BackgroundThe neuroprotective effect of remote ischemic preconditioning (RIPC) in patients undergoing elective cardiopulmonary bypass (CPB)-assisted coronary artery bypass graft (CABG) or valvular cardiac surgery remains unclear.MethodsA randomized, double-blind, placebo-controlled superior clinical trial was conducted in patients undergoing elective on-pump coronary artery bypass surgery or valve surgery. Before anesthesia induction, patients were randomly assigned to RIPC (three 5-min cycles of inflation and deflation of blood pressure cuff on the upper limb) or the control group. The primary endpoint was the changes in S-100 calcium-binding protein β (S100-β) levels at 6 h postoperatively. Secondary endpoints included changes in Neuron-specific enolase (NSE), Mini-mental State Examination (MMSE), and Montreal Cognitive Assessment (MoCA) levels.ResultsA total of 120 patients [mean age, 48.7 years; 36 women (34.3%)] were randomized at three cardiac surgery centers in China. One hundred and five patients were included in the modified intent-to-treat analysis (52 in the RIPC group and 53 in the control group). The primary result demonstrated that at 6 h after surgery, S100-β levels were lower in the RIPC group than in the control group (50.75; 95% confidence interval, 67.08 to 64.40 pg/ml vs. 70.48; 95% CI, 56.84 to 84.10 pg/ml, P = 0.036). Compared to the control group, the concentrations of S100-β at 24 h and 72 h and the concentration of NSE at 6 h, 24 h, and 72 h postoperatively were significantly lower in the RIPC group. However, neither the MMSE nor the MoCA revealed significant between-group differences in postoperative cognitive performance at 7 days, 3 months, and 6 months after surgery.ConclusionIn patients undergoing CPB-assisted cardiac surgery, RIPC attenuated brain damage as indicated with the decreased release of brain damage biomarker S100-β and NSE.Clinical trial registration[ClinicalTrials.gov], identifier [NCT01231789].

BackgroundAcute kidney injury (AKI) is a relevant complication after cardiac surgery and is associated with significant morbidity and mortality. Existing risk prediction tools have certain limitations and perform poorly in the Chinese population. We aimed to develop prediction models for AKI after valvular cardiac surgery in the Chinese population.MethodsModels were developed from a retrospective cohort of patients undergoing valve surgery from December 2013 to November 2018. Three models were developed to predict all-stage, or moderate to severe AKI, as diagnosed according to Kidney Disease: Improving Global Outcomes (KDIGO) based on patient characteristics and perioperative variables. Models were developed based on lasso logistics regression (LLR), random forest (RF), and extreme gradient boosting (XGboost). The accuracy was compared among three models and against the previously published reference AKICS score.ResultsA total of 3,392 patients (mean [SD] age, 50.1 [11.3] years; 1787 [52.7%] male) were identified during the study period. The development of AKI was recorded in 50.5% of patients undergoing valve surgery. In the internal validation testing set, the LLR model marginally improved discrimination (C statistic, 0.7; 95% CI, 0.66–0.73) compared with two machine learning models, RF (C statistic, 0.69; 95% CI, 0.65–0.72) and XGBoost (C statistic, 0.66; 95% CI, 0.63–0.70). A better calibration was also found in the LLR, with a greater net benefit, especially for the higher probabilities as indicated in the decision curve analysis. All three newly developed models outperformed the reference AKICS score.ConclusionAmong the Chinese population undergoing CPB-assisted valvular cardiac surgery, prediction models based on perioperative variables were developed. The LLR model demonstrated the best predictive performance was selected for predicting all-stage AKI after surgery.Clinical trial registrationTrial registration: Clinicaltrials.gov, NCT04237636.

Background: Currently, the effect of the 2022 nationwide coronavirus disease 2019 (COVID-19) wave on the perioperative prognosis of surgical patients in China is unclear. Thus, we aimed to explore its influence on postoperative morbidity and mortality in surgical patients. Methods: An ambispective cohort study was conducted at Xijing Hospital, China. We collected 10-day time-series data from December 29 until January 7 for the 2018–2022 period. The primary outcome was major postoperative complications (Clavien–Dindo class III–V). The association between COVID-19 exposure and postoperative prognosis was explored by comparing consecutive 5-year data at the population level and by comparing patients with and without COVID-19 exposure at the patient level. Results: The entire cohort consisted of 3350 patients (age: 48.5 ± 19.2 years), including 1759 females (52.5%). Overall, 961 (28.7%) underwent emergency surgery, and 553 (16.5%) had COVID-19 exposure (from the 2022 cohort). At the population level, major postoperative complications occurred in 5.9% (42/707), 5.7% (53/935), 5.1% (46/901), 9.4% (11/117), and 22.0% (152/690) patients in the 2018–2022 cohorts, respectively. After adjusting for potential confounding factors, the 2022 cohort (80% patients with COVID-19 history) had a significantly higher postoperative major complication risk than did the 2018 cohort (adjusted risk difference [aRD], 14.9% (95% confidence interval [CI], 11.5–18.4%); adjusted odds ratio [aOR], 8.19 (95% CI, 5.24–12.81)). At the patient level, the incidence of major postoperative complications was significantly greater in patients with (24.6%, 136/553) than that in patients without COVID-19 history (6.0% [168/2797]; aRD, 17.8% [95% CI, 13.6–22.1%]; aOR, 7.89 [95% CI, 5.76–10.83]). Secondary outcomes of postoperative pulmonary complications were consistent with primary findings. These findings were verified through sensitivity analyses using time-series data projections and propensity score matching. Conclusion: Based on a single-center observation, patients with recent COVID-19 exposure were likely to have a high incidence of major postoperative complications. Registration: NCT05677815 at https://clinicaltrials.gov/.