Anesthesiology最新文献

Estimating effects of interventions is a central task in perioperative and critical care outcomes research. While randomized trials remain the accepted standard for causal inference, trial data are not always available to inform clinical decisions, and some questions cannot be answered feasibly or efficiently with trials. In these settings, studies using observational healthcare data may be used to inform practice. Causal inference from observational data has been reconsidered in recent years, challenging the prevailing notion among clinical researchers that causal conclusions cannot be drawn from observational studies. The "target trial framework" is one contribution within a growing methodologic field that helps investigators avoid common pitfalls in observational study design and analysis. Importantly, researchers must understand which biases this framework can-and cannot-help avoid. The authors present an overview of target trial emulation and describe the promise and limitations of this framework for improving observational perioperative and critical care outcomes research.

Background: According to the model of the glymphatic system, the directed flow of cerebrospinal fluid (CSF) is a driver of waste clearance from the brain. In sleep, glymphatic transport is enhanced, but it is unclear how it is affected by anesthesia. Animal research indicates partially opposing effects of distinct anesthetics, but corresponding results in humans are lacking. Thus, this study aims to investigate the effect of sevoflurane anesthesia on CSF flow in humans, both during and after anesthesia.

Methods: Using data from a functional magnetic resonance imaging experiment in 16 healthy human subjects before, during, and 45 min after sevoflurane monoanesthesia of 2 volume percent (vol%), the authors related gray matter blood oxygenation level-dependent signals to CSF flow, indexed by functional magnetic resonance imaging signal fluctuations, across the basal cisternae. Specifically, CSF flow was measured by CSF functional magnetic resonance imaging signal amplitudes, global gray matter functional connectivity by the median of interregional gray matter functional magnetic resonance imaging Spearman rank correlations, and global gray matter-CSF basal cisternae coupling by Spearman rank correlations of functional magnetic resonance imaging signals.

Results: Anesthesia decreased cisternal CSF peak-to-trough amplitude (median difference, 1.00; 95% CI, 0.17 to 1.83; P = .013) and disrupted the global cortical blood oxygenation level-dependent and functional magnetic resonance imaging-based connectivity (median difference, 1.5; 95% CI, 0.67 to 2.33; P < 0.001) and global gray matter-CSF coupling (median difference, 1.19; 95% CI, 0.36 to 2.02; P = 0.002). Remarkably, the impairments of global connectivity (median difference, 0.94; 95% CI, 0.11 to 1.77; P = 0.022) and global gray matter-CSF coupling (median difference, 1.06; 95% CI, 0.23 to 1.89; P = 0.008) persisted after re-emergence from anesthesia.

Conclusions: Collectively, the authors' data show that sevoflurane impairs macroscopic CSF flow via a disruption of coherent global gray matter activity. This effect persists, at least for a short time, after regaining consciousness. Future studies need to elucidate whether this contributes to the emergence of postoperative neurocognitive symptoms, especially in older patients or those with dementia.

Background: Effective pain recognition and treatment in perioperative environments reduce length of stay and decrease risk of delirium and chronic pain. The authors sought to develop and validate preliminary computer vision-based approaches for nociception detection in hospitalized patients.

Methods: This was a prospective observational cohort study using red-green-blue camera detection of perioperative patients. Adults (18 yr or older) admitted for surgical procedures to the San Francisco Veterans Affairs Medical Center (San Francisco, California) were included across two study phases: (1) the algorithm development phase and (2) the internal validation phase. Continuous recordings occurred perioperatively across any postoperative setting. The authors inputted facial images into convolutional neural networks using a pretrained backbone to classify (1) the Critical Care Pain Observation Tool (CPOT) and (2) the numeric rating scale. Outcomes were binary pain/no pain. We performed external validation for CPOT and numerical rating scale classification on data from the University of Northern British Columbia (Prince George, Canada)-McMaster University (Hamilton, Canada) and the Delaware Pain Database. Perturbation models were used for explainability.

Results: The study included 130 patients for development, 77 patients for the validation cohort, and 25 patients from University of Northern British Columbia-McMaster University and 229 patients from Delaware datasets for external validation. Model areas under the curve of the receiver operating characteristic for CPOT models were 0.71 (95% CI, 0.70 to 0.74) on the development cohort, 0.91 (95% CI, 0.90 to 0.92) on the San Francisco Veterans Affairs Medical Center validation cohort, 0.91 (95% CI, 0.89 to 0.93) on University of Northern British Columbia-McMaster University, and 0.80 (95% CI, 0.75 to 0.85) on Delaware. The numeric rating scale model had lower performance (area under the receiver operating characteristics curve, 0.58 [95% CI, 0.55 to 0.61]). Brier scores improved after calibration across multiple different techniques. Perturbation models for CPOT models revealed eyebrows, nose, lips, and forehead were most important for model prediction.

Conclusions: Automated nociception detection using computer vision alone is feasible but requires additional testing and validation given the small datasets used. Future multicenter observational studies are required to better understand the potential for automated continuous assessments for nociception detection in hospitalized patients.