The present study was undertaken to investigate the effects and related mechanisms of hypothermia on oxidative stress and apoptosis caused by cardiac arrest (CA)-induced brain damage in rats. The C...

Cardiac arrest (CA) poses a significant global health challenge and often results in poor prognosis. We developed an interpretable and applicable machine learning (ML) model for predicting in-hospital mortality of CA patients who survived more than 72 h. A total of 721 patients were extracted from the Medical Information Mart for Intensive Care IV database, divided into the training set (n = 576) and the internal validation set (n = 145). The external validation set containing 856 cases were collected from four tertiary hospitals in Zhejiang Province. The primary outcome was in-hospital mortality. Eleven ML algorithms were utilized to establish prediction models based on data from 72 h after return of spontaneous circulation (ROSC). The results indicate that the CatBoost model exhibited the best performance at 72 h. Eleven variables were ultimately selected as key features by recursive feature elimination (RFE) to construct a compact model. The final model achieved the highest AUC of 0.86 (0.80, 0.92) in the internal validation and 0.76 (0.73, 0.79) in the external validation. SHAP summary plots and force plots visually explained the predicted outcomes. In conclusion, 72-h CatBoost showed promising performance in predicting in-hospital mortality of CA patients who survived more than 72 h. The model still requires further optimization and improvement.

Veno-arterial extracorporeal membrane oxygenation (VA-ECMO) is a critical life support technology for severely ill patients. Despite its benefits, patients face high costs and significant mortality risks. To improve clinical decision-making, this study aims to develop a non-invasive, efficient artificial intelligence (AI)-enabled model to predict the risk of mortality within 28 days post-weaning from VA-ECMO. A multicenter, retrospective cohort study was conducted across five hospitals in China, including all the patients who received VA-ECMO support between January 2020 and January 2024. Based on the innovatively selected 25 easily obtainable patient examination features as potentially relevant, this study involved developing ten predictive models using both classical and advanced machine learning techniques. The model’s performance is evaluated using various statistical metrics and the optimal predictive model are identified. Feature correlations are analyzed using Pearson correlation coefficients, and SHapley Additive exPlanations (SHAP) are employed to interpret feature importance. Decision curve analysis is used to evaluate the clinical utility of the predictive models. The study included 225 patients, with 66 patients from one hospital forming the training cohort. Three validation cohorts were used: internal validation with 16 patients from the training hospital and external validation with 30 and 60 patients from the other 4 hospitals. The random forest model emerged as the best predictor of 28-day mortality, achieving an AUROC of 1.00 in the training cohort and 1.00, 0.97, and 0.93 in the three validation cohorts, respectively. Despite the limited training data, the developed model, eCMoML, demonstrated high accuracy, generalizability and reliability. The model will be available online for immediate use by clinicians. The eCMoML model, validated in a multicenter cohort study, offers a rapid, stable, and accurate tool for predicting 28-day mortality post-VA-ECMO weaning. It has the potential to significantly enhance clinical decision-making, helping doctors better assess patient prognosis, optimize treatment plans, and improve survival rates.

Cardiac arrest (CA), characterized by its heterogeneity, poses challenges in patient management. This study aimed to identify clinical subphenotypes in CA patients to aid in patient classification, prognosis assessment, and treatment decision-making. For this study, comprehensive data were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) 2.0 database. We excluded patients under 18 years old, those not initially admitted to the intensive care unit (ICU), or treated in the ICU for less than 72 h. A total of 57 clinical parameters relevant to CA patients were selected for analysis. These included demographic data, vital signs, and laboratory parameters. After an extensive literature review and expert consultations, key factors such as temperature (T), sodium (Na), creatinine (CR), glucose (GLU), heart rate (HR), PaO2/FiO2 ratio (P/F), hemoglobin (HB), mean arterial pressure (MAP), platelets (PLT), and white blood cell count (WBC) were identified as the most significant for cluster analysis. Consensus cluster analysis was utilized to examine the mean values of these routine clinical parameters within the first 24 h post-ICU admission to categorize patient classes. Furthermore, in-hospital and 28-day mortality rates of patients across different CA subphenotypes were assessed using multivariate logistic and Cox regression analysis. After applying exclusion criteria, 719 CA patients were included in the study, with a median age of 67.22 years (IQR: 55.50-79.34), of whom 63.28% were male. The analysis delineated two distinct subphenotypes: Subphenotype 1 (SP1) and Subphenotype 2 (SP2). Compared to SP1, patients in SP2 exhibited significantly higher levels of P/F, HB, MAP, PLT, and Na, but lower levels of T, HR, GLU, WBC, and CR. SP2 patients had a notably higher in-hospital mortality rate compared to SP1 (53.01% for SP2 vs. 39.36% for SP1, P < 0.001). 28-day mortality decreased continuously for both subphenotypes, with a more rapid decline in SP2. These differences remained significant after adjusting for potential covariates (adjusted OR = 1.82, 95% CI: 1.26–2.64, P = 0.002; HR = 1.84, 95% CI: 1.40–2.41, P < 0.001). The study successfully identified two distinct clinical subphenotypes of CA by analyzing routine clinical data from the first 24 h following ICU admission. SP1 was characterized by a lower rate of in-hospital and 28-day mortality when compared to SP2. This differentiation could play a crucial role in tailoring patient care, assessing prognosis, and guiding more targeted treatment strategies for CA patients.

Cardiac arrest (CA) is a major disease burden worldwide and has a poor prognosis. Early prediction of CA outcomes helps optimize the therapeutic regimen and improve patients’ neurological function. As the current guidelines recommend, many factors can be used to evaluate the neurological outcomes of CA patients. Machine learning (ML) has strong analytical abilities and fast computing speed; thus, it plays an irreplaceable role in prediction model development. An increasing number of researchers are using ML algorithms to incorporate demographics, arrest characteristics, clinical variables, biomarkers, physical examination findings, electroencephalograms, imaging, and other factors with predictive value to construct multi-feature prediction models for neurological outcomes of CA survivors. In this review, we explore the current application of ML models using multiple features to predict the neurological outcomes of CA patients. Although the outcome prediction model is still in development, it has strong potential to become a powerful tool in clinical practice.