Background: To derive and validate a machine learning (ML) prediction model of acute kidney injury (AKI) that could be used for AKI surveillance and management to improve clinical outcomes. Methods: This retrospective cohort study was conducted in Fuwai Hospital, including patients aged 18 years and above undergoing cardiac surgery admitted between January 1, 2017, and December 31, 2018. Seventy percent of the observations were randomly selected for training and the remaining 30% for testing. The demographics, comorbidities, laboratory examination parameters, and operation details were used to construct a prediction model for AKI by logistic regression and eXtreme gradient boosting (Xgboost). The discrimination of each model was assessed on the test cohort by the area under the receiver operator characteristic (AUROC) curve, while calibration was performed by the calibration plot. Results: A total of 15,880 patients were enrolled in this study, and 4845 (30.5%) had developed AKI. Xgboost model had the higher discriminative ability compared with logistic regression (AUROC, 0.849 [95% CI, 0.837– 0.861] vs 0.803[95% CI 0.790– 0.817], P< 0.001) in the test dataset. The estimated glomerular filtration (eGFR) and creatine on intensive care unit (ICU) arrival are the two most important prediction parameters. A SHAP summary plot was used to illustrate the effects of the top 15 features attributed to the Xgboost model. Conclusion: ML models can provide clinical decision support to determine which patients should focus on perioperative preventive treatment to preemptively reduce acute kidney injury by predicting which patients are not at risk.

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.

Background: Prevention, screening, and early treatment are the aims of postoperative delirium management. The scoring system is an objective and effective tool to stratify potential delirium risk for patients undergoing cardiac surgery. Methods: Patients who underwent cardiac surgery between January 1, 2012, and 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 AD predictive systems were formulated using multivariate logistic regression analysis at three time points: preoperation, ICU admittance, and 24 h 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 µmol/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 were 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 the early recognition of and the interventions for AD.

Evidence for peritoneal dialysis catheter (PDC) usage in pediatric patients undergoing surgery for deteriorating cardiac dysfunction is lacking. This investigation explored factors associated with PDC usage and its effectiveness in children with anomalous origin of the left coronary artery from the pulmonary artery (ALCAPA). Eighty-four children undergoing left coronary artery transfer were retrospectively recruited. The primary endpoint was the postoperative ratio of the general ward/[intensive care unit (ICU)] length of stay. Univariable and multivariable analyses were fitted to assess factors related most strongly to PDC and the ratio of general ward/ICU length of stay. Of the 84 patients, 17 (20.2%) underwent postoperative PDC placement. Patients with extreme cardiac dysfunction [left ventricular ejection fraction (LVEF) ≤25%] were much more likely to require a PDC (OR, 9.88; 95% CI, 2.13–45.76; P = 0.003). Moreover, univariate analysis indicated that concomitant mitral repair significantly decreased the likelihood of PDC placement (OR, 0.25; 95% CI, 0.07–0.85; P = 0.026). In those with cardiac dysfunction (LVEF ≤50%), PDC use was associated with a reduced ratio of ward/ICU length of stay (B, − 1.62; 95% CI, − 2.77– -0.46; P = 0.008), as was age ≤ 12 months (B, − 1.57; 95% CI, − 2.88– -0.26; P = 0.02). At the 1-year follow-up, cardiac improvement was significantly greater in patients with PDC usage than in those without it (P < 0.001), and the number of mitral recoveries was comparable between the groups (64.2% vs. 53.3%, P = 0.434). In cohorts with ALCAPA, PDC placement following surgery may be necessary for patients with extreme cardiac compromise, while concomitant mitral repair can probably reduce their usage rate. PDC is beneficial in conferring an improvement in cardiac and mitral performance. Importantly, after patients are transferred from the ICU, recovery efficiency in the general ward can be enhanced by PDC placement, and hospital discharge can therefore be achieved early, especially for patients younger than 12 months or with LVEF ≤50%.