Emergency Endovascular Interventions on Descending Thoracic Aorta: A Single-Center ExperienceRead the full article
Emergency Medicine International publishes original research articles and review articles related to prehospital care, disaster preparedness and response, acute medical and paediatric emergencies, critical care and wound care
Emergency Medicine International maintains an Editorial Board of practicing researchers from around the world, to ensure manuscripts are handled by editors who are experts in the field of study.
Latest ArticlesMore articles
Comparison of Different Risk Scores for Prediction of In-Hospital Mortality in STEMI Patients Treated with PPCI
Background. Several risk scores have been developed to predict and analyze in-hospital mortality and short- and long-term outcomes of ST-elevation myocardial infarction (STEMI) patients after primary percutaneous coronary intervention (PPCI); these can classify patients as having a high or low risk of death or complications. Objective. To compare the prognostic precision of four risk scores for predicting in-hospital mortality in patients with STEMI treated with PPCI. Methods. We performed a retrospective cohort analysis of patients with STEMI who underwent PPCI between 2012 and 2019 (N = 1346). GRACE (Global Registry of Acute Cardiac Events), CADILLAC (Controlled Abciximab and Device Investigation to Lower Late Angioplasty Complications), Zwolle, and TIMI (Thrombolysis in Myocardial Infarction) risk scores were calculated for each patient according to different variables. We evaluated the predictive accuracy of these scores for in-hospital mortality using the C statistic, which was obtained using logistic regression and receiver operating characteristic curves. Results. The GRACE, CADILLAC, Zwolle, and TIMI risk scores all had good predictive precision for in-hospital mortality, with C statistics ranging from 0.842 to 0.923. The GRACE and CADILLAC risk scores were found to be superior. Conclusions. All GRACE, CADILLAC, Zwolle, and TIMI risk scores showed a high predictive value for in-hospital mortality due to all causes in patients with STEMI treated with PPCI. The GRACE and CADILLAC risk scores revealed a better accuracy for predicting in-hospital mortality than the Zwolle and TIMI risk scores.
The Risk Factors for Mortality among Septic Trauma Patients: A Retrospective Cohort Study Using the National Trauma Data Bank
Introduction. In trauma patients, the development of sepsis as a hospital complication is significantly associated with morbidity and mortality. We aimed to assess the risk factors associated with in-hospital mortality among trauma patients who developed sepsis during their hospital stay. Material and methods. Using the 2017 National Trauma Data Bank, a retrospective cohort study was conducted to identify adult trauma patients who developed sepsis during their hospital stay. The primary outcome of interest was in-hospital mortality. Multivariate analysis was used to determine the risk factors associated with in-hospital mortality. Results. 1782 trauma patients developed sepsis. 567 patients (31.8%) died during their hospital stay. The following patient factors were associated with higher odds of in-hospital mortality: age (OR = 1.045 95% CI = 1.036–1.054), chronic renal failure (OR = 2.564 95% CI = 1.528–4.301), and liver cirrhosis (OR = 3.699 95% CI = 2.267–6.033). Patients who developed cardiac arrest (OR = 4.994 95% CI = 3.381–7.378), acute kidney injury (OR = 3.808 95% CI = 2.837–5.110), acute respiratory distress syndrome (OR = 1.688 95% CI = 1.197–2.379), and stroke (OR = 1.998 95% CI = 1.075–3.714) during their hospital stay had higher odds of mortality. Higher Glasgow Coma Scale (13–15) at presentation was associated with lower odds of mortality (OR = 0.467 95% CI = 0.328–0.667). Conclusion. Among trauma patients who developed sepsis, age, chronic renal failure, cirrhosis, the development of cardiac arrest, acute kidney injury, acute respiratory distress syndrome, and stroke in the hospital were associated with in-hospital mortality. These factors can be used to identify patients who are at higher risk of adverse outcomes and implement standardized or protocol-driven methods to improve patient care.
Comparative Effect of Multi-Dose Contrast Median on Contrast-Enhanced Computed Tomography Workflow of Nurses and Hospital Efficiency: A Multi-CenterReal-World Prospective Observational Study in China
Objective. This study aims to evaluate and compare computed tomography (CT)-contrast operational workflow and hospital imaging efficiency when using a multi-dose bulk IV contrast delivery system and when using a single-dose packaging contrast. Materials and Methods. A multi-center prospective observational study was conducted in six regions in China. The operating time and workflow of radiology nursing staff were evaluated and observed using an investigational tool and recorded by the investigators using a stopwatch. Nursing staff’s knowledge and the imaging capabilities of hospitals were collected using a questionnaire. Rate, t-test, χ2 test, and partial correlation analysis were used to describe the knowledge of nursing staff. The operation time and frequency of the two contrast agent packages were further compared using the Stata 15.0 software. Results. A total of 42 radiology nurses and 1,167 CT contrast-operating procedures in six provinces in China were evaluated. The total operating times for the 100 ml contrast agent versus the 200 ml contrast agent were 80.67 s and 63.81 s, respectively (). According to the average annual hospital CT scans (49,807 scans) and the power injector (PI) market share, approximately 233 h yearly could be saved in a hospital. Regarding CT contrast knowledge, approximately 57.14% nurses expressed their willingness to use multi-dose packaging contrast agents. Conclusion. Through difference and correlation analysis on real-world data, this study suggests that, considering safety, the use of a multi-dose bulk IV contrast agent is more time-saving and efficient for Chinese nurses and medical institutions compared with that of a single-dose package.
Effect of Serum Ferritin on the Prognosis of Patients with Sepsis: Data from the MIMIC-IV Database
Background. The present study aimed to investigate the prognostic value of serum ferritin in critically ill patients with sepsis by using the MIMIC-IV database. Methods. Data were extracted from the MIMIC-IV database. Adult patients who met the sepsis-3 criteria and had the test of ferritin were included. Patients were divided into subgroups according to the initial serum ferritin. The association between initial serum ferritin and in-hospital mortality was performed by using Lowessregression, logistic regression, and ROC analysis. Subgroup analysis was used to search for the interacting factors and verify the robustness of the results. Results. Analysis of the 2,451 patients revealed a positive linear relationship between serum ferritin and in-hospital mortality. Patients with high-ferritin had a higher risk of in-hospital mortality, but no significant association was found in the low-ferritin subgroup compared with those whose ferritin was in the normal reference range. Serum ferritin had moderate predictive power for in-hospital mortality (AUC = 0.651), with an optimal cut-off value of 591.5 ng/ml. Ferritin ≥591.5 ng/ml acted as an independent prognostic predictor of in-hospital mortality, which increased the risk of in-hospital mortality by 119%. Our findings were still robust in subgroup analysis, and acute kidney injury and anemia were considered interactive factors. Conclusion. High-level serum ferritin was an independent prognostic marker for the prediction of mortality in patients with sepsis. Further high-quality research is needed to confirm the relationship between ferritin and the prognosis of septic patients.
Development and Validation of a Dynamic Prediction Model for Massive Hemorrhage in Trauma
Objectives. Early warning prediction of massive hemorrhages can greatly reduce mortality in trauma patients. This study aimed to develop and validate dynamic prediction models for massive hemorrhage in trauma patients. Methods. Based on vital signs (e.g., heart rate, respiratory rate, pulse pressure, and peripheral oxygen saturation) time-series data and the gated recurrent unit algorithm, we characterized a group of models to flexibly and dynamically predict the occurrence of massive hemorrhages in the subsequent T hours (where T = 1, 2, and 3). Models were evaluated in terms of accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and the area under the curve (AUC). Results. Results show that of the 2205 trauma patients selected for model development, a total of 265 (12.02%) had a massive hemorrhage. The AUCs of the model in the 1-h-group, 2-h-group, and 3-h-group were 0.763 (95% CI: 0.708–0.820), 0.775 (95% CI: 0.728–0.823), and 0.756 (95% CI: 0.715–0.797), respectively. Finally, the models were used in a web calculator and information system for the hospital emergency department. Conclusions. This study developed and validated a group of dynamic prediction models based on vital sign time-series data and a deep-learning algorithm to assist medical staff in the early diagnosis and dynamic prediction of a future massive hemorrhage in trauma.
Development of a Novel Nomogram Incorporating Red Blood Cell Distribution Width-Albumin Ratio for the Prediction of 30-day Mortality in Acute Pancreatitis Patients
Purpose. The available nomograms used to predict acute pancreatitis (AP) are not comprehensive. We sought to investigate the effect of red blood cell distribution width (RDW)-albumin ratio (RA) on prognosis of patients with AP and develop a new nomogram to identify AP patients at high risk for mortality. Methods. We used data from the Medical Information Mart for Intensive Care IV version 2.0 (MIMIC-IV v2.0). A total of 487 patients with acute pancreatitis were included. Patients enrolled in the study were randomly assigned to the training set and validation set at a 7 : 3 ratio. According to the 30-day mortality rate, the data were divided into a survival group and a death group. Multivariate logistic regression was used to establish a prognostic nomogram for predicting the 30-day mortality in AP patients. The area under the receiver operating characteristic curve (AUC), calibration curve, the net reclassification improvement (NRI), the integrated discrimination improvement (IDI), and a decision curve analysis (DCA) are used to verify the overall performance of the model. Results. Among 487 patients, 54 patients died (11.1%). 338 patients were assigned to the training cohort and 149 were assigned to the validation cohort. The multivariate analysis results showed that RA, age, heart rate, temperature, AST/ALT, BUN, hemoglobin, potassium, and bilirubin were independent risk factors. The prediction performance of the newly established nomogram was better than those of other common scoring systems (including SOFA, OASIS, and APSIII). The nomogram suggests that RA (OR = 1.706, 95% CI: 1.367–2.185) is the most significant laboratory test indicator influencing prognosis. Conclusion. The new nomogram incorporating RA performed well in predicting AP short-term mortality. A prospective study with a larger sample is needed to validate our findings.