Background: Hospital-acquired infection (HAI) after cardiac surgery is a common clinical concern associated with adverse prognosis and mortality. The objective of this study is to determine the prevalence of HAI and its associated risk factors in elderly patients following cardiac surgery and to build a nomogram as a predictive model. Methods: We developed and internally validated a predictive model from a retrospective cohort of 6405 patients aged ≥70 years, who were admitted to our hospital and underwent cardiac surgery. The primary outcome was HAI. Multivariable logistic regression analysis was used to identify independent factors significantly associated with HAI. The performance of the established nomogram was assessed by calibration, discrimination, and clinical utility. Internal validation was achieved by bootstrap sampling with 1000 repetitions to reduce the overfit bias. Results: Independent factors derived from the multivariable analysis to predict HAI were smoking, myocardial infarction, cardiopulmonary bypass use, intraoperative erythrocytes transfusion, extended preoperative hospitalization days and prolonged duration of mechanical ventilation postoperatively. The derivation model showed good discrimination, with a C-index of 0.706 [95% confidence interval 0.671-0.740], and good calibration [Hosmer-Lemeshow test P = 0.139]. Internal validation also maintained optimal discrimination and calibration. The decision curve analysis revealed that the nomogram was clinically useful. Conclusions: We developed a predictive nomogram for postoperative HAIs based on routinely available data. This predictive tool may enable clinicians to achieve better perioperative management for elderly patients undergoing cardiac surgery but still requires further external validation.

BackgroundOur previous showed that a blood management program in the cardiopulmonary bypass (CPB) department, reduced red blood cell (RBC) transfusion and complications, but assessing transfusion practice solely based on transfusion rates was insufficient. This study aimed to design a risk stratification score to predict perioperative RBC transfusion to guide targeted measures for on‐pump cardiac surgery patients.Study Design and MethodsWe analyzed data from 42,435 adult cardiac patients. Eight predictors were entered into the final model including age, sex, anemia, New York Heart Association classification, body surface area, cardiac surgery history, emergency surgery, and surgery type. We then simplified the score to an integer‐based system. The area under the receiver operating characteristic curve (AUC), Hosmer–Lemeshow goodness‐of‐fit test, and a calibration curve were used for its performance test. The score was compared to existing scores.ResultsThe final score included eight predictors. The AUC for the model was 0.77 (95% CI, 0.76–0.77) and 0.77 (95% CI, 0.76–0.78) in the training and test set, respectively. The calibration curves showed a good fit. The risk score was finally grouped into low‐risk (score of 0–13 points), medium‐risk (14–19 points), and high‐risk (more than 19 points). The score had better predictive power compared to the other two existing risk scores.DiscussionWe developed an effective risk stratification score with eight variables to predict perioperative RBC transfusion for on‐pump cardiac surgery. It assists perfusionists in proactively preparing blood conservation measures for high‐risk patients before surgery.

Background Prevention, screening, and early treatment are the mainstays of postoperative delirium management. Score system is an objective and effective tool to stratify potential delirium risk for patients undergoing cardiac surgery Methods Patients undergoing cardiac surgery from January 1, 2012, to January 1, 2019, were enrolled in our retrospective study. The patients were divided into a derivation cohort (n = 45,744) and a validation cohort (n = 11,436). The agitated delirium (AD) predictive systems were formulated using multivariate logistic regression analysis at three time points: preoperation, ICU admittance, and 24 hours after ICU admittance. Results The prevalence of AD after cardiac surgery in the whole cohort was 3.6% (2,085/57,180). The dynamic scoring system included preoperative LVEF ≤ 45%, serum creatinine > 100 umol/L, emergency surgery, coronary artery disease, hemorrhage volume > 600 mL, intraoperative platelet or plasma use, and postoperative LVEF ≤ 45%. The area under the receiver operating characteristic curve (AUC) values for AD prediction of 0.68 (preoperative), 0.74 (on the day of ICU admission), and 0.75 (postoperative). The Hosmer-Lemeshow test indicated that the calibration of the preoperative prediction model was poor (P = 0.01), whereas that of the pre- and intraoperative prediction model (P = 0.49) and the pre-, intra- and postoperative prediction model (P = 0.35) was good. Conclusions Using perioperative data, we developed a dynamic scoring system for predicting the risk of AD following cardiac surgery. The dynamic scoring system may improve early recognition of and interventions for AD.

Background Cardiopulmonary resuscitation (CPR) is an important technique of first aid. It is necessary to be popularized. Large-scale offline training has been affected after the outbreak of Coronavirus disease 2019 (COVID-19). Online training will be the future trend, but the quality of online assessment is unclear. This study aims to compare online and offline evaluations of CPR quality using digital simulator and specialist scoring methods. Methods Forty-eight out of 108 contestants who participated in the second Chinese National CPR Skill Competition held in 2020 were included in this study. The competition comprised two stages. In the preliminary online competition, the contestants practiced on the digital simulator while the specialist teams scored live videos. The final competition was held offline, and consisted of live simulator scoring and specialist scoring. The grades of the simulator and specialists in different stages were compared. Results There was no statistical significance for simulator grades between online and offline competition(37.7 ± 2.0 vs. 36.4 ± 3.4, p = 0.169). For specialists’ grades, the video scores were lower than live scores (55.0 ± 1.4 vs. 57.2 ± 1.7, p < 0.001). Conclusion Simulator scoring provided better reliability than specialist scoring in the online evaluation of CPR quality. However, the simulator could only collect quantified data. Specialist scoring is necessary in conjunction with online tests to provide a comprehensive evaluation. A complete and standardized CPR quality evaluation system can be established by combining simulator and specialist contributions.