Association between blood urea nitrogen to serum albumin ratio and in-hospital mortality in critical patients with diabetic ketoacidosis: a retrospective analysis of the eICU database.

Background: This study aimed to investigate the association between blood urea nitrogen to serum albumin ratio (BAR) and the risk of in-hospital mortality in patients with diabetic ketoacidosis. Methods: A total of 3,962 diabetic ketoacidosis patients from the eICU Collaborative Research Database were included in this analysis. The primary outcome was in-hospital death. Results: Over a median length of hospital stay of 3.1 days, 86 in-hospital deaths were identified. One unit increase in LnBAR was positively associated with the risk of in-hospital death (hazard ratio [HR], 1.82 [95% CI, 1.42-2.34]). Furthermore, a nonlinear, consistently increasing correlation between elevated BAR and in-hospital mortality was observed (P for trend =0.005 after multiple-adjusted). When BAR was categorized into quartiles, the higher risk of in-hospital death (multiple-adjusted HR, 1.99 [95% CI, (1.1-3.6)]) was found in participants in quartiles 3 to 4 (BAR≥6.28) compared with those in quartiles 1 to 2 (BAR<6.28). In the subgroup analysis, the LnBAR-hospital death association was significantly stronger in participants without kidney insufficiency (yes versus no, P-interaction=0.023). Conclusion: There was a significant and positive association between BAR and the risk of in-hospital death in patients with diabetic ketoacidosis. Notably, the strength of this association was intensified among those without kidney insufficiency.

The Tri-Steps Model of Critical Conditions in Intensive Care: Introducing a New Paradigm for Chronic Critical Illness.

Background: The prevailing model for understanding chronic critical illness is a biphasic model, suggesting phases of acute and chronic critical conditions. A major challenge within this model is the difficulty in determining the timing of the process chronicity. It is likely that the triad of symptoms (inflammation, catabolism, and immunosuppression [ICIS]) could be associated with this particular point. We aimed to explore the impact of the symptom triad (inflammation, catabolism, immunosuppression) on the outcomes of patients hospitalized in intensive care units (ICUs). Methods: The eICU-CRD database with 200,859 ICU admissions was analyzed. Adult patients with the ICIS triad, identified by elevated CRP (>20 mg/L), reduced albumin (<30 g/L), and low lymphocyte counts (<0.8 × 109/L), were included. The cumulative risk of developing ICIS was assessed using the Nelson-Aalen estimator. Results: This retrospective cohort study included 894 patients (485 males, 54%), with 60 (6.7%) developing ICIS. The cumulative risk of ICIS by day 21 was 22.5%, with incidence peaks on days 2-3 and 10-12 after ICU admission. Patients with the ICIS triad had a 2.5-fold higher mortality risk (p = 0.009) and double the likelihood of using vasopressors (p = 0.008). The triad onset day did not significantly affect mortality (p = 0.104). Patients with ICIS also experienced extended hospital (p = 0.041) and ICU stays (p < 0.001). Conclusions: The symptom triad (inflammation, catabolism, immunosuppression) during hospitalization increases mortality risk by 2.5 times (p = 0.009) and reflects the chronicity of the critical condition. Identifying two incidence peaks allows the proposal of a new Tri-steps model of chronic critical illness with acute, extended, and chronic phases.

Comparing Open-Access Database and Traditional Intensive Care Studies Using Machine Learning: Bibliometric Analysis Study.

BACKGROUND: Intensive care research has predominantly relied on conventional methods like randomized controlled trials. However, the increasing popularity of open-access, free databases in the past decade has opened new avenues for research, offering fresh insights. Leveraging machine learning (ML) techniques enables the analysis of trends in a vast number of studies. OBJECTIVE: This study aims to conduct a comprehensive bibliometric analysis using ML to compare trends and research topics in traditional intensive care unit (ICU) studies and those done with open-access databases (OADs). METHODS: We used ML for the analysis of publications in the Web of Science database in this study. Articles were categorized into "OAD" and "traditional intensive care" (TIC) studies. OAD studies were included in the Medical Information Mart for Intensive Care (MIMIC), eICU Collaborative Research Database (eICU-CRD), Amsterdam University Medical Centers Database (AmsterdamUMCdb), High Time Resolution ICU Dataset (HiRID), and Pediatric Intensive Care database. TIC studies included all other intensive care studies. Uniform manifold approximation and projection was used to visualize the corpus distribution. The BERTopic technique was used to generate 30 topic-unique identification numbers and to categorize topics into 22 topic families. RESULTS: A total of 227,893 records were extracted. After exclusions, 145,426 articles were identified as TIC and 1301 articles as OAD studies. TIC studies experienced exponential growth over the last 2 decades, culminating in a peak of 16,378 articles in 2021, while OAD studies demonstrated a consistent upsurge since 2018. Sepsis, ventilation-related research, and pediatric intensive care were the most frequently discussed topics. TIC studies exhibited broader coverage than OAD studies, suggesting a more extensive research scope. CONCLUSIONS: This study analyzed ICU research, providing valuable insights from a large number of publications. OAD studies complement TIC studies, focusing on predictive modeling, while TIC studies capture essential qualitative information. Integrating both approaches in a complementary manner is the future direction for ICU research. Additionally, natural language processing techniques offer a transformative alternative for literature review and bibliometric analysis.

Enhancing predictions with a stacking ensemble model for ICU mortality risk in patients with sepsis-associated encephalopathy.

OBJECTIVE: We identified predictive factors and developed a novel machine learning (ML) model for predicting mortality risk in patients with sepsis-associated encephalopathy (SAE). METHODS: In this retrospective cohort study, data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) and eICU Collaborative Research Database were used for model development and external validation. The primary outcome was the in-hospital mortality rate among patients with SAE; the observed in-hospital mortality rate was 14.74% (MIMIC IV: 1112, eICU: 594). Using the least absolute shrinkage and selection operator (LASSO), we built nine ML models and a stacking ensemble model and determined the optimal model based on the area under the receiver operating characteristic curve (AUC). We used the Shapley additive explanations (SHAP) algorithm to determine the optimal model. RESULTS: The study included 9943 patients. LASSO identified 15 variables. The stacking ensemble model achieved the highest AUC on the test set (0.807) and 0.671 on external validation. SHAP analysis highlighted Glasgow Coma Scale (GCS) and age as key variables. The model (https://sic1.shinyapps.io/SSAAEE/) can predict in-hospital mortality risk for patients with SAE. CONCLUSIONS: We developed a stacked ensemble model with enhanced generalization capabilities using novel data to predict mortality risk in patients with SAE.

Developing an early warning system for detecting sepsis in patients with trauma.

The purpose of this study was to analyse the risk factors for sepsis in patients with trauma and develop a new scoring system for predicting sepsis in patients with trauma based on these risk factors. This will provide a simple and effective early warning method for the rapid and accurate detection and evaluation of the probability of sepsis in patients with trauma to assist in planning timely clinical interventions. We undertook a retrospective analysis of the clinical data of 216 patients with trauma who were admitted to the emergency intensive care unit of the emergency medicine department of the Hebei Medical University Third Hospital, China, between November 2017 and October 2022. We conducted a preliminary screening of the relevant factors using univariate logistic regression analysis and included those factors with a p value of <0.075 in the multivariate logistic regression analysis, from which the risk factors were screened and assigned, and obtained a total score, which was the sepsis early warning score. The incidence of sepsis in patients in the intensive care unit with trauma was 36.9%, and the mortality rate due to sepsis was 19.4%. We found statistically significant differences in several factors for patients with sepsis. The risk factors for sepsis in patients with trauma were the activated partial thromboplastin time, the New Injury Severity Score, growth differentiation factor-15 levels, shock, mechanical ventilation and the Acute Physiology and Chronic Health Evaluation II score. The area under the receiver operating characteristic curve of the sepsis early warning score for predicting sepsis in patients with trauma was 0.725. When the cutoff value of the early warning score was set at 5.0 points, the sensitivity was 69.9% and the specificity was 60.3%. The incidence of sepsis in patients with trauma can be reduced by closely monitoring patients' hemodynamics, implementing adequate fluid resuscitation promptly and by early removal of the catheter to minimize the duration of unnecessary invasive mechanical ventilation. In this study, we found that the use of the sepsis early warning score helped in a more accurate and effective evaluation of the prognosis of patients with trauma.

Interpretable machine learning models for predicting 90-day death in patients in the intensive care unit with epilepsy.

PURPOSE: This study aims to develop a machine learning-based model for predicting mortality risk in patients with epilepsy admitted to the intensive care unit (ICU), providing clinicians with an accurate prognostic tool to guide individualized treatment. METHODS: We collected clinical data from clinical databases (MIMIC IV and eICU-CRD) of epilepsy patients 24 h after ICU admission. The clinical characteristics of ICU patients with epilepsy were carefully feature selected and processed. MIMIC IV as the training set and eICU-CRD database as the test set. Six models were developed and validated, and the best LightGBM model was selected by performance comparison and analysed for interpretability. RESULTS: The final cohort comprised 429 patients for training and 1217 for testing. The training set exhibited a 90-day mortality rate of 9.32 %, and the test set had an in-hospital 90-day mortality rate of 4.10 %. Utilizing the LightGBM model, we achieved an AUC of 0.956 in the training set. External validation demonstrated promising results with accuracy of 0.898, precision of 0.975, AUC of 0.781, F1 score of 0.945, highlighting the model's potential for guiding clinical decision-making. Significant factors influencing model performance included the severity of illness, as measured by the OASIS score, and clinical parameters like heart rate and body temperature. CONCLUSION: This study introduces a machine learning-based approach to predict mortality risk in ICU epilepsy patients, offering a valuable tool for clinicians to identify high-risk individuals and devise personalized treatment strategies, thus improving patient prognosis and treatment outcomes.

Using machine learning algorithms to predict 28-day mortality in critically ill elderly patients with colorectal cancer.

OBJECTIVE: To predict the 28-day mortality of critically ill, elderly patients with colorectal cancer (CRC) using five machine learning approaches. METHODS: Data were extracted from the eICU Collaborative Research Database (eICU-CRD) (version 2.0) for a training cohort and from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) and Wuhan Union hospital for validation cohorts. Clinical information (i.e., demographics; initial laboratory tests; vital signs; outcomes) were collected. Five machine learning algorithms (LightGBM, decision tree, XGBoost, random forest, and ensemble model) and a logistic regression were applied for the prediction of 28-day mortality. RESULTS: Overall, 693 patients were included from the eICU cohort, 181 patients from the MIMIC-IV cohort and 95 from the Wuhan Union cohort. Among the six machine learning models, the ensemble model exhibited the best predictive ability (AUC, 0.86), followed by random forest (AUC, 0.83) and LightGBM (AUC, 0.82) in the training cohort. The models also obtained the good predictive performance for the 28-day mortality in the validation cohorts. CONCLUSIONS: We showed that machine learning algorithms can be used for the 28-day mortality prediction in critically ill, elderly patients with CRC.

Cardiopulmonary prognosis of prophylactic endotracheal intubation in patients with upper gastrointestinal bleeding undergoing endoscopy.

BACKGROUND: It is controversial whether prophylactic endotracheal intubation (PEI) protects the airway before endoscopy in critically ill patients with upper gastrointestinal bleeding (UGIB). The study aimed to explore the predictive value of PEI for cardiopulmonary outcomes and identify high-risk patients with UGIB undergoing endoscopy. METHODS: Patients undergoing endoscopy for UGIB were retrospectively enrolled in the eICU Collaborative Research Database (eICU-CRD). The composite cardiopulmonary outcomes included aspiration, pneumonia, pulmonary edema, shock or hypotension, cardiac arrest, myocardial infarction, and arrhythmia. The incidence of cardiopulmonary outcomes within 48 h after endoscopy was compared between the PEI and non-PEI groups. Logistic regression analyses and propensity score matching analyses were performed to estimate effects of PEI on cardiopulmonary outcomes. Moreover, restricted cubic spline plots were used to assess for any threshold effects in the association between baseline variables and risk of cardiopulmonary outcomes (yes/no) in the PEI group. RESULTS: A total of 946 patients were divided into the PEI group (108/946, 11.4%) and the non-PEI group (838/946, 88.6%). After propensity score matching, the PEI group (n=50) had a higher incidence of cardiopulmonary outcomes (58.0% vs. 30.3%, P=0.001). PEI was a risk factor for cardiopulmonary outcomes after adjusting for confounders (odds ratio [OR] 3.176, 95% confidence interval [95% CI] 1.567-6.438, P=0.001). The subgroup analysis indicated the similar results. A shock index >0.77 was a predictor for cardiopulmonary outcomes in patients undergoing PEI (P=0.015). The probability of cardiopulmonary outcomes in the PEI group depended on the Charlson Comorbidity Index (OR 1.465, 95% CI 1.079-1.989, P=0.014) and shock index >0.77 (compared with shock index ≤0.77 [OR 2.981, 95% CI 1.186-7.492, P=0.020, AUC=0.764]). CONCLUSION: PEI may be associated with cardiopulmonary outcomes in elderly and critically ill patients with UGIB undergoing endoscopy. Furthermore, a shock index greater than 0.77 could be used as a predictor of a worse prognosis in patients undergoing PEI.

Association of inflammatory indicators with intensive care unit mortality in critically ill patients with coronary heart disease.

Objective: Coronary heart disease (CHD) is one of the major cardiovascular diseases, a common chronic disease in the elderly and a major cause of disability and death in the world. Currently, intensive care unit (ICU) patients have a high probability of concomitant coronary artery disease, and the mortality of this category of patients in the ICU is receiving increasing attention. Therefore, the aim of this study was to verify whether the composite inflammatory indicators are significantly associated with ICU mortality in ICU patients with CHD and to develop a simple personalized prediction model. Method: 7115 patients from the Multi-Parameter Intelligent Monitoring in Intensive Care Database IV were randomly assigned to the training cohort (n = 5692) and internal validation cohort (n = 1423), and 701 patients from the eICU Collaborative Research Database served as the external validation cohort. The association between various inflammatory indicators and ICU mortality was determined by multivariate Logistic regression analysis and Cox proportional hazards model. Subsequently, a novel predictive model for mortality in ICU patients with CHD was developed in the training cohort and performance was evaluated in the internal and external validation cohorts. Results: Various inflammatory indicators were demonstrated to be significantly associated with ICU mortality, 30-day ICU mortality, and 90-day ICU mortality in ICU patients with CHD by Logistic regression analysis and Cox proportional hazards model. The area under the curve of the novel predictive model for ICU mortality in ICU patients with CHD was 0.885 for the internal validation cohort and 0.726 for the external validation cohort. The calibration curve showed that the predicted probabilities of the model matched the actual observed probabilities. Furthermore, the decision curve analysis showed that the novel prediction model had a high net clinical benefit. Conclusion: In ICU patients with CHD, various inflammatory indicators were independent risk factors for ICU mortality. We constructed a novel predictive model of ICU mortality risk in ICU patients with CHD that had great potential to guide clinical decision-making.

Association between serum phosphate and in-hospital mortality of patients with AECOPD: A retrospective analysis on eICU database.

Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is an important adverse event in the development of chronic obstructive pulmonary disease (COPD). Hyperphosphatemia is associated with higher mortality in patients with multiple diseases. In this study, we aimed to determine the relationship between serum phosphate and the risk of in-hospital mortality in patients with AECOPD. Methods: In the present study, patients with AECOPD were enrolled in the electronic Intensive Care Unit Collaborative Research Database (eICU-CRD), and divided into three groups according to the tertiles of serum phosphate level. The primary outcome measure was all-cause in-hospital mortality. The association between serum phosphate level and in-hospital mortality was investigated using multivariate logistic regression analysis. Moreover, subgroup analysis was performed to explore whether the relationship was consistent among different subgroups. Results: A total of 1199 AECOPD patients were included in this study. Non-survivors had higher serum phosphate levels than survivors. All patients were classified into lowest tertile, median tertile, and highest tertile, respectively. Multivariate logistic regression analysis indicated that serum phosphate was positively associated with in-hospital mortality after adjusting for confounders. Moreover, there was a significant trend across tertiles when serum phosphate level was diverted as a categorical variable. In addition, subgroup analysis demonstrated that serum phosphate was consistently associated with a higher risk of in-hospital mortality in different subgroups. Conclusion: Higher serum phosphate was positively associated with the increased in-hospital mortality in patients with AECOPD. Hyperphosphatemia may be an underlying high-risk factor for in-hospital mortality owing to AECOPD.

Altered consciousness mediates the effect of hypernatremia, but not hyponatremia, on mortality among patients observed in the ICU.

Purpose: Dysnatremias - hypernatremia and hyponatremia - may be associated with mortality through their impact on altered consciousness. We examined the mediating effect of decreased consciousness on the relationship between dysnatremia and mortality. Methods: Among 195,568 critically ill patients in the United States contained in the eICU database, we categorized serum sodium into bands of 5mEq/L. Using causal mediation analysis, we compared bands in the hypernatremia and hyponatremia ranges to a reference band of 135-139mEq/L to determine the proportion of mortality mediated by decreased consciousness as determined by the Glasgow Coma Score (GCS). Results: Both hyponatremia (OR [95%CI] for bands: <120mEq/L: 1.58 [1.26-1.97]; 120-<125mEq/L: 1.92 [1.64-2.25]; 125-<130mEq/L: 1.76 [1.60-1.93]; 130-<135mEq/L: 1.32 [1.24-1.41]) and hypernatremia (OR [95%CI] for bands: 140-<145mEq/L: 1.12 [1.05-1.19]; 145-<150mEq/L: 1.89 [1.70-2.11]; ≥150mEq/L: 1.86 [1.57-2.19]) were significantly associated with increased mortality. GCS mediated the effect of hypernatremia on mortality risk (Proportion mediated [95%CI]: 140-144mEq/L: 0.38 [0.23 to 0.89]; 145-149mEq/L: 0.27 [0.22 to 0.34]; ≥150mEq/L: 0.53 [0.41 to 0.81]) but not hyponatremia (proportion mediated 95%CI upper bound <0.05 for all bands). Conclusion: Decreased consciousness mediates the association between increased mortality and hypernatremia, but not hyponatremia. Further studies are needed to explore neurologic mechanisms and directionality in this relationship.

Development and Validation of a Dynamic Nomogram for Predicting in-Hospital Mortality in Patients with Acute Pancreatitis: A Retrospective Cohort Study in the Intensive Care Unit.

Purpose: The aim of this study is to develop and validate a predictive model for the prediction of in-hospital mortality in patients with acute pancreatitis (AP) based on the intensive care database. Patients and Methods: We analyzed the data of patients with AP in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database and Electronic Intensive Care Unit Collaborative Research Database (eICU-CRD). Then, patients from MIMIC-IV were divided into a development group and a validation group according to the ratio of 8:2, and eICU-CRD was assigned as an external validation group. Univariate logistic regression and least absolute shrinkage and selection operator regression were used for screening the best predictors, and multivariate logistic regression was used to establish a dynamic nomogram. We evaluated the discrimination, calibration, and clinical efficacy of the nomogram, and compared the performance of the nomogram with Acute Physiology and Chronic Health Evaluation II (APACHE-II) score and Bedside Index of Severity in AP (BISAP) score. Results: A total of 1030 and 514 patients with AP in MIMIC-IV database and eICU-CRD were included in the study. After stepwise analysis, 8 out of a total of 37 variables were selected to construct the nomogram. The dynamic nomogram can be obtained by visiting https://model.sci-inn.com/KangZou/. The area under receiver operating characteristic curve (AUC) of the nomogram was 0.859, 0.871, and 0.847 in the development, internal, and external validation set respectively. The nomogram had a similar performance with APACHE-II (AUC = 0.841, p = 0.537) but performed better than BISAP (AUC = 0.690, p = 0.001) score in the validation group. Moreover, the calibration curve presented a satisfactory predictive accuracy, and the decision curve analysis suggested great clinical application value of the nomogram. Conclusion: Based on the results of internal and external validation, the nomogram showed favorable discrimination, calibration, and clinical practicability in predicting the in-hospital mortality of patients with AP.

A Multidatabase ExTRaction PipEline (METRE) for facile cross validation in critical care research.

Transforming raw EHR data into machine learning model-ready inputs requires considerable effort. One widely used EHR database is Medical Information Mart for Intensive Care (MIMIC). Prior work on MIMIC-III cannot query the updated and improved MIMIC-IV version. Besides, the need to use multicenter datasets further highlights the challenge of EHR data extraction. Therefore, we developed an extraction pipeline that works on both MIMIC-IV and eICU Collaborative Research Database and allows for model cross validation using these 2 databases. Under the default choices, the pipeline extracted 38,766 and 126,448 ICU records for MIMIC-IV and eICU, respectively. Using the extracted time-dependent variables, we compared the Area Under the Curve (AUC) performance with ​​prior works on clinically relevant tasks such as in-hospital mortality prediction. METRE achieved comparable performance with AUC 0.723-0.888 across all tasks with MIMIC-IV. Additionally, when we evaluated the model directly on MIMIC-IV data using a model trained on eICU, we observed that the AUC change can be as small as +0.019 or -0.015. Our open-source pipeline transforms MIMIC-IV and eICU into structured data frames and allows researchers to perform model training and testing using data collected from different institutions, which is of critical importance for model deployment under clinical contexts. The code used to extract the data and perform training is available here: https://github.com/weiliao97/METRE.

Comparison of correctly and incorrectly classified patients for in-hospital mortality prediction in the intensive care unit.

BACKGROUND: The use of machine learning is becoming increasingly popular in many disciplines, but there is still an implementation gap of machine learning models in clinical settings. Lack of trust in models is one of the issues that need to be addressed in an effort to close this gap. No models are perfect, and it is crucial to know in which use cases we can trust a model and for which cases it is less reliable. METHODS: Four different algorithms are trained on the eICU Collaborative Research Database using similar features as the APACHE IV severity-of-disease scoring system to predict hospital mortality in the ICU. The training and testing procedure is repeated 100 times on the same dataset to investigate whether predictions for single patients change with small changes in the models. Features are then analysed separately to investigate potential differences between patients consistently classified correctly and incorrectly. RESULTS: A total of 34 056 patients (58.4%) are classified as true negative, 6 527 patients (11.3%) as false positive, 3 984 patients (6.8%) as true positive, and 546 patients (0.9%) as false negatives. The remaining 13 108 patients (22.5%) are inconsistently classified across models and rounds. Histograms and distributions of feature values are compared visually to investigate differences between groups. CONCLUSIONS: It is impossible to distinguish the groups using single features alone. Considering a combination of features, the difference between the groups is clearer. Incorrectly classified patients have features more similar to patients with the same prediction rather than the same outcome.

Red Cell Distribution Width/Albumin Ratio: A Predictor of In-Hospital All-Cause Mortality in Patients with Acute Myocardial Infarction in the ICU.

Purpose: Red cell distribution width (RDW) and albumin level are linked to adverse outcomes in patients with acute myocardial infarction (AMI). Nonetheless, it remains unknown whether the RDW/albumin ratio (RAR) is associated with the short-term prognosis of AMI. Using a large cohort, we aimed to explore the association between RAR and in-hospital all-cause mortality in intensive care unit (ICU) patients with AMI. Patients and Methods: The patients' data analyzed in this retrospective cohort investigation were obtained from the eICU Collaborative Research Data Resource. RAR was calculated based on the serum albumin level and RDW. The primary outcome was in-hospital all-cause mortality. Receiver operating characteristic curve, multiple logistic regression model, and Kaplan-Meier survival analysis were performed to explore the prognostic value of RAR. Results: We enrolled 2594 patients in this study. After correcting for confounding factors, the RAR was an independent predictor for in-hospital mortality in our model (odds ratio [OR] 1.27, 95% confidence interval [CI] 1.12, 1.43). A similar relationship was observed with mechanical ventilation use. RAR showed a better predictive value with an area under the curve (AUC) of 0.738 (cutoff, 4.776) for in-hospital all-cause mortality compared to RDW or albumin alone. Kaplan-Meier estimator curve analyses for RAR demonstrated that the group with RAR ≥4.776%/g/dL had poorer survival than the group with RAR <4.776%/g/dL (p< 0.0001). The subgroup analysis revealed no significant interaction between RAR and in-hospital all-cause mortality in all strata. Conclusion: RAR was an independent risk factor for in-hospital all-cause mortality in ICU patients with AMI. Higher RAR values corresponded to higher mortality rates. RAR is a more accurate predictor of in-hospital all-cause mortality in patients with AMI in the ICU than albumin or RDW. Thus, RAR may be a potential biomarker of AMI.

Association of early postoperative serum magnesium with acute kidney injury after cardiac surgery.

INTRODUCTION: Dysmagnesemia has been demonstrated to be involved in the pathophysiology of kidney diseases and is common in cardiac surgical patients. It remains unknown whether changes of serum magnesium after cardiac surgery affect AKI. We aimed to investigate the association of early postoperative magnesium with cardiac surgery-associated AKI in adults. METHODS: We conducted a multicenter retrospective cohort study involving patients who underwent cardiac surgery in the eICU Collaborative Research Database between 2014 and 2015. AKI within 7 days after surgery was defined using both serum creatinine and urine output criteria of Kidney Disease Improving Global Outcomes definition. Postoperative AKI was analyzed using multivariable logistic regression with early postoperative serum magnesium measured within the first 24 h after surgery as a continuous variable and categorically by quartiles. RESULTS: Postoperative AKI was identified in 3498 of 6124 (57.1%) patients receiving cardiac surgery. The median (25th-75th percentiles) early postoperative serum magnesium level of the study population was 2.3 (2.0-2.7) mg/dL. Higher serum magnesium level was associated with a higher risk of developing postoperative AKI (adjusted odds ratio (OR), 1.46 per 1 mg/dL increase; 95% confidence interval (CI), 1.31-1.62; p<.001). The multivariable-adjusted ORs (95% CIs) of postoperative AKI across increasing quartiles of serum magnesium were 1.00 (referent), 1.11 (0.95-1.29), 1.30 (1.12-1.52), and 1.72 (1.47-2.02) (p for trend <.001). CONCLUSIONS: These data demonstrate a significantly higher incidence of AKI in patients with higher early postoperative serum magnesium who underwent cardiac surgery.

Integration Strategy for e-Intensive Care Unit: A Narrative Review and Implementation Plan.

Introduction: The objectives of this article are to review previously established tele-intensive care unit (ICU) services describing their impact at the technical and medical level, and to propose an implementation plan to equip health care facilities in need of telehealth. Materials and Methods: We searched MEDLINE, EMBASE, PubMed, and ISI web of knowledge, using terms related to "e-ICU" and "tele-ICU" from inception to May 2021. Discussion: At the technical level, an increase in private insurance enrollment and routine checkups, as well as a reduction in hospital utilization rates and improvement in health outcomes was seen in the aftermath of the adoption of telehealth insurance mandates. Moreover, e-ICU helped reducing mortality and length of hospital stay of critically ill patients. The main approach to implementation should include features that are widely accepted for quality improvement, including being focused on patient-centered outcomes, having strong executive support, and targeting changes that were known to improve outcomes. HL7 Fast Healthcare Interoperability Resources stands out as one of the best candidates to achieve structural interoperability for patient health records. Conclusions: Adoption of tele-ICU services requires a substantial up-front investment and ongoing cost of maintenance. This could be challenging for hospitals with low budgets. Hence the importance of further investigating more efficient strategies of e-ICU services integration and implementation.

Association between the blood urea nitrogen to creatinine ratio and in­hospital mortality among patients with acute myocardial infarction: A retrospective cohort study.

The present study aimed to determine the association between the blood urea nitrogen (BUN) and creatinine (Cr) ratio and in-hospital mortality in patients with acute myocardial infarction (AMI). The present retrospective cohort study included adult patients (≥18 years of age) who were admitted to the intensive care unit (ICU) with a primary diagnosis of AMI. Medical records were obtained from the electronic ICU collaborative research database, which includes data from throughout continental USA. Data included demographic characteristics, vital signs, laboratory tests and comorbidities. The clinical endpoint was in-hospital mortality. The Cox proportional hazards model was used to evaluate the prognostic values of the basic BUN/Cr ratio and the Kaplan-Meier method was used to plot survival curves. Subgroup analyses were performed to measure mortality across various subgroups. In total, 5,965 eligible patients were included. In the Cox regression analysis, after being adjusted for age, sex, ethnicity and other confounding factors, the BUN/Cr ratio was found to be a significant risk predictor of in-hospital mortality. There was a non-linear relationship between the BUN/Cr ratio and in-hospital mortality after adjusting for potential confounders. A two-piecewise regression model was used to obtain a threshold inflection point value of 18. Furthermore, after adjusting for additional confounding factors (age, sex, ethnicity, BMI, heart rate, oxygen saturation, platelets, total protein, AMI category, heart failure, history of diabetes, history of hypertension, percutaneous coronary intervention, and administration of norepinephrine, dopamine and epinephrine), the BUN/Cr ratio remained a significant predictor of in-hospital mortality (third vs. first tertile: Hazard ratio, 1.50; 95% CI, 1.08-2.09; P<0.05). The Kaplan-Meier curve for tertiles of the BUN/Cr ratio indicated that in-hospital mortality rates were highest when the BUN/Cr ratio was ≥18.34 after adjustment for age, sex and ethnicity (P<0.05). The present findings demonstrated that a higher BUN/Cr ratio was associated with an increased risk of in-hospital mortality in patients with non-ST-segment elevation myocardial infarction. These results support a revision of how the prognosis of patients with AMI is predicted.

Cardiopulmonary prognosis of prophylactic endotracheal intubation in patients with upper gastrointestinal bleeding undergoing endoscopy / 世界急诊医学杂志（英文）

@#BACKGROUND: It is controversial whether prophylactic endotracheal intubation (PEI) protects the airway before endoscopy in critically ill patients with upper gastrointestinal bleeding (UGIB). The study aimed to explore the predictive value of PEI for cardiopulmonary outcomes and identify high-risk patients with UGIB undergoing endoscopy. METHODS: Patients undergoing endoscopy for UGIB were retrospectively enrolled in the eICU Collaborative Research Database (eICU-CRD). The composite cardiopulmonary outcomes included aspiration, pneumonia, pulmonary edema, shock or hypotension, cardiac arrest, myocardial infarction, and arrhythmia. The incidence of cardiopulmonary outcomes within 48 h after endoscopy was compared between the PEI and non-PEI groups. Logistic regression analyses and propensity score matching analyses were performed to estimate effects of PEI on cardiopulmonary outcomes. Moreover, restricted cubic spline plots were used to assess for any threshold effects in the association between baseline variables and risk of cardiopulmonary outcomes (yes/no) in the PEI group. RESULTS: A total of 946 patients were divided into the PEI group (108/946, 11.4%) and the non-PEI group (838/946, 88.6%). After propensity score matching, the PEI group (n=50) had a higher incidence of cardiopulmonary outcomes (58.0% vs. 30.3%, P=0.001). PEI was a risk factor for cardiopulmonary outcomes after adjusting for confounders (odds ratio [OR] 3.176, 95% confidence interval [95% CI] 1.567-6.438, P=0.001). The subgroup analysis indicated the similar results. A shock index >0.77 was a predictor for cardiopulmonary outcomes in patients undergoing PEI (P=0.015). The probability of cardiopulmonary outcomes in the PEI group depended on the Charlson Comorbidity Index (OR 1.465, 95% CI 1.079-1.989, P=0.014) and shock index >0.77 (compared with shock index ≤0.77 [OR 2.981, 95% CI 1.186-7.492, P=0.020, AUC=0.764]). CONCLUSION: PEI may be associated with cardiopulmonary outcomes in elderly and critically ill patients with UGIB undergoing endoscopy. Furthermore, a shock index greater than 0.77 could be used as a predictor of a worse prognosis in patients undergoing PEI.

Factor analysis based on SHapley Additive exPlanations for sepsis-associated encephalopathy in ICU mortality prediction using XGBoost - a retrospective study based on two large database.

Objective: Sepsis-associated encephalopathy (SAE) is strongly linked to a high mortality risk, and frequently occurs in conjunction with the acute and late phases of sepsis. The objective of this study was to construct and verify a predictive model for mortality in ICU-dwelling patients with SAE. Methods: The study selected 7,576 patients with SAE from the MIMIC-IV database according to the inclusion criteria and randomly divided them into training (n = 5,303, 70%) and internal validation (n = 2,273, 30%) sets. According to the same criteria, 1,573 patients from the eICU-CRD database were included as an external test set. Independent risk factors for ICU mortality were identified using Extreme Gradient Boosting (XGBoost) software, and prediction models were constructed and verified using the validation set. The receiver operating characteristic (ROC) and the area under the ROC curve (AUC) were used to evaluate the discrimination ability of the model. The SHapley Additive exPlanations (SHAP) approach was applied to determine the Shapley values for specific patients, account for the effects of factors attributed to the model, and examine how specific traits affect the output of the model. Results: The survival rate of patients with SAE in the MIMIC-IV database was 88.6% and that of 1,573 patients in the eICU-CRD database was 89.1%. The ROC of the XGBoost model indicated good discrimination. The AUCs for the training, test, and validation sets were 0.908, 0.898, and 0.778, respectively. The impact of each parameter on the XGBoost model was depicted using a SHAP plot, covering both positive (acute physiology score III, vasopressin, age, red blood cell distribution width, partial thromboplastin time, and norepinephrine) and negative (Glasgow Coma Scale) ones. Conclusion: A prediction model developed using XGBoost can accurately predict the ICU mortality of patients with SAE. The SHAP approach can enhance the interpretability of the machine-learning model and support clinical decision-making.