Triglyceride-glucose index and clinical outcomes in sepsis: A retrospective cohort study of MIMIC-IV.

Although accumulating researches were done for investigating the relationship between triglyceride-glucose index (TyG index) and different diseases, none of the researches have been made in sepsis yet. In this study, we aimed to explore the relationship between TyG index and clinical outcomes in sepsis based on a large critical care public database. Sepsis patients in Medical Information Mart for Intensive Care IV (MIMIC-IV) Database were included. The exposure was TyG index, which was calculated by the equation: ln (TG (mg/dL) × FBG (mg/dL)/2). The outcomes were in-hospital mortality and 1-year mortality. The relationship between TyG index and outcomes was performed by Cox regression analysis. Smooth fitting curves were constructed by using generalized additive model. Kaplan-Meier analyses for cumulative hazard of 1-year mortality in different groups were done. 1103 sepsis patients were included with a median TyG index of 9.78. The mortalities of in-hospital and 1-year were 37.53% (n = 414) and 42.25% (n = 466), respectively. After adjusting confounders, there was a significantly negative relationship between TyG index and mortalities of in-hospital and 1-year. With the per unit increment in TyG index, the risk of in-hospital and 1-year mortality both decreased by 21% (HR = 0.79, 95% CI: 0.66-0.94, p = 0.0086 and HR = 0.79, 95% CI: 0.66-0.94, p = 0.0080, respectively). A negative relationship between TyG index and clinical outcomes in sepsis was found.

Risk analysis of the association between different hemoglobin glycation index and poor prognosis in critical patients with coronary heart disease-A study based on the MIMIC-IV database.

BACKGROUND: The hemoglobin glycation index (HGI) is the difference between the observed and predicted values of glycosylated hemoglobin (HbA1c), which is closely associated with a variety of poor prognoses. However, there are still no studies on the correlation between HGI and poor prognosis in patients with critical coronary artery disease. The purpose of this study was to analyze the correlation between HGI and all-cause mortality in patients with critical coronary artery disease using the MIMIC-IV database. METHODS: The HGI was calculated by constructing a linear regression equation between HbA1c and fasting plasma glucose (FPG). A Kaplan‒Meier survival analysis model was constructed based on the HGI quartiles to clarify the differences in all-cause mortality rates between groups, and the log-rank test was used to assess the differences between groups. The hazard ratio (HR) of HGI as a risk factor for outcome events was assessed using the Cox proportional risk model and restricted cubic spline (RCS), with the Q2 group serving as the reference group. RESULTS: A total of 5260 patients were included in this study. The 30-day mortality rate of the patients was 4.94% and the mortality rate within 365 days was 13.12%. A low HGI was significantly associated with 30-day mortality (HR, 1.96; 95% CI, (1.38, 2.78); P < 0.001) and 365-day mortality (HR, 1.48; 95% CI, (1.19, 1.85); P < 0.001) in patients with critical coronary artery disease in the completely adjusted Cox proportional risk model. In addition, high levels of HGI were associated with 365-day mortality (HR, 1.31; 95% CI, (1.02, 1.69); P < 0.05). RCS analysis revealed a U-shaped relationship between HGI and outcome events. According to the stratified analysis, the interaction test revealed that the correlation between HGI and outcome events remained stable. CONCLUSION: There was a significant correlation between HGI and all-cause mortality in patients with critical coronary artery disease, particularly in those with low HGI. HGI can be used as a potential indicator for assessing the short- and long-term risk of mortality in such patients.

Simultaneous assessment of stress hyperglycemia ratio and glycemic variability to predict mortality in patients with coronary artery disease: a retrospective cohort study from the MIMIC-IV database.

BACKGROUND: Stress hyperglycemia and glycemic variability (GV) can reflect dramatic increases and acute fluctuations in blood glucose, which are associated with adverse cardiovascular events. This study aimed to explore whether the combined assessment of the stress hyperglycemia ratio (SHR) and GV provides additional information for prognostic prediction in patients with coronary artery disease (CAD) hospitalized in the intensive care unit (ICU). METHODS: Patients diagnosed with CAD from the Medical Information Mart for Intensive Care-IV database (version 2.2) between 2008 and 2019 were retrospectively included in the analysis. The primary endpoint was 1-year mortality, and the secondary endpoint was in-hospital mortality. Levels of SHR and GV were stratified into tertiles, with the highest tertile classified as high and the lower two tertiles classified as low. The associations of SHR, GV, and their combination with mortality were determined by logistic and Cox regression analyses. RESULTS: A total of 2789 patients were included, with a mean age of 69.6 years, and 30.1% were female. Overall, 138 (4.9%) patients died in the hospital, and 404 (14.5%) patients died at 1 year. The combination of SHR and GV was superior to SHR (in-hospital mortality: 0.710 vs. 0.689, p = 0.012; 1-year mortality: 0.644 vs. 0.615, p = 0.007) and GV (in-hospital mortality: 0.710 vs. 0.632, p = 0.004; 1-year mortality: 0.644 vs. 0.603, p < 0.001) alone for predicting mortality in the receiver operating characteristic analysis. In addition, nondiabetic patients with high SHR levels and high GV were associated with the greatest risk of both in-hospital mortality (odds ratio [OR] = 10.831, 95% confidence interval [CI] 4.494-26.105) and 1-year mortality (hazard ratio [HR] = 5.830, 95% CI 3.175-10.702). However, in the diabetic population, the highest risk of in-hospital mortality (OR = 4.221, 95% CI 1.542-11.558) and 1-year mortality (HR = 2.013, 95% CI 1.224-3.311) was observed in patients with high SHR levels but low GV. CONCLUSIONS: The simultaneous evaluation of SHR and GV provides more information for risk stratification and prognostic prediction than SHR and GV alone, contributing to developing individualized strategies for glucose management in patients with CAD admitted to the ICU.

Triglyceride-glucose index: a novel evaluation tool for all-cause mortality in critically ill hemorrhagic stroke patients-a retrospective analysis of the MIMIC-IV database.

BACKGROUND: Hemorrhagic stroke (HS), including non-traumatic intracerebral hemorrhage (ICH) and subarachnoid hemorrhage (SAH), constitutes a substantial proportion of cerebrovascular incidents, accounting for around 30% of stroke cases. The triglyceride-glucose index (TyG-i) represents a precise insulin resistance (IR) indicator, a crucial metabolic disturbance. Existing literature has demonstrated an association between TyG-i and all-cause mortality (ACM) among individuals suffering from ischemic stroke (IS). Yet, the TyG-i prognostic implications for severe HS patients necessitating intensive care unit (ICU) admission are not clearly understood. Considering the notably elevated mortality and morbidity associated with HS relative to IS, investigating this association is warranted. Our primary aim was to investigate TyG-i and ACM association among critically ill HS patients within an ICU context. METHODS: Herein, patients with severe HS were identified by accessing the Medical Information Mart for Intensive Care-IV (MIMIC-IV, version 2.2) database, using the International Classification of Diseases (ICD)-9/10 as diagnostic guidelines. Subsequently, we stratified the subjects into quartiles, relying on their TyG-i scores. Moreover, we measured mortality at ICU, in-hospital, 30 days, 90 days, and 1 year as the outcomes. Cox proportional hazards regression analysis and restricted cubic splines (RCS) were deployed for elucidating the relation between the TyG-i and ACM while utilizing the Kaplan-Meier (K-M) method to estimate survival curves. The findings' robustness was assessed by conducting subgroup analysis and interaction tests employing likelihood ratio tests. RESULTS: The analysis included 1475 patients, with a male predominance of 54.4%. Observed mortality rates in the ICU, hospital, 30 days, 90 days, and 1 year were 7.3%, 10.9%, 13.8%, 19.7%, and 27.3%, respectively. Multivariate Cox regression analysis results manifested that heightened TyG-i was significantly related to ACM at 30 days (adjusted hazard ratio [aHR]: 1.32; 95% confidence interval [CI]: 1.05-1.67; P = 0.020), 90 days (aHR: 1.27; 95% CI: 1.04-1.55; P = 0.019), and 1 year (aHR: 1.22; 95% CI: 1.03-1.44; P = 0.023). The results of RCS analysis demonstrated a progressive elevation in ACM risk with rising TyG-i levels. Interaction tests found no significant effect modification in this relationship. CONCLUSION: In summary, TyG-i exhibits a significant correlation with ACM among patients enduring critical illness due to HS. This correlation underscores the probable utility of TyG-i as a prognostic tool for stratifying HS patients according to their risk of mortality. Applying TyG-i in clinical settings could enhance therapeutic decision-making and the management of disease trajectories. Additionally, this investigation augments existing research on the linkage between the TyG-i and IS, elucidating the TyG-i's role in predicting mortality across diverse stroke categories.

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.

Association between lactate/albumin ratio and prognosis in patients with acute myocardial infarction.

BACKGROUND: The association between the lactate/albumin ratio (L/A) as a diagnostic indicator and unfavourable clinical outcomes has been established in patients with community-acquired pneumonia, sepsis and heart failure, but the connection between L/A and all-cause mortality in patients with acute myocardial infarction (AMI) has yet to be fully understood. METHODS: This was a retrospective cohort study using MIMIC-IV (v2.2) data, with 2816 patients enrolled and all-cause mortality during hospitalization as the primary outcome. Kaplan-Meier (KM) analysis was used to compare the all-cause mortality between high-level and low-level L/A groups. Receiver operating characteristic (ROC) curve, Restricted cubic splines (RCS) and Cox proportional hazards analysis were performed to investigate the relationship between L/A ratio and in-hospital all-cause mortality. RESULTS: L/A values were significantly higher in the non-survivor groups than the survival groups (1.14 [.20] vs. .60 [.36], p < .05), and area under the ROC curve [.734 (95% confidence interval, .694-.775)] was better than other indicators. Data of COX regression analysis showed that higher L/A value supposed to be an independent risk factor for in-hospital mortality. RCS analysis showed evidence of an increasing trend and a non-linear relationship between L/A and in-hospital mortality (p-value was non-linear <.05). KM survival curves were significantly lower in the high L/A group than the low L/A group (p < .001), and the former group had an increased risk of in-hospital mortality compared with the latter one (Log Rank p < .001). CONCLUSIONS: L/A demonstrates significant independent predictive power for elevated all-cause mortality during hospitalization in patients diagnosed with AMI.

Association between Albumin-Corrected Anion Gap and Mortality in Patients with Cardiogenic Shock.

Background: Cardiogenic shock (CS) is a critical illness with a high mortality rate in clinical practice. Although some biomarkers have been found to be associated with mortality in patients suffering from CS in previous studies. The albumin-corrected anion gap (ACAG) has not been studied in depth. Our study aimed to explore the relationship between ACAG and mortality in patients with CS. Methods: All baseline data was extracted from Medical Information Mart for Intensive Care-IV version: 2.0 (MIMIC-IV). According to the prognosis at 30 days of follow-up, they were divided into survivors and non-survivors groups. The survival curves between the two groups were drawn using the Kaplan-Meier method and the log-rank test. Valid factors were selected using the least absolute shrinkage and selection operator (LASSO) logistic analysis model. Analysis was performed to investigate the relationship between mortality and all enrolled patients using restricted cubic spline (RCS) and Cox proportional hazards models. Receiver operating characteristic (ROC) curves were used to assess the predictive ability of ACAG. Evaluation of final result stability using sensitivity analysis. Results: 839 cases were selected to meet the inclusion criteria and categorized into survivors and non-survivors groups in the final analysis. The ACAG value measured for the first time at the time of admission was selected as the research object. Kaplan-Meier (K-M) survival curves showed that cumulative 30- and 90-day survival decreased progressively with elevated ACAG (p < 0.001), and multifactorial Cox regression analyses showed ACAG to be an independent risk factor for increased 30- and 90-day mortality in patients suffering from CS (p < 0.05). RCS curves revealed that all-cause mortality in this group of patients increased with increasing ACAG ( χ 2 = 5.830, p = 0.120). The ROC curve showed that the best cutoff value for ACAG for predicting 30-day mortality in patients with CS was 22.625, with a sensitivity of 44.0% and a specificity of 74.7%. The relationship between ACAG and CS short-term mortality remained stable in all sensitivity analyses (All p < 0.05). Conclusions: The ACAG is an independent risk factor for 30- and 90-day mortality in CS patients and predicts poor clinical outcomes in CS patients. According to our study, elevated ACAG at admission, especially when ACAG > 20 mmol/L, was an independent predictor of all-cause mortality in CS.

Correlation between Lactate Dehydrogenase to Albumin Ratio and the Prognosis of Patients with Cardiac Arrest.

Background: Cardiac arrest (CA) is a common event in the intensive care unit (ICU), which seriously threatens the prognosis of patients. Therefore, it is crucial to determine a simple and effective clinical indicator to judge the prognosis of patients after a CA for later treatments. The purpose of this study was to investigate the relationship between the lactate dehydrogenase to albumin ratio (LAR) and the prognosis of patients after a CA. Methods: The clinical data of participants was obtained from the Medical Information Mart for Intensive Care IV (MIMIC-IV, v2.0; 2008 to 2019). According to the 30-day prognosis, patients were divided into a survivors group (n = 216) and a non-survivors group (n = 304). The optimal LAR threshold was determined using restricted cubic spline (RCS), which divided patients into a high LAR group ( ≥ 15.50, n = 257) and a low LAR group ( < 15.50, n = 263). The ICU hospitalization and 30-day accumulative survival curves of the two groups were plotted following the Kaplan-Meier survival analysis. Multivariate Cox regression was used to analyze the relationship between the LAR and the prognosis of CA patients. Receiver operating characteristic (ROC) curves were drawn to evaluate the predictive efficacy of the LAR on 30-day all-cause mortality, and sensitivity analysis was used to check the reliability of the findings. Results: A total of 520 patients with CA were enrolled and the 30-day mortality was 58.46%. The LAR in the non-survivors group was higher than in the survivors group. The RCS showed a linear trend relationship between the LAR and the mortality risk in patients during their ICU stay and 30 days; moreover, as the LAR increased, so did the risk of mortality. The Kaplan-Meier survival curve showed that compared with the low LAR group, the cumulative survival rates of ICU hospitalization and 30 days were lower in the high LAR group among CA patients (p < 0.001). Multivariate Cox regression analysis showed that an elevated LAR ( ≥ 15.50) was an independent risk factor for mortality during ICU stay and 30 days (p < 0.005). ROC analysis suggested that the LAR was superior to the sequential organ failure assessment (SOFA) score in predicting the 30-day all-cause mortality in CA patients (area under the curve (AUC) = 0.676, 95% confidence interval [CI]: 0.629-0.723). To verify the reliability of our findings, we performed sensitivity analyses and found that the findings were reliable. Conclusions: An elevated LAR might be a predictor of mortality in patients following a CA during ICU hospitalization and 30 days, thereby it can be used to provide a reference for the clinical management of these patients.

Association between the ROX index and mortality in patients with acute hypoxemic respiratory failure: a retrospective cohort study.

BACKGROUND: Although ROX index is frequently used to assess the efficacy of high-flow nasal cannula treatment in acute hypoxemic respiratory failure (AHRF) patients, the relationship between the ROX index and the mortality remains unclear. Therefore, a retrospective cohort study was conducted to evaluate the ability of the ROX index to predict mortality risk in patients with AHRF. METHOD: Patients diagnosed with AHRF were extracted from the MIMIC-IV database and divided into four groups based on the ROX index quartiles. The primary outcome was 28-day mortality, while in-hospital mortality and follow-up mortality were secondary outcomes. To investigate the association between ROX index and mortality in AHRF patients, restricted cubic spline curve and COX proportional risk regression were utilized. RESULT: A non-linear association (L-shaped) has been observed between the ROX index and mortality rate. When the ROX index is below 8.28, there is a notable decline in the 28-day mortality risk of patients as the ROX index increases (HR per SD, 0.858 [95%CI 0.794-0.928] P < 0.001). When the ROX index is above 8.28, no significant association was found between the ROX index and 28-day mortality. In contrast to the Q1 group, the mortality rates in the Q2, Q3, and Q4 groups had a substantial reduction (Q1 vs. Q2: HR, 0.749 [0.590-0.950] P = 0.017; Q3: HR, 0.711 [0.558-0.906] P = 0.006; Q4: HR, 0.641 [0.495-0.830] P < 0.001). CONCLUSION: The ROX index serves as a valuable predictor of mortality risk in adult patients with AHRF, and that a lower ROX index is substantially associated with an increase in mortality.

Association between pre-ICU aspirin administration and ARDS mortality in the MIMIC-IV database: A cohort study.

BACKGROUND: Acute Respiratory Distress Syndrome (ARDS) is a severe condition with high mortality and morbidity rates. Evidence on the effectiveness of pharmacological interventions for ARDS treatment is limited. Recent studies suggest that aspirin may prevent ARDS development, but its efficacy in established ARDS is uncertain. METHODS: We enrolled patients with ARDS using data from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database. Primary outcomes were 30- and 90-day mortality rates and length of ICU stay. We employed multivariable Cox regression and linear regression models for statistical analysis and used propensity score matching (PSM) to ensure robust results. RESULTS: The study included 10,042 participants with an average age of 61.8 ± 15.3 years. Kaplan-Meier analysis showed significantly lower 30- and 90-day mortality rates in patients treated with pre-ICU admission aspirin compared with non-aspirin use (p < 0.0001). Multivariable Cox regression models revealed a significant 63% reduction in 30-day mortality for pre-ICU aspirin users (HR = 0.37, 95% CI: 0.31-0.44, p < 0.001). Aspirin use in the ICU was associated with a 59% reduction in ICU mortality and a 0.68-day reduction in length of ICU stay (p < 0.05). These findings consistently indicate that aspirin may improve survival in patients with ARDS, even after further stratification of aspirin use and PSM analysis. CONCLUSION: Our findings suggest that aspirin treatment before ICU admission is associated with significantly reduced 30- and 90-day mortality rates and decreased length of ICU stay in patients with ARDS.

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.

Nomogram predictive model for in-hospital mortality risk in elderly ICU patients with urosepsis.

BACKGROUND: Urinary tract infection (UTI) is a common cause of sepsis. Elderly patients with urosepsis in intensive care unit (ICU) have more severe conditions and higher mortality rates owing to factors such as advanced age, immunosenescence, and persistent host inflammatory responses. However, comprehensive studies on nomograms to predict the in-hospital mortality risk in elderly patients with urosepsis are lacking. This study aimed to construct a nomogram predictive model to accurately assess the prognosis of elderly patients with urosepsis and provide therapeutic recommendations. METHODS: Data of elderly patients with urosepsis were extracted from the Medical Information Mart for Intensive Care (MIMIC) IV 2.2 database. Patients were randomly divided into training and validation cohorts. A predictive nomogram model was constructed from the training set using logistic regression analysis, followed by internal validation and sensitivity analysis. RESULTS: This study included 1,251 patients. LASSO regression analysis revealed that the Glasgow Coma Scale (GCS) score, red cell distribution width (RDW), white blood count (WBC), and invasive ventilation were independent risk factors identified from a total of 43 variables studied. We then created and verified a nomogram. The area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA) of the nomogram were superior to those of the traditional SAPS-II, APACHE-II, and SOFA scoring systems. The Hosmer-Lemeshow test results and calibration curves suggested good nomogram calibration. The IDI and NRI values showed that our nomogram scoring tool performed better than the other scoring systems. The DCA curves showed good clinical applicability of the nomogram. CONCLUSIONS: The nomogram constructed in this study is a convenient tool for accurately predicting in-hospital mortality in elderly patients with urosepsis in ICU. Improving the treatment strategies for factors related to the model could improve the in-hospital survival rates of these 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.

Association between heart rate and mortality in patients with septic shock: an analysis revealed by time series data.

BACKGROUND: Heart rate is crucial for patients with septic shock, but there are few studies on the scope of heart rate. Therefore, we studied the relationship between different heart rates and mortality of critically ill patients with septic shock, and explored the optimal heart rate range, in order to provide new insights for clinical treatment of septic shock. METHODS: This retrospective study utilized time-series heart rate data from the Medical Information Mart for Intensive Care (MIMIC) IV database. Patients with septic shock were identified as the Sepsis 3.0 criteria and received vasopressor therapy in the first 24 h since ICU admission. We calculated the time-weighted average heart rate (TWA-HR) based on the time-series data. The restricted cubic spline (RCS) analysis was employed to investigate the nonlinear relationship between heart rate and 28-day mortality, aiming to explore the optimal heart rate control target for septic patients and using this target as the exposure factor. The primary outcome was 28-day mortality, and the secondary outcome were ICU and in-hospital mortality. For the original cohort, we applied the log-rank test to infer the relationship between heart rate and mortality. To control for bias introduced by confounders, we utilized propensity score matching (PSM) to reduce imbalances between normal TWA-HR and high TWA-HR groups, and we established a series of models [the multivariable Cox model, matching weight (MW)-adjusted Cox model, multivariable logistic regression, MW-adjusted logistic regression, and doubly robust model] as sensitivity analyses and subgroup analyses to demonstrate the robustness of our findings. RESULTS: A total of 13492 patients were included in our study. The RCS analysis based on Cox and logistic regression showed increased risk of mortality (P < 0.001, non-linear P < 0.001) when TWA-HR > 85 beats per minute (bpm). The log-rank test revealed in terms of the 28-day mortality, the hazard ratio (HR) (95% confidence interval [CI]) was 1.92 (1.78-2.06, P < 0.001) for patients with high TWA-HR compared to normal TWA-HR group. Similarly, for the ICU mortality, the HR (95% CI) was 1.64 (1.52-1.78, P < 0.001), and for the in-hospital mortality, the HR (95% CI) was 1.61 (1.48-1.76, P < 0.001). Collectively, the sensitivity analysis consistently demonstrated higher 28-day mortality, ICU mortality, and in-hospital mortality in patients with TWA-HR > 85 bpm. CONCLUSION: Patients with septic shock whose heart rate was controlled no more than 85 bpm during ICU stay received survival benefit in terms of 28-day, ICU and in-hospital mortality. .

Association between lactate-to-albumin ratio and 28-days all-cause mortality in patients with sepsis-associated liver injury: a retrospective cohort study.

BACKGROUND: The mortality rate of sepsis-associated liver injury (SALI) is relatively high, but there is currently no authoritative prognostic criterion for the outcome of SALI. Meanwhile, lactate-to-albumin ratio (LAR) has been confirmed to be associated with mortality rates in conditions such as sepsis, heart failure, and respiratory failure. However, there is a scarcity of research reporting on the association between LAR and SALI. This study aimed to elucidate the association between LAR and the 28-day mortality rate of SALI. METHODS: In this retrospective cohort study, data were obtained from the Medical Information Mart for Intensive Care IV (v2.2). Adult patients with SALI were admitted to the intensive care unit in this study. The LAR level at admission was included, and the primary aim was to assess the relationship between the LAR and 28-day all-cause mortality. RESULTS: A total of 341 patients with SALI (SALI) were screened. They were divided into a survival group (241) and a non-survival group (100), and the 28-day mortality rate was 29.3%. Multivariable Cox regression analysis revealed that for every 1-unit increase in LAR, the 28-day mortality risk for SALI patients increased by 21%, with an HR of 1.21 (95% CI 1.11 ~ 1.31, p < 0.001). CONCLUSIONS: This study indicates that in patients with SALI, a higher LAR is associated with an increased risk of all-cause mortality within 28 days of admission. This suggests that LAR may serve as an independent risk factor for adverse outcomes in SALI patients.

Prediction of 28-Day All-Cause Mortality in Heart Failure Patients with Clostridioides difficile Infection Using Machine Learning Models: Evidence from the MIMIC-IV Database.

INTRODUCTION: Heart failure (HF) may induce bowel hypoperfusion, leading to hypoxia of the villa of the bowel wall and the occurrence of Clostridioides difficile infection (CDI). However, the risk factors for the development of CDI in HF patients have yet to be fully illustrated, especially because of a lack of evidence from real-world data. METHODS: Clinical data and survival situations of HF patients with CDI admitted to ICU were extracted from the Medical Information Mart for Intensive Care (MIMIC)-IV database. For developing a model that can predict 28-day all-cause mortality in HF patients with CDI, the Recursive Feature Elimination with Cross-Validation (RFE-CV) method was used for feature selection. And nine machine learning (ML) algorithms, including logistic regression (LR), decision tree, Bayesian, adaptive boosting, random forest (RF), gradient boosting decision tree, XGBoost, light gradient boosting machine, and categorical boosting, were applied for model construction. After training and hyperparameter optimization of the models through grid search 5-fold cross-validation, the performance of models was evaluated by the area under curve (AUC), accuracy, sensitivity, specificity, precision, negative predictive value, and F1 score. Furthermore, the SHapley Additive exPlanations (SHAP) method was used to interpret the optimal model. RESULTS: A total of 526 HF patients with CDI were included in the study, of whom 99 cases (18.8%) experienced death within 28 days. Eighteen of the 57 variables were selected for the model construction algorithm for model construction. Among the ML models considered, the RF model emerged as the optimal model achieving the accuracy, F1-score, and AUC values of 0.821, 0.596, and 0.864, respectively. The net benefit of the model surpassed other models at 16%-22% threshold probabilities based on decision curve analysis. According to the importance of features in the RF model, red blood cell distribution width, blood urea nitrogen, Simplified Acute Physiology Score II, Sequential Organ Failure Assessment, and white blood cell count were highlighted as the five most influential variables. CONCLUSIONS: We developed ML models to predict 28-day all-cause mortality in HF patients associated with CDI in the ICU, which are more effective than the conventional LR model. The RF model has the best performance among all the ML models employed. It may be useful to help clinicians identify high-risk HF patients with CDI.

Variation in monitoring: Glucose measurement in the ICU as a case study to preempt spurious correlations.

OBJECTIVE: Health inequities can be influenced by demographic factors such as race and ethnicity, proficiency in English, and biological sex. Disparities may manifest as differential likelihood of testing which correlates directly with the likelihood of an intervention to address an abnormal finding. Our retrospective observational study evaluated the presence of variation in glucose measurements in the Intensive Care Unit (ICU). METHODS: Using the MIMIC-IV database (2008-2019), a single-center, academic referral hospital in Boston (USA), we identified adult patients meeting sepsis-3 criteria. Exclusion criteria were diabetic ketoacidosis, ICU length of stay under 1 day, and unknown race or ethnicity. We performed a logistic regression analysis to assess differential likelihoods of glucose measurements on day 1. A negative binomial regression was fitted to assess the frequency of subsequent glucose readings. Analyses were adjusted for relevant clinical confounders, and performed across three disparity proxy axes: race and ethnicity, sex, and English proficiency. RESULTS: We studied 24,927 patients, of which 19.5% represented racial and ethnic minority groups, 42.4% were female, and 9.8% had limited English proficiency. No significant differences were found for glucose measurement on day 1 in the ICU. This pattern was consistent irrespective of the axis of analysis, i.e. race and ethnicity, sex, or English proficiency. Conversely, subsequent measurement frequency revealed potential disparities. Specifically, males (incidence rate ratio (IRR) 1.06, 95% confidence interval (CI) 1.01 - 1.21), patients who identify themselves as Hispanic (IRR 1.11, 95% CI 1.01 - 1.21), or Black (IRR 1.06, 95% CI 1.01 - 1.12), and patients being English proficient (IRR 1.08, 95% CI 1.01 - 1.15) had higher chances of subsequent glucose readings. CONCLUSION: We found disparities in ICU glucose measurements among patients with sepsis, albeit the magnitude was small. Variation in disease monitoring is a source of data bias that may lead to spurious correlations when modeling health data.

Association between serum anion gap and 28-day mortality in critically ill patients with infective endocarditis: a retrospective cohort study from MIMIC IV database.

BACKGROUND: The relationship between serum anion gap (AG) and 28-day mortality in critically ill patients with infective endocarditis is currently not well established. OBJECTIVE: This study aims to investigate the impact of serum AG on 28-day mortality in critically ill patients with infective endocarditis. METHODS: A retrospective cohort study was conducted involving 449 participants diagnosed with infective endocarditis and admitted to intensive care units (ICU). Vital signs, laboratory parameters and comorbidity were collected for all participants to analyze the association between anion gap levels and 28-day mortality. RESULTS: A total of 449 critically ill patients with infective endocarditis (IE) were included in the study. The mean age was 57 years, and 64% were male. The overall 28-day mortality rate was 20%. A greater AG on admission were significantly associated with increased 28-day mortality in unadjusted analysis (hazard ratio [HR] 1.13; 95% confidence interval [CI] 1.09-1.18; p < 0.001). After adjusting for all confounders, the association remained significant (adjusted HR 1.07; 95% CI 1.02-1.13; p = 0.003). When AG was converted into categorial variables (quartiles), the risk of 28-day mortality in the greatest Q4 group was significantly higher compared with that in the lowest Q1 group (model 4: HR = 2.62, 95%CI: 1.17-5.83, p = 0.019). Subgroup analysis showed consistent results across different groups. CONCLUSION: A greater AG on admission were independently associated with increased 28-day mortality in critically ill patients with IE. These findings suggest that the AG can serve as a prognostic marker in this population, aiding in risk stratification and guiding clinical management.

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.

Comparison of different intensive care scoring systems and Glasgow Aneurysm score for aortic aneurysm in predicting 28-day mortality: a retrospective cohort study from MIMIC-IV database.

OBJECTIVE: This study aims to assess the performance of various scoring systems in predicting the 28-day mortality of patients with aortic aneurysms (AA) admitted to the intensive care unit (ICU). METHODS: We utilized data from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) to perform a comparative analysis of various predictive systems, including the Glasgow Aneurysm Score (GAS), Simplified Acute Physiology Score (SAPS) III, SAPS II, Logical Organ Dysfunction System (LODS), Sequential Organ Failure Assessment (SOFA), Systemic Inflammatory Response Syndrome (SIRS), and The Oxford Acute Illness Severity Score (OASIS). The discrimination abilities of these systems were compared using the area under the receiver operating characteristic curve (AUROC). Additionally, a 4-knotted restricted cubic spline regression was employed to evaluate the association between the different scoring systems and the risk of 28-day mortality. Finally, we conducted a subgroup analysis focusing on patients with abdominal aortic aneurysms (AAA). RESULTS: This study enrolled 586 patients with AA (68.39% male). Among them, 26 patients (4.4%) died within 28 days. Comparative analysis revealed higher SAPS II, SAPS III, SOFA, LODS, OASIS, and SIRS scores in the deceased group, while no statistically significant difference was observed in GAS scores between the survivor and deceased groups (P = 0.148). The SAPS III system exhibited superior predictive value for the 28-day mortality rate (AUROC 0.805) compared to the LODS system (AUROC 0.771), SOFA (AUROC 0.757), SAPS II (AUROC 0.759), OASIS (AUROC 0.742), SIRS (AUROC 0.638), and GAS (AUROC 0.586) systems. The results of the univariate and multivariate logistic analyses showed that SAPS III was statistically significant for both 28-day and 1-year mortality. Subgroup analyses yielded results consistent with the overall findings. No nonlinear relationship was identified between these scoring systems and 28-day all-cause mortality (P for nonlinear > 0.05). CONCLUSION: The SAPS III system demonstrated superior discriminatory ability for both 28-day and 1-year mortality compared to the GAS, SAPS II SIRS, SOFA, and OASIS systems among patients with AA.