Long-term survival in stroke patients: insights into triglyceride-glucose body mass index from ICU data.

BACKGROUND: The Triglyceride Glucose-Body Mass Index (TyG-BMI) has been established as a robust indicator of insulin resistance (IR), reflecting metabolic health across various populations. In general, lower TyG-BMI values are often associated with better metabolic health outcomes and a reduced risk of adverse health events in non-critically ill populations. Previous studies have highlighted a significant negative association between TyG-BMI and all-cause mortality (ACM) among critically ill atrial fibrillation patients. Given the high prevalence and severe outcomes associated with stroke, understanding how TyG-BMI at the time of ICU admission correlates with ACM in critically ill stroke patients becomes imperative. This study aims to assess the correlation between TyG-BMI and ACM in this specific patient cohort, exploring how traditional associations between TyG-BMI and metabolic health may differ in the context of acute, life-threatening illness. METHODS: Patient data were retrieved by accessing the Medical Information Mart for Intensive Care IV (MIMIC-IV 2.2) database, categorizing patients into three groups on the basis of TyG-BMI tertiles. The study evaluated both primary and secondary outcomes: the primary outcomes included the 90-day, 180-day, and 1-year ACM, while secondary outcomes encompassed ICU, in-hospital, and 30-day ACM. Our study employed the Kaplan-Meier (K-M) curve method for outcome comparison across the groups while utilizing multivariate Cox proportional-hazards regression models and restricted cubic splines (RCS) to explore TyG-BMI association with these outcomes. Additionally, interaction and subgroup analyses were performed, focusing on different mortality time points. RESULTS: Among a cohort of 1707 individuals diagnosed with stroke, the average age was 68 years (interquartile range [IQR]: 58-78 years), with 946 (55.42%) of the participants being male. The analysis of K-M curves suggested that patients having a lower TyG-BMI level faced a heightened risk of long-term ACM, whereas the short-term ACM exhibited no statistically significant differences across the three TyG-BMI groups. Furthermore, Cox proportional-hazards regression analysis validated a statistically significant increased risk of long-term ACM among patients belonging to the lowest TyG-BMI tertile. Additionally, RCS analysis results demonstrated L-shaped correlations between the TyG-BMI index and both short- and long-term ACM. These findings underscore the TyG-BMI predictive value for long-term mortality in stroke patients, highlighting a nuanced relationship that varies over different time frames. The results revealed no interactions between TyG-BMI and the stratified variables, with the exception of age. CONCLUSION: In our study, lower TyG-BMI levels in critically ill stroke patients are significantly related to a higher risk of long-term ACM within the context of the United States. This finding suggests the potential of TyG-BMI as a marker for stratifying long-term risk in this patient population. However, it's crucial to note that this association was not observed for short-term ACM, indicating that the utility of TyG-BMI may be more pronounced in long-term outcome prediction. Additionally, our conclusion that TyG-BMI could serve as a reliable indicator for managing and stratifying stroke patients over the long term is preliminary. To confirm our findings and assess the universal applicability of TyG-BMI as a prognostic tool, it is crucial to conduct rigorously designed research across various populations.

Malignant tumor increases the risk of all causes in-hospital mortality in critically ill patients with ischemic stroke: analysis of the MIMIC-IV database.

BACKGROUND: Ischemic stroke (IS) and malignant tumor (MT) have high morbidity and mortality rates worldwide, and several associations exist between them. This study aimed to determine the effect of MT on hospital mortality in patients with IS. METHODS: Based on their MT status, participants with IS in the Medical Information Mart for Intensive Care IV (MIMIC-IV) were divided into two groups. The primary outcome was in-hospital all causes mortality. Kaplan-Meier survival analysis was performed to evaluate the intergroup in-hospital mortality, and three Cox regression models were used to determine the association between MT and in-hospital mortality. RESULTS: A total of 1605 participants (749 males and 856 females) were included in the study. The mean age was 72.030 ± 15.463 years. Of these, 257 (16%) patients died in the hospital. Kaplan-Meier analysis showed that the MT group had a significantly lower possibility of in-hospital survival than the non-MT group. In the unadjusted model, in-hospital mortality among MT patients had a higher odds ratio (OR) of 1.905 (95% CI, 1.320-2.748; P < 0.001) than the non-MT group. After adjusting for basic information, vital signs, and laboratory data, MT was also associated with increased in-hospital mortality (OR = 1.844, 95% CI: 1.255-2.708; P = 0.002). CONCLUSIONS: Among the patients with IS, the risk of all causes in-hospital mortality was higher for MT than for patients non-MT. This finding can assist clinicians in more accurately assessing prognosis and making informed treatment decisions.

Association between estimated pulse wave velocity and the risk of mortality in patients with subarachnoid hemorrhage: a retrospective cohort study based on the MIMIC database.

BACKGROUND: The estimated pulse wave velocity (ePWV) is a recently developed, simple and useful tool to measure arterial stiffness and to predict long-term cardiovascular mortality. However, the association of ePWV with mortality risk in patients with subarachnoid hemorrhage (SAH) is unclear. Herein, this study aims to assess the potential prediction value of ePWV on short- and long-term mortality of SAH patients. METHODS: Data of adult patients with no traumatic SAH were extracted from the Medical Information Mart for Intensive Care (MIMIC) III and IV database in this retrospective cohort study. Weighted univariate and multivariable Cox regression analyses were used to explore the associations of ePWV levels with 30-day mortality and 1-year mortality in SAH patients. The evaluation indexes were hazard ratios (HRs) and 95% confidence intervals (CIs). In addition, subgroup analyses of age, the sequential organ failure assessment (SOFA) score, surgery, atrial fibrillation (AF), renal failure (RF), hepatic diseases, chronic obstructive pulmonary disease (COPD), sepsis, hypertension, and diabetes mellitus (DM) were also performed. RESULTS: Among 1,481 eligible patients, 339 died within 30 days and 435 died within 1 year. After adjusting for covariates, ePWV ≥ 12.10 was associated with higher risk of both 30-day mortality (HR = 1.77, 95%CI: 1.17-2.67) and 1-year mortality (HR = 1.97, 95%CI: 1.36-2.85), compared to ePWV < 10.12. The receiver operator characteristic (ROC) curves showed that compared to single SOFA score, ePWV combined with SOFA score had a relative superior predictive performance on both 30-day mortality and 1-year mortality, with the area under the curves (AUCs) of 0.740 vs. 0.664 and 0.754 vs. 0.658. This positive relationship between ePWV and mortality risk was also found in age ≥ 65 years old, SOFA score < 2, non-surgery, non-hepatic diseases, non-COPD, non-hypertension, non-DM, and sepsis subgroups. CONCLUSION: Baseline ePWV level may have potential prediction value on short- and long-term mortality in SAH patients. However, the application of ePWV in SAH prognosis needs further clarification.

Interpretable machine learning model for new-onset atrial fibrillation prediction in critically ill patients: a multi-center study.

BACKGROUND: New-onset atrial fibrillation (NOAF) is the most common arrhythmia in critically ill patients admitted to intensive care and is associated with poor prognosis and disease burden. Identifying high-risk individuals early is crucial. This study aims to create and validate a NOAF prediction model for critically ill patients using machine learning (ML). METHODS: The data came from two non-overlapping datasets from the Medical Information Mart for Intensive Care (MIMIC), with MIMIC-IV used for training and subset of MIMIC-III used as external validation. LASSO regression was used for feature selection. Eight ML algorithms were employed to construct the prediction model. Model performance was evaluated based on identification, calibration, and clinical application. The SHapley Additive exPlanations (SHAP) method was used for visualizing model characteristics and individual case predictions. RESULTS: Among 16,528 MIMIC-IV patients, 1520 (9.2%) developed AF post-ICU admission. A model with 23 variables was built, with XGBoost performing best, achieving an AUC of 0.891 (0.873-0.888) in validation and 0.769 (0.756-0.782) in external validation. Key predictors included age, mechanical ventilation, urine output, sepsis, blood urea nitrogen, percutaneous arterial oxygen saturation, continuous renal replacement therapy and weight. A risk probability greater than 0.6 was defined as high risk. A friendly user interface had been developed for clinician use. CONCLUSION: We developed a ML model to predict the risk of NOAF in critically ill patients without cardiac surgery and validated its potential as a clinically reliable tool. SHAP improves the interpretability of the model, enables clinicians to better understand the causes of NOAF, helps clinicians to prevent it in advance and improves patient outcomes.

Association of alactic base excess with in-hospital mortality in patients with acute myocardial infarction: a retrospective cohort study.

BACKGROUND: Alactic base excess (ABE) is a novel biomarker to evaluate the renal capability of handling acid-base disturbances, which has been found to be associated with adverse prognosis of sepsis and shock patients. This study aimed to evaluate the association between ABE and the risk of in-hospital mortality in patients with acute myocardial infarction (AMI). METHODS: This retrospective cohort study collected AMI patients' clinical data from the Medical Information Mart for Intensive Care (MIMIC)-IV database. The outcome was in-hospital mortality after intensive care unit (ICU) admission. Univariate and multivariate Cox proportional hazards models were performed to assess the association of ABE with in-hospital mortality in AMI patients, with hazard ratios (HRs) and 95% confidence intervals (CI). To further explore the association, subgroup analyses were performed based on age, AKI, eGFR, sepsis, and AMI subtypes. RESULTS: Of the total 2779 AMI patients, 502 died in hospital. Negative ABE (HR = 1.26, 95%CI: 1.02-1.56) (neutral ABE as reference) was associated with a higher risk of in-hospital mortality in AMI patients, but not in positive ABE (P = 0.378). Subgroup analyses showed that negative ABE was significantly associated with a higher risk of in-hospital mortality in AMI patients aged>65 years (HR = 1.46, 95%CI: 1.13-1.89), with eGFR<60 (HR = 1.35, 95%CI: 1.05-1.74), with AKI (HR = 1.32, 95%CI: 1.06-1.64), with ST-segment elevation acute myocardial infarction (STEMI) subtype (HR = 1.79, 95%CI: 1.18-2.72), and without sepsis (HR = 1.29, 95%CI: 1.01-1.64). CONCLUSION: Negative ABE was significantly associated with in-hospital mortality in patients with AMI.

Association between serum sodium trajectory and mortality in patients with acute kidney injury: a retrospective cohort study.

INTRODUCTION: Dysnatremia is strongly associated with poor prognosis in acute kidney injury (AKI); however, the impact of sodium trajectories on the prognosis of patients with AKI has not yet been well elucidated. This study aimed to assess the association between sodium trajectories in patients with AKI and mortality at 30-day and 1-year follow-up. METHODS: This retrospective cohort study used data from Medical Information Mart for Intensive Care (MIMIC)-IV database, and patients diagnosed with AKI within 48 h after admission were enrolled. Group-based trajectory models (GBTM) were applied to map the developmental course of the serum sodium fluctuations. Kaplan-Meier survival curve was used to compare differences in mortality in AKI patients with distinct serum sodium trajectories. Hazard ratios (HRs) were calculated to determine the association between trajectories and prognosis using Cox proportional hazard models. RESULTS: A total of 9,314 AKI patients were enrolled. Three distinct sodium trajectories were identified including: (i) stable group (ST, in which the serum sodium levels remained relatively stable, n = 4,935; 53.0%), (ii) descending group (DS, in which the serum sodium levels declined, n = 2,994; 32.15%) and (iii) ascending group (AS, in which the serum sodium levels were elevated, n = 1,383; 14.85%). There was no significant difference in age and gender distribution among the groups. The 30-day mortality rates were 7.9% in ST, 9.5% in DS and 16.6% in AS (p < 0.001). The results of 1-year mortality rates were similar (p < 0.001). In adjusted analysis, patients in the DS (HR = 1.22, 95% confidence interval [CI], 1.04-1.43, p = 0.015) and AS (HR = 1.68, 95% CI, 1.42-2.01, p = 0.013) groups had higher risks of 30-day mortality compared to those in the ST group. CONCLUSION: In patients with AKI, the serum sodium trajectories were independently associated with 30-day and 1-year mortality. Association between serum sodium level trajectories and prognosis in patients with AKI deserve further study.

Association between lactate dehydrogenase and ventilator-associated pneumonia risk: an analysis of the MIMIC database 2001-2019.

BACKGROUND: Serum lactate dehydrogenase (LDH) is a nonspecific inflammatory biomarker and has been reported to be associated with pneumonia prognosis. This study aimed to evaluate the relationship between LDH levels and ventilator-associated pneumonia (VAP) risk in intensive care unit (ICU) patients. METHODS: This retrospective cohort study used data from the Multiparameter Intelligent Monitoring in Intensive Care database from 2001 to 2019. ICU patients aged ≥ 18 years and receiving mechanical ventilation were included. LDH levels were analyzed as continuous and categorical variables (< 210, 210-279, 279-390, > 390 IU/L), respectively. Restricted cubic spline (RCS) curves and quartiles were used to categorize LDH levels. Logistic regression and linear regression were utilized to assess the relationship of LDH levels with VAP risk and duration of mechanical ventilation, respectively. RESULTS: A total of 9,164 patients were enrolled, of which 646 (7.05%) patients developed VAP. High levels of LDH increased the risk of VAP [odds ratio (OR) = 1.15, 95% confidence interval (CI): 1.06-1.24] and LDH levels were positively correlated with the duration of mechanical ventilation [ß = 4.49, 95%CI: (3.42, 5.56)]. Moreover, patients with LDH levels of 279-390 IU/L (OR = 1.38, 95%CI: 1.08-1.76) and > 390 IU/L (OR = 1.50, 95%CI: 1.18-1.90) had a higher risk of VAP than patients with LDH levels < 210 IU/L. Patients with LDH levels of 279-390 IU/L [ß = 3.84, 95%CI: (0.86, 6.82)] and > 390 IU/L [ß = 11.22, 95%CI: (8.21, 14.22)] (vs. <210 IU/L) had a longer duration of mechanical ventilation. CONCLUSION: Elevated serum LDH levels were related to a higher risk of VAP and longer duration of mechanical ventilation and may be useful for monitoring VAP risk.

A novel higher performance nomogram based on explainable machine learning for predicting mortality risk in stroke patients within 30 days based on clinical features on the first day ICU admission.

BACKGROUND: This study aimed to develop a higher performance nomogram based on explainable machine learning methods, and to predict the risk of death of stroke patients within 30 days based on clinical characteristics on the first day of intensive care units (ICU) admission. METHODS: Data relating to stroke patients were extracted from the Medical Information Marketplace of the Intensive Care (MIMIC) IV and III database. The LightGBM machine learning approach together with Shapely additive explanations (termed as explain machine learning, EML) was used to select clinical features and define cut-off points for the selected features. These selected features and cut-off points were then evaluated using the Cox proportional hazards regression model and Kaplan-Meier survival curves. Finally, logistic regression-based nomograms for predicting 30-day mortality of stroke patients were constructed using original variables and variables dichotomized by cut-off points, respectively. The performance of two nomograms were evaluated in overall and individual dimension. RESULTS: A total of 2982 stroke patients and 64 clinical features were included, and the 30-day mortality rate was 23.6% in the MIMIC-IV datasets. 10 variables ("sofa (sepsis-related organ failure assessment)", "minimum glucose", "maximum sodium", "age", "mean spo2 (blood oxygen saturation)", "maximum temperature", "maximum heart rate", "minimum bun (blood urea nitrogen)", "minimum wbc (white blood cells)" and "charlson comorbidity index") and respective cut-off points were defined from the EML. In the Cox proportional hazards regression model (Cox regression) and Kaplan-Meier survival curves, after grouping stroke patients according to the cut-off point of each variable, patients belonging to the high-risk subgroup were associated with higher 30-day mortality than those in the low-risk subgroup. The evaluation of nomograms found that the EML-based nomogram not only outperformed the conventional nomogram in NIR (net reclassification index), brier score and clinical net benefits in overall dimension, but also significant improved in individual dimension especially for low "maximum temperature" patients. CONCLUSIONS: The 10 selected first-day ICU admission clinical features require greater attention for stroke patients. And the nomogram based on explainable machine learning will have greater clinical application.

Triglyceride glucose index is a significant predictor of severe disturbance of consciousness and all-cause mortality in critical cerebrovascular disease patients.

OBJECTIVE: The association of the triglyceride-glucose (TyG) index with severe consciousness disturbance and in-hospital mortality in patients with cerebrovascular disease in the intensive care unit (ICU) is unclear. This study aimed to investigate the TyG index's predictive ability on the severity of impaired consciousness and in-hospital mortality in patients with cerebrovascular disease in the ICU. METHOD: Patients diagnosed with non-traumatic cerebral hemorrhage and cerebral infarction were extracted from the MIMIC-IV database and analyzed as two cohorts. The association between the TyG index and the severity of patients' impairment of consciousness and in-hospital mortality was analyzed using logistic regression models. Using restricted cubic spline curves, we analyzed potential nonlinear relationships between TyG indices and outcome indicators. receiver operating characteristic (ROC) curves were utilized to evaluate the predictive ability of the TyG index for outcome indicators. RESULT: The study's last two cohorts comprised 537 patients with traumatic cerebral hemorrhage and 872 patients with cerebral infarction. TyG index was a significant predictor of the severity of impaired consciousness and in-hospital mortality in patients with cerebrovascular disease, as determined by logistic regression. The risk of severe consciousness impairment and in-hospital mortality increased roughly linearly with increasing TyG index. CONCLUSION: The TyG index was found to be a significant predictor for severe impairment of consciousness and in-hospital death in patients with cerebrovascular disease in the ICU, and it provides some predictive value for the severity of consciousness disturbances and in-hospital mortality in cerebrovascular disease patients.

Timing of vasopressin initiation and mortality in patients with septic shock: analysis of the MIMIC-III and MIMIC-IV databases.

BACKGROUND: vasopressin is commonly used as a second-line vasopressor for patients with septic shock, but the optimal timing of initiation is uncertain. This study was designed to investigate when vasopressin initiation may be beneficial for 28-day mortality in septic shock patients. METHODS: This was a retrospective observational cohort study from the MIMIC-III v1.4 and MIMIC-IV v2.0 databases. All adults diagnosed with septic shock according to Sepsis-3 criteria were included. Patients were stratified into two groups based on norepinephrine (NE) dose at the time of vasopressin initiation, defined as the low doses of NE group (NE<0.25 µg/kg/min) and the high doses of NE group (NE ≥ 0.25 µg/kg/min). The primary end-point was 28-day mortality after diagnosis of septic shock. The analysis involved propensity score matching (PSM), multivariable logistic regression, doubly robust estimation, the gradient boosted model, and an inverse probability-weighting model. RESULTS: A total of 1817 eligible patients were included in our original cohort (613 in the low doses of NE group and 1204 in the high doses of NE group). After 1:1 PSM, 535 patients from each group with no difference in disease severity were included in the analysis. The results showed that vasopressin initiation at low doses of NE was associated with reduced 28-day mortality (odds ratio [OR] 0.660, 95% confidence interval [CI] 0.518-0.840, p < 0.001). Compared with patients in the high doses of NE group, patients in the low doses of NE group received significantly shorter duration of NE, with less intravenous fluid volume on the first day after initiation of vasopressin, more urine on the second day, and longer mechanical ventilation-free days and CRRT-free days. Nevertheless, there were no significant differences in hemodynamic response to vasopressin, duration of vasopressin, and ICU or hospital length of stay. CONCLUSIONS: Among adults with septic shock, vasopressin initiation when low-dose NE was used was associated with an improvement in 28-day mortality.

Development and validation of the prediction model for mortality in patients with diabetic kidney disease in intensive care unit: a study based on medical information Mart for intensive care.

We aimed to explore factors associated with mortality of diabetic kidney disease (DKD), and to establish a prediction model for predicting the mortality of DKD. This was a cohort study. In total, 1,357 DKD patients were identified from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database, with 505 DKD patients being identified from the MIMIC-III as the testing set. The outcome of the study was 1-year mortality. COX proportional hazard models were applied to screen the predictive factors. The prediction model was conducted based on the predictive factors. A receiver operating characteristic (ROC) curve with the area under the curve (AUC) was calculated to evaluate the performance of the prediction model. The median follow-up time was 365.00 (54.50,365.00) days, and 586 patients (43.18%) died within 1 year. The predictive factors for 1-year mortality in DKD included age, weight, sepsis, heart rate, temperature, Charlson Comorbidity Index (CCI), Simplified Acute Physiology Score (SAPS) II, and Sequential Organ Failure Assessment (SOFA), lymphocytes, red cell distribution width (RDW), serum albumin, and metformin. The AUC of the prediction model for predicting 1-year mortality in the training set was 0.771 [95% confidence interval (CI): 0.746-0.795] and the AUC of the prediction model in the testing set was 0.795 (95% CI: 0.756-0.834). This study establishes a prediction model for predicting mortality of DKD, providing a basis for clinical intervention and decision-making in time.

Early prediction of ventilator-associated pneumonia in critical care patients: a machine learning model.

BACKGROUND: This study was performed to develop and validate machine learning models for early detection of ventilator-associated pneumonia (VAP) 24 h before diagnosis, so that VAP patients can receive early intervention and reduce the occurrence of complications. PATIENTS AND METHODS: This study was based on the MIMIC-III dataset, which was a retrospective cohort. The random forest algorithm was applied to construct a base classifier, and the area under the receiver operating characteristic curve (AUC), sensitivity and specificity of the prediction model were evaluated. Furthermore, We also compare the performance of Clinical Pulmonary Infection Score (CPIS)-based model (threshold value ≥ 3) using the same training and test data sets. RESULTS: In total, 38,515 ventilation sessions occurred in 61,532 ICU admissions. VAP occurred in 212 of these sessions. We incorporated 42 VAP risk factors at admission and routinely measured the vital characteristics and laboratory results. Five-fold cross-validation was performed to evaluate the model performance, and the model achieved an AUC of 84% in the validation, 74% sensitivity and 71% specificity 24 h after intubation. The AUC of our VAP machine learning model is nearly 25% higher than the CPIS model, and the sensitivity and specificity were also improved by almost 14% and 15%, respectively. CONCLUSIONS: We developed and internally validated an automated model for VAP prediction using the MIMIC-III cohort. The VAP prediction model achieved high performance based on its AUC, sensitivity and specificity, and its performance was superior to that of the CPIS model. External validation and prospective interventional or outcome studies using this prediction model are envisioned as future work.

Prolonged Elevated Heart Rate and 90-Day Survival in Acutely Ill Patients: Data From the MIMIC-III Database.

PURPOSE: We sought to evaluate the association of prolonged elevated heart rate (peHR) with survival in acutely ill patients. METHODS: We used a large observational intensive care unit (ICU) database (Multiparameter Intelligent Monitoring in Intensive Care III [MIMIC-III]), where frequent heart rate measurements were available. The peHR was defined as a heart rate >100 beats/min in 11 of 12 consecutive hours. The outcome was survival status at 90 days. We collected heart rates, disease severity (simplified acute physiology scores [SAPS II]), comorbidities (Charlson scores), and International Classification of Diseases (ICD) diagnosis information in 31 513 patients from the MIMIC-III ICU database. Propensity score (PS) methods followed by inverse probability weighting based on the PS was used to balance the 2 groups (the presence/absence of peHR). Multivariable weighted logistic regression was used to assess for association of peHR with the outcome survival at 90 days adjusting for additional covariates. RESULTS: The mean age was 64 years, and the most frequent main disease category was circulatory disease (41%). The mean SAPS II score was 35, and the mean Charlson comorbidity score was 2.3. Overall survival of the cohort at 90 days was 82%. Adjusted logistic regression showed a significantly increased risk of death within 90 days in patients with an episode of peHR (P < .001; odds ratio for death 1.79; confidence interval, 1.69-1.88). This finding was independent of median heart rate. CONCLUSION: We found a significant association of peHR with decreased survival in a large and heterogenous cohort of ICU patients.

Aspirin is associated with improved outcomes in patients with sepsis-induced myocardial injury: An analysis of the MIMIC-IV database.

BACKGROUND: The effectiveness of aspirin treatment in septic patients remains a subject of debates. OBJECTIVE: To explore the association between aspirin usage and the prognosis of patients with sepsis-induced myocardial injury (SIMI), as well as the timing of aspirin administration. METHODS: Patients with SIMI were screened in the MIMIC-IV database and categorized into aspirin and non-aspirin groups based on their medications during intensive care unit (ICU) stay, and propensity matching analysis (PSM) was subsequently performed to reduce bias at baseline between the groups. The primary outcome was 28-day all-cause mortality. Cox multivariate regression analysis was conducted to evaluate the impact of aspirin on the prognosis of patients with SIMI. RESULTS: The pre-PSM and post-PSM cohorts included 1170 and 1055 patients, respectively. In the pre-PSM cohort, the aspirin group is older, has a higher proportion of chronic comorbidities, and lower SOFA and SAPS II scores when compared to the non-aspirin group. In the PSM analysis, most of the baseline characterization biases were corrected, and aspirin use was also associated with lower 28-day mortality (hazard ratio [HR] = 0.51, 95 % confidence interval [CI]: 0.42-0.63, P < 0.001), 90-day mortality (HR = 0.58, 95 % CI: 0.49-0.69, P < 0.001) and 1-year mortality (HR = 0.65, 95 % CI: 0.56-0.76, P < 0.001), irrespective of aspirin administration timing. A sensitivity analysis based on the original cohort confirmed the robustness of the findings. Additionally, subsequent subgroup analysis revealed that the use of vasopressin have a significant interaction with aspirin's efficacy. CONCLUSION: Aspirin was associated with decreased mortality in SIMI patients, and this beneficial effect persisted regardless of pre-ICU treatment.

Association between short-term changes in serum magnesium and in-hospital mortality following acute myocardial infarction: a cohort study based on the MIMIC database.

The association between short-term changes in serum magnesium level and risk of in-hospital mortality was investigated in patients with acute myocardial infarction (AMI). In this retrospective cohort study, data of 2,716 patients with AMI were extracted from the Medical Information Mart for Intensive Care (MIMIC-III and MIMIC-IV) database for 2001-2012. Univariate and multivariate Cox proportional hazards models were used to explore the association between serum magnesium level and short-term change and in-hospital mortality in patients with AMI. In addition, subgroups according to age, gender, Sequential Organ Failure Assessment (SOFA) score, and Simplified Acute Physiology Score (SAPS-II) were also analysed. In total, 504 (18.6%) patients died in hospital. After adjusting for covariates, all AMI patients with high magnesium levels at ICU admission (HR=1.03, 95% CI: 0.83-1.27) or 48 hours after ICU admission (all p<0.05), or those demonstrating a change in magnesium level within the first 48 hours of ICU stay (all p<0.05) were shown to have a high risk of in-hospital mortality. Moreover, this correlation was retained irrespective of age, gender, SOFA score, and SAPS-II (all p<0.05). Serum magnesium levels at different time points after ICU admission and change in serum magnesium level during the first 48 hours were associated with in-hospital mortality in patients with AMI, indicating that clinical attention should be paid to short-term changes in serum magnesium levels regarding treatment adjustment, which may further reduce the risk of mortality.

Association between hyperglycemia at ICU admission and postoperative acute kidney injury in patients undergoing cardiac surgery: Analysis of the MIMIC-IV database.

Background: This study aimed to explore the correlation between hyperglycemia at intensive care unit (ICU) admission and the incidence of acute kidney injury (AKI) in patients after cardiac surgery. Methods: We conducted a retrospective cohort study, in which clinical data were extracted from the Medical Information Mart for Intensive Care (MIMIC)-IV database. Adults (≥18 years) in the database who were admitted to the cardiovascular intensive care unit after cardiac surgery were enrolled. The primary outcome was the incidence of AKI within 7 days following ICU admission. Secondary outcomes included ICU mortality, hospital mortality, ICU length of stay, and the 28-day and 90-day mortality. Multivariable Cox regression analysis was used to assess the association between ICU-admission hyperglycemia and AKI incidence within 7 days of ICU admission. Different adjustment strategies were used to adjust for potential confounders. Patients were divided into three groups according to their highest blood glucose levels recorded within 24 h of ICU admission: no hyperglycemia (<140 mg/dL), mild hyperglycemia (140-200 mg/dL), and severe hyperglycemia (≥200 mg/dL). Results: Of the 6905 included patients, 2201 (31.9%) were female, and the median (IQR) age was 68.2 (60.1-75.9) years. In all, 1836 (26.6%) patients had severe hyperglycemia. The incidence of AKI within 7 days of ICU admission, ICU mortality, and hospital mortality was significantly higher in patients with severe admission hyperglycemia than those with mild hyperglycemia or no hyperglycemia (80.3% vs. 73.6% and 61.2%, respectively; 2.8% vs. 0.9% and 1.9%, respectively; and 3.4% vs. 1.2% and 2.5%, respectively; all P <0.001). Severe hyperglycemia was a risk factor for 7-day AKI (Model 1: hazard ratio [HR]=1.4809, 95% confidence interval [CI]: 1.3126 to 1.6707; Model 2: HR=1.1639, 95% CI: 1.0176 to 1.3313; Model 3: HR=1.2014, 95% CI: 1.0490 to 1.3760; all P <0.050). Patients with normal glucose levels (glucose levels <140 mg/dL) had a higher 28-day mortality rate than those with severe hyperglycemia (glucose levels ≥200 mg/dL) (4.0% vs. 3.8%, P <0.001). Conclusions: In post-cardiac surgery patients, severe hyperglycemia within 24 h of ICU admission increases the risk of 7-day AKI, ICU mortality, and hospital mortality. Clinicians should be extra cautious regarding AKI among patients with hyperglycemia at ICU admission after cardiac surgery.

A nomogram for predicting neonatal apnea: a retrospective analysis based on the MIMIC database.

Introduction: The objective of this study is to develop a model based on indicators in the routine examination of neonates to effectively predict neonatal apnea. Methods: We retrospectively analysed 8024 newborns from the MIMIC IV database, building logistic regression models and decision tree models. The performance of the model is examined by decision curves, calibration curves and ROC curves. Variables were screened by stepwise logistic regression analysis and LASSO regression. Results: A total of 7 indicators were ultimately included in the model: gestational age, birth weight, ethnicity, gender, monocytes, lymphocytes and acetaminophen. The mean AUC (the area under the ROC curve) of the 5-fold cross-validation of the logistic regression model in the training set and the AUC in the validation set are 0.879 and 0.865, respectively. The mean AUC (the area under the ROC curve) of the 5-fold cross-validation of the decision tree model in the training set and the AUC in the validation set are 0.861 and 0.850, respectively. The calibration and decision curves in the two cohorts also demonstrated satisfactory predictive performance of the model. However, the logistic regression model performs relatively well. Discussion: Our results proved that blood indicators were valuable and effective predictors of neonatal apnea, which could provide effective predictive information for medical staff.

Development of a nomogram to predict the prognosis of patients with secondary bone tumors in the intensive care unit: a retrospective analysis based on the MIMIC IV database.

PURPOSE: The present study aimed to develop a nomogram to predict the prognosis of patients with secondary bone tumors in the intensive care unit to facilitate risk stratification and treatment planning. METHODS: We used the MIMIC IV 2.0 (the Medical Information Mart for Intensive Care IV) to retrieve patients with secondary bone tumors as a study cohort. To evaluate the predictive ability of each characteristic on patient mortality, stepwise Cox regression was used to screen variables, and the selected variables were included in the final Cox proportional hazard model. Finally, the performance of the model was tested using the decision curve, calibration curve, and receiver operating characteristic (ROC) curve. RESULTS: A total of 1028 patients were enrolled after excluding cases with missing information. In the training cohort, albumin, APSIII (Acute Physiology Score III), chemotherapy, lactate, chloride, hepatic metastases, respiratory failure, SAPSII (Simplified Acute Physiology Score II), and total protein were identified as independent risk factors for patient death and then incorporated into the final model. The model showed good and robust prediction performance. CONCLUSION: We developed a nomogram prognostic model for patients with secondary bone tumors in the intensive care unit, which provides effective survival prediction information.

Enhancing Sepsis prognosis: Integrating social determinants and demographic variables into a comprehensive model for critically ill patients.

BACKGROUND: The Sequential Organ Failure Assessment (SOFA) score monitors organ failure and defines sepsis but may not fully capture factors influencing sepsis mortality. Socioeconomic and demographic impacts on sepsis outcomes have been highlighted recently. OBJECTIVE: To evaluate the prognostic value of SOFA scores against demographic and social health determinants for predicting sepsis mortality in critically ill patients, and to assess if a combined model increases predictive accuracy. METHODS: The study utilized retrospective data from the MIMIC-IV database and prospective external validation from the Penn State Health cohort. A Random Forest model incorporating SOFA scores, demographic/social data, and the Charlson Comorbidity Index was trained and validated. FINDINGS: In the MIMIC-IV dataset of 32,970 sepsis patients, 6,824 (20.7%) died within 30 days. A model including demographic, socioeconomic, and comorbidity data with SOFA scores improved predictive accuracy beyond SOFA scores alone. Day 2 SOFA, age, weight, and comorbidities were significant predictors. External validation showed consistent performance, highlighting the importance of delta SOFA between days 1 and 3. CONCLUSION: Adding patient-specific demographic and socioeconomic information to clinical metrics significantly improves sepsis mortality prediction. This suggests a more comprehensive, multidimensional prognostic approach is needed for accurate sepsis outcome predictions.

Blood Urea Nitrogen-to-Albumin Ratio May Predict Mortality in Patients with Traumatic Brain Injury from the MIMIC Database: A Retrospective Study.

Traumatic brain injury (TBI), a major global health burden, disrupts the neurological system due to accidents and other incidents. While the Glasgow coma scale (GCS) gauges neurological function, it falls short as the sole predictor of overall mortality in TBI patients. This highlights the need for comprehensive outcome prediction, considering not just neurological but also systemic factors. Existing approaches relying on newly developed biomolecules face challenges in clinical implementation. Therefore, we investigated the potential of readily available clinical indicators, like the blood urea nitrogen-to-albumin ratio (BAR), for improved mortality prediction in TBI. In this study, we investigated the significance of the BAR in predicting all-cause mortality in TBI patients. In terms of research methodologies, we gave preference to machine learning methods due to their exceptional performance in clinical support in recent years. Initially, we obtained data on TBI patients from the Medical Information Mart for Intensive Care database. A total of 2602 patients were included, of whom 2260 survived and 342 died in hospital. Subsequently, we performed data cleaning and utilized machine learning techniques to develop prediction models. We employed a ten-fold cross-validation method to obtain models with enhanced accuracy and area under the curve (AUC) (Light Gradient Boost Classifier accuracy, 0.905 ± 0.016, and AUC, 0.888; Extreme Gradient Boost Classifier accuracy, 0.903 ± 0.016, and AUC, 0.895; Gradient Boost Classifier accuracy, 0.898 ± 0.021, and AUC, 0.872). Simultaneously, we derived the importance ranking of the variable BAR among the included variables (in Light Gradient Boost Classifier, the BAR ranked fourth; in Extreme Gradient Boost Classifier, the BAR ranked sixth; in Gradient Boost Classifier, the BAR ranked fifth). To further evaluate the clinical utility of BAR, we divided patients into three groups based on their BAR values: Group 1 (BAR < 4.9 mg/g), Group 2 (BAR ≥ 4.9 and ≤10.5 mg/g), and Group 3 (BAR ≥ 10.5 mg/g). This stratification revealed significant differences in mortality across all time points: in-hospital mortality (7.61% vs. 15.16% vs. 31.63%), as well as one-month (8.51% vs. 17.46% vs. 36.39%), three-month (9.55% vs. 20.14% vs. 41.84%), and one-year mortality (11.57% vs. 23.76% vs. 46.60%). Building on this observation, we employed the Cox proportional hazards regression model to assess the impact of BAR segmentation on survival. Compared to Group 1, Groups 2 and 3 had significantly higher hazard ratios (95% confidence interval (CI)) for one-month mortality: 1.77 (1.37-2.30) and 3.17 (2.17-4.62), respectively. To further underscore the clinical potential of BAR as a standalone measure, we compared its performance to established clinical scores, like sequential organ failure assessment (SOFA), GCS, and acute physiology score III(APS-III), using receiver operator characteristic curve (ROC) analysis. Notably, the AUC values (95%CI) of the BAR were 0.67 (0.64-0.70), 0.68 (0.65-0.70), and 0.68 (0.65-0.70) for one-month mortality, three-month mortality, and one-year mortality. The AUC value of the SOFA did not significantly differ from that of the BAR. In conclusion, the BAR is a highly influential factor in predicting mortality in TBI patients and should be given careful consideration in future TBI prediction research. The blood urea nitrogen-to-albumin ratio may predict mortality in TBI patients.