Association between lactate/albumin ratio and prognosis in critically ill patients with acute kidney injury undergoing continuous renal replacement therapy.

BACKGROUND: The primary objective was to examine the association between the lactate/albumin ratio (LAR) and the prognosis of patients with acute kidney injury (AKI) undergoing continuous renal replacement therapy (CRRT). METHODS: Utilizing the Medical Information Mart for Intensive Care IV (MIMIC-IV, v2.0) database, we categorized 703 adult AKI patients undergoing CRRT into survival and non-survival groups based on 28-day mortality. Patients were further grouped by LAR tertiles: low (< 0.692), moderate (0.692-1.641), and high (> 1.641). Restricted cubic splines (RCS), Least Absolute Shrinkage and Selection Operator (LASSO) regression, inverse probability treatment weighting (IPTW), and Kaplan-Meier curves were employed. RESULTS: In our study, the patients had a mortality rate of 50.07% within 28 days and 62.87% within 360 days. RCS analysis revealed a non-linear correlation between LAR and the risk of mortality at both 28 and 360 days. Cox regression analysis, which was adjusted for nine variables identified by LASSO, confirmed that a high LAR (>1.641) served as an independent predictor of mortality at these specific time points (p < 0.05) in AKI patients who were receiving CRRT. These findings remained consistent even after IPTW adjustment, thereby ensuring a reliable and robust outcome. Kaplan-Meier survival curves exhibited a gradual decline in cumulative survival rates at both 28 and 360 days as the LAR values increased (log-rank test, χ2 = 48.630, p < 0.001; χ2 = 33.530, p < 0.001). CONCLUSION: A high LAR (>1.641) was found to be an autonomous predictor of mortality at both 28 and 360 days in critically ill patients with AKI undergoing CRRT.

Normalized lactate load as an independent prognostic indicator in patients with cardiogenic shock.

BACKGROUND: Early prognosis evaluation is crucial for decision-making in cardiogenic shock (CS) patients. Dynamic lactate assessment, for example, normalized lactate load, has been a better prognosis predictor than single lactate value in septic shock. Our objective was to investigate the correlation between normalized lactate load and in-hospital mortality in patients with CS. METHODS: Data were extracted from the Medical Information Mart for Intensive Care (MIMIC)-IV database. The calculation of lactate load involved the determination of the cumulative area under the lactate curve, while normalized lactate load was computed by dividing the lactate load by the corresponding period. Receiver Operating Characteristic (ROC) curves were constructed, and the evaluation of areas under the curves (AUC) for various parameters was performed using the DeLong test. RESULTS: Our study involved a cohort of 1932 CS patients, with 687 individuals (36.1%) experiencing mortality during their hospitalization. The AUC for normalized lactate load demonstrated significant superiority compared to the first lactate (0.675 vs. 0.646, P < 0.001), maximum lactate (0.675 vs. 0.651, P < 0.001), and mean lactate (0.675 vs. 0.669, P = 0.003). Notably, the AUC for normalized lactate load showed comparability to that of the Sequential Organ Failure Assessment (SOFA) score (0.675 vs. 0.695, P = 0.175). CONCLUSION: The normalized lactate load was an independently associated with the in-hospital mortality among CS patients.

Association between frailty index based on laboratory tests and all-cause mortality in critically ill patients with heart failure.

BACKGROUND: The frailty index based on laboratory tests (FI-lab) can identify individuals at increased risk for adverse health outcomes. The association between the FI-lab and all-cause mortality in patients with heart failure (HF) in the intensive care unit (ICU) remains unknown. This study aimed to determine the correlation between FI-lab and all-cause mortality to evaluate the impact of FI-lab on the prognosis of critically ill patients with HF. METHODS: This retrospective observational study utilized data extracted from the Medical Information Mart for Intensive Care IV database. The FI-lab, which consists of 33 laboratory tests, was constructed. Patients were then grouped into quartiles (Q1-Q4) based on their FI-lab scores. Kaplan-Meier analysis was used to compare all-cause mortality among the four groups. A Cox proportional hazard analysis was conducted to examine the association between the FI-lab score and all-cause mortality. The incremental predictive value of adding FI-lab to classical disease severity scores was assessed using Harrell's C statistic, integrated discrimination improvement (IDI) and net reclassification improvement (NRI). RESULTS: Among 3021 patients, 838 (27.74%) died within 28 days, and 1400 (46.34%) died within a 360 day follow-up period. Kaplan-Meier analysis indicated that patients with higher FI-lab scores had significantly higher risks of all-cause mortality (log-rank P < 0.001). Multivariable Cox regression suggested that FI-lab, evaluated as a continuous variable (for each 0.01 increase), was associated with increased 28 day mortality [hazard ratio (HR) 1.02, 95% confidence interval (CI) (1.01-1.03), P < 0.001] and 360 day mortality [HR 1.02, 95% CI (1.01-1.02), P < 0.001]. When assessed in quartiles, the 28 day mortality risk [HR 1.66, 95% CI (1.28-2.15), P < 0.001] and 360 day mortality risk [HR 1.48, 95% CI (1.23-1.8), P < 0.001] were significantly higher for FI-lab Q4 compared with FI-lab Q1. FI-lab significantly improved the predictive capability of classical disease severity scores for 28 and 360 day mortality. CONCLUSIONS: In ICU patients diagnosed with HF, the FI-lab is a potent predictor of short-term and long-term mortality in critically ill patients with HF. The active use of FI-lab to identify high-risk groups among critically ill HF patients and initiate timely interventions may have significant value in improving the prognosis of critically ill patients with HF.

Association between advanced lung cancer inflammation index and in-hospital mortality in ICU patients with community-acquired pneumonia: A retrospective analysis of the MIMIC-IV database.

Objective: The objective of the present study was to explore the correlation between the advanced lung cancer inflammation index (ALI) and in-hospital mortality among patients diagnosed with community-acquired pneumonia (CAP). Methods: Data from the Medical Information Mart for Intensive Care-IV database were adopted to analyze the in-hospital mortality of ICU patients with CAP. Upon admission to the ICU, fundamental data including vital signs, critical illness scores, comorbidities, and laboratory results, were collected. The in-hospital mortality of all CAP patients was documented. Multivariate logistic regression (MLR) models and restricted cubic spline (RCS) analysis together with subgroup analyses were conducted. Results: This study includes 311 CAP individuals, involving 218 survivors as well as 93 nonsurvivors. The participants had an average age of 63.57 years, and the females accounted for approximately 45.33%. The in-hospital mortality was documented to be 29.90%. MLR analysis found that ALI was identified as an independent predictor for in-hospital mortality among patients with CAP solely in the Q1 group with ALI ≤ 39.38 (HR: 2.227, 95% CI: 1.026-4.831, P = 0.043). RCS analysis showed a nonlinear relationship between the ALI and in-hospital mortality, with a turning point at 81, and on the left side of the inflection point, a negative correlation was observed between ALI and in-hospital mortality (HR: 0.984, 95% CI: 0.975-0.994, P = 0.002). The subgroup with high blood pressure showed significant interaction with the ALI. Conclusion: The present study demonstrated a nonlinear correlation of the ALI with in-hospital mortality among individuals with CAP. Additional confirmation of these findings requires conducting larger prospective investigations.

Association between early central venous pressure measurement and all-cause mortality in critically ill patients with heart failure: A cohort of 11,241 patients.

Background: The timing of central venous pressure (CVP) measurement may play a crucial role in heart failure management, yet no studies have explored this aspect. Methods: Clinical information pertaining to patients in critical condition with a diagnosis of heart failure was retrieved from the MIMIC-IV database. The association between initial measurements of central venous pressure (CVP) and the incidence of mortality from all causes was analyzed using the Cox proportional hazards approach. Subgroup analysis and propensity score matching were conducted for sensitivity analyses. Results: This study included 11,241 participants (median age, 75 years; 44.70 % female). Utilizing restricted cubic spline and Kaplan-Meier survival analyses, it was determined that prognostic outcomes were better when CVP was measured within the initial 5-h window. Multivariate-adjusted 1-year (HR: 0.69; 95 % CI: 0.61-0.77), 90-day (HR: 0.70; 95 % CI: 0.62-0.80), and 30-day (HR: 0.67; 95 % CI: 0.57-0.78) all-cause mortalities were significantly lower in patients with early CVP measurement, which was proved robustly in subgroup analysis. Subsequent to the application of propensity score matching, a cohort of 1536 matched pairs was established, with the observed mortality rates continuing to be significantly lower among participants who underwent early CVP assessment. Conclusions: Early CVP measurement (within 5 h) demonstrated an independent correlation with a decrease in both immediate and extended all-cause mortality rates among patients in critical condition suffering from heart failure.

Exploring Predictive Factors for Heart Failure Progression in Hypertensive Patients Based on Medical Diagnosis Data from the MIMIC-IV Database.

Heart failure is associated with a significant mortality rate, and an elevated prevalence of this condition has been noted among hypertensive patients. The identification of predictive factors for heart failure progression in hypertensive individuals is crucial for early intervention and improved patient outcomes. In this study, we aimed to identify these predictive factors by utilizing medical diagnosis records for hypertension patients from the MIMIC-IV database. In particular, we employed only diagnostic history prior to hypertension to enable patients to anticipate the onset of heart failure at the moment of hypertension diagnosis. In the methodology, chi-square tests and XGBoost modeling were applied to examine age-specific predictive factors across four groups: AL (all ages), G1 (0 to 65 years), G2 (65 to 80 years), and G3 (over 80 years). As a result, the chi-square tests identified 34, 28, 20, and 10 predictive factors for the AL, G1, G2, and G3 groups, respectively. Meanwhile, the XGBoost modeling uncovered 19, 21, 27, and 33 predictive factors for these respective groups. Ultimately, our findings reveal 21 overall predictive factors, encompassing conditions such as atrial fibrillation, the use of anticoagulants, kidney failure, obstructive pulmonary disease, and anemia. These factors were assessed through a comprehensive review of the existing literature. We anticipate that the results will offer valuable insights for the risk assessment of heart failure in hypertensive patients.

Relationship between the magnitude of haemoglobin changes and long-term mortality in patients with sepsis: a retrospective cohort study.

BACKGROUND: Sepsis is a common and severe disease with a high mortality rate in intensive care unit (ICU). The hemoglobin (HGB) level is a key parameter for oxygen supply in sepsis. Although HGB is associated with the progression of inflammation in sepsis patients, its role as a marker following sepsis treatment remains unclear. Here, we studied the correlation between early temporal changes in HGB levels and long-term mortality rates in septic patients. METHOD: In this retrospective study of data on patients with sepsis from the Medical Information Mart for Intensive Care (MIMIC) IV database, the outcome was long-term mortality. Patients were divided based on the cut-off of the HGB percentage for receiver operating characteristic (ROC) curve calculation. Kaplan-Meier (KM) survival curves and Cox proportional hazards regression models were used to analyse the associations between groups and outcomes. Propensity score matching (PSM) was used to verify the results. RESULTS: In this study, 2042 patients with sepsis and changes in HGB levels at day 4 after admission compared to day 1 were enrolled and divided into two groups: group 1 (n = 1147) for those with reduction of HGB < 7% and group 2 (n = 895) for those with dropping ≥ 7%. The long-term survival chances of sepsis with less than a 7% reduction in the proportion of HGB at day four were significantly higher than those of patients in the group with a reduction of 7% or more. After adjusting for covariates in the Cox model, the hazard ratios (HRs) with 95% confidence intervals (CIs) for long-term all-cause mortality in the group with a reduction of 7% or more were as follows: 180 days [HR = 1.41, 95% CI (1.22 to 1.63), P < 0.001]; 360 days [HR = 1.37, 95% CI (1.21 to 1.56), P < 0.001]; 540 days [HR = 1.35, 95% CI (1.20 to 1.53), P < 0.001]; 720 days [HR = 1.45, 95% CI (1.29 to 1.64), P < 0.001]. Additionally, the long-term survival rates, using Kaplan-Meier analysis, for the group with a reduction of 7% or more were lower compared to the group with less than 7% reduction at 180 days (54.3% vs. 65.3%, P < 0.001), 360 days (42.3% vs. 50.9%, P < 0.001), 540 days (40.2% vs. 48.6%, P < 0.001), and 720 days (35.5% vs. 46.1%, P < 0.001). The same trend was obtained after using PSM. CONCLUSION: A ≥ 7% decrease in HGB levels on Day 4 after admission was associated with worse long-term prognosis in sepsis patients admitted to the ICU.

Correlation between serum phosphate and all-cause mortality in critically ill patients with coronary heart disease accompanied by chronic kidney disease: a retrospective study using the MIMIC-IV database.

Background: The adverse clinical endpoints of cardiovascular and kidney diseases are correlated with increased serum phosphate levels. However, in critically ill patients with coronary heart disease (CHD) accompanied by chronic kidney disease (CKD), the prognostic value of serum phosphate remains unclear. Methods: Patients' medical records from the Medical Information Mart for Intensive Care IV database who had concomitant CKD and CHD were classified into four distinct groups in this large retrospective observational cohort study based on the quartiles of serum phosphate levels. Vital status and the duration of hospital and ICU stays within the short-term follow-up periods of 30 and 90 days constituted the primary outcomes. All-cause mortality in the intensive care unit (ICU) and hospital constituted the secondary outcomes. Further, the Cox proportional hazard and restricted cubic spline (RCS) regression models were employed to ascertain how serum phosphate levels correlated with the primary outcomes. In addition, the occurrence rate of the secondary outcomes across the four quartiles was determined utilizing the Kaplan-Meier method. Results: Among the total 3,557 patients (67.6% male) included, the hospital and ICU all-cause mortality rates were 14.6% and 10%, separately. Higher quartiles of serum phosphate concentrations were associated with shorter short-term survival rates, as shown by the Kaplan-Meier curves. Additionally, the Cox proportional hazards analysis illustrated that serum phosphate was independently linked to a higher death risk in the hospital [HR, 1.10 (95% CI: 1.03-1.18), P = 0.007] and ICU [HR, 1.14 (95% CI: 1.07-1.22), P < 0.001]. Lastly, the RCS regression models suggested a robust non-linear correlation between serum phosphate concentrations and death risk in the ICU and hospital (both P for non-linearity <0.001). Conclusions: The prognostic value of serum phosphate is significant in critically ill patients with CHD accompanied by CKD. Furthermore, serum phosphate is potentially valuable for identifying patients with this concomitant condition.

Prognostic value of the systemic immune-inflammation index in critically ill elderly patients with hip fracture: evidence from MIMIC (2008-2019).

Background: The systemic immune-inflammation index (SII) showed an extensive link between immunological dysfunction and the activation of systemic inflammation. Several studies have confirmed the application of SII to orthopedic diseases. However, the significance of SII in critically ill elderly individuals with hip fracture who require intensive care unit (ICU) admission is not yet known. This study centered on exploring the relationship between SII and clinical outcomes among critically ill elderly hip fracture individuals. Methods: The study centered around elderly patients experiencing severe illness following hip fractures and requiring admission to the ICU. These patients from the MIMIC-IV database formed the basis of this study's cohort. We stratified them into quartiles according to their SII levels. The results involved the mortality at 30 days and 1 year post-admission. Then we employ Cox proportional hazards regression analysis as well as restricted cubic splines to explore the association between the SII and clinical results in critically ill elderly patients with hip fracture. Results: The study encompassed 991 participants, among whom 63.98% identified as females. Notably, the mortality rates attributed to any cause within 30 days and 1 year after hospitalization stood at 19.68 and 33.40%, respectively. The multivariate Cox proportional hazards model disclosed a significant correlation between an elevated SII and all-cause mortality. Following adjustments for confounding variables, individuals with a high SII showed a notable correlation with 30-day mortality [adjusted hazard ratio (HR), 1.065; 95% confidence interval (CI), 1.044-1.087; p < 0.001] and 1-year mortality (adjusted HR, 1.051; 95% CI, 1.029-1.074; p < 0.001). Furthermore, the analysis of restricted cubic splines demonstrated a progressive increase in the risk of all-cause death as the SII value rose. Conclusion: Among critically ill elderly patients with hip fracture, the SII exhibits a non-linear association that positively correlates with both 30-day and 1-year all-cause mortality rates. The revelation indicates that the SII may play a vital role in identifying patients with hip fractures who face an escalated risk of mortality due to any cause.

Triglyceride-glucose index and prognosis in individuals afflicted with heart failure and chronic kidney disease.

BACKGROUND: The triglyceride-glucose (TyG) index has demonstrated correlations with adverse clinical outcomes in patients with ischaemic stroke, coronary heart disease and cardiac failure. However, its association with overall mortality in individuals concurrently experiencing heart failure (HF) and chronic kidney disease (CKD) remains inadequately explored. METHODS: Utilizing the Medical Information Mart for Intensive Care IV (Version 2.2) repository, subjects underwent quartile stratification based on the TyG index. The primary endpoint was all-cause mortality during hospitalization. Cox proportional hazard models were employed to examine the correlation between TyG and all-cause mortality in HF patients with CKD. Evaluation involved Kaplan-Meier (KM) analysis and restricted cubic splines (RCSs) to compare mortality rates during hospitalization and 1 year after admission across cohorts with varying TyG index levels. RESULTS: A cohort of 1537 HF and CKD patients participated. Cox regression analysis revealed elevated TyG levels as an independent risk factor for both in-hospital and 1 year mortality. RCS analysis indicated a rising, non-linear association between TyG levels and all-cause mortality (P value for non-linear <0.001). KM survival curves demonstrated a statistically significant reduction in survival rates within the high TyG index group compared with the low one (log-rank P < 0.001). CONCLUSIONS: The TyG index exhibited substantial independent prognostic value for elevated in-hospital and 1 year all-cause mortality among the cohort with HF and CKD. These findings suggest that assessing the TyG index could play a crucial role in developing novel therapeutic strategies to improve outcomes for this high-risk demographic.

Comparative analysis of dexmedetomidine, midazolam, and propofol impact on epilepsy-related mortality in the ICU: insights from the MIMIC-IV database.

BACKGROUND: Dexmedetomidine (Dex), midazolam, and propofol are three distinct sedatives characterized by varying pharmacological properties. Previous literature has indicated the positive impact of each of these sedatives on ICU patients. However, there is a scarcity of clinical evidence comparing the efficacy of Dex, midazolam, and propofol in reducing mortality among people with epilepsy (PWE). This study aimed to assess the impact of Dex, midazolam, and propofol on the survival of PWE. METHODS: The data were retrospectively retrieved from the Medical Information Mart for Intensive Care (MIMIC)-IV database (version 2.0). PWE were categorized into Dex, midazolam, and propofol groups based on the intravenously administered sedatives. PWE without standard drug therapy were included in the control group. Comparative analyses were performed on the data among the groups. RESULTS: The Dex group exhibited a significantly lower proportion of in-hospital deaths and a markedly higher in-hospital survival time compared to the midazolam and propofol groups (p < 0.01) after propensity score matching. Kaplan-Meier curves demonstrated a significant improvement in survival rates for the Dex group compared to the control group (p = 0.025). Analysis of Variance (ANOVA) revealed no significant differences in survival rates among the Dex, midazolam, and propofol groups (F = 1.949, p = 0.143). The nomogram indicated that compared to midazolam and propofol groups, Dex was more effective in improving the survival rate of PWE. CONCLUSION: Dex might improve the survival rate of PWE in the ICU compared to no standard drug intervention. However, Dex did not exhibit superiority in improving survival rates compared to midazolam and propofol.

The association between frailty and in-hospital mortality in critically ill patients with congestive heart failure: results from MIMIC-IV database.

Background: Frailty correlates with adverse outcomes in many cardiovascular diseases and is prevalent in individuals with heart failure (HF). The Hospital Frailty Risk Score (HFRS) offers an integrated, validated solution for frailty assessment in acute care settings, but its application in critically ill patients with congestive HF lacks exploration. This study aimed to identify the association between frailty assessed by the HFRS and in-hospital mortality in critically ill patients with congestive HF. Methods: This observational study retrospectively enrolled 12,179 critically ill patients with congestive HF. Data from the Medical Information Mart for Intensive Care IV database was used. The HFRS was calculated to assess frailty. Patients were categorized into three groups: non-frailty (HFRS < 5, n = 7,961), pre-frailty (5 ≤ HFRS < 15, n = 3,684), and frailty (HFRS ≥ 15, n = 534). Outcomes included in-hospital mortality, length of intensive care unit stay, and length of hospital stay. Multiple logistic regression and Locally Weighted Scatterplot Smoothing (LOWESS) smoother were used to investigate the association between frailty and outcomes. Subgroup analysis was employed to elucidate the correlation between frailty levels and in-hospital mortality across diverse subgroups. Results: 12,179 patients were enrolled, 6,679 (54.8%) were male, and the average age was 71.05 ± 13.94 years. The overall in-hospital mortality was 11.7%. In-hospital mortality increased with the escalation of frailty levels (non-frailty vs. pre-frailty vs. frailty: 9.7% vs. 14.8% vs. 20.2%, P < 0.001). The LOWESS curve demonstrated that the HFRS was monotonically positively correlated with in-hospital mortality. Upon controlling for potential confounders, both pre-frailty and frailty statuses were found to be independently linked to a heightened risk of mortality during hospitalization (odds ratio [95% confidence interval]: pre-frailty vs. non-frailty: 1.27 [1.10-1.47], P = 0.001; frailty vs. non-frailty: 1.40 [1.07-1.83], P = 0.015; P for trend < 0.001). Significant interactions between frailty levels and in-hospital mortality were observed in the following subgroups: race, heart rate, creatinine, antiplatelet drug, diabetes, cerebrovascular disease, chronic renal disease, and sepsis. Conclusion: In critically ill patients with congestive HF, frailty as assessed by the HFRS emerged as an independent predictor for the risk of in-hospital mortality. Prospective, randomized studies are required to determine whether improvement of frailty levels could improve clinical prognosis.

Association of the TyG index with prognosis in surgical intensive care patients: data from the MIMIC-IV.

BACKGROUND: The triglyceride-glucose (TyG) index, a tool for assessing insulin resistance, is increasingly recognized for its ability to predict cardiovascular and metabolic risks. However, its relationship with trauma and surgical patient prognosis is understudied. This study investigated the correlation between the TyG index and mortality risk in surgical/trauma ICU patients to identify high-risk individuals and improve prognostic strategies. METHODS: This study identified patients requiring trauma/surgical ICU admission from the Medical Information Mart for Intensive Care (MIMIC-IV) database, and divided them into tertiles based on the TyG index. The outcomes included 28-day mortality and 180-day mortality for short-term and long-term prognosis. The associations between the TyG index and clinical outcomes in patients were elucidated using Cox proportional hazards regression analysis and RCS models. RESULTS: A total of 2103 patients were enrolled. The 28-day mortality and 180-day mortality rates reached 18% and 24%, respectively. Multivariate Cox proportional hazards analysis revealed that an elevated TyG index was significantly related to 28-day and 180-day mortality after covariates adjusting. An elevated TyG index was significantly associated with 28-day mortality (adjusted hazard ratio, 1.19; 95% confidence interval 1.04-1.37) and 180-day mortality (adjusted hazard ratio, 1.24; 95% confidence interval 1.11-1.39). RCS models revealed that a progressively increasing risk of mortality was related to an elevated TyG index. According to our subgroup analysis, an elevated TyG index is associated with increased risk of 28-day and 180-day mortality in critically ill patients younger than 60 years old, as well as those with concomitant stroke or cardiovascular diseases. Additionally, in nondiabetic patients, an elevated TyG index is associated with 180-day mortality. CONCLUSION: An increasing risk of mortality was related to an elevated TyG index. In critically ill patients younger than 60 years old, as well as those with concomitant stroke or cardiovascular diseases, an elevated TyG index is associated with adverse short-term and long-term outcomes. Furthermore, in non-diabetic patients, an elevated TyG index is associated with adverse long-term prognosis.

J-shaped relationship between stress hyperglycemia ratio and 90-day and 180-day mortality in patients with a first diagnosis of acute myocardial infarction: analysis of the MIMIC-IV database.

AIMS: The Stress Hyperglycemia Ratio (SHR) potently predicts adverse outcomes in patients with cardiovascular and cerebrovascular diseases. However, the relationship between SHR and short-term mortality risk in patients with a first diagnosis of acute myocardial infarction (AMI) remains contentious. This study sought to understand better the relationship between SHR and short-term mortality risk in patients with a first diagnosis of AMI. METHODS: We conducted a cohort study using data from 1961 patients with a first diagnosis of AMI from the MIMIC-IV (version 2.2) database. Patients were divided into three groups based on SHR tertiles. The Cox proportional hazards model and a two-segmented Cox proportional hazards model were used to elucidate the nonlinear relationship between SHR in patients with a first diagnosis of AMI and mortality. RESULTS: Of the surveyed population, 175 patients (8.92%) died within 90 days, and 210 patients (10.71%) died within 180 days. After multivariate adjustments, elevated SHR levels were significantly and non-linearly associated with a higher risk of 90-day and 180-day mortality in patients with a first diagnosis of AMI, showing a J-shaped correlation with an inflection point at 0.9. Compared to participants with SHR levels below the inflection point, those with higher SHR levels had a fivefold increased risk of 90-day mortality (hazard ratio [HR] 5.74; 95% confidence interval [CI] 3.19, 10.33) and a fourfold increased risk of 180-day mortality (HR 4.56; 95% CI 2.62, 7.95). In the subgroup analysis, patients with pre-diabetes mellitus (pre-DM) and higher SHR levels had increased 90-day (HR 6.90; 95% CI 1.98, 24.02) and 180-day mortality risks (HR 5.30; 95% CI 1.96, 14.27). CONCLUSION: In patients with a first diagnosis of AMI, there is a J-shaped correlation between SHR and 90-day and 180-day mortality, with an adverse prognostic inflection point of SHR at 0.9.

Machine learning for the prediction of in-hospital mortality in patients with spontaneous intracerebral hemorrhage in intensive care unit.

This study aimed to develop a machine learning (ML)-based tool for early and accurate prediction of in-hospital mortality risk in patients with spontaneous intracerebral hemorrhage (sICH) in the intensive care unit (ICU). We did a retrospective study in our study and identified cases of sICH from the MIMIC IV (n = 1486) and Zhejiang Hospital databases (n = 110). The model was constructed using features selected through LASSO regression. Among five well-known models, the selection of the best model was based on the area under the curve (AUC) in the validation cohort. We further analyzed calibration and decision curves to assess prediction results and visualized the impact of each variable on the model through SHapley Additive exPlanations. To facilitate accessibility, we also created a visual online calculation page for the model. The XGBoost exhibited high accuracy in both internal validation (AUC = 0.907) and external validation (AUC = 0.787) sets. Calibration curve and decision curve analyses showed that the model had no significant bias as well as being useful for supporting clinical decisions. XGBoost is an effective algorithm for predicting in-hospital mortality in patients with sICH, indicating its potential significance in the development of early warning systems.

Estimated plasma volume status as a simple and accessible predictor of 28-day mortality in septic shock: insights from a retrospective study of the MIMIC-IV database.

Background: Assessing volume status in septic shock patients is crucial for tailored fluid resuscitation. Estimated plasma volume status (ePVS) has emerged as a simple and effective tool for evaluating patient volume status. However, the prognostic value of ePVS in septic shock patients remains underexplored. Methods: The study cohort consisted of septic shock patients admitted to the ICU, sourced from the MIMIC-IV database. Patients were categorized into two groups based on 28-day survival outcomes, and their baseline characteristics were compared. According to the ePVS (6.52 dL/g) with a hazard ratio of 1 in the restricted cubic spline (RCS) analysis, patients were further divided into high and low ePVS groups. A multivariable Cox regression model was utilized to evaluate the association between ePVS and 28-day mortality rate. The Kaplan-Meier survival curve was plotted, and all-cause mortality was compared between the high and low groups using the log-rank test. Results: A total of 7,607 septic shock patients were included in the study, among whom 2,144 (28.2%) died within 28 days. A J-shaped relationship was observed between ePVS at ICU admission and 28-day mortality, with an increase in mortality risk noted when ePVS exceeded 6.52 dL/g. The high ePVS group exhibited notably higher mortality rates compared to the low ePVS group (28-day mortality: 26.2% vs. 30.2%; 90-day mortality: 35% vs. 42.3%). After adjustment for confounding factors, ePVS greater than 6.52 dL/g independently correlated with an increased risk of 28-day mortality (HR: 1.20, 95% CI: 1.10-1.31, p < 0.001) and 90-day mortality (HR: 1.25, 95% CI: 1.15-1.35, p < 0.001). Kaplan-Meier curves demonstrated a heightened risk of mortality associated with ePVS values exceeding 6.52 dL/g. Conclusion: A J-shaped association was observed between ePVS and 28-day mortality in septic shock patients, with higher ePVS levels associated with increased risk of mortality.

The lactate-to-albumin ratio relationship with all-cause mortality in cerebral infarction patients: analysis from the MIMIC-IV database.

Objective: To examine the association of lactate-to-albumin ratio (LAR) with 30-day and 90-day mortality in patients with cerebral infarction admitted to the intensive care unit (ICU). Methods: In this retrospective observational study, 1,089 patients with cerebral infarction were recruited. The concentration of blood lactate and serum albumin on the first day of ICU admission were recorded. The relationship between LAR levels and mortality was evaluated through univariate and multivariate Cox regression analyses, four-knot multivariate restricted cubic spline regression, and Kaplan-Meier (KM) curves. Results: The overall 30-day and 90-day mortality rates in the entire cohort were 27.3 and 35.8%, respectively. KM analysis revealed a significant relationship between high LAR index and the risk of all-cause mortality (log-rank p < 0.001). Furthermore, multivariate Cox proportional risk analysis showed that the LAR index independently predicted the risk of 30-day mortality (HR: 1.38, 95% CI 1.15-1.64, p = 0.004) and 90-day mortality (HR: 1.53, 95% CI 1.32-1.77, p < 0.001) in the study population. Furthermore, a higher LAR exceeding 0.53 was positively correlated with the risk of 30-day and 90-day mortalities. Subsequent subgroup analyses demonstrated that LAR could predict the primary outcome. Conclusion: In summary, the LAR index is a reliable and independent predictor of increased mortality among critically ill patients suffering from cerebral infarction. Nonetheless, there is a need for additional comprehensive prospective studies to validate these findings.

Establishment and validation of a predictive model for tracheotomy in critically ill patients and analysis of the impact of different tracheotomy timing on patient prognosis.

BACKGROUND: In critically ill patients receiving invasive mechanical ventilation (IMV), it is unable to determine early which patients require tracheotomy and whether early tracheotomy is beneficial. METHODS: Clinical data of patients who were first admitted to the ICU and underwent invasive ventilation for more than 24 h in the Medical Information Marketplace in Intensive Care (MIMIC)-IV database were retrospectively collected. Patients were categorized into successful extubation and tracheotomy groups according to whether they were subsequently successfully extubated or underwent tracheotomy. The patients were randomly divided into model training set and validation set in a ratio of 7:3. Constructing predictive models and evaluating and validating the models. The tracheotomized patients were divided into the early tracheotomy group (< = 7 days) and the late tracheotomy group (> 7 days), and the prognosis of the two groups was analyzed. RESULTS: A total of 7 key variables were screened: Glasgow coma scale (GCS) score, pneumonia, traumatic intracerebral hemorrhage, hemorrhagic stroke, left and right pupil responses to light, and parenteral nutrition. The area under the receiver operator characteristic (ROC) curve of the prediction model constructed through these seven variables was 0.897 (95% CI: 0.876-0.919), and 0.896 (95% CI: 0.866-0.926) for the training and validation sets, respectively. Patients in the early tracheotomy group had a shorter length of hospital stay, IMV duration, and sedation duration compared to the late tracheotomy group (p < 0.05), but there was no statistically significant difference in survival outcomes between the two groups. CONCLUSION: The prediction model constructed and validated based on the MIMIC-IV database can accurately predict the outcome of tracheotomy in critically ill patients. Meanwhile, early tracheotomy in critically ill patients does not improve survival outcomes but has potential advantages in shortening the duration of hospitalization, IMV, and sedation.

Relationship between sodium level and in-hospital mortality in traumatic brain injury patients of MIMIC IV database.

Background: An association between prognosis and high sodium levels in Traumatic Brain Injury (TBI) patients in Intensive Care Units (ICUs) has been noted, but limited research exists on the ideal sodium level in these patients or the impact on early mortality, using the MIMIC-IV database. Methods: A retrospective survey was conducted on TBI patients from the MIMIC-IV database. Patients were divided into two categories based on their highest serum sodium level within 24 h of admission exceeding 145 mmol/L: those with hypernatremia, and those with moderate-to-low sodium levels. Collected covariates encompasses demographic, clinical, laboratory, and intervention variables. A multivariate logistic regression model was implemented to forecast in-hospital mortality. Results: The study included 1749 TBI patients, with 209 (11.5%) experiencing in-hospital deaths. A non-linear test exposed an L-shaped correlation between sodium level and in-hospital mortality, with mortality rates increasing after a turning point at 144.1 mmol/L. Compared to the moderate-to-low group's 9.3% mortality rate, the hypernatremia group had a significantly higher mortality rate of 25.3% (crude odds ratio = 3.32, 95% confidence interval: 2.37 ~ 4.64, p < 0.001). After adjusting for all covariates, the hypernatremia group continued to show a significant correlation with higher mortality risk (adjusted odds ratio = 2.19, 95% confidence interval: 1.38 ~ 3.47, p = 0.001). This trend remained consistent regardless of the analyses stratification. Conclusion: The study reveals an L-shaped relationship between sodium levels and in-hospital deaths, with a pivotal point at 144.1 mmol/L. TBI patients displaying hypernatremia were independently linked to higher in-hospital mortality, underlining the need for further studies into targeted management of sodium levels in these patients.

Independent effect of the triglyceride-glucose index on all-cause mortality in critically ill patients with chronic obstructive pulmonary disease and asthma: A retrospective cohort study.

BACKGROUND: The triglyceride-glucose (TyG) index serves as a reliable proxy for insulin resistance (IR). IR has been linked to heightened incidence, prevalence, or severity of chronic obstructive pulmonary disease (COPD) and asthma. Prior research indicates that critically ill patients are prone to developing IR. Nevertheless, few studies have delved into the correlation between IR and all-cause mortality in critically ill patients with COPD and asthma. Therefore, the aim of this study is to explore the association between the TyG index and all-cause mortality in patients with COPD and asthma, with the goal of assessing the impact of IR on the prognosis of this patient population. METHODS: This is a retrospective study, and all data are from the Medical Information Mart for Intensive Care IV (MIMIC-IV) critical care database. This study included 684 ICU patients with COPD and asthma and divided them into quartiles based on TyG index levels. The primary outcomes of this study were all-cause mortality during follow-up, encompassing mortality at 30 days, 90 days, and 180 days. The Kaplan-Meier analysis was used to compare all-cause mortality among the above four groups. Cox proportional hazards analyses were performed to examine the association between TyG index and all-cause mortality in critically ill patients with COPD and asthma. Restricted cubic spline analysis was used to assess potential nonlinear association between the TyG index and the primary outcome. RESULTS: A total of 684 patients (53.9% female) were included. The 90-days all-cause mortality rate and 180-days all-cause mortality were 11.7% and 12.3%, respectively. Kaplan-Meier analysis revealed a significant association between the TyG index and both 90-days all-cause mortality (log-rank p = .039) and 180-days all-cause mortality (log-rank p = .017). Cox proportional hazards analysis revealed a significant association between the TyG index and 90-days all-cause mortality in both the unadjusted model (HR, 1.30 [95% CI 1.08-1.57] p = .005) and the model adjusted for age, gender, and diabetes (HR, 1.38 [95% CI 1.15-1.67] p < .001). Similarly, the TyG index was associated with 180-days all-cause mortality in the unadjusted model (HR, 1.30 [95% CI 1.09-1.56] p = .004) and the model adjusted for age, sex, and diabetes (HR, 1.38 [95% CI 1.15-1.66] p < .001). The restricted cubic splines (RCS) regression model indicated a significant nonlinear association between the TyG index and both 90-days and 180-days all-cause mortality. Specifically, TyG index >4.8 was associated with an increased risk of mortality at both 90 days and 180 days. CONCLUSIONS: In summary, our results extend the utility of the TyG index to critically ill patients with COPD and asthma. Our study shows that the TyG index is a potential predictor of all-cause mortality in critically ill patients with COPD and asthma. In addition, in patients with a TyG index exceeding 4.8, there was a heightened risk of mortality. Measuring the TyG index may help with risk stratification and prognosis prediction in critically ill patients with COPD and asthma. Further prospective studies are needed to confirm our findings.