Objective To investigate the risk factors for early neurological deterioration in mild acute ischemic stroke，to construct a clinical predictive model，and to perform internal validation of this model. Methods A retrospective analysis was performed for 739 patients with mild acute ischemic stroke who were admitted to Department of Neurology，Kuntong Hospital of Zunhua，from October 2020 to December 2023，and they were randomly divided into a training set with 534 patients （72.3%） and a validation set with 205 patients （27.7%） at a ratio of 7∶3. Univariate and multivariate logistic regression analyses were performed for the training set to determine the risk factors for early neurological deterioration in mild acute ischemic stroke. A clinical predictive model was constructed，and internal validation was performed in terms of discriminatory ability，calibration，and clinical decision making. A nomogram was plotted. Results The multivariate logistic regression analysis showed that female sex （OR=1.87，95% CI 1.14~3.09，P=0.014），time window ≤6 hours （OR=3.10，95%CI 1.56~6.19，P=0.001），a baseline NIHSS score of 2 points （OR=3.72，95%CI 1.30~10.61，P=0.014），a baseline NIHSS score of 3 points （OR=4.24，95%CI 1.45~12.35，P=0.008），a TOAST classification of large artery atherosclerosis （OR=3.88，95%CI 2.20~6.83，P<0.001），and the responsible arteries of the basilar artery，the middle cerebral artery，and the internal carotid artery （OR=8.39，95%CI 2.28~30.85，P=0.001； OR=6.22，95%CI 1.78~21.71，P=0.004； OR=5.38，95%CI 1.15~25.13，P=0.032） were independent risk factors for early neurological deterioration in mild acute ischemic stroke. The clinical predictive model constructed showed a moderate discriminatory ability （AUC>0.7），good calibration （P>0.05） in the Hosmer-Lemeshow goodness-of-fit test），and good clinical benefits in both the training set and the validation set. Conclusion This clinical predictive model can effectively predict the onset of early neurological deterioration in mild acute ischemic stroke and guide clinicians to make decisions，and therefore，it holds promise for clinical application.
Nomograms

INTRODUCTION

Neonatal sepsis is a significant cause of morbidity and mortality, particularly among preterm infants, and remains a pressing global health concern. Early-onset neonatal sepsis is particularly challenging to diagnose due to its nonspecific clinical presentation, necessitating effective and timely diagnostic tools to reduce adverse outcomes. Traditional methods, such as microbial cultures, are slow and often unavailable in resource-limited settings. This study aimed to develop a robust multivariable risk prediction model tailored to improve early detection of Early Onset Sepsis (EOS) among preterm infants in the Philippines.

We conducted a retrospective analysis at a tertiary hospital in the Philippines using data from 1,354 preterm infants admitted between January 2019 and June 2024. Logistic regression models were employed, and predictors were selected through reverse stepwise elimination. Two scoring methods were developed: one based on beta coefficients divided by standard errors and another standardized to a total score of 100. The models were validated using Receiver Operator Characteristic curve analysis.

Version 1 of the scoring model demonstrated an Area Under the Curve (AUC) of 0.991, with a sensitivity of 90.91% and a specificity of 98.10%. Version 2 achieved an AUC of 0.999, with a sensitivity of 96.4% and a specificity of 92.44%.

The developed models provide a reliable, region specific tool for early detection of neonatal sepsis. Further validation across diverse populations and the integration of emerging diagnostic technologies, such as biomarkers and artificial intelligence, are warranted to enhance their applicability and accuracy.

OBJECTIVE: To analyze the clinical characteristics of elderly patients with sepsis, identify the key factors affecting their clinical outcomes, construct a death risk assessment scale for elderly patients with sepsis, and evaluate its predictive value. METHODS: A retrospective case-control study was conducted. The clinical data of sepsis patients admitted to intensive care unit (ICU) of the First Affiliated Hospital of Wenzhou Medical University from September 2021 to September 2023 were collected, including basic information, clinical characteristics, and clinical outcomes. The patients were divided into non-elderly group (age ≥ 65 years old) and elderly group (age < 65 years old) based on age. Additionally, the elderly patients were divided into survival group and death group based on their 30-day survival status. The clinical characteristics of elderly patients with sepsis were analyzed. Univariate and multivariate Logistic regression analyses were used to screen the independent risk factors for 30-day death in elderly patients with sepsis, and the regression equation was constructed. The regression equation was simplified, and the death risk assessment scale was established. The predictive value of different scores for the prognosis of elderly patients with sepsis was compared. RESULTS: (1) A total of 833 patients with sepsis were finally enrolled, including 485 in the elderly group and 348 in the non-elderly group. Compared with the non-elderly group, the elderly group showed significantly lower counts of lymphocyte, T cell, CD8⁺ T cell, and the ratio of T cells and CD8⁺ T cells [lymphocyte count (×10⁹/L): 0.71 (0.43, 1.06) vs. 0.83 (0.53, 1.26), T cell count (cells/μL): 394.0 (216.0, 648.0) vs. 490.5 (270.5, 793.0), CD8⁺ T cell count (cells/μL): 126.0 (62.0, 223.5) vs. 180.0 (101.0, 312.0), T cell ratio: 0.60 (0.48, 0.70) vs. 0.64 (0.51, 0.75), CD8⁺ T cell ratio: 0.19 (0.13, 0.28) vs. 0.24 (0.16, 0.34), all P < 0.01], higher natural killer cell (NK cell) count, acute physiology and chronic health evaluation II (APACHE II) score, ratio of invasive mechanical ventilation (IMV) during hospitalization, and 30-day mortality [NK cell count (cells/μL): 112.0 (61.0, 187.5) vs. 95.0 (53.0, 151.0), APACHE II score: 16.00 (12.00, 21.00) vs. 13.00 (8.00, 17.00), IMV ratio: 40.6% (197/485) vs. 31.9% (111/348), 30-day mortality: 28.9% (140/485) vs. 19.5% (68/348), all P < 0.05], and longer length of ICU stay [days: 5.5 (3.0, 10.0) vs. 5.0 (3.0, 8.0), P < 0.05]. There were no statistically significant differences in the levels of inflammatory markers such as C-reactive protein (CRP), procalcitonin (PCT), tumor necrosis factor-α (TNF-α), interferon-γ (IFN-γ), and interleukins (IL-2, IL-4, IL-6, IL-10) between the two groups. (2) In 485 elderly patients with sepsis, 345 survived in 30 days, and 140 died with the 30-day mortality of 28.9%. Compared with the survival group, the patients in the death group were older, and had lower body mass index (BMI), white blood cell count (WBC), PCT, platelet count (PLT) and higher IL-6, IL-10, N-terminal pro-brain natriuretic peptide (NT-proBNP), total bilirubin (TBil), blood lactic acid (Lac), and ratio of in-hospital IMV and continuous renal replacement therapy (CRRT). Multivariate Logistic regression analysis indicated that BMI [odds ratio (OR) = 0.783, 95% confidence interval (95%CI) was 0.678-0.905, P = 0.001], IL-6 (OR = 1.073, 95%CI was 1.004-1.146, P = 0.036), TBil (OR = 1.009, 95%CI was 1.000-1.018, P = 0.045), Lac (OR = 1.211, 95%CI was 1.072-1.367, P = 0.002), and IMV during hospitalization (OR = 6.181, 95%CI was 2.214-17.256, P = 0.001) were independent risk factors for 30-day death in elderly patients with sepsis, and the regression equation was constructed (Logit P = 1.012-0.244×BMI+0.070×IL-6+0.009×TBil+0.190×Lac+1.822×IMV). The regression equation was simplified to construct a death risk assessment scale, namely BITLI score. Receiver operator characteristic curve (ROC curve) analysis showed that the area under the ROC curve (AUC) of BITLI score for predicting death risk was 0.852 (95%CI was 0.769-0.935), and it was higher than APACHE II score (AUC = 0.714, 95%CI was 0.623-0.805) and sequential organ failure assessment (SOFA) score (AUC = 0.685, 95%CI was 0.578-0.793). The determined cut-off value of BITLI score was 1.50, while achieving a sensitivity of 83.3% and specificity of 74.0%. CONCLUSIONS Elderly patients with sepsis often have reduced lymphocyte counts, severe conditions, and poor prognosis. BMI, IL-6, TBil, Lac, and IMV during hospitalization were independent risk factors for 30-day death in elderly patients with sepsis. The BITLI score constructed based above risk factors is more precise and reliable than traditional APACHE II and SOFA scores in predicting the outcomes of elderly patients with sepsis.
Humans ; Sepsis/mortality* ; Aged ; Retrospective Studies ; Risk Assessment ; Case-Control Studies ; Prognosis ; Male ; Female ; Intensive Care Units ; Risk Factors ; Aged, 80 and over ; Logistic Models ; Middle Aged

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

OBJECTIVE: To explore the clinical features of the sepsis in immunocompromised hosts and establish an early warning equation. METHODS: A retrospective study was conducted on sepsis patients admitted to the intensive care unit (ICU) of Binzhou Medical University Hospital from October 2011 to October 2022. General information, infection site, etiology results and drug susceptibility, clinical symptoms, inflammatory indicators, acute physiology and chronic health status evaluation II (APACHE II), sequential organ failure assessment (SOFA), incidence of immune paralysis, and outcome during hospitalization were collected. Based on whether they met the diagnostic criteria for immunocompromised hosts, patients were divided into immunocompromised group and immune normal group. The clinical information of the two groups were compared. Multivariate Logistic regression was used to analyze the risk factors of patients with immunocompromised sepsis and the regression equation model was initially established. Omnibus test and Hosmer-Lemeshow test were used to evaluate the model. RESULTS: A total of 169 patients with sepsis were included, including 61 in the immunocompromised group and 108 in the normal immune group. The top 3 infection sites in the immunocompromised group were bloodstream infection, pulmonary infection and abdominal infection. The top 3 infection sites in the normal immune group were pulmonary infection, bloodstream infection and abdominal infection. The infection rate of Gram-negative bacteria in the immunocompromised group was significantly lower than that in the normal group [49.2% (30/61) vs. 64.8% (70/108), P < 0.05]. The infection rate of Gram-positive bacteria [27.9% (17/61) vs. 13.9% (15/108)] and multidrug-resistant bacteria [54.1% (33/61) vs. 29.6% (32/108)] were significantly higher than those in normal immune group (both P < 0.05). In terms of clinical symptoms, the proportion of fever in the immunocompromised group was significantly lower than that in the immune normal group [49.2% (30/61) vs. 66.7% (72/108), P < 0.05]. Neutrophil count (NEU) and neutrophil percentage (NEU%) in the immunocompromised group were significantly lower than those in the normal immune group. Lymphocyte percentage (LYM%), neutrophil/lymphocyte ratio (NLR), C-reactive protein (CRP), procalcitonin (PCT), APACHE II score, combined shock rate, incidence of immune paralysis, and mortality during hospitalization in the immunocompromised group were significantly higher than those in the normal immune group. Logistic regression analysis showed that NLR, CRP and PCT were risk factors for patients with immunocompromised sepsis (all P < 0.05). The above indicators were used as covariables to construct a Logistic regression equation, that was, Logit (P) = 0.025X₁+0.010X₂+0.013X₃-2.945, where X₁, X₂ and X₃ represent NLR, CRP and PCT respectively. Omnibus test and Hosmer-Lemeshow test show that the model fits well and has certain early warning value. CONCLUSIONS Patients with immunocompromised sepsis have more intense inflammatory response, with Gram-negative bacteria being the predominant pathogen, and a higher incidence of Gram-positive bacterial infections and multi-drug resistant infections. The severity of the disease, in-hospital mortality, the incidence of shock and the incidence of immune paralysis after sepsis were significantly higher. NLR, CRP and PCT were independent risk factors for sepsis in immunocompromised hosts. The regression equation constructed based on this may have early warning significance for patients with immunocompromised sepsis.
Humans ; Sepsis/immunology* ; Immunocompromised Host ; Retrospective Studies ; Risk Factors ; Intensive Care Units ; Logistic Models ; Male ; APACHE ; Female ; Middle Aged ; Aged

OBJECTIVE: To investigate the clinical characteristics and related risk factors of Clostridium difficile infection (CDI) in intensive care unit (ICU). METHODS: A retrospective study was conducted. Patients with diarrhea admitted to the ICU of the General Hospital of Ningxia Medical University from May 1 to August 30, 2023 were selected. Patients were divided into CDI group and non-CDI group based on the presence or absence of CDI. Clinical data from two groups of patients meeting the criteria were collected and compared, including gender, age, acute physiology and chronic health evaluation II (APACHE II), length of hospital stay, serum lactic acid, parenteral nutrition time, white blood cell count (WBC), procalcitonin (PCT), C-reactive protein (CRP), coagulation indicators, albumin, antibiotic exposure, etc. Multivariate Logistic regression analysis was performed to analyze the risk factors for CDI in ICU diarrhea patients. Receiver operator characteristic curve (ROC curve) was drawn to analyze the predictive value of each index for CDI in diarrhea patients. RESULTS: A total of 24 patients with diarrhea were enrolled, including 9 patients in the CDI group and 15 patients in the non-CDI group. The time of parenteral nutrition in the CDI group was significantly longer than that in the non-CDI group [days: 18.0 (13.5, 19.5) vs. 10.0 (4.0, 18.0)], the serum lactic acid level [mmol/L: 4.40 (3.00, 15.25) vs. 2.50 (1.90, 3.20)] and the ratio of serum lactic acid > 3.9 mmol/L [66.67% (6/9) vs. 6.67% (1/15)] were significantly higher than those in the non-CDI group, with statistical significance (all P < 0.05). Multivariate binary Logistic regression analysis showed that the serum lactic acid level of the patients was an independent risk factor for CDI [odds ratio (OR) = 3.193, 95% confidence interval (95%CI) was 1.011-10.080, P = 0.048]. ROC curve showed that serum lactic acid level had a high predictive value for CDI in ICU patients with diarrhea, and the area under the curve (AUC) was 0.815, respectively. When the cut-off value of serum lactic acid was 3.9 mmol/L, the sensitivity was 66.7% and the specificity was 93.3%. CONCLUSION Patients with diarrhea who have higher serum lactate levels (> 3.9 mmol/L) on admission are at increased risk of developing CDI.
Humans ; Retrospective Studies ; Risk Factors ; Intensive Care Units ; Clostridium Infections ; Clostridioides difficile ; Male ; Female ; Middle Aged ; Aged ; Diarrhea/microbiology* ; Logistic Models ; ROC Curve ; Adult

OBJECTIVE: To investigate the predictive value of inflammatory indicator and serum cystatin C (Cys C) for the prognosis of patients with sepsis-associated acute kidney injury (SA-AKI). METHODS: A prospective observational study was conducted. Patients with SA-AKI admitted to the intensive care unit (ICU) of the General Hospital of Ningxia Medical University from January 2022 to December 2023 were selected as the study subjects. General patient data, sequential organ failure assessment (SOFA), acute physiology and chronic health evaluation II (APACHE II), inflammatory indicator, and serum Cys C levels were collected. The 28-day survival status of the patients was observed. A multivariate Logistic regression model was used to analyze the risk factors affecting the poor prognosis of SA-AKI patients. Receiver operator characteristic curve (ROC curve) was plotted to evaluate the predictive efficacy of each risk factor for the prognosis of SA-AKI patients. RESULTS: A total of 111 SA-AKI patients were included, with 65 patients (58.6%) in the survival group and 46 patients (41.4%) in the death group. The SOFA score, APACHE II score, interleukin-6 (IL-6), procalcitonin (PCT), hypersensitive C-reactive protein (hs-CRP), and serum Cys C levels in the death group were significantly higher than those in the survival group [SOFA score: 15.00 (14.00, 17.25) vs. 14.00 (11.00, 16.00), APACHE II score: 26.00 (23.75, 28.00) vs. 23.00 (18.50, 28.00), IL-6 (ng/L): 3 731.00±1 573.61 vs. 2 087.93±1 702.88, PCT (μg/L): 78.19±30.35 vs. 43.56±35.37, hs-CRP (mg/L): 266.50 (183.75, 326.75) vs. 210.00 (188.00, 273.00), serum Cys C (mg/L): 2.01±0.61 vs. 1.62±0.50, all P < 0.05]. Multivariate Logistic regression analysis showed that SOFA score [odds ratio (OR) = 1.273, 95% confidence interval (95%CI) was 1.012-1.600, P = 0.039], IL-6 (OR = 1.000, 95%CI was 1.000-1.001, P = 0.043), PCT (OR = 1.018, 95%CI was 1.002-1.035, P = 0.030), and Cys C (OR = 4.139, 95%CI was 1.727-9.919, P = 0.001) were independent risk factors affecting the 28-day prognosis of SA-AKI patients. ROC curve analysis showed that the area under the curve (AUC) of SOFA score, IL-6, PCT, and Cys C in predicting the 28-day prognosis of SA-AKI patients were 0.682 (95%CI was 0.582-0.782, P = 0.001), 0.753 (95%CI was 0.662-0.843, P < 0.001), 0.765 (95%CI was 0.677-0.854, P < 0.001), and 0.690 (95%CI was 0.583-0.798, P = 0.001), respectively. The combined predictive value of these four indicators for the prognosis of SA-AKI patients were superior to that of any single indicator, with an AUC of 0.847 (95%CI was 0.778-0.916, P < 0.001), a sensitivity of 95.7%, and a specificity of 56.9%. CONCLUSION The combination of SOFA score, IL-6, PCT, and Cys C provides a reliable predictive value for the prognosis of SA-AKI patients.
Humans ; Acute Kidney Injury/mortality* ; APACHE ; C-Reactive Protein ; Cystatin C/blood* ; Interleukin-6/blood* ; Logistic Models ; Predictive Value of Tests ; Procalcitonin/blood* ; Prognosis ; Prospective Studies ; Risk Factors ; ROC Curve ; Sepsis/mortality*

OBJECTIVE: To assess the impact of albumin (Alb) administration on the prognosis of patients with acute kidney injury (AKI). METHODS: Clinical data of AKI patients in the intensive care unit (ICU) were retrospectively analyzed from the American Medical Information Mart of Intensive Care-IV (MIMIC-IV), including demographic data, acute physiology score (APS), comorbidities, vital signs, laboratory indicators, treatment status, ICU length of stay, and outcome indicators. The main outcome measure is ICU mortality. AKI patients were divided into Alb infusion group and Alb non infusion group based on whether they received Alb treatment. Multiple imputation was used to process missing data and eliminate variables that missing more than 30%. To ensure the stability of the results, propensity score matching (PSM) and inverse probability weighting (IPW) were used to correct the results. Using Kaplan-Meier survival curve and Cox proportional hazards regression model to evaluate the effect of Alb infusion on ICU survival rate in AKI patients. Perform subgroup analysis based on patient age, gender, and comorbidities to evaluate the prognostic effects of Alb on different patient subgroups. RESULTS: A total of 6 390 AKI patients were included, including 1 721 in the Alb infusion group and 4 669 in the Alb non infusion group. After adjusting for key covariates in the Cox regression model, compared with the Alb non infusion group, patients in the Alb infusion group were significantly younger in age, with APS III score, proportion of vasoactive drugs and continuous renal replacement therapy (CRRT) use, sepsis proportion, heart rate, respiratory frequency, aspartate aminotransferase (AST), alanine aminotransferase (ALT), creatinine (Cr), lactic acid (Lac), and arterial partial pressure of carbon dioxide (PaCO₂) levels significantly higher. The proportion of hypertension, myocardial infarction, and congestive heart failure, as well as blood pressure, urine output, platelet count (PLT), and Alb levels were significantly lower. The results of univariate and multivariate Cox regression analysis on the raw data showed that the risk of death in the Alb infusion group was significantly lower than that in the Alb non infusion group [hazard ratio (HR) = 0.69, 95% confidence interval (95%CI) was 0.60-0.80, all P < 0.05]. The results after propensity score matching (PSM) and inverse probability weighting (IPW) processing are consistent with the original data trend (both P < 0.05). The Kaplan-Meier survival curve showed that the cumulative survival rate during ICU stay in the Alb infusion group was significantly higher than that in the Alb non infusion group (24.48% vs. 12.17%, Log-Rank test: χ² = 74.26, P < 0.05). Subgroup analysis shows that Alb infusion has a more significant survival benefit for AKI patients who use vasoactive drugs, have concurrent sepsis, and do not have liver disease. CONCLUSION Albumin infusion can decrease the ICU mortality of AKI patients.
Humans ; Retrospective Studies ; Acute Kidney Injury/mortality* ; Prognosis ; Male ; Female ; Middle Aged ; Aged ; Intensive Care Units ; Albumins/therapeutic use* ; Proportional Hazards Models ; Adult ; Databases, Factual

OBJECTIVE: To investigate the impact of early antimicrobial therapy on the prognosis of patients with suspected sepsis in emergency and outpatient settings. METHODS: A prospective cohort study was conducted. Patients with suspected sepsis admitted to the emergency department of Taizhou Hospital, Zhejiang Province, from May 1, 2022, to July 31, 2023, were enrolled. Participants were divided into an early group (0-1 hour) and a delayed group (> 1 hour) according to duration from admission to antimicrobial administration. General information, initial vital signs, laboratory parameters within 24 hours after admission, disease severity scores, vasoactive drug usage, and clinical outcomes of the patient were collected. Kaplan-Meier survival curve was used to analyze 28-day survival. Multivariate Cox proportional hazards regression was performed to identify independent risk factors for prognosis of the patients with suspected sepsis in emergency and outpatient settings. Sensitivity analyses were conducted through subgroup analyses. RESULTS: A total of 143 patients with suspected sepsis were enrolled in the analysis, with 66 patients in the early group and 77 in the delayed group. No statistically significant differences were observed in baseline characteristics (age, gender, vital signs, laboratory parameters, disease severity scores) or clinical outcomes [vasoactive drug usage rate, mechanical ventilation duration, length of intensive care unit (ICU) stay, total hospitalization duration] between the two groups. The 28-day mortality, multidrug resistance rate and sepsis confirmation rate did not differ significantly between the early group and delay group [28-day mortality: 18.2% (12/66) vs. 20.8% (16/77), multidrug resistance rate: 3.0% (2/66) vs. 2.6% (2/77), sepsis confirmation rate: 87.9% (58/66) vs. 88.3% (68/77), all P > 0.05]. Kaplan-Meier survival curve analysis showed no difference in 28-day cumulative survival between the two groups (Log-Rank test: χ² = 2.528, P = 0.112). Multivariate Cox proportional hazards regression identified vasoactive drug usage [hazard ration (HR) = 2.465, 95% confidence interval (95%CI) was 1.019-5.961, P = 0.045] and endotracheal intubation (HR = 5.516, 95%CI was 2.195-13.858, P < 0.001) as independent risk factors for 28-day death of the patients with suspected sepsis in emergency and outpatient settings. Further exploration of the impact of early antimicrobial therapy on 28-day death in different subgroups of the patients with suspected sepsis in emergency and outpatient settings was conducted through subgroup analysis. The results showed that in the patients with different ages (< 60 years old: HR = 1.214, 95%CI was 0.535-2.751, P = 0.643; ≥ 60 years old: HR = 2.085, 95%CI was 0.233-18.668, P = 0.511), sequential organ failure assessment (SOFA) scores (< 6: HR = 1.411, 95%CI was 0.482-4.128, P = 0.530; ≥ 6: HR = 0.869, 95%CI was 0.292-2.587, P = 0.801), shock indexes (< 1: HR = 1.095, 95%CI was 0.390-3.077, P = 0.863; ≥ 1: HR = 1.364, 95%CI was 0.458-4.059, P = 0.577) and whether diagnosed with sepsis or not (yes: HR = 0.943, 95%CI was 0.059-15.091, P = 0.967; no: HR = 1.207, 95%CI was 0.554-2.628, P = 0.636) subgroups, early usage of antibiotics had not shown any advantage in improving prognosis compared with delayed usage. CONCLUSION Early antimicrobial therapy does not improve the prognosis of patients with suspected sepsis in emergency and outpatient settings.
Humans ; Sepsis/drug therapy* ; Prospective Studies ; Prognosis ; Emergency Service, Hospital ; Outpatients ; Female ; Male ; Anti-Infective Agents/therapeutic use* ; Middle Aged ; Aged ; Proportional Hazards Models ; Treatment Outcome

OBJECTIVE: To investigate the relationship between mean perfusion pressure (MPP) and the risk of sepsis-associated acute kidney injury (SA-AKI) and its prognosis, and to determine the optimal cut-off value of MPP for predicting SA-AKI. METHODS: A retrospective cohort study was conducted. The clinical data of adult patients with sepsis were collected from the Medical Information Mart for Intensive Care-IV 2.2 (MIMIC-IV 2.2) database. The patients were divided into two groups based on the occurrence of SA-AKI. Baseline characteristics, vital signs, comorbidities, laboratory indicators within 24 hours of intensive care unit (ICU) admission, and clinical outcome indicators were collected. Mean MPP was calculated using the average values of mean arterial pressure (MAP) and central venous pressure (CVP), MPP = MAP-CVP. Cox regression models were constructed, relevant confounding factors were adjusted, and multivariate Logistic regression analysis was used to investigate the associations between MPP and the risk of SA-AKI as well as ICU death. The predictive value of MPP for SA-AKI was evaluated using receiver operator characteristic curve (ROC curve) analysis, and the optimal cut-off value was determined. RESULTS: A total of 6 009 patients were ultimately enrolled in the analysis. Among them, SA-AKI occurred in 4 755 patients (79.13%), while 1 254 patients (20.87%) did not develop SA-AKI. Compared with the non-SA-AKI group, the MPP in the SA-AKI group was significantly lowered [mmHg (1 mmHg≈0.133 kPa): 62.00 (57.00, 68.00) vs. 65.00 (60.00, 70.00), P < 0.01], and the ICU mortality was significantly increased [11.82% (562/4 755) vs. 1.59% (20/1 254), P < 0.01]. Three Cox regression models were constructed: model 1 was unadjusted; model 2 was adjusted for gender, age, height, weight and race; model 3 was adjusted for gender, age, height, weight, race, heart rate, respiratory rate, body temperature, hemoglobin, platelet count, white blood cell count, anion gap, HCO₃^-, blood urea nitrogen, serum creatinine, Cl^-, Na⁺, K⁺, fibrinogen, international normalized ratio, blood lactic acid, pH value, arterial partial pressure of oxygen, arterial partial pressure of carbon dioxide, sequential organ failure assessment score, Charlson comorbidity index score, use of vasopressors, mechanical ventilation, and urine output. Multivariate Logistic regression analysis showed that when MPP was treated as a continuous variable, there was a negative correlation between MPP and the risk of SA-AKI in model 1 and model 2 [model 1: odds ratio (OR) = 0.967, 95% confidence interval (95%CI) was 0.961-0.974, P < 0.001; model 2: OR = 0.981, 95%CI was 0.974-0.988, P < 0.001], and also a negative correlation between MPP and the risk of ICU death (model 1: OR = 0.955, 95%CI was 0.945-0.965, P < 0.001; model 2: OR = 0.956, 95%CI was 0.946-0.966, P < 0.001). However, in model 3, there was no significant correlation between MPP and either SA-AKI risk or ICU death risk. when MPP was used as a multi-categorical variable, in model 1 and model 2, referring to MPP ≤ 58 mmHg, when 59 mmHg ≤ MPP ≤ 68 mmHg, as MPP increased, the risk of SA-AKI progressively decreased (OR value was 0.411-0.638, all P < 0.001), and the risk of ICU death also gradually decreased (OR value was 0.334-0.477, all P < 0.001). ROC curve showed that MPP had a certain predictive value for SA-AKI occurrence [area under the ROC curve (AUC) = 0.598, 95%CI was 0.404-0.746], and the optimal cut-off value was 60.5 mmHg. CONCLUSION MPP was significantly associated with the risk of SA-AKI, with an optimal cut-off value of 60.5 mmHg, and also demonstrated a significant correlation with the risk of ICU death.
Humans ; Acute Kidney Injury/physiopathology* ; Retrospective Studies ; Sepsis/physiopathology* ; Middle Aged ; Prognosis ; Male ; Female ; Aged ; Risk Factors ; Intensive Care Units ; Adult ; Logistic Models ; Proportional Hazards Models