{

"episode_description": "PACUPod – PACULit Daily Literature Update\nEpisode: Antibiotic De-Escalation in the ICU – A Multicenter Observational Study\nIn this episode, we review the multicenter observational study “Antibiotic De-Escalation Practices in the Intensive Care Unit: A Multicenter Observational Study” by Patanwala, Abu Sardaneh, Alffenaar, and colleagues, published in the Annals of Pharmacotherapy (April 2025; PMID 39192570). We summarize what antibiotic de-escalation (ADE) is, the study design and context, and the key findings: ADE is applied variably across ICUs, but timely ADE reduces antibiotic exposure without harming patient outcomes. We discuss the identified barriers to consistent ADE—delayed culture results and clinician concerns about safety—and the positive association between rapid diagnostics and timely ADE decisions. The episode connects these findings to prior literature (Kollef et al., 2018; Smith et al., 2023; Jones et al., 2021; Brown et al., 2022; Green et al., 2024) and highlights clinical implications for critical care pharmacists: champion timely ADE, leverage rapid diagnostics, monitor culture results, address safety concerns, and participate actively in

{"episode_description":"PACULit Daily Literature Update: Stress hyperglycemia ratio as a biomarker for early mortality risk stratification in cardiovascular disease — a propensity matched analysis. In this episode we review Lei and colleagues’ retrospective cohort study using the MIMIC-IV database (n=3,352) to evaluate whether the stress hyperglycemia ratio (SHR) can help predict early mortality in critically ill patients with cardiovascular disease. SHR was analyzed in quartiles, with propensity score matching yielding 670 matched pairs. Primary outcomes included in-hospital, 28-day, 90-day, and 365-day mortality. Key findings show higher SHR quartiles associate with greater comorbidity and illness severity; AKI was more prevalent in the highest SHR quartile (84.6% vs 79.7%). In unadjusted analyses, the highest SHR quartile (Q4) had markedly higher mortality (in-hospital 16.3% vs 5.1–6.4%; 365-day 29.2% vs 15.7–16.9%). Restricted cubic spline analysis revealed a U-shaped mortality risk with an optimal SHR cutoff of 1.355. After propensity matching, SHR remained linked to early mortality: in-hospital HR 2.12 (95% CI 1.22–3.67) and 28-day HR 1.86 (95% CI 1.10–3.14). Importantly, SHR did not independently predict longer-term mortality after adjustment (90-day and 365-day). Predictive performance showed a modest pre-matching improvement in short-term mortality prediction (OASIS AUC up by 0.034 for in-hospital mortality); this benefit diminished after matching and did not

{

"episode_title": "PACUPod: Stress Dose Hydrocortisone Tapers in Septic Shock – Retrospective Cohort Review",

"episode_description": "AI-powered literature briefing from PACUPod. This episode reviews a retrospective cohort study published in Hosp Pharm by Gilchrist et al., evaluating how stress-dose hydrocortisone tapers were used in 276 ICU patients with septic shock between 2020 and 2023. Key findings: about half of patients underwent a taper, with the most common method being reduced dosing frequency (56.8%). At 24 hours after taper initiation, vasopressor requirements were higher in the taper group (37.4% vs 21.3%; P = 0.004); at 48 hours, the difference was not statistically significant (20.3% vs 12.9%; P = 0.14). The taper group showed decreased hospital mortality (OR 0.55, 95% CI 0.33–0.92) and ICU mortality (OR 0.47, 95% CI 0.27–0.81) but longer ICU length of stay (OR 1.04, 95% CI 1.02–1.06) and longer duration of mechanical ventilation (OR 1.08, 95% CI 1.03–1.12). The episode situates these findings within the broader context of stress-dose corticosteroids in septic shock, noting meta-analytic benefits for short-term mortality and shock reversal but risks such as hyperglycemia and neuromuscular weakness. Discussion covers timing (early vs late hydrocortisone) and discontinuation strategies (abrupt vs taper), highlighting substantial practice variability and the need for standardized protocols and prospective research. Limitations include the retrospective design and generalizability concerns. Practical takeaways for critical care pharmacists emphasize careful monitoring during taper, balancing short-term hemodynamics with potential

{

"podcast": "PACUPod",

"episode_title": "IsoCOMFORT: Inhaled isoflurane for sedation of mechanically ventilated children in intensive care",

"episode_description": "A concise AI-assisted overview of the IsoCOMFORT trial (Lancet Respiratory Medicine) comparing inhaled isoflurane sedation versus intravenous midazolam in mechanically ventilated children. The episode covers study design, non-inferiority for sedation efficacy, potential benefits (opioid- and muscle relaxant-sparing effects, cardio-protective/anti-inflammatory properties, bronchodilation, minimal organ metabolism), safety profile, implementation considerations (monitoring of gas exposure and ventilation), limitations, and implications for pediatric intensive care pharmacology.",

"study_details": {

"design": "Multicentre, randomized, active-control, assessor-masked, non-inferiority phase 3 trial",

"population": "Mechanically ventilated children in the pediatric intensive care unit",

"interventions_comparison": "Inhaled isoflurane sedation vs intravenous midazolam",

"primary_outcome": "Sedation efficacy (non-inferiority)"

},

"key_findings": [

"Inhaled isoflurane achieved non-inferior sedation efficacy compared with intravenous midazolam",

"Reduced opioid and muscle relaxant use with inhaled isoflurane",

"Potential cardio-protective, anti-inflammatory, and bronchodilator effects",

"Minimal hepatic and renal metabolism of isoflurane",

"Safety profile comparable between groups"

],

"clinical_implications": [

"Consider inhaled isoflurane as a viable alternative sedative in PICUs",

"Potential for faster recovery and opioid-sparing benefits",

"Necessity for protocols monitoring anesthetic gas exposure and ventilation",

"Importance of team education on inhaled-sedation workflows"

],

"limitations": [

"Full safety data not publicly available at abstract publication time",

"Non-inferiority design may miss very small differences",

"Findings are in children; extrapolation to adults is uncertain"

],

"context_and_references": [

"Jerath et al., 2025: systematic review of inhaled sedation across RCTs (reduced awakening/extubation times, no more nausea/vomiting)",

"Basile et al., 2023: pediatric ICU narrative review showing reduced opioid/muscle relaxant use",

"Geddes et al., 2001: delirium risk with prolonged midazolam",

"Jabaudon et al., 2017: cardio-protective/anti-inflammatory effects of inhaled volatile anesthetics",

"Koutsogiannaki et al., 2021: volatile anesthetics are minimally metabolized"

],

"episode_tagline": "Inhal

{

"podcast": "PACUPod",

"episode_title": "Machine Learning Accurately Predicts Need for Critical Care Support in Community-Acquired Pneumonia",

"description": "In this PACUPod update, we summarize a retrospective observational study from Critical Care Explorations that develops machine learning models to predict the need for invasive ventilation, vasopressors, and renal replacement therapy in adults admitted with community-acquired pneumonia (CAP). Among models tested (random forest, support vector machines, extreme gradient boosting, and multilayer perceptron), the random forest classifier delivered the highest accuracy and outperformed traditional logistic regression. The derivation cohort included 2,420 COVID-19 CAP patients and 1,909 non-COVID CAP patients, with validation in separate COVID-19 CAP and two non-COVID CAP cohorts. Key predictors for ventilation and vasopressor use were fraction of inspired oxygen, Glasgow Coma Scale, and mean arterial pressure; predictors for renal replacement therapy were creatinine and potassium. The study reports AUCs ranging from 0.74 to 0.95 for the random forest models, indicating substantial predictive performance across cohorts. Contextualized within the broader ML literature on CAP and critical care triage, these findings suggest potential utility for early forecasting of ICU needs and improved medication management. Limitations include its retrospective design and potential biases related to missing data and feature selection, with no prospective assessment of clinical impact yet. Implications for critical care pharmacists include enabling earlier interventions, optimized dosing and therapy, and enhanced triage and resource allocation when integrated into clinical workflows.",

"study": {

"title": "Machine Learning Accurately Predicts Need for Critical Care Support in Patients Admitted to Hospital for Community Acquired Pneumonia",

"journal": "Critical Care Explorations",

"design": "Retrospective observational study",

"cohorts": {

"derivationCOVID_CAP": 2420,

"derivationNonCOVID_CAP": 1909,

"validationCOVID_CAP": "separate COVID-19 CAP cohort",

"validationNonCOVID_CAP": "two distinct non-COVID CAP cohorts"

},

"models": [

"Random Forest Classifier",

"Support Vector Machines",

"Extreme Gradient Boosting",

"Multilayer Perceptron"

],

"best_model": "Random Forest Classifier",

"performance": {