Background Cardiac arrest is currently one of the leading causes of mortality in clinical practice, and the Charlson Comorbidity Index (CCI) is widely utilized to assess the severity of comorbidities. We aimed to evaluate the relationship between the age-adjusted CCI score and in-hospital mortality in intensive care unit (ICU) patients with the diagnosis of cardiac arrest, which is important but less explored previously. Methods This was a retrospective study including patients aged over 18 years from the MIMIC-IV database. We calculated the age-adjusted CCI using age information and ICD codes. The univariate analysis for varied predictors’ differences between the survival and the non-survival groups was performed. In addition, a multiple factor analysis was conducted based on logistic regression analysis with the primary result set as hospitalization death. An additional multivariate regression analysis was conducted to estimate the influence of hospital and ICU stay. Results A total of 1772 patients were included in our study, with median age of 66, among which 705 (39.8%) were female. Amongst these patients, 963 (54.3%) died during the hospitalization period. Patients with higher age-adjusted CCI scores had a higher likelihood of dying during hospitalization (P < 0.001; OR: 1.109; 95% CI: 1.068–1.151). With the age-adjusted CCI incorporated into the predictive model, the area under the receiver operating characteristic curve was 0.794 (CI: 0.773–0.814), showing that the prediction model is effective. Additionally, patients with higher age-adjusted CCI scores stayed longer in the hospital (P = 0.026, 95% CI: 0.056–0.896), but there was no significant difference between patients with varied age-adjusted CCI scores on the days of ICU stay. Conclusion The age-adjusted CCI is a valid indicator to predict death in ICU patients with cardiac arrest, which can offer enlightenment for both theory literatures and clinical practice.

Background Worldwide, cardiac arrest is highly prevalent and associated with a high mortality rate. Despite timely CPR, a substantial proportion of cardiac arrest deaths occur in patients who return to spontaneous circulation (ROSC).Therefore, the purpose of this study was to explore the relevant factors affecting the prognosis of patients with cardiac arrest and develop an accurate and fast prognostic prediction model through machine learning with convenient clinical information. Methods We conducted a retrospective observational study. Data from 1772 cardiac arrest patients above 18 years of age from the MIMIC database were used to develop three machine learning models, including SVM, LR, and XGBoost models, for predicting in-hospital mortality. The areas under the receiver operating characteristic curve (AUC), accuracy, precision, recall and F1 score were calculated to evaluate these models. Results In our study, the XGBoost algorithm outperformed the other algorithms. The accuracy, recall value, precision value and F1 score of the XGBoost algorithm were 0.762, 0.812, 0.765, and 0.788, respectively. In addition, the AUC of the XGBoost model was larger than those of the LR and SVM models (0.847 vs. 0.834 vs. 0.747, respectively). The top 10 most important features of the XGboost algorithm were lactate_min,gcs_min,temperature_max,weight_kg,CK_MB_max,bun_min,glucose_min,spo2_min,wbc_min,and heart_rate_min. The XGBoost algorithm provided more personalized and reliable prognostic information for cardiac arrest patients than the other algorithms. Conclusions The prognostic prediction model for patients with cardiac arrest established by the XGBoost algorithm includes indicators that had certain predictive value for disease severity in previous studies. Compared with other models, this model can provide more accurate and considerable prognostic information, facilitate communication between patients' families and doctors about the disease, and help doctors make clinical decisions.

Background Cardiac arrest is one of the main causes of adult mortality worldwide. However, the impact of the application of echocardiography on the prognosis of cardiac arrest patients is still lacking sufficient research. Objectives We aimed to explore the association between the echocardiography utilization and the prognosis of patients with cardiac arrest, which can offer some evidence to Improving the quality of diagnosis and treatment for patients with cardiac arrest. Methods This study was a retrospective study including adult patients aged over 18 with cardiac arrest diagnosis and hospitalized in the Intensive Care Unit (ICU) from the Medical Information Mart for Intensive Care IV (MIMIC-IV). Patients were grouped according to whether echocardiography was performed during the hospitalization, analysis models including dual robust estimation were used to evaluate the association between the application of echocardiography and the prognosis of patients with cardiac arrest. Results Compared with the control group without echocardiography, patients receiving echocardiography showed better outcomes in both hospitalization survival (Odds ratio = 0.94, 95% CI: 0.90–0.98, p = 0.007) and 28-day survival (p < 0.001). Conclusions In patients with cardiac arrest, the use of echocardiography was associated with a reduction in mortality rate.