Daily Anesthesiology Research Analysis

BACKGROUND: Suboptimal hand hygiene compliance (HHC) among operating room (OR) nurses contributes to surgical site infections (SSIs). Conventional interventions often lack sustainability. We evaluated an intervention leveraging social network analysis (SNA) to engage key influencers for HHC improvement. METHODS: We conducted a 12-month cluster randomised controlled trial across two hospital campuses (86 OR nurses) in Nanjing, China. The intervention campus received an SNA-informed strategy; the control received standard education. Six key influencers (KIs), identified via weighted in-degree and betweenness centrality within the professional advice-seeking network, were trained as peer champions to model best practices. The primary outcome was HHC rates (covert observation) at baseline, 3, 6, and 12 months. Secondary outcomes included SSI rates and team climate. RESULTS: Baseline HHC was similar between groups (41.8% vs. 42.6%; p=0.62). Identified KIs had centrality scores 2.4-fold higher than the network average. At 12 months, intervention HHC increased to 79.2% (+37.4%) versus 59.3% (+16.7%) in controls. The mixed-effects model confirmed a significant group-by-time interaction (p<0.001). The intervention was associated with reduced SSI rates (1.3 vs 2.2 per 1,000 procedures; 43.2% relative reduction) and significantly improved nurse knowledge and team climate scores (p<0.01). CONCLUSION: An intervention targeting key influencers identified through SNA is a highly effective strategy for sustaining HHC among OR nurses. This precision approach offers a resource-efficient alternative to traditional infection control programmes.

BACKGROUND: Sepsis recognition in the ICU remains variable and relies on consensus clinical criteria rather than biomarker-defined rules. Routine laboratory and physiologic data often overlap with noninfectious critical illness, obscuring early identification. We evaluated whether discovery proteomics could prioritize a concise set of routinely obtainable clinical variables, yielding a practical, clinic-first model that distinguishes sepsis from other critical illness. METHODS: In a prospective, single-center pilot at an academic medical center, we enrolled adults within 48 h of critical illness onset (sepsis and non-sepsis comparators). Plasma proteomics by LC-MS/MS with diaPASEF identified proteins differentiating groups and guided selection of proteome-enriched routine variables for modeling. A Random Forest classifier was trained in a Discovery cohort (n = 55) and evaluated in an independent Validation cohort (n = 59), with prespecified attention to discrimination, parsimony, and feasibility for electronic health record (EHR) deployment. RESULTS: Twelve plasma proteins differed between groups at FDR < 0.10, supporting biological separation. A parsimonious model using routine predictors ± CCL3 achieved AUC 0.73 in Discovery and AUC 0.76 in the independent Validation cohort. Recursive feature elimination demonstrated a parsimony plateau at ~ 9 variables; beyond this threshold, further reduction degraded accuracy. Notably, blood urea nitrogen, CCL3 (measured by multiplex immunoassay), and creatinine were the final features retained before performance declined, aligning with renal stress and inflammatory signaling. Figures present ROC curves and the parsimony profile, highlighting a minimal variable set compatible with typical ICU workflows and decision-support systems. CONCLUSIONS: A proteomics-informed, clinic-first strategy produced a parsimonious set of routine variables that discriminated sepsis from other ICU critical illness with clinically meaningful accuracy and an immediately actionable footprint. Because most predictors are routinely captured in the EHR, the model is EHR-compatible; CCL3 is readily measurable on standard immunoassay platforms if adopted locally. These findings justify multicenter studies to confirm generalizability and calibration, evaluate real-time integration into ICU workflows, and test whether an early recognition adjunct improves timeliness of sepsis care and patient outcomes.

BACKGROUND: Cardiovascular-kidney-metabolic (CKM) syndrome represents a complex interplay among obesity, metabolic dysfunction, kidney disease, and cardiovascular disease. The relationship between CKM staging and myocardial injury after noncardiac surgery (MINS) has not been comprehensively studied. OBJECTIVES: This study sought to investigate the association between the CKM syndrome and MINS risk in patients undergoing noncardiac surgery. METHODS: This single-center retrospective cohort study included 25,040 patients aged ≥45 years who underwent noncardiac surgery between January 2019 and December 2023. Patients were classified according to CKM stages 0 to 4. MINS was defined as postoperative troponin elevation with ischemic etiology within 30 days. Four progressive multivariable logistic regression models were constructed, and subgroup analyses were performed stratified by age, sex, and surgery type. RESULTS: Among 25,040 patients (median time to MINS: 2.0 days [IQR: 0.8-6.6]), CKM stages 0 to 4 comprised 13.0%, 15.0%, 43.7%, 18.7%, and 9.7%, respectively. MINS occurred in 1,782 patients (7.12% [6.80-7.44]), demonstrating a J-shaped distribution: lowest in stage 1 at 141 of 3,754 (3.76% [3.19-4.41]), intermediate in stages 0 (166 of 3,246, 5.11% [4.40-5.93]) and 2 (586 of 10,943, 5.36% [4.94-5.80]), and highest in stages 3 (512 of 4,670, 10.96% [10.09-11.90]) and 4 (377 of 2,427, 15.53% [14.14-17.04]). With stage 0 as reference, stages 3 (OR: 1.95 [1.60-2.37]) and 4 (OR: 2.16 [1.75-2.66]) independently predicted increased MINS risk (both P < 0.001), with significant age interaction (P = 0.013) showing stronger associations in younger patients. CONCLUSIONS: Advanced CKM stages independently predicted an increased risk of MINS. These findings may improve perioperative risk assessment and guide preventive strategies.