Daily Anesthesiology Research Analysis

BACKGROUND: General anesthesia, such as isoflurane, induces analgesia (loss of pain) and loss of consciousness through mechanisms that are not fully understood. A distinct population of γ-aminobutyric acid-mediated neurons has been recently identified in the central amygdala (CeA) that can be activated by general anesthesia (CeA GA ) and exert antinociceptive functions. In this study, the authors aimed to explore the underlying cellular mechanisms of CeA GA neurons across different phases of nerve injury-induced nociceptive sensitization in mice. METHODS: This study used 107 mice, including 57 males and 50 females. The authors induced c-fos activation in the mice brains using 1.2% isoflurane and validated Fos expression via RNAscope (Advanced Cell Diagnostics, USA) in situ hybridization. Unlike previous studies using the capturing activated neuronal ensembles method, CeA GA neurons (tdTomato + ) were labeled using the Fos-Targeted Recombination in Active Populations (TRAP2) method. The authors then performed ex vivo electrophysiologic recordings to assess the properties of both Fos-positive/CeA GA neurons and Fos-negative CeA neurons. Using chemogenetic strategy to selectively activate the CeA GA neurons, the authors investigated pain-like behaviors and associated comorbidities in mice after spared nerve injury (SNI). RESULTS: Isoflurane induced robust Fos expression in CeA γ-aminobutyric acid-mediated neurons. Electrophysiologic recordings in brain slices revealed that compared to Fos-negative CeA neurons, CeA GA neurons had higher excitability and exhibited distinct patterns of action potentials. Chemogenetic activation of Fos-TRAPed CeA GA neurons increased nociceptive thresholds in naive mice and in mice 2 weeks after SNI, but demonstrated modest antinociception 8 weeks after SNI. Finally, Fos-negative CeA neurons, but not CeA GA neurons, exhibited increased excitability in the chronic phase of SNI, which was correlated with a downregulation of K + -Cl - cotransporter-2 (KCC2) in the CeA (sham vs . SNI 8 weeks). CONCLUSIONS: These results validate the antinociceptive power of CeA GA neurons using a different approach. Additionally, the authors highlight distinct roles of CeA GA neurons in governing physiologic pain, acute pain, and the transition to chronic pain through KCC2 dysregulation.

IMPORTANCE: Automating the identification of risk for developing hospital delirium with models that use machine learning (ML) could facilitate more rapid prevention, identification, and treatment of delirium. However, there are very few reports on the performance of ML models for delirium risk stratification in live clinical practice. OBJECTIVE: To report on development, operationalization, and validation of a multimodal ML model for delirium risk stratification in live clinical practice and its associations with workflow and clinical outcomes. DESIGN, SETTING, AND PARTICIPANTS: This quality improvement study developed an ML model supported by automated electronic medical records to stratify the risk of non-intensive care unit delirium in live clinical practice using the Confusion Assessment Method as the diagnostic reference standard, with an iterative model update method. Data from patients aged at least 60 years admitted to non-intensive care units at Mount Sinai Hospital between January 2016 and January 2020 were used to train and test the ML model presented. The model was validated in live clinical practice from March 2023 to March 2024. Analysis of the model's associations with workflow and clinical outcomes was conducted retrospectively in 2024, comparing hospitalized patients prior to deployment of any model version (pre-ML cohort) and during model clinical deployment (post-ML cohort). MAIN OUTCOMES AND MEASURES: Outcomes of interest were area under the receiver operating characteristic curve, monthly delirium detection rates, median length of hospital stay, and daily doses of opiate, benzodiazepine, and antipsychotic medications administered. RESULTS: The overall sample included 32 284 inpatient admissions (mean [SD] age, 73.56 (9.67) years, 15 157 [46.9%] women). A total of 25 261 inpatient admissions of older patients with both medical and surgical primary diagnoses represented the combined model testing and training cohort (median age, 73.37 [66.42-81.36] years) and live clinical deployment validation cohort (median [IQR] age, 72.11 [62.26-78.97] years), while 7023 inpatient admissions of older patients with both medical and surgical primary diagnoses represented the combined pre-ML (median [IQR] age, 74.00 [68.00-81.00] years) and post-ML (median [IQR] age, 75.33 [68.34-82.91] years) cohorts. The model presented is a fusion of electronic medical record patient data features and clinical note features processed by natural language processing. The results of model validation in live clinical practice included an area under the curve of 0.94 (95% CI, 0.93-0.95). Median (IQR) monthly delirium detection rates of inpatients assessed for delirium with the Confusion Assessment Method increased from 4.42% (95% CI, 3.70%-5.14%) in the pre-ML cohort to 17.17% (95% CI, 15.54%-18.80%) in the post-ML cohort (P < .001). Post-ML vs pre-ML cohorts received lower daily doses of benzodiazepines (median [IQR] 0.93 [0.42-2.28] diazepam dose equivalents vs 1.60 [0.66-4.27] diazepam dose equivalents; P < .001) and olanzapine (median [IQR], 1.09 [0.38-2.46] mg vs 2.50 [1.17-6.65] mg; P < .001). CONCLUSIONS AND RELEVANCE: This quality improvement study demonstrates the feasibility of a novel multimodal ML model to automate delirium risk stratification in live clinical practice. The model demonstrated acceptable performance in live clinical practice and may facilitate resource allocation to enhance delirium identification and care.

BACKGROUND: Intraoperative cardiac arrest (IOCA) is a rare but catastrophic event with significant morbidity, mortality, and health care costs. This study aimed to characterize the frequency, risk factors, and outcomes of IOCA. METHODS: Adults undergoing noncardiac surgery were identified in the 2016 to 2021 National Inpatient Sample. IOCA events were identified using the relevant International Classification of Diseases code. Multivariable regression models examined factors independently associated with IOCA and in-hospital mortality. The significance of temporal trends was calculated using Cuzick's nonparametric test. RESULTS: Among 2671,834 noncardiac surgical admissions, 1294 (0.05%) experienced IOCA. The incidence increased from 0.05% to 0.06% during the study period, coinciding with an increase in nonelective operations during the coronavirus disease-2019 (COVID-19) pandemic. IOCA was associated with a 39.3% in-hospital mortality rate and increases in length of stay and hospitalization costs. Key risk factors for IOCA included advanced age, male sex, Black race (adjusted odds ratio [AOR] 1.40, 95% CI, 1.20-1.65), low-income status (AOR 1.21, 95% CI, 1.02-1.43), treatment at government nonfederal hospitals (AOR 1.22, 95% CI, 1.08-1.50), high-risk surgical procedures, and significant comorbidities such as congestive heart failure, cardiac arrhythmias, and valvular disease. CONCLUSIONS: Despite the initial reduction in the incidence of IOCA, this study highlights a temporal increase coinciding with the COVID-19 pandemic and an increase in nonelective surgeries. Future research should explore more granular predictors of IOCA and its outcomes to develop targeted interventions for at-risk populations and tailor guidelines to manage emerging challenges in population health.