Daily Anesthesiology Research Analysis
Analyzed 37 papers and selected 3 impactful papers.
Summary
Three impactful studies span perioperative pharmacology, neuro-predictive analytics, and resuscitation technology. A meta-analysis of 38 RCTs shows esketamine reduces postoperative nausea and vomiting but modestly prolongs recovery. Multimodal MRI+EHR modeling enhances prediction of postoperative delirium, while transthoracic impedance-based detection of gasping offers a feasible pathway to earlier cardiac arrest recognition.
Research Themes
- Perioperative antiemetic strategies and recovery trade-offs
- Multimodal data fusion for postoperative delirium prediction
- Automation of early cardiac arrest recognition using impedance signals
Selected Articles
1. Efficacy of esketamine in reducing nausea and vomiting after anesthesia: a systematic review and meta-analysis of randomized controlled trials.
Across 38 randomized trials (3,425 patients), perioperative esketamine reduced postoperative nausea and vomiting and accelerated gastrointestinal recovery, while modestly prolonging emergence time and PACU stay. Benefits were accompanied by decreased rescue analgesic needs within 48 hours.
Impact: This synthesis provides high-level evidence supporting esketamine as an adjunct for PONV prophylaxis, quantifying both benefits and recovery delays. It informs risk–benefit discussions and protocol design.
Clinical Implications: Consider esketamine for PONV prophylaxis in high-risk patients with careful dosing and monitoring, balancing antiemetic benefits against potential delays in emergence and PACU discharge. Integrate into multimodal pathways and tailor to patient priorities (e.g., rapid discharge vs PONV avoidance).
Key Findings
- Reduced postoperative nausea (RR 0.69, 95% CI 0.53-0.90) and vomiting (RR 0.75, 95% CI 0.57-0.98).
- Shortened time to first flatus (SMD -0.81, 95% CI -1.48 to -0.15).
- Lower rescue analgesic needs within 2 days (SMD 0.32, 95% CI 0.2-0.5).
- Prolonged anesthesia recovery time (SMD 0.97) and PACU stay (SMD 0.76).
Methodological Strengths
- Comprehensive meta-analysis of 38 randomized controlled trials.
- PROSPERO-registered protocol with sensitivity and subgroup analyses.
Limitations
- Between-study heterogeneity in dosing, timing, and comparator regimens.
- Potential publication bias and limited reporting on adverse neuropsychiatric effects.
Future Directions: Head-to-head trials versus standard antiemetics, dose-finding to minimize recovery delays, and stratified analyses by PONV risk profiles and ambulatory pathways.
BACKGROUND: Postoperative nausea and vomiting (PONV) are significant perioperative challenges. This study evaluated the efficacy of perioperative esketamine in preventing PONV. MATERIALS AND METHODS: We systematically searched Embase, PubMed, Web of Science, and the Cochrane Library from inception to August 2025 for randomized controlled trials investigating the effect of perioperative esketamine on PONV. The primary outcome was PONV incidence. Secondary outcomes included time to first flatus, postoperative pain degree, anxiety scores, agitation, anesthesia recovery time, and post-anesthesia care unit (PACU) stay duration. Data were analyzed using RevMan 5.4 and STATA 15.0 software. Sensitivity and subgroup analyses were performed to assess result stability and explore potential sources of heterogeneity. RESULTS: Thirty-eight randomized trials (3,425 patients) were included. Esketamine reduced the risk of nausea (RR=0.69, 95% CI: 0.53-0.90) and vomiting (RR=0.75, 95% CI: 0.57-0.98), shortened time to first flatus (SMD=-0.81, 95% CI: -1.48 to -0.15), and decreased rescue analgesic needs within 2 days (SMD=0.32, 95% CI: 0.2-0.5). However, it prolonged anesthesia recovery time (SMD=0.97, 95% CI: 0.28-1.67) and PACU stay (SMD=0.76, 95% CI: 0.27-1.26). CONCLUSIONS: Perioperative esketamine may reduce PONV and aid gastrointestinal recovery, but its potential to delay anesthesia recovery and PACU discharge requires consideration. Further studies are needed to clarify its risk-benefit profile. DATE OF FIRST SUBMISSION TO PROSPERO: 10 March 2024. DATE OF THE START OF STUDY SCREENING AGAINST ELIGIBILITY CRITERIA: 21 March 2024.
2. Detection of Gasping through Transthoracic Impedance: a New Approach to Early Cardiac Arrest Recognition.
In untreated porcine cardiac arrest, gasp-related inspiratory efforts produced large, characteristic transthoracic impedance fluctuations measurable via defibrillator pads. A human volunteer simulation corroborated signal detectability, supporting the feasibility of automated gasping detection to accelerate cardiac arrest recognition and bystander CPR initiation.
Impact: Introduces a practical, sensor-ready physiological signal for automated early cardiac arrest recognition, directly compatible with existing AED hardware. This could reduce recognition delays that cost lives.
Clinical Implications: AED algorithms could incorporate TTI-based gasping detection to distinguish agonal breathing from normal respiration, prompting earlier shock analysis and CPR prompts in public and in-hospital settings.
Key Findings
- Gasping produced large, distinctive TTI fluctuations in a porcine model of untreated cardiac arrest.
- Signal characteristics were replicated in a human volunteer simulating gasping under open and closed airway conditions.
- Findings support feasibility of automated TTI-based gasping detection within AEDs to improve early cardiac arrest recognition.
Methodological Strengths
- Translational setup bridging animal model and human simulation.
- Non-invasive, continuously measured signal using standard defibrillator pads.
Limitations
- Small-scale proof-of-concept without real-world cardiac arrest human validation.
- No diagnostic accuracy metrics (sensitivity/specificity) reported for clinical settings.
Future Directions: Prospective validation in out-of-hospital and in-hospital cardiac arrest cohorts, algorithm development for real-time AED integration, and assessment of impact on time-to-CPR and survival.
Timely recognition of cardiac arrest (CA) is often delayed because gasping, a frequent sign of CA, is misinterpreted as normal breathing. In a porcine model of untreated CA with frequent gasps, we tested whether transthoracic impedance (TTI), continuously measured through defibrillator pads, could capture gasping-related thoracic volume changes. Gasping-induced inspiratory efforts produced large, distinctive TTI fluctuations. This observation was then confirmed in a healthy human volunteer simulating gasping under open and closed airway conditions. These translational findings support automated, TTI-based gasping detection via automated external defibrillators as a feasible strategy to improve early CA recognition and accelerate bystander CPR initiation.
3. Fusion of clinical magnet resonance images and electronic health records promotes multimodal predictions of postoperative delirium.
Across two surgical cohorts, multimodal fusion of MRI-derived brain morphometry and EHR features predicted postoperative delirium with AUROC up to 0.86. Temporal cortical thickness and thalamic/brainstem volumes emerged as key neuroanatomical correlates, with benefits of fusion most pronounced in less critically ill patients.
Impact: Demonstrates practical value of routine clinical brain imaging to augment risk stratification for POD using interpretable neuroanatomical signals and modern machine learning.
Clinical Implications: In settings where preoperative MRI exists (e.g., oncology, neurosurgical workups), integrating morphometry with EHRs could identify high-risk patients, enabling targeted prevention (non-pharmacologic bundles, anesthetic plans) and postoperative monitoring.
Key Findings
- MLP-based multimodal models achieved AUROC up to 0.86 for POD prediction.
- Temporal cortical thickness and thalamic/brainstem volumes correlated with POD risk after age adjustment.
- Multimodal fusion improved prediction especially in less critically ill patients.
- Model weights implicated atrophy in higher-order regions (temporal pole, superior frontal gyrus, insula).
Methodological Strengths
- Use of two cohorts with confounding adjustment via mixed-effects models.
- Direct comparison of EHR-only, MRI-only, and fused models with modern ML.
Limitations
- Observational design without prospective external validation.
- MRI availability limits generalizability; workflow and timing constraints.
- Potential model interpretability and transport issues across institutions.
Future Directions: Prospective multicenter validation, pragmatic integration into perioperative pathways, and evaluation of clinical impact (e.g., POD incidence reduction) via randomized implementation studies.
Brain morphometry derived from clinical imaging has an underexplored potential for the multimodal prediction of postoperative delirium (POD), an acute encephalopathy that can lead to long-term adverse outcomes or death. This study conducted a comprehensive analysis of patient trajectories, integrating magnetic resonance imaging (MRI) data and electronic health records (EHRs) across two general surgical cohorts. We applied univariate test methods and linear mixed-effects models correcting for confounding. Non-linear multi-layer perceptrons (MLPs), boosted decision trees, and logistic regressions were trained on EHR data, brain morphometry measures, and their multimodal fusion to predict POD. Age-adjusted correlations identified cortical thickness of temporal gyri, as well as thalamic and brainstem volumes to be POD-relevant neuroanatomical features. MLP models demonstrated robust predictive capability, achieving notably high performances up to 86% AUROC (area under the receiver operating characteristic). Multimodal fusion yielded pronounced benefits in less critically ill patients. MLP model weights showed high predictive potential for cerebral atrophy in higher-order cortical regions, including the temporal pole, superior frontal gyrus, and the insula. These findings reveal the previously unrecognized potential of clinically derived brain morphometry in enhancing early multimodal predictions of POD. A better understanding of brain vulnerability in POD may translate into improved clinical decision making based on multimodal health care data.