomness). Performance was assessed using task-specific evaluation metrics including precision (positive predictive value), recall (sensitivity), F1-score, accuracy, and correctness consistency across trials. Results: Across six benchmarks, LLaMA3-70B demonstrated the highest performance in four tasks: drug-formulation matching (F1, 54.0% [95 CI: 50.1-58]), drug-order generation (accuracy, 88.0%), drug-route identification (F1, 74.3% [95 CI: 71-78]), and drug-indication identification (accuracy, 97.6% [95 CI: 95.6-99.2]). In the drug–drug interaction task, GPT-4o-mini achieved the highest overall accuracy (70.4% [95 CI: 64.8-75.7]). For renal dose–adjustment identification, GPT-4o-mini demonstrated the highest F1 score (83.3% [95 CI: 77.6-88]). Correctness-consistency scores ranged from 8.0% to 97.6% across benchmarks, with no model exhibiting uniformly superior consistency. Conclusions: Model performance varied substantially across medication-related tasks. LLaMA3-70B demonstrated promising baseline performance in tasks involving formulation, ordering, route, and indication. GPT-4o-mini showed potential advantages in drug–drug interaction detection and renal dose adjustment. These findings underscore the need for task-specific evaluation when deploying models for medication-focused clinical decision support.

Abstract Background The purpose of this study was to develop a series of benchmarks specifically for large language model (LLM) performance in the identification of drug-drug interactions (DDIs). Methods We evaluated six LLMs using a clinician-designed dataset of 750 DDI scenarios. Tasks included: (1) a pointwise two-drug classification task (2) a pairwise three-drug discrimination task and (3) a listwise 4–6 drug selection task. Zero-shot prompting was applied and performance was assessed using precision, recall, F1 score, accuracy, and self-consistency. Results Model performance varied by task structure and interaction severity. GPT-5-Chat demonstrated the highest recall and F1 score in the pointwise task (80.7% and 80.2%), whereas DrugGPT achieved superior accuracy in the pairwise (93.7%) and listwise task (92.6%). All models had improved performance with more severe compared to less severe DDIs (accuracy 72.0-99.2% vs. 58.3-86.7%). All models tended to have higher self-consistency in the pairwise and listwise experiments compared to the pointwise experiment (68.8-93.6% vs. 31.0-74.2%). No model exhibited uniformly high reliability across all tasks. Conclusions Current LLMs show inconsistent capabilities in identifying DDIs. Performance degrades as reasoning space expands with limited stability. These findings emphasize the need for multi-format evaluation frameworks and reliability-aware assessment when considering LLMs for medication-safety applications.