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

OBJECTIVES: To evaluate the correlation of mean arterial oxygen tension (PaO₂) during the first 24 h following intensive care unit (ICU) admission with mortality in critically ill patients with acute kidney injury (AKI) and determine the optimal PaO₂ threshold for devising oxygen therapy strategies for these patients. METHODS: We collected the clinical data of ICU patients with AKI from the MIMIC-IV database. Based on the optimal first 24-h PaO₂ threshold determined by receiver operating characteristic (ROC) curve analysis and the Youden index maximization principle, we classified the patients into hyperoxia group (with PaO₂ ≥137.029 mmHg) and hypoxemia group (PaO₂<137.029 mm Hg). Multivariable logistic regression and propensity score matching were used to evaluate the correlation of first 24-h PaO₂ levels with in-hospital mortality of the patients. RESULTS: Among the 18 335 patients, 46.7% were in the hyperoxia group, who had an overall mortality rate of 16.9%. The optimal PaO₂ threshold (137.029 mm Hg) had a sensitivity of 78.3%, a specificity of 63.7%, and an AUC of 0.76 (95% CI: 0.74=0.78). Hyperoxia within the first 24 h after ICU admission was associated with a significantly lower in-hospital mortality (OR=0.78) and 90-day mortality (OR=0.77), particularly in stage 1 AKI patients. A non-linear relationship was identified between PaO₂ and mortality of the patients (P<0.001). Kaplan-Meier survival curves indicated a significantly increased 90-day survival rate in the patients in hyperoxia group (P<0.001), who also had shorter durations of mechanical ventilation, less vasopressor use, and shorter lengths of hospital/ICU stay. CONCLUSIONS Maintenance of a PaO₂ level ≥137.029 mmHg within 24 h after ICU admission may improve clinical outcomes of critically ill AKI patients, which underscores the importance of targeted oxygen delivery in ICU care.
Humans ; Acute Kidney Injury/blood* ; Male ; Female ; Middle Aged ; Intensive Care Units ; Aged ; Oxygen/blood* ; Hospital Mortality ; Partial Pressure ; Adult ; Databases, Factual

OBJECTIVE: To investigate the predictive value of oxygenation index (PaO₂/FiO₂) at intensive care unit (ICU) admission on 30-day mortality in patients with sepsis. METHODS: A retrospective study was conducted. Patients with sepsis who were hospitalized in the ICU of the Affiliated Hospital of Jining Medical University from April 2015 to October 2023 were enrolled. The demographic information, comorbidities, sites of infection, vital signs and laboratory test indicators at the time of admission to the ICU, disease severity scores within 24 hours of admission to the ICU, treatment process and prognostic indicators were collected. According to the PaO₂/FiO₂ at ICU admission, patients were divided into Q1 group (PaO₂/FiO₂ of 4.1-16.4 cmHg, 1 cmHg ≈ 1.33 kPa), Q2 group (PaO₂/FiO₂ of 16.5-22.6 cmHg), Q3 group (PaO₂/FiO₂ of 22.7-32.9 cmHg), and Q4 group (PaO₂/FiO₂ of 33.0-94.8 cmHg). Differences in the indicators across the four groups were compared. Multifactorial Cox regression analysis was used to assess the relationship between PaO₂/FiO₂ and 30-day mortality of patients with sepsis. The predictive value of PaO₂/FiO₂, sequential organ failure assessment (SOFA) and acute physiology and chronic health evaluation II (APACHE II) on 30-day prognosis of patients with sepsis was analyzed by receiver operator characteristic curve (ROC curve). RESULTS: A total of 1 711 patients with sepsis were enrolled, including 428 patients in Q1 group, 424 patients in Q2 group, 425 patients in Q3 group, and 434 patients in Q4 group. 622 patients died at 30-day, the overall 30-day mortality was 36.35%. There were statistically significant differences in age, body mass index (BMI), history of smoking, history of alcohol consumption, admission heart rate, respiratory rate, APACHE II score, SOFA score, Glasgow coma score (GCS), site of infection, Combined chronic obstructive pulmonary disease (COPD), blood lactic acid (Lac), prothrombin time (PT), albumin (Alb), total bilirubin (TBil), pH, proportion of mechanical ventilation, duration of mechanical ventilation, proportion of vasoactive medication used, and maximal concentration, length of ICU stay, hospital stay, incidence of acute kidney injury, in-hospital mortality, 30-day mortality among the four groups. Multivariate Cox regression analysis showed that after adjusting for confounding factors, for every 1 cmHg increase in PaO₂/FiO₂ at ICU admission, the 30-day mortality risk decreased by 2% [hazard ratio (HR) = 0.98, 95% confidence interval (95%CI) was 0.98-0.99, P < 0.001]. The 30-day mortality risk in the Q4 group was reduced compared with the Q1 group by 41% (HR = 0.59, 95%CI was 0.46-0.76, P < 0.001). The fitted curve showed that a curvilinear relationship between PaO₂/FiO₂ and 30-day mortality after adjustment for confounders. In the inflection point analysis, for every 1 cmHg increase in PaO₂/FiO₂ at PaO₂/FiO₂ < 28.55 cmHg, the risk of 30-day death in sepsis patients was reduced by 5% (HR = 0.95, 95%CI was 0.94-0.97, P < 0.001); when PaO₂/FiO₂ ≥ 28.55 cmHg, there was no statistically significant association between PaO2/FiO2 and the increase in the risk of 30-day death in sepsis (HR = 1.01, 95%CI was 0.99-1.02, P = 0.512). ROC curve analysis showed that the area under the curve (AUC) for the prediction of 30-day mortality by admission PaO₂/FiO₂ in ICU sepsis patients was 0.650, which was lower than the predictive ability of the SOFA score (AUC = 0.698) and APACHE II score (AUC = 0.723). CONCLUSION In patients with sepsis, PaO₂/FiO₂ at ICU admission is strongly associated with 30-day mortality risk, alerting healthcare professionals to pay attention to patients with low PaO₂/FiO₂ for timely interventions.
Humans ; Sepsis/mortality* ; Intensive Care Units ; Retrospective Studies ; Prognosis ; Hospital Mortality ; Oxygen ; Male ; Predictive Value of Tests ; Female ; Middle Aged ; Aged

OBJECTIVE: To investigate the predictive value of early lactate/albumin ratio (LAR) combined with quick sequential organ failure assessment (qSOFA) for the 28-day prognosis of patients with sepsis caused by emergency community-acquired pneumonia (CAP). METHODS: The clinical data of patients with sepsis caused by CAP admitted to the department of emergency of Beijing Haidian Hospital from June 2021 to August 2022 were retrospectively analyzed, including gender, age, comorbidities, lactic acid (Lac), serum albumin (Alb), LAR, procalcitonin (PCT) within 1 hour, and 28-day prognosis. Patients were divided into two groups based on 28-day prognosis, and risk factors affecting patients' prognosis were analyzed using univariate and multivariate Cox regression methods. Patients were divided into two groups according to the best cut-off value of LAR, and Kaplan-Meier survival curves were used to analyze the 28-day cumulative survival of patients in each group. Time-dependent receiver operator characteristic curve (ROC curve) were plotted to analyze the predictive value of sequential organ failure assessment (SOFA), acute physiology and chronic health evaluation II (APACHE II), and qSOFA+LAR score on the prognosis of patients with sepsis caused by CAP at 28 days. The area under the curve (AUC) was calculated and compared. RESULTS: A total of 116 patients with sepsis caused by CAP were included, of whom 80 survived at 28 days and 36 died, 28-day mortality of 31.0%. There were no statistically significant differences in age, gender, comorbidities, pH, platelet count, and fibrinogen between the survival and death groups, and there were significantly differences in blood urea nitrogen (BUN), white blood cell count (WBC), hemoglobin, Lac, Alb, PCT, D-dimer, LAR, as well as qSOFA score, SOFA score, and APACHE II score. Univariate Cox regression analyses showed that BUN, WBC, pH, Lac, Alb, PCT, LAR, qSOFA score, SOFA score, and APACHE II score were associated with mortality outcome. Multifactorial Cox regression analysis of the above variables showed that BUN, WBC, PCT, and APACHE II score were independent risk factors for 28-day death in the emergency department in patients with sepsis caused by CAP [hazard ratio (HR) were 1.081, 0.892, 1.034, and 1.135, respectively, all P < 0.05]. The best cut-off value of early LAR for predicting the 28-day prognosis of sepsis patients was 0.088, the Kaplan-Meier survival curve showed that the 28-day cumulative survival rate of sepsis patients in the LAR ≤ 0.088 group was significantly higher than that in the LAR > 0.088 group [82.9% (63/76) vs. 42.5% (17/40), Log-Rank test: χ² = 22.51, P < 0.001]. The qSOFA+LAR score was calculated based on the LAR cut-off value and qSOFA score, and ROC curve analysis showed that the AUCs of SOFA score, APACHE II score, and qSOFA+LAR score for predicting 28-day death of patients with sepsis caued by CAP were 0.741, 0.774, and 0.709, respectively, with the AUC of qSOFA+LAR score slightly lower than those of SOFA score and APACHE II score, but there were no significantly differences. When the best cut-off value of qSOFA+LAR score was 1, the sensitivity was 63.9% and the specificity was 80.0%. CONCLUSION The qSOFA+LAR score has predictive value for the 28-day prognosis of patients with sepsis caused by CAP in the emergency department, its predictive value is comparable to the SOFA score and the APACHE II score, and it is more convenient for early use in the emergency department.
Emergency Service, Hospital/statistics & numerical data* ; Sepsis/etiology* ; Prognosis ; Community-Acquired Pneumonia/mortality* ; Organ Dysfunction Scores ; Predictive Value of Tests ; Lactic Acid/blood* ; Serum Albumin, Human/analysis* ; Biomarkers/blood* ; Retrospective Studies ; Hospital Mortality ; Kaplan-Meier Estimate ; APACHE ; Procalcitonin/blood* ; ROC Curve ; Area Under Curve ; Humans

OBJECTIVE: To explore the risk factors affecting the prognosis of patients with sepsis complicated with acute pulmonary embolism, and to construct and validate a nomogram predictive model for in-hospital mortality risk. METHODS: Based on the American Medical Information Mart for Intensive Care (MIMIC-III, MIMIC-IV) databases, the data were collected on patients with sepsis complicated with acute pulmonary embolism from 2001 to 2019, including baseline characteristics, and vital signs, disease scores, laboratory tests within 24 hours of admission to the intensive care unit (ICU), and interventions. In-hospital mortality was the outcome event. The total samples were divided into training and testing sets in a 7:3 ratio by random sampling. Univariate Cox regression analysis was used to verify the impact of all variables on the risk of in-hospital mortality, thereby screen potential influencing factors. Subsequently, a stepwise bi-directional regression method was applied to select factors one by one, leading to the construction of a nomogram prediction model. Collinearity testing was used to demonstrate the absence of strong multicollinearity among the influencing factors in the nomogram prediction model. The discrimination of the nomogram model, sequential organ failure assessment (SOFA), and simplified pulmonary embolism severity index (sPESI) was evaluated using C-index in the test set. Receiver operator characteristic curve (ROC curve) was drawn to evaluate the predictive value of various models for in-hospital mortality in patients with sepsis complicated with acute pulmonary embolism. RESULTS: A total of 562 patients with sepsis complicated with acute pulmonary embolism were included, including 393 in the training set and 169 in the testing set. Univariate Cox regression analysis showed that 30 factors associated with in-hospital mortality in patients with sepsis complicated with acute pulmonary embolism. Through stepwise bi-directional regression, 12 variables were ultimately selected, including gender, presence of malignant tumors, body temperature, red cell distribution width (RDW), blood urea nitrogen (BUN), serum potassium, prothrombin time (PT), 24-hour urine output, mechanical ventilation, vasoactive drugs, warfarin use, and sepsis-induced coagulopathy (SIC). Collinearity testing indicated no strong multicollinearity among the influencing factors [all variance inflation factor (VIF) > 10]. A nomogram model was constructed using the 12 variables mentioned above. The nomogram model predicted the C-index and its 95% confidence interval (95%CI) of in-hospital mortality in patients with sepsis complicated with acute pulmonary embolism better than SOFA score and sPESI [0.771 (0.725-0.816) vs. 0.579 (0.519-0.639), 0.608 (0.554-0.663)]. The ROC curve showed that the area under the curve (AUC) and its 95%CI of the nomogram model were higher than those of the SOFA score and sPESI [0.811 (0.766-0.857) vs. 0.630 (0.568-0.691), 0.623 (0.566-0.680)]. These findings were consistently replicated in the internal validation of the testing set. In both the training and testing sets, Delong's test showed that the AUC of the nomogram model was significantly higher than the SOFA score and sPESI (both P < 0.05). CONCLUSION The nomogram model demonstrated good predictive effectiveness for the risk of in-hospital mortality in patients with sepsis complicated with acute pulmonary embolism, enabling clinicians to predict mortality risk in advance and take timely interventions to reduce mortality.
Humans ; Pulmonary Embolism/mortality* ; Hospital Mortality ; Nomograms ; Sepsis/complications* ; Prognosis ; Risk Factors ; Intensive Care Units ; Male ; Female ; Middle Aged ; Aged