Daily Anesthesiology Research Analysis

INTRODUCTION: Significant blood loss (≥ 500 mL) during spinal surgery is common and linked to greater transfusion requirements and worse outcomes. Preoperative tools for risk stratification and individualized blood-management strategies are lacking. We aimed to develop a machine-learning model predicting intraoperative significant blood loss and to assess whether a risk-adapted blood management improves clinical benefit. METHODS: We used data from 3944 spinal surgery patients at Peking Union Medical College Hospital (December 2018-October 2021) to train 26 machine-learning algorithms. We used Shapley additive explanations to identify key predictors from 49 candidate variables to train simplified models. The optimal simplified model was externally validated in 843 patients from West China Hospital. Decision-curve analysis and spline analysis were used to evaluate Cell Saver benefit across model-predicted risk. RESULTS: The 12-variable ranger model achieved an AUC of 0.814 (95% CI, 0.790-0.839) in the test set and 0.820 (0.785-0.854) in the external cohort. Decision-curve analysis demonstrated that the risk-adapted Cell Saver strategy provided greater net benefit than current practice. Spline analysis demonstrated that Cell Saver benefit rose with increasing predicted risk: for risk >0.53, Cell Saver use was associated with higher postoperative hemoglobin; for risk >0.58, it reduced allogeneic transfusion requirements. Among patients requiring allogeneic transfusion, Cell Saver use decreased red-cell unit volume at all risk levels, with larger reductions in higher-risk patients. DISCUSSION: This 12-variable machine-learning model can accurately predict significant blood loss risk in spinal surgery. Risk-adapted Cell Saver use guided by predicted risk provides greater net clinical benefit than experienced-based real-world strategy.

IMPORTANCE: Despite nearly universal adherence to the Surgical Care Improvement Project (SCIP), surgical site infections (SSIs) persist. Compared with SCIP, which largely focuses on antibiotic timing, the Infectious Diseases Society of America (IDSA) guidelines provide a more comprehensive framework of antibiotic metrics, including procedure-specific antibiotic selection, weight-adjusted dosing, timing of the first dose, and appropriate redosing. OBJECTIVE: To assess whether nonadherence to each antibiotic administration metric of IDSA guidelines is associated with SSIs. DESIGN, SETTING, AND PARTICIPANTS: In this nationwide, multicenter, cross-sectional study, patients aged 18 years or older who underwent noncardiac surgeries involving a skin incision between January 1, 2014, and August 31, 2022, were included from merged data of the Multicenter Perioperative Outcomes Group, National Surgical Quality Improvement Program, and Michigan Surgical Quality Collaborative registries. Analyses were conducted between July 2, 2024, and April 24, 2025. EXPOSURE: Nonadherence to IDSA-defined antibiotic metrics. MAIN OUTCOMES AND MEASURES: The primary end point was SSI, defined as any superficial, deep tissue, or organ-space infection as recorded in the National Surgical Quality Improvement Program and Michigan Surgical Quality Collaborative registries. The association of nonadherence to IDSA guidelines (both overall and individually) was examined using hierarchical generalized linear mixed models. RESULTS: Of 134 413 eligible surgical cases, a total of 119 236 patients (mean [SD] age, 56.2 [15.9] years; 58.1% women) from 37 institutions met the inclusion criteria, among whom 6796 (5.7%) had incomplete covariate data. Failure to adhere to any IDSA metric was common in 26.1% of cases, with individual nonadherence rates as follows: 13.3% for antibiotic choice, 9.0% for weight-adjusted dosing, 3.0% for timing relative to incision, and 4.8% for correct intraoperative redosing interval. Overall, SSIs occurred in 4.4% of cases. After adjusted analysis, guideline-nonadherent antibiotic administration was significantly associated with SSIs (relative risk [RR], 1.34 [95% CI, 1.26-1.43]). Nonadherence to antibiotic choice (RR, 1.43 [95% CI, 1.33-1.53]) and failure to appropriately redose intraoperatively (RR, 1.12 [95% CI, 1.02-1.24]) were significantly associated with SSIs. CONCLUSIONS AND RELEVANCE: This cross-sectional study found that IDSA guideline nonadherence, including incorrect antibiotic choice and missed intraoperative redosing, was common and associated with increased SSI risk, despite high adherence to SCIP timing metrics. Improving adherence to IDSA-recommended antibiotic selection and redosing may meaningfully reduce SSIs.

BACKGROUND: Propofol is a commonly used anesthetic, and its impact on brain function has been a significant focus of neuroscience research. However, previous studies have primarily focused on the effects of propofol on gray matter function. White matter in the brain is a pathway for transmitting information between different brain regions. Recently, blood oxygen level-dependent signals in white matter have been shown to have physiological significance. However, the effects of propofol on white matter function remain unclear. The purpose of this study is to investigate changes in white matter functional connectivity during propofol-induced sedation. METHODS: Resting-state functional magnetic resonance imaging was performed on 21 healthy participants in four states: awake, mild propofol-induced sedation, deep propofol-induced sedation, and postsedation recovery. White matter functional connectivity, including white to gray matter functional connectivity and white to white matter functional connectivity, was compared between different states. The white matter tracts primarily affected by propofol were identified by calculating white matter functional connectivity strength from white to gray matter functional connectivity and performing a Friedman test across four states. Additionally, considering that white matter promotes gray matter communication, white matter-mediated functional networks were constructed through white to gray matter functional connectivity. The global efficiency of white matter-mediated functional networks across different states was studied. RESULTS: The white to gray matter functional connectivity and white to white matter functional connectivity significantly decreased during deep sedation compared to the awake state (P < .05). Several fiber tracts, including the posterior limb of the internal capsule, the cingulum near the cingulate gyrus, the genu of corpus callosum, and the retrolenticular part of the internal capsule, showed significant differences in white matter functional connectivity strength across the four states (P < .01). The global efficiency of the whole brain network, as well as the visual, somatomotor, attention, frontoparietal, limbic, and default mode networks, decreased during deep sedation and returned to the awake level after recovery (P < .05). CONCLUSIONS: Propofol disrupts white matter functional connectivity, with deep sedation inducing widespread functional connectivity reductions, particularly in key tracts and networks. The disruption of white matter functional connectivity may reflect a breakdown in large-scale brain integration and could serve as a biomarker for deep propofol-induced sedation, although not necessarily its mechanistic driver.