OBJECTIVES: The Charlson comorbidity index reflects overall comorbidity burden and has been applied in cardiovascular medicine. However, its role in predicting in-hospital mortality in patients with acute myocardial infarction (AMI) complicated by ventricular arrhythmias (VA) remains unclear. This study aims to evaluate the predictive value of the Charlson comorbidity index in this setting and to construct a nomogram model for early risk identification and individualized management to improve outcomes. METHODS: Using the open-access critical care database MIMIC-IV (Medical Information Mart for Intensive Care IV), we identified intensive care unit (ICU) patients diagnosed with AMI complicated by VA. Patients were grouped according to in-hospital survival. The predictive performance of the Charlson comorbidity index and other clinical variables for in-hospital mortality was analyzed. Key predictors were selected using the least absolute shrinkage and selection operator (LASSO) regression, followed by multivariable Logistic regression. A nomogram model was constructed based on the regression results. Model performance was assessed using receiver operating characteristic (ROC) curves and calibration plots. RESULTS: A total of 1 492 patients with AMI and VA were included, of whom 340 died and 1 152 survived during hospitalization. Significant differences were observed between survivors and non-survivors in sex distribution, vital signs, comorbidity burden, organ function, and laboratory parameters (all P<0.05). The area under the curve (AUC) of the Charlson comorbidity index for predicting in-hospital mortality was 0.712 (95% CI 0.681 to 0.742), significantly higher than albumin, international normalized ratio (INR), hemoglobin, body temperature, and platelet count (all P<0.001), but comparable to Sequential Organ Failure Assessment (SOFA) score (P>0.05). LASSO regression identified seven key predictors: the Charlson comorbidity index (quartile groups: T1, <6; T2, ≥6-<7; T3, ≥7-<9; T4, ≥9), ventricular fibrillation, age, systolic blood pressure, respiratory rate, body temperature, and SOFA score. Multivariate Logistic regression showed that compared with T1, mortality risk increased significantly in T2 (OR=1.996, 95% CI 1.135 to 3.486, P=0.016), T3 (OR=3.386, 95% CI 2.192 to 5.302, P<0.001), and T4 (OR=5.679, 95% CI 3.711 to 8.842, P<0.001). Age (OR=1.056, P<0.001), respiratory rate (OR=1.069, P<0.001), SOFA score (OR=1.223, P<0.001), and ventricular fibrillation (OR=2.174, P<0.001) were independent risk factors, while systolic blood pressure (OR=0.984, P<0.001) and body temperature (OR=0.648, P<0.001) were protective factors. The nomogram incorporating these predictors achieved an AUC of 0.849 (95% CI 0.826 to 0.871) with high discrimination and good calibration (mean absolute error=0.014). CONCLUSIONS The Charlson comorbidity index is an independent predictor of in-hospital mortality in AMI patients complicated by VA, with performance comparable to the SOFA score. The nomogram model based on the Charlson comorbidity index and additional clinical variables effectively estimates mortality risk and provides a valuable reference for clinical decision-making.
Humans ; Nomograms ; Hospital Mortality ; Myocardial Infarction/complications* ; Male ; Female ; Comorbidity ; Middle Aged ; Aged ; Arrhythmias, Cardiac/complications* ; ROC Curve ; Intensive Care Units

OBJECTIVES: Global epidemiological data indicate that 20% to 30% of intensive care unit (ICU) sepsis patients progress to deep vein thrombosis (DVT) due to coagulopathy, with an associated mortality rate of 25% to 40%. Existing prognostic tools have limitations. This study aims to develop and validate nomogram and machine learning models to predict in-hospital mortality in sepsis patients with DVT and assess their clinical applicability. METHODS: This multicenter retrospective study drew on data from the Medical Information Mart for Intensive Care IV (MIMIC-IV; n=2 235), the eICU Collaborative Research Database (eICU-CRD; n=1 274), and the Patient Admission Dataset from the ICU of Third Xiangya Hospital, Central South University (CSU-XYS-ICU; n=107). MIMIC-IV was split into a training set (n=1 584) and internal validation set (n=651), with the remaining datasets used for external validation. Predictors were selected via least absolute shrinkage and selection operator (LASSO) regression and Bayesian Information Criterion (BIC), and a nomogram model was constructed. An extreme gradient boosting (XGBoost) algorithm was used to build the machine learning model. Model performance was assessed by the concordance index (C-index), calibration curves, Brier score, decision curve analysis (DCA), and net reclassification improvement index (NRI). RESULTS: Five key predictors, age [odds ratio (OR)=1.02, 95% CI 1.01 to 1.03, P<0.001], minimum activated partial thromboplastin (APTT; OR=1.09, 95% CI 1.08 to 1.11, P<0.001), maximum APTT (OR=1.01, 95% CI 1.00 to 1.01, P<0.001), maximum lactate (OR=1.56, 95% CI 1.39 to 1.75, P<0.001), and maximum serum creatinine (OR=2.03, 95% CI 1.79 to 2.30, P<0.001), were included in the nomogram. The model showed robust performance in internal validation (C-index=0.845, 95% CI 0.811 to 0.879) and external validation (eICU-CRD: C-index=0.827, 95% CI 0.800 to 0.854; CSU-XYS-ICU: C-index=0.779, 95% CI 0.687 to 0.871). Calibration curves indicated good agreement between predicted and observed outcomes (Brier score<0.25), and DCA confirmed clinical benefit. The XGBoost model achieved an area under the receiver operating characteristic curve (AUC) of 0.982 (95% CI 0.969 to 0.985) in the training set, but performance declined in external validation (eICU-CRD, AUC=0.825, 95% CI 0.817 to 0.861; CSU-XYS-ICU, AUC=0.766, 95% CI 0.700 to 0.873), though it remained above clinical thresholds. Net reclassification improvement was slightly lower for XGBoost compared with the nomogram (NRI=0.58). CONCLUSIONS Both the nomogram and XGBoost models effectively predict in-hospital mortality in sepsis patients with DVT. However, the nomogram offers superior generalizability and clinical usability. Its visual scoring system provides a quantitative tool for identifying high-risk patients and implementing individualized interventions.
Humans ; Sepsis/complications* ; Machine Learning ; Nomograms ; Venous Thrombosis/complications* ; Retrospective Studies ; Hospital Mortality ; Male ; Female ; Middle Aged ; Aged ; Intensive Care Units ; Prognosis ; Bayes Theorem

INTRODUCTION: Out-of-hospital-cardiac-arrest (OHCA) is a major public health challenge and post-return-of-spontaneous-circulation (ROSC) goals have shifted from just survival to survival with intact neurology. Although post-ROSC care is crucial for survival with intact neurology, there are insufficient well-established protocols for post-resuscitation care. We aimed to evaluate post-resuscitation care in the emergency department (ED) of adult (aged ≥16 years) OHCA patients with sustained ROSC and its associated neurologically intact survival. METHODS: A retrospective review of electronic medical records was conducted for OHCA patients with sustained ROSC at the ED. Data including demographics, pre-hospital resuscitation, ED resuscitation, post-resuscitation care and eventual outcomes were analysed. RESULTS: Among 921 OHCA patients, 85 (9.2%) had sustained ROSC at the ED. Nineteen patients (19/85, 22.4%) survived, with 13 (13/85, 15.3%) having intact neurology at discharge. Electrocardiogram and chest X-ray were performed in all OHCA patients, whereas computed tomography (CT) was performed inconsistently, with CT brain being most common (74/85, 87.1%), while CT pulmonary angiogram (6/85, 7.1%), abdomen and pelvis (4/85, 4.7%) and aortogram (2/85, 2.4%) were done infrequently. Only four patients (4.7%) had all five neuroprotective goals of normoxia, normocarbia, normotension, normothermia and normoglycaemia achieved in the ED. The proportion of all five neuroprotective goals being met was significantly higher ( P = 0.01) among those with neurologically intact survival (3/13, 23.1%) than those without (1/72, 1.4%). CONCLUSION Post-resuscitation care at the ED showed great variability, indicating gaps between recommended guidelines and clinical practice. Good quality post-resuscitation care, centred around neuroprotection goals, must be initiated promptly to achieve meaningful survival with intact neurology.
Humans ; Out-of-Hospital Cardiac Arrest/mortality* ; Retrospective Studies ; Male ; Female ; Middle Aged ; Emergency Service, Hospital ; Cardiopulmonary Resuscitation/methods* ; Return of Spontaneous Circulation ; Aged ; Adult ; Treatment Outcome ; Electrocardiography ; Tomography, X-Ray Computed ; Aged, 80 and over

BACKGROUND

Pediatric Early Warning Scores (PEWS) help identify children at risk of clinical deterioration, but their accuracy across diverse settings, populations, interventions, and outcomes remains unexplored.

To determine the accuracy of PEWS in detecting clinical deterioration events (CDE) among pediatric patients seen at the emergency department (ED).

Retrospective cohort study.

Pediatric patients aged 1 month to 18 years seen at the ED.

Southern Philippines Medical Center Emergency Department, Davao City, Philippines from January 2021 to December 2022.

Area under the curve (AUC) of PEWS in detecting CDE; Brighton PEWS optimal cut-off and its sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (+LR), and negative likelihood ratio (-LR).

Among the 345 patients, 56 experienced CDE and 289 did not. Patients with CDE had significantly lower median age (1.00 year vs 5.00 years; p < 0.001), oxygen saturation (93.00% vs 98.00%; p < 0.001), and pediatric Glasgow Coma Scale scores (8.00 vs 15.00; p < 0.0001) compared to those without CDE. Heart rate (135.00 vs 111.00 beats per minute; p < 0.001), and respiratory rate (32.50 vs 24.00 breaths per minute; p < 0.001) were significantly higher in patients with CDE. The two groups also differed significantly in terms of comorbidity distribution (p < 0.001) and diagnosis (p < 0.001). The AUC of Brighton PEWS was 0.9064 (95% CI 0.8716 to 0.9357), with an optimal cut-off score of ≥4.00. This threshold yielded 76.79% sensitivity, 88.58% specificity, 56.60% PPV, 95.20% NPV, 6.72 LR+, and 0.26 LR-.

The Brighton PEWS demonstrates strong diagnostic accuracy in predicting CDE among pediatric patients. A cut-off score of ≥4.00 offers a balanced combination of sensitivity, specificity, and likelihood ratios for ED application.

OBJECTIVE: To assess the relationship between blood pressure reactivity index (BPRI) and in-hospital mortality risk in patients with sepsis-associated acute kidney injury (SA-AKI). METHODS: A retrospective cohort study was conducted to collect data from patients admitted to the intensive care unit (ICU) and clinically diagnosed with SA-AKI between 2008 and 2019 in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database in the United States. The collected data included demographic characteristics, comorbidities, vital signs, laboratory parameters, sequential organ failure assessment (SOFA) and simplified acute physiology scoreII(SAPSII) within 48 hours of SA-AKI diagnosis, stages of AKI, treatment regimens, mean BPRI during the first and second 24 hours (BPRI_0_24, BPRI_24_48), and outcome measures including primary outcome (in-hospital mortality) and secondary outcomes (ICU length of stay and total hospital length of stay). Variables with statistical significance in univariate analysis were included in LASSO regression analysis for variable selection, and the selected variables were subsequently incorporated into multivariate Logistic regression analysis to identify independent predictors associated with in-hospital mortality in SA-AKI patients. Restricted cubic spline (RCS) analysis was employed to examine whether there was a linear relationship between BPRI within 48 hours and in-hospital mortality in SA-AKI patients. Basic prediction models were constructed based on the independent predictors identified through multivariate Logistic regression analysis, and receiver operator characteristic curve (ROC curve) was plotted to evaluate the predictive performance of each basic prediction model before and after incorporating BPRI. RESULTS: A total of 3 517 SA-AKI patients admitted to the ICU were included, of whom 826 died during hospitalization and 2 691 survived. The BPRI values within 48 hours of SA-AKI diagnosis were significantly lower in the death group compared with the survival group [BPRI_0_24: 4.53 (1.81, 8.11) vs. 17.39 (5.16, 52.43); BPRI_24_48: 4.76 (2.42, 12.44) vs. 32.23 (8.85, 85.52), all P < 0.05]. LASSO regression analysis identified 20 variables with non-zero coefficients that were included in the multivariate Logistic regression analysis. The results showed that respiratory rate, temperature, pulse oxygen saturation (SpO₂), white blood cell count (WBC), hematocrit (HCT), activated partial thromboplastin time (APTT), lactate, oxygenation index, SOFA score, fluid balance (FB), BPRI_0_24, and BPRI_24_48 were all independent predictors for in-hospital mortality in SA-AKI patients (all P < 0.05). RCS analysis revealed that both BPRI showed "L"-shaped non-linear relationships with the risk of in-hospital mortality in SA-AKI patients. When BPRI_0_24 ≤ 14.47 or BPRI_24_48 ≤ 24.21, the risk of in-hospital mortality in SA-AKI increased as BPRI values decreased. Three basic prediction models were constructed based on the identified independent predictors: Model 1 (physiological indicator model) included respiratory rate, temperature, SpO₂, and oxygenation index; Model 2 (laboratory indicator model) included WBC, HCT, APTT, and lactate; Model 3 (scoring indicator model) included SOFA score and FB. ROC curve analysis showed that the predictive performance of the basic models ranked from high to low as follows: Model 3, Model 2, and Model 1, with area under the curve (AUC) values of 0.755, 0.661, and 0.655, respectively. The incorporation of BPRI indicators resulted in significant improvement in the discriminative ability of each model (all P < 0.05), with AUC values increasing to 0.832 for Model 3+BPRI, 0.805 for Model 2+BPRI, and 0.808 for Model 1+BPRI. CONCLUSIONS BPRI is an independent predictor factor for in-hospital mortality in SA-AKI patients. Incorporating BPRI into the prediction model for in-hospital mortality risk in SA-AKI can significantly improve its predictive capability.
Humans ; Acute Kidney Injury/mortality* ; Sepsis/complications* ; Retrospective Studies ; Hospital Mortality ; Prognosis ; Blood Pressure ; Intensive Care Units ; Male ; Female ; Length of Stay ; Middle Aged ; Aged ; Adult ; Logistic Models

OBJECTIVE: To construct a longitudinal trajectory model of arterial oxygen tension (PaO₂) within 24 hours after cardiac arrest (CA). METHODS: A retrospective cohort study was conducted. CA patients admitted to the ICU from 2014 to 2015 were selected from the eICU Collaborative Research Database (eICU-CRD). Data about patients' demographic characteristics, history of comorbidities, laboratory test indicators within 24 hours of intensive care unit (ICU) admission [including all PaO₂ data and arterial carbon dioxide tension (PaCO₂)], vasopressor use, and clinical outcomes were extracted from the database. The primary outcome variable was all-cause in-hospital mortality. Group-based trajectory model (GBTM) were built based on the changes in PaO₂ within 24 hours of ICU admission, and patients were grouped according to their initial static PaO₂ values upon ICU admission. Multivariable adjusted Poisson regression analysis was used to compare the in-hospital mortality risk among patients in different PaO₂ dynamic trajectory groups. Sensitivity analyses were performed using multivariable logistic regression and multivariable adjusted Poisson regression without imputation of missing values. RESULTS: A total of 3 866 CA patients were included. Three GBTM trajectory groups were identified based on PaO₂ changes within 24 hours of ICU admission: Group-1 (low level first increased then decreased, 148 cases), Group-2 (sustained low level, 3 040 cases), and Group-3 (first high level then decreased, 678 cases). Significant differences were found among the three groups in age, body weight, maximum serum potassium, maximum PaCO₂, minimum hemoglobin (Hb), vasopressor use, total hospitalization time, ICU stay, and hospital mortality. After incorporating variables with significant differences into the multivariable adjusted Poisson regression model, results showed that compared to Group-2 patients, patients in Group-1 and Group-3 had an increased risk of all-cause in-hospital mortality [Group-1 adjusted relative risk (aRR) = 1.20, 95% confidence interval (95%CI) was 1.02-1.41; Group-3 aRR = 1.11, 95%CI was 1.01-1.24]. Based on initial static PaO₂ values at ICU admission, patients were divided into four groups: PaO₂ < 100 mmHg (1 mmHg = 0.133 kPa; 1 217 cases), PaO₂ 100-200 mmHg (569 cases), PaO₂ 201-300 mmHg (547 cases), and PaO₂ > 300 mmHg (1 082 cases). Multivariable adjusted Poisson regression analysis indicated a significant upward trend in aRR for the latter three groups compared to the PaO₂ < 100 mmHg group. Sensitivity analyses revealed that compared to Group-2, patients in Group-1 and Group-3 had a significantly increased risk of all-cause in-hospital mortality (both P < 0.05). CONCLUSIONS Within 24 hours after return of spontaneous circulation in CA patients, PaO₂ exhibits different dynamic trajectories, and patients with hyperoxia have an increased risk of in-hospital mortality.
Humans ; Retrospective Studies ; Hospital Mortality ; Heart Arrest/blood* ; Prognosis ; Oxygen/blood* ; Intensive Care Units ; Cardiopulmonary Resuscitation ; Male ; Female ; Middle Aged

OBJECTIVE: To explore the timing of weaning extracorporeal membrane oxygenation (ECMO) and analyze the risk factors that affect survival outcomes before weaning. METHODS: A retrospective case-control study was conducted. Patients who received ECMO treatment and were weaned according to physicians' orders at the Second Xiangya Hospital of Central South University from January 2020 to June 2024 were enrolled as the study subjects. The general information, underlying diseases, indications and processes of ECMO, vital signs and arterial blood gas analysis 1 hour before weaning test, and biochemical indicators 24 hours before weaning test were collected through the hospital electronic medical record system. The primary outcome measure was the hospital mortality. The variables with P < 0.1 in univariate analysis and correlation analysis were included into binary Logistic regression analysis to identify risk factors. A nomogram model was constructed to predict the risk of weaning death in patients with ECMO, and receiver operator characteristic curve (ROC curve) and calibration curve were drawn to evaluate the model. Decision curve analysis (DCA) was used to evaluate the clinical net benefit rate of the model. RESULTS: A total of 32 ECMO patients were included, among whom 10 received veno-arterial ECMO (VA-ECMO) and 22 received veno-venous ECMO (VV-ECMO). During the hospitalization period, 23 patients survived, while 9 died. The time from mechanical ventilation to ECMO activation in the death group was significantly longer than that in the survival group, and the time from ECMO cessation to discharge was significantly shorter than that in the survival group. The levels of diastolic blood pressure (DBP) and albumin (Alb) before weaning were significantly lower than those in the survival group, and the level of procalcitonin (PCT) was significantly higher than that in the survival group (all P < 0.05). Spearman correlation analysis showed that DBP, PCT, Alb, and thrombin time (TT) were correlated with the weaning outcomes of ECMO patients (r values were -0.450, 0.373, -0.376, -0.346, all P < 0.1). Binary Logistic regression analysis showed that the final indicators entering the regression equation included DBP [odds ratio (OR) = 0.864, 95% confidence interval (95%CI) was 0.756-0.982], PCT (OR = 1.157, 95%CI was 0.679-1.973), and TT (OR = 0.852, 95%CI was 0.693-1.049), and a nomogram model was constructed to predict the weaning outcomes of ECMO patients. ROC curve analysis showed that the area under the curve (AUC) of the nomogram model for predicting the weaning outcome of ECMO patients was 0.831, with a sensitivity of 77.8% and a specificity of 65.2%. Its predictive value was better than that of single indicators DBP, PCT, and TT (AUC of 0.787, 0.739, and 0.722, respectively). The calibration curve showed that the prediction probability of the model was in good consistency with the actual observed results, the Hosmer-Lemeshow goodness of fit test showed that, χ ² = 8.3521, P = 0.400, indicating that the model fits well. DCA showed that across risk threshold of 0-0.8, the net benefit rate was greater than 0, which was significantly better than that of single indicator. CONCLUSIONS The nomogram model constructed with DBP, PCT, and TT has certain predictive value for the weaning outcomes of ECMO patients and can be used as a screening indicator for ECMO weaning timing.
Humans ; Extracorporeal Membrane Oxygenation ; Retrospective Studies ; Risk Factors ; Case-Control Studies ; Hospital Mortality ; Male ; Female ; Nomograms ; Logistic Models ; ROC Curve ; Middle Aged ; Adult ; Ventilator Weaning ; Time Factors

OBJECTIVE: To investigate the correlation between nucleated red blood cell (NRBC) level on the first day of intensive care unit (ICU) admission and 28-day mortality in adult septic patients, and to evaluate the value of NRBC as an independent predictor of death. METHODS: Single-cell transcriptomic analysis was performed using the GSE167363 dataset from the Gene Expression Omnibus (including 2 healthy controls, 3 surviving septic patients, and 2 non-surviving septic patients). A retrospective clinical analysis was conducted using the America Medical Information Mart for Intensive Care-IV (MIMIC-IV) database, including adult patients (≥ 18 years) with first-time admission who met the Sepsis-3.0 criteria, excluding those without NRBC testing on the first ICU day. The demographic information, vital signs, laboratory test indicators, disease severity score and survival data on the first day of admission were collected. The restricted cubic spline (RCS) curve was used to determine the optimal cut-off value of NRBC for predicting 28-day mortality in patients. Patients were divided into low-risk and high-risk groups based on this cut-off value for intergroup comparison, with Kaplan-Meier survival curve analysis conducted. Independent risk factors for 28-day mortality were analyzed using Logistic regression and Cox regression analysis, followed by the construction of regression models. RESULTS: NRBC were detected in the peripheral blood of septic patients by single-cell transcriptomic. A total of 1 291 sepsis patients were included in the clinical analysis, with 576 deaths within 28 days, corresponding to a 28-day mortality of 44.6%. RCS curve analysis showed a nonlinear relationship between the first-day NRBC level and the 28-day mortality. When NRBC ≥ 1%, the 28-day mortality of patients increased significantly. Compared to the low-risk group (NRBC < 1%), the high-risk group (NRBC ≥ 1%) had significantly higher respiratory rate, heart rate, sequential organ failure assessment (SOFA), and simplified acute physiology score II (SAPSII), and significantly lower hematocrit and platelet count. The high-risk group also had a significantly higher 28-day mortality [49.8% (410/824) vs. 35.5% (166/467), P < 0.05], and shorter median survival time (days: 29.8 vs. 208.6, P < 0.05). Kaplan-Meier survival curve showed that compared with the low-risk group, the survival time of high-risk group was significantly shortened (Log-rank test: χ ² = 25.1, P < 0.001). After adjusting for potential confounding factors including body mass, temperature, heart rate, respiratory rate, mean arterial pressure, serum creatinine, pulse oximetry saturation, hemoglobin, hematocrit, Na⁺, K⁺, platelet count, and SOFA score, multivariate regression analysis confirmed that NRBC ≥ 1% was an independent risk factor for 28-day mortality [Logistic regression: odds ratio (OR) = 1.464, 95% confidence interval (95%CI) was 1.126-1.902, P = 0.004; Cox regression: hazard ratio (HR) = 1.268, 95%CI was 1.050-1.531, P = 0.013]. CONCLUSIONS NRBC ≥ 1% on the first day of ICU admission is an independent risk factor for 28-day mortality in septic patients and can serve as a practical indicator for early prognostic assessment.
Humans ; Sepsis/blood* ; Intensive Care Units ; Risk Factors ; Retrospective Studies ; Prognosis ; Male ; Female ; Hospital Mortality ; Middle Aged ; Aged

OBJECTIVE: To explore the correlation between albumin (Alb) combined with diuretic treatment and the mortality risk of septic patients with pre-existing congestive heart failure based on the United States Critical Care Medical Information Database-IV (MIMIC-IV), and to conduct the external validation. METHODS: A retrospective cohort study was conducted. The clinical data of septic patients with pre-existing congestive heart failure admitted to the intensive care unit (ICU) from 2008 to 2019 in the MIMIC-IV 2.0 were extracted, including demographic characteristics, comorbidities, laboratory indicators on the first day of ICU admission, severity of illness, treatment measures, etc. For external validation, clinical data were collected from septic patients with pre-existing congestive heart failure admitted to the ICU of the Second People's Hospital of Shenzhen from October 2022 to December 2023. The patients were divided into Alb alone group and Alb combined with diuretic group. The ICU mortality was defined as the primary outcome event, and the 30-day and 60-day mortality were defined as the secondary outcomes. Multivariate Cox proportional hazard regression analysis was conducted to investigate the relationship between Alb combined with diuretic treatment and the mortality risk of ICU and 30 days in septic patients with pre-existing congestive heart failure, and subgroup analysis was performed. Kaplan-Meier survival curve was plotted to compared the 60-day cumulative survival rate between the Alb alone group and Alb combined with diuretic group. RESULTS: (1) Analysis results of data from MIMIC-IV: a total 1 754 patients were enrolled, of which 378 in the Alb alone group, and 1 376 in the Alb combined with diuretic group. Compared with the Alb alone group, the patients in the Alb combined with diuretic group had significantly lower ICU, 30-day, and 60-day mortality [ICU mortality: 19.11% (263/1 376) vs. 30.42% (115/378), 30-day mortality: 18.90% (260/1 376) vs. 32.54% (123/378), 60-day mortality: 24.49% (337/1 376) vs. 39.15% (148/378), all P < 0.05]. Based on the multivariate Cox proportional hazard regression adjusted models considering demographic characteristics, comorbidities, laboratory indicators, severity of illness, and treatment measures, it was shown that the use of Alb combined with diuretic was significantly associated with a reduced risk death of ICU and 30 days [ICU mortality risk: hazard ratio (HR) = 0.597, 95% confidence interval (95%CI) was 0.460-0.774, P < 0.001; 30-day mortality risk: HR = 0.557, 95%CI was 0.433-0.716, P < 0.001]. Subgroup analysis revealed that after adjusting for variables, regardless of gender, age, and whether or not patients had comorbidities such as hypertension, diabetes, severe liver disease, acute renal insufficiency, and sequential organ failure assessment (SOFA) score, the ICU mortality risk was significantly reduced in patients treated with Alb combined with diuretic (all HR < 1, P < 0.05), with no interaction observed (all P > 0.05). Kaplan-Meier survival curve showed the 60-day cumulative survival rate of patients in the Alb combined with diuretic group was significantly higher than that in the Alb alone group (Log-rank test: χ ² = 49.62, P < 0.05). (2) External validation: a total of 385 patients were enrolled, of which 144 in the Alb alone group, and 241 in the Alb combined with diuretic group. Compared with the Alb alone group, the patients of the Alb combined with diuretic group had significantly lower ICU, 30-day, and 60-day mortality [ICU mortality: 19.92% (48/241) vs. 31.25% (45/144), 30-day mortality: 19.09% (46/241) vs. 28.47% (41/144), 60-day mortality: 24.07% (58/241) vs. 34.03% (49/144), all P < 0.05]. The results of multivariate Cox proportional hazard regression analysis, subgroup analysis, and Kaplan-Meier survival curve analysis were consistent with the data analysis of the MIMIC-IV database. CONCLUSIONS Combination therapy of Alb and diuretic was associated with reduced mortality risk in septic patients with pre-existing congestive heart failure.
Humans ; Heart Failure/mortality* ; Retrospective Studies ; Sepsis/drug therapy* ; Intensive Care Units ; Diuretics/therapeutic use* ; Male ; Female ; Aged ; Middle Aged ; Proportional Hazards Models ; Hospital Mortality

OBJECTIVE: To explore the association between blood glucose trajectories within 7 days of intensive care unit (ICU) admission and mortality in patients with sepsis-associated acute respiratory distress syndrome (ARDS). METHODS: Based on the MIMIC-IV database, sepsis-associated ARDS patients with daily blood glucose monitoring data within 7 days of ICU admission were selected. Blood glucose trajectories were analyzed using group-based trajectory modeling (GBTM), and the optimal number of groups was determined based on the minimum Akaike information criterion (AIC), Bayesian information criterion (BIC), average posterior probability (AvePP), odds of correct classification (OCC), and proportion of group membership (Prop). Baseline characteristics including demographics, comorbidities, severity scores, vital signs, laboratory indicators within the first 24 hours of ICU admission, and treatments were collected. Kaplan-Meier survival curves were used to compare 28-day and 1-year survival across trajectory groups. Multivariate Logistic regression was performed to evaluate the associations between glucose trajectory groups and in-hospital mortality, ICU mortality. The incidence of hypoglycemia within 7 days in the ICU was analyzed among different groups. RESULTS: A total of 3 869 patients with sepsis-associated ARDS were included, with a median age of 63.52 (52.13, 73.54) years; 59.6% (2 304/3 869) were male. Based on glucose levels within 7 days, patients were categorized into three groups: persistent hyperglycemia group (glucose maintained at 10.6-13.1 mmol/L, n = 894), moderate glucose group (7.8-8.9 mmol/L, n = 1 452), and low-normal glucose group (6.1-7.0 mmol/L, n = 1 523). There were statistically significant differences in 28-day mortality and 1-year mortality among low-normal glucose group, moderate glucose group, and persistent hyperglycemia group [28-day mortality: 11.42% (174/1 523), 19.83% (288/1 452), 25.50% (228/894), χ ² = 82.545, P < 0.001; 1-year mortality: 23.31% (355/1 523), 33.75% (490/1 452), 39.49% (353/894), χ ² = 77.376, P < 0.001]. Kaplan-Meier analysis showed that higher glucose trajectories were associated with significantly lower 28-day and 1-year cumulative survival rates (Log-rank test: χ ² were 83.221 and 85.022, both P < 0.001). There were statistically significant differences in in-hospital mortality and ICU mortality among the low-normal glucose group, moderate glucose group, and persistent hyperglycemia group [in-hospital mortality: 9.65% (147/1 523), 19.70% (286/1 452), 24.50% (219/894), χ ² = 102.020, P < 0.001; ICU mortality: 7.22% (110/1 523), 16.05% (233/1 452), 20.13% (180/894), χ ² = 93.050, P < 0.001]. Logistic regression confirmed that, using the persistent hyperglycemia group as the reference, the low-normal glucose group had significantly lower risks of in-hospital mortality and ICU mortality after multiple factor adjustment. Although the moderate glucose group showed a trend toward lower mortality, the differences were not statistically significant. Using the moderate glucose group as a reference, the low-normal glucose group had 43.1% lower in-hospital mortality [odds ratio (OR) = 0.569, 95% confidence interval (95%CI) was 0.445-0.726, P < 0.001] and 42.0% lower ICU mortality (OR = 0.580, 95%CI was 0.439-0.762, P < 0.001). There was no statistically significant difference in the incidence of hypoglycemia within 7 days of ICU admission among low-normal glucose group, moderate glucose group, and persistent hyperglycemia group [2.82% (43/1 523), 2.69% (39/1 452), 3.02% (27/894), χ ² = 0.226, P = 0.893]. CONCLUSIONS Blood glucose trajectories during ICU stay are closely associated with prognosis in patients with sepsis-associated ARDS. Persistent hyperglycemia (10.6-13.1 mmol/L) is linked to significantly higher short- and long-term mortality.
Humans ; Respiratory Distress Syndrome/etiology* ; Sepsis/blood* ; Intensive Care Units ; Male ; Female ; Middle Aged ; Blood Glucose/metabolism* ; Hospital Mortality ; Aged