The eICU Database is a multi-centre dataset from 208 hospitals in the United States. It contains diagnoses, flat features and time series from all adult patients (>18 years) with a length of stay of at least 5 hours and at least one observation.

Despite advances in intensive care, sepsis remains a leading cause of mortality in intensive care unit (ICU) patients, especially middle-aged and elderly individuals. Given the limitations of conventional scoring systems and the interpretability challenges of machine learning models, this study aims to develop and temporally validate a nomogram for predicting 28-day ICU mortality in middle-aged and elderly sepsis patients via the eICU database (2014-2015), providing a clinically practical prediction tool. This retrospective study included 13,717 sepsis patients aged ≥ 45 years. The cohort was temporally divided into training (n = 6,397; 2014) and validation (n = 7,320; 2015) sets. Variable selection was performed via random forest importance ranking and LASSO regression. A nomogram was developed on the basis of multivariable logistic regression analysis. The 28-day ICU mortality rates were 9.08% and 9.49% in the training and validation cohorts, respectively. The final nomogram inco..., , ## Description of the data and file structure

This dataset was extracted from the eICU Collaborative Research Database, a multi-center intensive care unit (ICU) database containing de-identified clinical data from critically ill patients. The eICU database includes detailed information from adult ICU patients across multiple hospitals in the United States, capturing demographic information, vital signs, laboratory measurements, treatment interventions, and outcomes. For this research, relevant clinical data was extracted according to our study objectives. The data collection process followed the eICU database usage guidelines and data protection protocols, utilizing only de-identified data. No on-site experiments were conducted; the data collection relied solely on organizing and filtering information from the existing electronic health record database.

For additional context: The eICU Collaborative Research Database v2.0 contains anonymized clinical data from over 200,000 ICU stays a..., Ethics approval and consent to participate Owing to the retrospective nature of the study and the established security framework, the requirement for informed consent was waived. No additional institutional review board approval was required for the use of this database, as detailed at https://eicu-crd.mit.edu/about/acknowledgments/.

Consent for publication Not applicable.

Summary of the eICU dataset.

The eICU Research Institute (eRI) database contains clinical and microbiological data for patients who had a complete hospitalization between January 1, 2007 and March 31, 2013.

Discover the booming eICU market! This comprehensive analysis reveals key growth drivers, trends, and challenges, featuring leading companies like GE Healthcare and Philips. Explore market size projections, regional breakdowns, and future opportunities in this rapidly evolving sector of healthcare technology.

BackgroundInvasive mechanical ventilation plays an important role in the prognosis of patients with sepsis. However, there are, currently, no tools specifically designed to assess weaning from invasive mechanical ventilation in patients with sepsis. The aim of our study was to develop a practical model to predict weaning in patients with sepsis.MethodsWe extracted patient information from the Medical Information Mart for Intensive Care Database-IV (MIMIC-IV) and the eICU Collaborative Research Database (eICU-CRD). Kaplan–Meier curves were plotted to compare the 28-day mortality between patients who successfully weaned and those who failed to wean. Subsequently, MIMIC-IV was divided into a training set and an internal verification set, and the eICU-CRD was designated as the external verification set. We selected the best model to simplify the internal and external validation sets based on the performance of the model.ResultsA total of 5020 and 7081 sepsis patients with invasive mechanical ventilation in MIMIC-IV and eICU-CRD were included, respectively. After matching, weaning was independently associated with 28-day mortality and length of ICU stay (p < 0.001 and p = 0.002, respectively). After comparison, 35 clinical variables were extracted to build weaning models. XGBoost performed the best discrimination among the models in the internal and external validation sets (AUROC: 0.80 and 0.86, respectively). Finally, a simplified model was developed based on XGBoost, which included only four variables. The simplified model also had good predictive performance (AUROC:0.75 and 0.78 in internal and external validation sets, respectively) and was developed into a web-based tool for further review.ConclusionsWeaning success is independently related to short-term mortality in patients with sepsis. The simplified model based on the XGBoost algorithm provides good predictive performance and great clinical applicablity for weaning, and a web-based tool was developed for better clinical application.

The global Electronic Intensive Care Unit (eICU) market is experiencing robust growth, driven by the increasing prevalence of chronic diseases, a surge in the aging population, and the rising demand for improved patient care and reduced healthcare costs. Technological advancements in remote monitoring, data analytics, and telehealth are further fueling market expansion. The market size in 2025 is estimated at $2.5 billion, demonstrating significant potential for growth. Considering a conservative Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, based on industry trends and technological advancements, the market is projected to reach approximately $8.2 billion by 2033. This growth is fueled by the increasing adoption of eICU systems in both general and specialized hospitals, with a preference for integrated systems over standalone ones due to enhanced data management and workflow integration. The integration of AI and machine learning capabilities for predictive analytics and early warning systems is a key trend transforming the eICU market, further improving patient outcomes and operational efficiency. Key market segments include general and specialized hospitals, with specialized hospitals showing faster adoption rates due to their focus on critical care and higher patient acuity. The increasing preference for integrated eICU systems, which combine hardware, software, and remote monitoring capabilities, is driving the segment's growth. Geographic segmentation reveals strong market penetration in North America and Europe, primarily due to advanced healthcare infrastructure and higher adoption rates of advanced medical technologies. However, emerging economies in Asia-Pacific and the Middle East & Africa present significant growth opportunities, fueled by increasing healthcare investments and rising awareness of eICU benefits. While high initial investment costs and the need for skilled personnel present challenges, the long-term cost savings and improved patient outcomes associated with eICU technology are expected to outweigh these restraints. Major players like Medical Informatics (Sickbay), GE HealthCare, and Philips are driving innovation and expanding their market presence through strategic partnerships, acquisitions, and product development.

Demographics and clinical characteristics on EICU admission by SCHE activity.

IntroductionSepsis associated thrombocytopenia (SAT) is a common complication of sepsis. We designed this study to investigate factors influencing SAT.MethodsPatients with sepsis (2984 in Peking union medical college hospital [PUMCH] database, 13165 in eICU Collaborative Research [eICU] database, 11101 in Medical Information Mart for Intensive Care IV [MIMIC-IV] database) were enrolled. Variables included basic information, comorbidities, and organ functions. Multi-variable logistic regression models and artificial neural network model were applied to determine the factors related to SAT.Main resultsAge and body mass index (BMI) were inversely correlated with the incidence of SAT (p-value 0.175 and 0.049 [PUMCH], p-value 0.000 and 0.000 [eICU], p-value 0.000 and 0.000 [MIMIC-IV]). Hematologic malignancies and other malignancies were positively correlated with the incidence of SAT (p-value 0.000 and 0.000 [PUMCH], p-value 0.000 and 0.000 [eICU], p-value 0.000 and 0.020 [MIMIC-IV]) except other malignancies was inversely correlated with the incidence of SAT in PUMCH database. Norepinephrine (NE) equivalents, total bilirubin (TBIL) and creatinine were positively correlated with the incidence of SAT (p-value 0.000, 0.000 and 0.011 [PUMCH], p-value 0.028, 0.000 and 0.013 [eICU], p-value 0.028, 0.000 and 0.027 [MIMIC-IV]). PaO2 / FiO2 was inversely correlated with the incidence of SAT in PUMCH database (p-value 0.021 [PUMCH]), while it was positively correlated with the incidence of SAT (p-value 0.000 [MIMIC-IV]). PaO2 / FiO2 and SAT was not related (p-value 0.111 [eICU]). TBIL, hematologic malignancies, PaO2 / FiO2 and NE equivalents ranked in the top five significant variables in all three datasets.ConclusionsHematologic malignancies and other malignancies were positively correlated with the incidence of SAT. NE equivalents, TBIL and creatinine were positively correlated with the incidence of SAT. TBIL, hematologic malignancies, PaO2 / FiO2 and NE equivalents ranked in the top significant variables in factors influencing SAT.

The size of the Electronic Intensive Care Unit (eICU) market was valued at USD XXX million in 2024 and is projected to reach USD XXX million by 2033, with an expected CAGR of XX% during the forecast period.

Decompensation risk prediction in eICU.

and the eICU

(Num. and Cat. indicate presence of numerical and categorical variables respectively. Repn. indicates representation of categorical variables, either One Hot Encoding (OHE) or embedding (EMB)).

Objective: Congestive heart failure (CHF) is a clinical syndrome in which heart disease progresses to a severe stage. Risk assessment and early diagnosis of death in patients with CHF are critical to patient prognosis and treatment. The purpose of this study was to establish a nomogram predicting in-hospital death for CHF patients in the ICU. Design: A retrospective observational cohort study. Setting and participants: The data of study from 30,411 CHF patients in the Medical Information Mart for Intensive Care (MIMIC-IV) database and the eICU Collaborative Research Database (eICU-CRD). Primary outcome: In-hospital mortality. Results: The inclusion criteria were met by 15983 subjects, whose in-hospital mortality rate was 12.4%. Multivariate analysis determined that the independent risk factors were age, race, norepinephrine, dopamine, phenylephrine, vasopressin, mechanical ventilation, intubation, HepF, heart rate, respiratory rate, temperature, SBP, AG, BUN, creatinine, chloride, MCV, RDW, and WBC. The C-index of the nomogram (0.767, 95%CI: 0.759–0.779) was superior to that of the traditional SOFA, APSIII and GWTGHF score, indicating its discrimination power. Calibration plots demonstrated that the predicted results are in good agreement with the observed results. The decision curves of the derivation and validation sets both had net benefits. Conclusion: The twenty independent risk factors for in-hospital mortality of CHF patients were age, race, norepinephrine, dopamine, phenylephrine, vasopressin, mechanical ventilation, intubation, HepF, heart rate, respiratory rate, temperature, SBP, AG, BUN, creatinine, chloride, MCV, RDW, and WBC. The nomogram that included these factors accurately predicted the in-hospital mortality of CHF patients. The novel nomogram has the potential to be a clinical practice aided predictive tool for predicting and assessing mortality in CHF patients in the ICU. Methods Univariate logistic regression analysis were used to select risk factors associated with the in-hospital mortality of CHF patients, and multivariate logistic regression was used to build the prediction model. The discrimination, calibration and clinical validity of the model were evaluated by AUC, calibration curve, Hosmer-Lemeshow χ2 test and DCA curve, respectively. Finally, data from 15,503 CHF patients in the multi-center eICU-CRD were used for external validation of the established nomogram.

BackgroundIn the ICU, patients with acute myocardial infarction and cardiogenic shock (AMI-CS) often face high mortality rates, making timely and precise mortality risk prediction crucial for clinical decision-making. Despite existing models, machine learning algorithms hold the potential for improved predictive accuracy.MethodsIn this study, a predictive model was developed using the MIMIC-IV database, with external validation performed on the eICU-CRD database. We included ICU patients diagnosed with AMI-CS. Feature selection was conducted using the Boruta algorithm, followed by the construction and comparison of four machine learning models: Logistic Regression (LR), eXtreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Gaussian Naive Bayes (GNB). Model performance was evaluated based on metrics such as AUC (Area Under the Curve), accuracy, sensitivity, specificity, and so on. The SHAP method was employed to visualize and interpret the importance of model features. Finally, we constructed an online prediction model and conducted external validation in the eICU-CRD database.ResultsIn this study, a total of 570 and 391 patients with AMI-CS were included from the MIMIC-IV and eICU-CRD databases, respectively. Among all machine learning algorithms evaluated, LR exhibited the best performance with a validation set AUC of 0.841(XGBoost: 0.835, AdaBoost: 0.839, GNB: 0.826). The model incorporated five variables: prothrombin time, blood urea nitrogen, age, beta-blockers and Angiotensin-Converting Enzyme Inhibitors or Angiotensin II Receptor Blockers. SHAP plots are employed to visualize the importance of model features and to interpret the results. An online prediction tool was developed, externally validated with the eICU-CRD database, achieving an AUC of 0.755.ConclusionEmploying the LR algorithm, we developed a predictive model for assessing the mortality risk among AMI-CS patients in the ICU setting. Through model predictions, this facilitates early detection of high-risk individuals, ensures judicious allocation of healthcare resources.

Number of patients and records in four tasks.

Background: Many severity scores are widely used for clinical outcome prediction for critically ill patients in the intensive care unit (ICU). However, for patients identified by sepsis-3 criteria, none of these have been developed. This study aimed to develop and validate a risk stratification score for mortality prediction in sepsis-3 patients.Methods: In this retrospective cohort study, we employed the Medical Information Mart for Intensive Care III (MIMIC III) database for model development and the eICU database for external validation. We identified septic patients by sepsis-3 criteria on day 1 of ICU entry. The Least Absolute Shrinkage and Selection Operator (LASSO) technique was performed to select predictive variables. We also developed a sepsis mortality prediction model and associated risk stratification score. We then compared model discrimination and calibration with other traditional severity scores.Results: For model development, we enrolled a total of 5,443 patients fulfilling the sepsis-3 criteria. The 30-day mortality was 16.7%. With 5,658 septic patients in the validation set, there were 1,135 deaths (mortality 20.1%). The score had good discrimination in development and validation sets (area under curve: 0.789 and 0.765). In the validation set, the calibration slope was 0.862, and the Brier value was 0.140. In the development dataset, the score divided patients according to mortality risk of low (3.2%), moderate (12.4%), high (30.7%), and very high (68.1%). The corresponding mortality in the validation dataset was 2.8, 10.5, 21.1, and 51.2%. As shown by the decision curve analysis, the score always had a positive net benefit.Conclusion: We observed moderate discrimination and calibration for the score termed Sepsis Mortality Risk Score (SMRS), allowing stratification of patients according to mortality risk. However, we still require further modification and external validation.

BackgroundHeart failure is a cardiovascular disorder, while sepsis is a common non-cardiac cause of mortality. Patients with combined heart failure and sepsis have a significantly higher mortality rate and poor prognosis, making early identification of high-risk patients and appropriate allocation of medical resources critically important.MethodsWe constructed a survival prediction model for patients with heart failure and sepsis using the eICU-CRD database and externally validated it using the MIMIC-IV database. Our primary outcome is the 28-day all-cause mortality rate. The Boruta method is used for initial feature selection, followed by feature ranking using the XGBoost algorithm. Four machine learning models were compared, including Logistic Regression (LR), eXtreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Gaussian Naive Bayes (GNB). Model performance was assessed using metrics such as area under the curve (AUC), accuracy, sensitivity, and specificity, and the SHAP method was utilized to visualize feature importance and interpret model results. Additionally, we conducted external validation using the MIMIC-IV database.ResultsWe developed a survival prediction model for heart failure complicated by sepsis using data from 3891 patients in the eICU-CRD and validated it externally with 2928 patients from the MIMIC-IV database. The LR model outperformed all other machine learning algorithms with a validation set AUC of 0.746 (XGBoost: 0.726, AdaBoost: 0.744, GNB: 0.722), alongside accuracy (0.685), sensitivity (0.666), and specificity (0.712). The final model incorporates 10 features: age, ventilation, norepinephrine, white blood cell count, total bilirubin, temperature, phenylephrine, respiratory rate, neutrophil count, and systolic blood pressure. We employed the SHAP method to enhance the interpretability of the model based on the LR algorithm. Additionally, external validation was conducted using the MIMIC-IV database, with an external validation AUC of 0.699.ConclusionBased on the LR algorithm, a model was constructed to effectively predict the 28-day all-cause mortality rate in patients with heart failure complicated by sepsis. Utilizing our model predictions, clinicians can promptly identify high-risk patients and receive guidance for clinical practice.

Background: Sepsis-induced coagulopathy (SIC) denotes an increased mortality rate and poorer prognosis in septic patients.Objectives: Our study aimed to develop and validate machine-learning models to dynamically predict the risk of SIC in critically ill patients with sepsis.Methods: Machine-learning models were developed and validated based on two public databases named Medical Information Mart for Intensive Care (MIMIC)-IV and the eICU Collaborative Research Database (eICU-CRD). Dynamic prediction of SIC involved an evaluation of the risk of SIC each day after the diagnosis of sepsis using 15 predictive models. The best model was selected based on its accuracy and area under the receiver operating characteristic curve (AUC), followed by fine-grained hyperparameter adjustment using the Bayesian Optimization Algorithm. A compact model was developed, based on 15 features selected according to their importance and clinical availability. These two models were compared with Logistic Regression and SIC scores in terms of SIC prediction.Results: Of 11,362 patients in MIMIC-IV included in the final cohort, a total of 6,744 (59%) patients developed SIC during sepsis. The model named Categorical Boosting (CatBoost) had the greatest AUC in our study (0.869; 95% CI: 0.850–0.886). Coagulation profile and renal function indicators were the most important features for predicting SIC. A compact model was developed with an AUC of 0.854 (95% CI: 0.832–0.872), while the AUCs of Logistic Regression and SIC scores were 0.746 (95% CI: 0.735–0.755) and 0.709 (95% CI: 0.687–0.733), respectively. A cohort of 35,252 septic patients in eICU-CRD was analyzed. The AUCs of the full and the compact models in the external validation were 0.842 (95% CI: 0.837–0.846) and 0.803 (95% CI: 0.798–0.809), respectively, which were still larger than those of Logistic Regression (0.660; 95% CI: 0.653–0.667) and SIC scores (0.752; 95% CI: 0.747–0.757). Prediction results were illustrated by SHapley Additive exPlanations (SHAP) values, which made our models clinically interpretable.Conclusions: We developed two models which were able to dynamically predict the risk of SIC in septic patients better than conventional Logistic Regression and SIC scores.

*LoS (Length of Stay). Continuous variables are presented as Median [Interquartile Range Q1–Q3]; binary or categorical variables as Count (%).