Daily Anesthesiology Research Analysis

INTRODUCTION: Renal replacement therapy (RRT) is a life-saving intervention in severe acute kidney injury (AKI). After a clinician-initiated discontinuation, the likelihood of sustained liberation remains uncertain. We aimed to identify predictors of successful RRT weaning and to develop a pragmatic bedside tool (the UNDERSCORE) to support post-discontinuation management. METHODS: This post-hoc analysis of two multicenter randomized trials (AKIKI and AKIKI2) included ICU patients with KDIGO stage 3 AKI managed with a conservative initiation approach. Patients were eligible if they underwent an RRT weaning attempt, defined as discontinuation for ≥ 3 consecutive days. The primary outcome was successful weaning, i.e., no RRT resumption within seven days. Independent predictors were identified using multivariable logistic regression, and the resulting model (UNDERSCORE) was externally validated in an independent Swiss ICU cohort. RESULTS: Among 554 patients who received RRT, 180 underwent a weaning attempt and 101 (56%) were successfully weaned. Six predictors were retained to construct the UNDERSCORE: RRT duration before the attempt, septic shock on admission, baseline serum creatinine, and three clinical variables assessed after the weaning attempt (use of vasopressors, invasive mechanical ventilation, and urine output). The score showed strong discrimination in the derivation cohort (AUC 0.86, 95% CI 0.80-0.91). In the external Swiss cohort (n = 415), 338 patients (81%) were successfully weaned, with fair performance across a broader case mix (AUC 0.73, 95% CI 0.66-0.80). CONCLUSION: The UNDERSCORE, derived from a homogeneous conservative RRT initiation cohort and validated in a diverse ICU population, provides a bedside tool to estimate the probability of sustained RRT discontinuation after an initial clinician-initiated stop.

BACKGROUND: Postoperative delirium (POD) is a frequent and serious complication after surgery. Parameters of the electroencephalogram (EEG), such as the Bispectral Index and the occurrence of burst suppression, have been associated with POD. We analyzed the predictive properties of periodic and aperiodic parameters of the EEG power spectrum. METHODS: In a secondary post-hoc analysis of a prospective observational study, patients with an age of at least 60 years undergoing major cardiac or non-cardiac surgery were analyzed. The frontal intraoperative raw EEG was recorded by a BIS monitor and offline analyzed with the FOOOF toolbox, revealing the periodic and aperiodic parameters of the power spectrum. Patients were screened for POD and divided in a 2:1 ratio into a training and a validation cohort. Predictors of POD were identified by uni- and multivariable logistic regression. RESULTS: Thirty-two out of the 120 training group patients developed POD. These showed a significantly longer median duration of surgery (286 [interquartile range {IQR} 236-391] vs 223 [127-331] min, P = .005), lower median BIS (40.4 [IQR 38.1-43.4] vs 42.7 [39.5-46.0], P = .038), and a higher mean aperiodic exponent (2.09 ± 0.19 vs 1.99 ± 0.17, P = .017). Duration of surgery (odds ratio [OR] = 1.01; 95% confidence interval [CI], 1.00-1.01, P = .005), peak center frequency (OR = 0.79; CI, 0.62-0.97, P = .039) and aperiodic exponent (OR = 23.2; CI, 2.1-318.2, P = .013) were predictors of POD according to univariable logistic regression. At a cutoff of 1.967, the aperiodic exponent had a sensitivity of 0.813 and a specificity of 0.478 for the prediction of POD. A low duration of time spent in burst suppression was observed in both patients with POD (67 [6-363] s) and without POD (173 [4-641] s, P = .30), and cumulative burst suppression time was not a predictor of POD. In a stepwise regression model, age, duration of surgery, peak power, and the aperiodic exponent were associated with POD (AUROC = 0.80 (CI, 0.71-0.89, P < .001). The predictive model was confirmed in the validation group (n = 60) with an AUROC = 0.77 (CI, 0.65-0.90, P = .001). CONCLUSIONS: A higher aperiodic exponent, for example, a less complex EEG signal, is associated with a greater POD risk, especially in combination with known POD predictors such as age and duration of surgery.

INTRODUCTION: Alterations in the blood metabolome are closely associated with sepsis, while the gut microbiota (GM) plays a crucial role in modulating both sepsis progression and circulating metabolites. However, whether the effects of the GM on sepsis are mediated through blood metabolites remains unclear. METHODS: To determine whether the effects of the GM on sepsis are mediated through blood metabolites, we performed a two-sample Mendelian randomization (MR) analysis combined with a two-step MR framework to identify potential metabolic mediators. Comprehensive bioinformatics analyses were integrated to construct interaction networks using Cytoscape, and pharmacodynamic experiments were conducted in a murine sepsis model. RESULTS: We identified 23 GM taxa and 169 blood metabolites significantly associated with sepsis. Two-step MR analysis revealed that 15 metabolites mediated the causal relationships between 12 GM taxa and sepsis, with mediation proportions ranging from 3.70% to 13.70%. A total of 131 potential molecular targets were predicted for these metabolites, and network analysis highlighted five key metabolites and seven central targets. Molecular docking demonstrated strong binding affinities between these metabolites and their targets. Notably, gulonic acid (GA) and 4-hydroxyphenylacetic acid (4-HPA), driven by Lentisphaerae, Lentisphaeria, and Victivallales, significantly improved survival and attenuated organ injury and inflammation in septic mice. DISCUSSION: Collectively, this study provides evidence supporting a causal role of the GM in sepsis, which mediated in part by blood metabolites. These findings highlight the therapeutic potential of targeting both the GM and GM-driven metabolites as novel interventions for sepsis.