Journal of critical care最新文献

Purpose: To evaluate the clinical impact of cardiac surgery-associated acute kidney injury requiring continuous venovenous haemofiltration by assessing its impact on short- and long-term outcomes.

Methods: Data for all adult cardiac surgeries performed between 2015 and 2024 were retrieved from our institutional database. 1:2 propensity-score matching of patients requiring postoperative haemofiltration and those not requiring haemofiltration was performed based on the EuroSCORE II covariates. In-hospital outcomes (mortality, postoperative complications, postoperative hospitalisation duration) and long-term survival were evaluated.

Results: After excluding patients requiring renal replacement therapy preoperatively, 16,681 patients were included. Propensity matching yielded Group H (postoperative haemofiltration, n = 510) and Group C (controls, n = 1020). Groups had generally similar demographics and preoperative clinical characteristics. Group H exhibited worse in-hospital outcomes compared to Group C. Hospital mortality was significantly higher in Group H (23.1% vs 6.2%, p < 0.001), with higher mortality up to five years and poorer long-term survival (HR =1.81 (95% CI: 1.50-2.18), p < 0.001). Conditional survival analyses demonstrated that among patients who survived past hospital discharge, long-term survival was comparable between groups (HR 1.23, 95% CI 0.95-1.58, p = 0.115).

Conclusions: Postoperative AKI requiring haemofiltration is associated with poor outcomes following cardiac surgery. However, in patients surviving the acute postoperative phase, there was no significant difference in long-term survival compared to those who did not require haemofiltration. These findings underscore the importance of early recognition and management of acute kidney injury after cardiac surgery whilst offering a more nuanced understanding of long-term prognosis.

Objectives: To identify relevant objective variables for retrospective identification of 'suspected infection' and sepsis, using behavioural artificial intelligence technology (BAIT), and to explore the accuracy of this approach for sepsis surveillance.

Methods: BAIT uses choice behaviour analysis to make implicit expert knowledge explicit. Online choice experiments with 25-30 hypothetical retrospective patient scenarios were composed, each consisting of objective variables relevant to sepsis surveillance. Experts reviewed these scenarios and labelled them as 'sepsis' or 'no sepsis'. Two rounds were conducted: round 1 focused on a sepsis surveillance definition, round 2 only on 'suspected infection' in patients with a qSOFA≥2. Relative importance (RI) of variables was calculated using binary logistic regression. Model accuracy was assessed using an expert adjudicated sepsis database.

Results: In round 1, 22 experts participated. Temperature (RI 24 %), CRP (RI 18 %) and systolic blood pressure (RI 16 %) contributed most to sepsis identification, respectively. Model accuracy was 74 % (sensitivity 87 %, specificity 66 %). Round 2 involved 21 experts. Focusing on 'suspected infection', CRP (RI 27 %), temperature (RI 18 %) and leukocyte count (RI 11 %) were most important, respectively. Model accuracy was 75 % (sensitivity 83 %, specificity 71 %).

Conclusion: Inflammatory parameters contributed most to retrospective sepsis and 'suspected infection' identification by experts. BAIT-model accuracy for surveillance was 74-75 %.