This is a preprocessed dataset derived from patient records in MIMIC-III and eICU, two large-scale electronic health record (EHR) databases. It contains features and labels for 5 prediction tasks involving 3 adverse outcomes (prediction times listed in parentheses): in-hospital mortality (48h), acute respiratory failure (4h and 12h), and shock (4h and 12h). We extracted comprehensive, high-dimensional feature representations (up to ~8,000 features) using FIDDLE (FlexIble Data-Driven pipeLinE), an open-source preprocessing pipeline for structured clinical data. These 5 prediction tasks were designed in consultation with a critical care physician for their clinical importance, and were used as part of the proof-of-concept experiments in the original paper to demonstrate FIDDLE's utility in aiding the feature engineering step of machine learning model development. The intent of this release is to share preprocessed MIMIC-III and eICU datasets used in the experiments to support and enable reproducible machine learning research on EHR data.

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.

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, ...

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.

BackgroundThe mean perfusion pressure (MPP) was recently proposed to personalize tissue perfusion pressure management in critically ill patients. Severe fluctuation in MPP may be associated with adverse outcomes. We sought to determine if higher MPP variability was correlated with increased mortality in critically ill patients with CVP monitoring.MethodsWe designed a retrospective observational study and analyzed data stored in the eICU Collaborative Research Database. Validation test was conducted in MIMIC-III database. The exposure was the coefficient of variation (CV) of MPP in the primary analyses, using the first 24 hours MPP data recorded within 72 hours in the first ICU stay. Primary endpoint was in-hospital mortality.ResultsA total of 6,111 patients were included. The in-hospital mortality of 17.6% and the median MPP-CV was 12.3%. Non-survivors had significantly higher MPP-CV than survivors (13.0% vs 12.2%, p 19.2%) were associated with increased risk of hospital mortality compared with those in the fifth and sixth decile (adjusted OR: 1.38, 95% Cl: 1.07–1.78). These relationships remained remarkable in the multiple sensitivity analyses. The validation test with 4,153 individuals also confirmed the results when MPP-CV > 21.3% (adjusted OR: 1.46, 95% Cl: 1.05–2.03).ConclusionsSevere fluctuation in MPP was associated with increased short-term mortality in critically ill patients with CVP monitoring.

The Electronic Intensive Care Unit (eICU) market has gained remarkable traction in the healthcare sector, owing to the increasing demand for advanced monitoring solutions that ensure patient safety and enhance care delivery in intensive care units (ICUs). eICUs leverage telemedicine technology to provide remote surv

Summary of the eICU dataset.

Contains quarterly performance data regarding average customer wait times at MVA branch offices, traffic fatalities on all roads in Maryland, and average time to certify new MBE Program applicants

ObjectiveIn an era of increasingly expensive intensive care costs, it is essential to evaluate early whether the length of stay (LOS) in the intensive care unit (ICU) of obesity patients with sepsis will be prolonged. On the one hand, it can reduce costs; on the other hand, it can reduce nosocomial infection. Therefore, this study aimed to verify whether ICU prolonged LOS was significantly associated with poor prognosis poor in obesity patients with sepsis and develop a simple prediction model to personalize the risk of ICU prolonged LOS for obesity patients with sepsis.MethodIn total, 14,483 patients from the eICU Collaborative Research Database were randomized to the training set (3,606 patients) and validation set (1,600 patients). The potential predictors of ICU prolonged LOS among various factors were identified using logistic regression analysis. For internal and external validation, a nomogram was developed and performed.ResultsICU prolonged LOS was defined as the third quartile of ICU LOS or more for all sepsis patients and demonstrated to be significantly associated with the mortality in ICU by logistic regression analysis. When entering the ICU, seven independent risk factors were identified: maximum white blood cell, minimum white blood cell, use of ventilation, Glasgow Coma Scale, minimum albumin, maximum respiratory rate, and minimum red blood cell distribution width. In the internal validation set, the area under the curve was 0.73, while in the external validation set, it was 0.78. The calibration curves showed that this model predicted probability due to actually observed probability. Furthermore, the decision curve analysis and clinical impact curve showed that the nomogram had a high clinical net benefit.ConclusionIn obesity patients with sepsis, we created a novel nomogram to predict the risk of ICU prolonged LOS. This prediction model is accurate and reliable, and it can assist patients and clinicians in determining prognosis and making clinical decisions.

Risk of mortality in low predialysis creatinine patients compared with high predialysis creatinine patients.

Baseline characteristics of the study population by the median of creatinine level.

Baseline characteristics of patients included in the analysis according to ICU cohort.

ObjectivesSepsis-associated encephalopathy (SAE) patients in the intensive care unit (ICU) and perioperative period are administrated supplemental oxygen. However, the correlation between oxygenation status with SAE and the target for oxygen therapy remains unclear. This study aimed to examine the relationship between oxygen therapy and SAE patients.MethodsPatients diagnosed with sepsis 3.0 in the intensive care unit (ICU) were enrolled. The data were collected from the Medical Information Mart for Intensive Care IV (MIMIC IV) database and the eICU Collaborative Research Database (eICU-CRD) database. The generalized additive models were adopted to estimate the oxygen therapy targets in SAE patients. The results were confirmed by multivariate Logistic, propensity score analysis, inversion probability-weighting, doubly robust model, and multivariate COX analyses. Survival was analyzed by the Kaplan-Meier method.ResultsA total of 10055 patients from eICU-CRD and 1685 from MIMIC IV were included. The incidence of SAE patients was 58.43%. The range of PaO2 (97-339) mmHg, PaO2/FiO2 (189-619), and SPO2≥93% may reduce the incidence of SAE, which were verified by multivariable Logistic regression, propensity score analysis, inversion probability-weighting, and doubly robust model estimation in MIMIC IV database and eICU database. The range of PaO2/FiO2 (189-619) and SPO2≥93% may reduce the hospital mortality of SAE were verified by multivariable COX regression.ConclusionsSAE patients in ICU, including perioperative period, require conservative oxygen therapy. We should maintain SPO2≥93%, PaO2 (97-339) mmHg and PaO2/FiO2 (189-619) in SAE patients.

Advantages and disadvantages of each models.

Comparison of mortality prediction performance among the four models of AKI patients.

Background: Present researches exploring the prognostic value of calcium concentration are undermined by sample size and study design. Our study investigated the association of both total calcium (tCa) and ionized Ca (iCa) to short- and long-term mortality and other outcomes in post-cardiovascular surgery (PCS) patients admitted to intensive care unit (ICU) from two large public data sets.Methods: The Medical Information Mart for Intensive Care III (MIMIC-III) database and the eICU Collaborative Research Database (eICU) were inspected to identify PCS patients. The primary outcome was 28-day mortality. Multivariate regression was used to elucidate the relationship between calcium concentration and outcomes. The propensity score estimation was performed to validate our findings.Results: A total of 6122 and 914 patients were included from the MIMIC III and eICU data sets, respectively. The groups with the most patients were the mild hypo-iCa and hypo-tCa groups. The mild hypo-iCa group showed significant association with worse short-term and long-term prognosis, less use of ventilation, longer ICU and hospital stay, and more incidence of 7-day acute kidney injury.Conclusions: The mild hypo-iCa (0.9–1.15 mmol/L) within the first day of admission to the ICU could serve as an independent prognosis factor for PCS patients.

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.

BackgroundPrior studies have linked the aspartate aminotransferase to alanine aminotransferase ratio (AAR) with negative health outcomes in the elderly and specific populations. However, the impact of AAR on the prognosis of the entire population in the intensive care unit (ICU) remains unclear. This study aimed to determine the correlation between AAR and the mortality among adult ICU patients.MethodPatient data were retrieved from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and stratified into quartiles by AAR. Survival analysis using the Kaplan-Meier curves was conducted to compare survival across quartiles. The primary outcome was 28-day mortality, with secondary outcomes including 60-day, 90-day, and 365-day mortality, along with ICU-free, ventilator-free, and vasopressor-free days within the first 28 days. The association between AAR and mortality was evaluated using Cox proportional hazards regression analysis complemented by a restricted cubic spline. Furthermore, the eICU Collaborative Research Database (eICU-CRD) was used as an external validation cohort for sensitivity analysis.ResultThe study included 20,225 patients with a mean age of 63.7 ± 17.5 years. Kaplan-Meier analysis indicated a higher risk of 28-day mortality for patients with higher AAR (log-rank P 

Background: Present researches exploring the prognostic value of calcium concentration are undermined by sample size and study design. Our study investigated the association of both total calcium (tCa) and ionized Ca (iCa) to short- and long-term mortality and other outcomes in post-cardiovascular surgery (PCS) patients admitted to intensive care unit (ICU) from two large public data sets.Methods: The Medical Information Mart for Intensive Care III (MIMIC-III) database and the eICU Collaborative Research Database (eICU) were inspected to identify PCS patients. The primary outcome was 28-day mortality. Multivariate regression was used to elucidate the relationship between calcium concentration and outcomes. The propensity score estimation was performed to validate our findings.Results: A total of 6122 and 914 patients were included from the MIMIC III and eICU data sets, respectively. The groups with the most patients were the mild hypo-iCa and hypo-tCa groups. The mild hypo-iCa group showed significant association with worse short-term and long-term prognosis, less use of ventilation, longer ICU and hospital stay, and more incidence of 7-day acute kidney injury.Conclusions: The mild hypo-iCa (0.9–1.15 mmol/L) within the first day of admission to the ICU could serve as an independent prognosis factor for PCS patients.

BackgroundPrior studies have linked the aspartate aminotransferase to alanine aminotransferase ratio (AAR) with negative health outcomes in the elderly and specific populations. However, the impact of AAR on the prognosis of the entire population in the intensive care unit (ICU) remains unclear. This study aimed to determine the correlation between AAR and the mortality among adult ICU patients.MethodPatient data were retrieved from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and stratified into quartiles by AAR. Survival analysis using the Kaplan-Meier curves was conducted to compare survival across quartiles. The primary outcome was 28-day mortality, with secondary outcomes including 60-day, 90-day, and 365-day mortality, along with ICU-free, ventilator-free, and vasopressor-free days within the first 28 days. The association between AAR and mortality was evaluated using Cox proportional hazards regression analysis complemented by a restricted cubic spline. Furthermore, the eICU Collaborative Research Database (eICU-CRD) was used as an external validation cohort for sensitivity analysis.ResultThe study included 20,225 patients with a mean age of 63.7 ± 17.5 years. Kaplan-Meier analysis indicated a higher risk of 28-day mortality for patients with higher AAR (log-rank P