OBJECTIVES: To investigate the expression of soluble factor-related apoptosis ligand (sFasL) in peripheral blood and microRNA-147b (miR-147b) in monocytes in children with sepsis and their value in assessing prognosis. METHODS: A prospective study was conducted on 124 children with sepsis (sepsis group), 60 children with common infections (infection group), and 60 healthy children undergoing physical examinations (healthy control group). The independent risk factors for poor prognosis in children with sepsis were analyzed, and the value of serum sFasL and monocyte miR-147b in predicting poor prognosis in children with sepsis was assessed. RESULTS: The serum level of sFasL and the relative expression of miR-147b in monocytes were highest in the sepsis group, followed by the infection group and the healthy control group (P<0.05). The multivariate logistic regression analysis showed that the serum level of sFasL and the relative expression of miR-147b in monocytes were closely associated with the poor prognosis of children with sepsis (P<0.05). The receiver operating characteristic curve analysis showed that the combination of serum sFasL level and relative expression of miR-147b in monocytes had a larger area under the curve compared to each indicator alone in predicting the prognosis of children with sepsis (P<0.05). CONCLUSIONS There are significant increases in the level of sFasL in peripheral blood and the relative expression of miR-147b in monocytes in children with sepsis. The combined use of these two indicators has relatively high clinical value in assessing the prognosis of children with sepsis.
Humans ; Sepsis/diagnosis* ; MicroRNAs/blood* ; Male ; Female ; Monocytes/metabolism* ; Prognosis ; Child, Preschool ; Prospective Studies ; Child ; Infant ; TNF-Related Apoptosis-Inducing Ligand/blood* ; Logistic Models

OBJECTIVE: To investigate the application value of pediatric sepsis-induced coagulation (pSIC) score and mean platelet volume/platelet count (MPV/PLT) ratio in the diagnosis of pediatric sepsis and the determination of critical pediatric sepsis. METHODS: A retrospective cohort study was conducted, selecting 112 children with sepsis (sepsis group) admitted to pediatric intensive care unit (PICU) of Liaocheng Second People's Hospital from January 2020 to December 2023 as the study objects, and 50 children without sepsis admitted to the pediatric surgery department of our hospital during the same period for elective surgery due to inguinal hernia as the control (control group). The children with sepsis were divided into two groups according to the pediatric critical case score (PCIS). The children with PCIS score of ≤ 80 were classified as critically ill group, and those with PCIS score of > 80 was classified as non-critically ill group. pSIC score, coagulation indicators [prothrombin time (PT), international normalized ratio (INR), activated partial thromboplastin time (APTT), and fibrinogen (FIB)], and platelet related indicators (PLT, MPV, and MPV/PLT ratio) were collected. Pearson correlation method was used to analyze the correlation between pSIC score and MPV/PLT ratio as well as their correlation with coagulation indicators. Multivariate Logistic regression analysis was used to screen the independent risk factors for pediatric sepsis and critical pediatric sepsis. Receiver operator characteristic curve (ROC curve) was drawn to evaluate the application value of the above independent risk factors on the diagnosis of pediatric sepsis and the determination of critical pediatric sepsis. RESULTS: 112 children with sepsis and 50 children without sepsis were enrolled in the final analysis. pSIC score, PT, INR, APTT, FIB, MPV, and MPV/PLT ratio in the sepsis group were significantly higher than those in the control group [pSIC score: 0.93±0.10 vs. 0.06±0.03, PT (s): 14.76±0.38 vs. 12.23±0.15, INR: 1.26±0.03 vs. 1.06±0.01, APTT (s): 40.08±0.94 vs. 32.47±0.54, FIB (g/L): 3.51±0.11 vs. 2.31±0.06, MPV (fL): 8.86±0.14 vs. 7.62±0.11, MPV/PLT ratio: 0.037±0.003 vs. 0.022±0.001, all P < 0.01], and PLT was slightly lower than that in the control group (×10⁹/L: 306.00±11.01 vs. 345.90±10.57, P > 0.05). Among 112 children with sepsis, 46 were critically ill and 66 were non-critically ill. pSIC score, PT, INR, APTT, MPV, and MPV/PLT ratio in the critically ill group were significantly higher than those in the non-critically ill group [pSIC score: 1.74±0.17 vs. 0.36±0.07, PT (s): 16.55±0.80 vs. 13.52±0.23, INR: 1.39±0.07 vs. 1.17±0.02, APTT (s): 43.83±1.72 vs. 37.77±0.95, MPV (fL): 9.31±0.23 vs. 8.55±0.16, MPV/PLT ratio: 0.051±0.006 vs. 0.027±0.001, all P < 0.05], PLT was significantly lower than that in the non-critically ill group (×10⁹/L: 260.50±18.89 vs. 337.70±11.90, P < 0.01), and FIB was slightly lower than that in the non-critically ill group (g/L: 3.28±0.19 vs. 3.67±0.14, P > 0.05). Correlation analysis showed that pSIC score was significantly positively correlated with MPV/PLT ratio and coagulation indicators including PT, APTT and INR in pediatric sepsis (r value was 0.583, 0.571, 0.296 and 0.518, respectively, all P < 0.01), and MPV/PLT ratio was also significantly positively correlated with PT, APTT and INR (r value was 0.300, 0.203 and 0.307, respectively, all P < 0.05). Multivariate Logistic regression analysis showed that pSIC score and MPV/PLT ratio were independent risk factors for pediatric sepsis and critical pediatric sepsis [pediatric sepsis: odds ratio (OR) and 95% confidence interval (95%CI) for pSIC score was 14.117 (4.190-47.555), and the OR value and 95%CI for MPV/PLT ratio was 1.128 (1.059-1.202), both P < 0.01; critical pediatric sepsis: the OR value and 95%CI for pSIC score was 8.142 (3.672-18.050), and the OR value and 95%CI for MPV/PLT ratio was 1.068 (1.028-1.109), all P < 0.01]. ROC curve analysis showed that pSIC score and MPV/PLT ratio had certain application value in the diagnosis of pediatric sepsis [area under the ROC curve (AUC) and 95%CI was 0.754 (0.700-0.808) and 0.720 (0.643-0.798), respectively] and the determination of critical pediatric sepsis [AUC and 95%CI was 0.849 (0.778-0.919) and 0.731 (0.632-0.830)], and the combined AUC of the two indictors was 0.815 (95%CI was 0.751-0.879) and 0.872 (95%CI was 0.806-0.938), respectively. CONCLUSIONS pSIC score and MPV/PLT ratio have potential application value in the diagnosis of pediatric sepsis and the determination of critical pediatric sepsis, and the combined application of both is more valuable.
Humans ; Sepsis/complications* ; Platelet Count ; Mean Platelet Volume ; Retrospective Studies ; Child ; Blood Coagulation Disorders/diagnosis* ; Intensive Care Units, Pediatric ; Male ; Female ; Partial Thromboplastin Time ; Child, Preschool ; Blood Coagulation ; International Normalized Ratio ; Infant

Sepsis constitutes one of the principal causes of death globally, and the mortality rate of patients complicated with sepsis-induced cardiomyopathy (SIC) surges by over 50%. Early identification of patients with sepsis, particularly SIC, and implementing clinical intervention are vital measures to reduce the mortality. In recent years, biomarkers for the diagnosis and prognosis of SIC have emerged rapidly. Among classical myocardial injury biomarkers, cardiac troponin (cTn), brain natriuretic peptide (BNP), and soluble growth stimulation gene 2 protein (sST2) have predictive value for the prognosis of SIC. Meanwhile, heart-type fatty acid-binding protein (h-FABP) possess relatively high value in diagnosis. Moreover, plasma metabolites, microRNA (miRNA), as well as recently identified markers related to sepsis or cardiovascular diseases also demonstrate outstanding predictive value in both the diagnosis and prognosis of SIC. For instance, exosomal miR-150-5p, blood miR-155, blood miR-378a-3p, blood miR-21-3p, blood miR-233, blood miR-23b, blood miR-135, lipocalin (LCN), heme oxygenase-1 (HO-1), fibroblast growth factor-21 (FGF-21), and growth differentiation factor-15 (GDF-15) show varying degrees of predictive value when it comes to diagnosing SIC. S100A8/A9 protein, triglyceride-glucose (TyG) index, angiotensinogen II (Ang II) and lactoferrin are correlated with the prognosis of SIC. Meanwhile, it has been discovered that the combination of multiple biomarkers outperforms a single biomarker, and certain combinations exhibit superior diagnostic performance. However, most of these studies use single-center clinical data, which has certain limitations and still calls for more high-quality evidence support. Therefore, identifying biomarker combinations that are supported by high-quality evidence, have bedside application potential, and possess high sensitivity and specificity is of crucial importance for the prevention, diagnosis, and treatment of SIC. This review is carried out on the current articles that report biomarkers with predictive value and the diagnosis and prediction of multiple biomarkers in combination, in the hope of continuously optimizing the diagnostic strategy for the specific identification of early SIC.
Humans ; Biomarkers/blood* ; Cardiomyopathies/etiology* ; Sepsis/diagnosis* ; MicroRNAs/blood* ; Prognosis ; Fatty Acid-Binding Proteins/blood*

OBJECTIVE: To evaluate the predictive value of plasma heparin-binding protein (HBP) combined with albumin (Alb) for predicting 28-day mortality in patients with sepsis. METHODS: The clinical data of patients with sepsis admitted to the emergency intensive care unit (EICU) of the People's Hospital of Shenzhen Baoan District from March 2020 to March 2024 were retrospectively analyzed. The study began at the time of the first diagnosis of sepsis upon EICU admission and ended upon patient death or at 28 days. The gender, age, length of stay in EICU, underlying diseases, and infection sites were recorded. Within 24 hours of sepsis diagnosis, blood culture results, white blood cell count (WBC), C-reactive protein (CRP), procalcitonin (PCT), blood lactate acid (Lac), HBP, Alb, sequential organ failure assessment (SOFA), acute physiology and chronic health evaluation II (APACHE II), mortality in emergency department sepsis score (MEDS), modified early warning score (MEWS), number of organ failures, use of vasopressors, application of mechanical ventilation, renal replacement therapy, and 28-day prognosis were recorded, the differences in these indicators between two groups were compared. Univariate and multivariate Logistic regression analyses were used to analyze the risk factors of 28-day mortality in patients with sepsis. Receiver operator characteristic curve (ROC curve) was drawn, and the area under the ROC curve (AUC) was calculated to evaluate the early predictive value of various risk factors for 28-day mortality in patients with sepsis. RESULTS: A total of 300 patients with sepsis were included, with 16 excluded, resulting in 284 patients being analyzed. Among them, 191 survived and 93 died within 28 days. There were no statistically significant differences between the two groups in terms of gender, age, underlying diseases, infection sites, blood culture positivity rate, number of organ failures, and length of stay in EICU. Univariate analysis showed that the rate of vasopressor use, the rate of mechanical ventilation, HBP, PCT, CRP, Lac, SOFA score, APACHE II score, MEDS score, and MEWS score were significantly higher in the death group than those in the survival group, while Alb was significantly lower in the death group than that in the survival group. Multivariate Logistic regression analysis showed that HBP and Alb were independent risk factors for predicting 28-day mortality in patients with sepsis [odds ratio (OR) and 95% confidence interval (95%CI) were 1.093 (0.989-1.128) and 1.174 (1.095-1.259), both P < 0.05]. ROC curve analysis showed that both HBP and Alb had certain predictive value for 28-day mortality in patients with sepsis [AUC and 95%CI were 0.820 (0.717-0.923) and 0.786 (0.682-0.890), both P < 0.05]. When the critical value of HBP was 117.50 μg/L, the sensitivity was 85.90%, and the specificity was 70.50%. When the critical value of Alb was 28.30 g/L, the sensitivity was 69.30%, and the specificity was 81.20%. When the two indexes were combined for diagnosis, the AUC was 0.881 (95%CI was 0.817-0.945, P < 0.001), the sensitivity was 92.70%, and the specificity was 76.80%. CONCLUSIONS HBP and Alb are independent risk factors for predicting 28-day mortality in patients with sepsis. The combined prediction efficiency of HBP and Alb for 28-day mortality in patients with sepsis is superior to a single indicator.
Humans ; Sepsis/diagnosis* ; Retrospective Studies ; Predictive Value of Tests ; Intensive Care Units ; Blood Proteins/analysis* ; Prognosis ; Antimicrobial Cationic Peptides/blood* ; APACHE ; Male ; Female ; Organ Dysfunction Scores ; ROC Curve ; Middle Aged ; C-Reactive Protein/analysis* ; Emergency Service, Hospital ; Aged ; Hospital Mortality ; Serum Albumin/analysis*

OBJECTIVE: To explore the influencing factors of prognosis in patients with sepsis-induced acute kidney injury undergoing continuous renal replacement therapy (CRRT), and to construct a mortality risk prediction model. METHODS: A retrospective research method was adopted, patients with sepsis-induced acute kidney injury who received CRRT at Fuyang People's Hospital from February 2021 to September 2023 were included in this study. Collect general information, comorbidities, vital signs, laboratory indicators, disease severity scores, treatment status, length of stay in the intensive care unit (ICU), and 28-day prognosis were collected within 24 hours of patient enrollment. The Cox regression model was used to identify the factors influencing prognosis in patients with sepsis-induced acute kidney injury, and a nomogram model was developed to predict mortality in these patients. Receiver operator characteristic curve (ROC curve), calibration curve, and Hosmer-Lemeshow test were used to validate the predictive performance of the nomogram model. RESULTS: A total of 146 patients with sepsis-induced acute kidney injury were included, of which 98 survived and 48 died (with a mortality of 32.88%) after 28 days of treatment. The blood lactic acid, interleukin-6 (IL-6), serum cystatin C, acute physiology and chronic health evaluation II (APACHE II), sequential organ failure assessment (SOFA), and proportion of mechanical ventilation in the death group were significantly higher than those in the survival group. The ICU stay was significantly longer than that in the survival group, and the glomerular filtration rate was significantly lower than that in the survival group. Cox regression analysis showed that blood lactic acid [odds ratio (OR) = 2.992, 95% confidence interval (95%CI) was 1.023-8.754], IL-6 (OR = 3.522, 95%CI was 1.039-11.929), serum cystatin C (OR = 3.999, 95%CI was 1.367-11.699), mechanical ventilation (OR = 4.133, 95%CI was 1.413-12.092), APACHE II score (OR = 5.013, 95%CI was 1.713-14.667), SOFA score (OR = 3.404, 95%CI was 1.634-9.959) were risk factors for mortality in patients with sepsis-induced acute kidney injury (all P < 0.05), glomerular filtration rate (OR = 0.294, 95%CI was 0.101-0.860) was a protective factor for mortality in patients with sepsis-induced acute kidney injury (P < 0.05). The ROC curve showed that the column chart model has a sensitivity of 80.0% (95%CI was 69.1%-89.2%) and a specificity of 89.3% (95%CI was 83.1%-95.2%) in predicting 28-day mortality in patients with acute kidney injury caused by sepsis. CONCLUSIONS Blood lactic acid, IL-6, mechanical ventilation, APACHEII score, SOFA score, glomerular filtration rate, and serum cystatin C are associated with the risk of death in patients with sepsis-induced acute kidney injury. The nomogram model could help early identification of mortality risk in these patients.
Humans ; Acute Kidney Injury/diagnosis* ; Sepsis/therapy* ; Retrospective Studies ; Prognosis ; Continuous Renal Replacement Therapy/methods* ; Nomograms ; Intensive Care Units ; ROC Curve ; Interleukin-6/blood* ; Proportional Hazards Models ; Female ; Male ; Cystatin C/blood* ; Middle Aged ; Risk Factors ; Lactic Acid/blood*

OBJECTIVE: To investigate the association between the glucose-to-lymphocyte ratio (GLR) and prognosis of patients with sepsis-associated acute kidney injury (SA-AKI). METHODS: Based on the Medical Information Mart for Intensive Care-IV (MIMIC-IV), SA-AKI patients aged ≥ 18 years were selected. According to the tertiles of GLR, the patients were divided into GLR1 group (GLR ≤ 4.97×10^-9 mmol), GLR2 group (4.97×10^-9 mmol < GLR < 9.75×10^-9 mmol) and GLR3 group (GLR ≥ 9.75×10^-9 mmol). Patients with SA-AKI were divided into survival group and death group according to whether they survived 28 days after admission. The patient's gender, age, vital signs, laboratory test results, comorbidities, sequential organ failure assessment (SOFA), acute physiology score III (APS III) score and treatment measures were extracted from the database. Kaplan-Meier survival analysis was used to make the survival curves of patients with SA-AKI at 28 days, 90 days, 180 days and 1 year. Multivariate Logistic regression analysis model was used to explore the independent risk factors of 28-day mortality in patients with SA-AKI. Receiver operator characteristic curve (ROC curve) was used to analyze the predictive efficacy of GLR for the prognosis of patients with SA-AKI. RESULTS: A total of 1 524 patients with SA-AKI were included, with a median age of 68.28 (58.96, 77.24) years old, including 612 females (40.16%) and 912 males (59.84%). There were 507 patients in the GLR1 group, 509 patients in the GLR2 group and 508 patients in the GLR3 group. There were 1 181 patients in the 28-day survival group and 343 patients in the death group. Grouping according to GLR tertiles showed that with the increase of GLR, the 28-day, 90-day, 180-day and 1-year mortality of SA-AKI patients gradually increased (28-day mortality were 11.64%, 22.00%, 33.86%, respectively; 90-day mortality were 15.98%, 26.72%, 40.55%, respectively; 180-day mortality were 17.16%, 28.29% and 41.73%, and the 1-year mortality were 17.95%, 29.27% and 42.72%, respectively, all P < 0.01). According to 28-day survival status, the GLR of the death group was significantly higher than that of the survival group [×10^-9 mmol: 9.81 (5.75, 20.01) vs. 6.44 (3.64, 10.78), P < 0.01]. Multivariate Logistic regression analysis showed that GLR was an independent risk factor for 28-day mortality in patients with SA-AKI [when GLR was used as a continuous variable: odds ratio (OR) = 1.065, 95% confidence interval (95%CI) was 1.045-1.085, P < 0.001; when GLR was used as a categorical variable, compared with GLR1 group: GLR2 group OR = 1.782, 95%CI was 1.200-2.647, P = 0.004; GLR3 group OR = 2.727, 95%CI was 1.857-4.005, P < 0.001]. ROC curve analysis showed that the area under the ROC curve (AUC) of GLR for predicting 28-day mortality in patients with SA-AKI was 0.674, when the optimal cut-off value was 8.769×10^-9 mmol, the sensitivity was 57.1% and the specificity was 67.1%. The predictive performance was improved when GLR was combined with APS III score and SOFA score, and the AUC was 0.806, the sensitivity was 74.6% and the specificity was 71.4%. CONCLUSIONS GLR is an independent risk factor of 28-day mortality in patients with SA-AKI, and high GLR is associated with poor prognosis in patients with SA-AKI.
Male ; Female ; Humans ; Blood Glucose ; Glucose ; ROC Curve ; Prognosis ; Sepsis/diagnosis* ; Acute Kidney Injury ; Retrospective Studies ; Intensive Care Units

Blood stream infection (BSI),a blood-borne disease caused by microorganisms such as bacteria,fungi,and viruses,can lead to bacteremia,sepsis,and infectious shock,posing a serious threat to human life and health.Identifying the pathogen is central to the precise treatment of BSI.Traditional blood culture is the gold standard for pathogen identification,while it has limitations in clinical practice due to the long time consumption,production of false negative results,etc.Nanopore sequencing,as a new generation of sequencing technology,can rapidly detect pathogens,drug resistance genes,and virulence genes for the optimization of clinical treatment.This paper reviews the current status of nanopore sequencing technology in the diagnosis of BSI.
Humans ; Nanopore Sequencing ; Sepsis/diagnosis* ; Bacteremia/microbiology* ; Bacteria ; Blood Culture/methods*

OBJECTIVE: To investigate the correlation between early-stage blood pressure indexes and prognosis in sepsis patients. METHODS: A retrospective cohort study was conducted on the medical records of patients diagnosed with sepsis from 2001 to 2012 in the Medical Information Mart for Intensive Care-III (MIMIC-III) database. Patients were divided into survival group and death group according to the 28-day prognosis. General data of patients and heart rate (HR) and blood pressure at admission to ICU and within 24 hours after admission were collected. The blood pressure indexes including the maximum, median and mean value of systolic index, diastolic index and mean arterial pressure (MAP) index were calculated. The data were randomly divided into training set and validation set (4 : 1). Univariate Logistic regression analysis was used to screen covariates, and multivariate Logistic stepwise regression models were further developed. Model 1 (including HR, blood pressure, and blood pressure index related variables with P < 0.1 and other variables with P < 0.05) and Model 2 (including HR, blood pressure, and blood pressure index related variables with P < 0.1) were developed respectively. The receiver operator characteristic curve (ROC curve), precision recall curve (PRC) and decision curve analysis (DCA) curve were used to evaluate the quality of the two models, and the influencing factors of the prognosis of sepsis patients were analyzed. Finally, nomogram model was developed according to the better model and effectiveness of it was evaluated. RESULTS: A total of 11 559 sepsis patients were included in the study, with 10 012 patients in the survival group and 1 547 patients in the death group. There were significant differences in age, survival time, Elixhauser comorbidity score and other 46 variables between the two groups (all P < 0.05). Thirty-seven variables were preliminarily screened by univariate Logistic regression analysis. After multivariate Logistic stepwise regression model screening, among the indicators related to HR, blood pressure and blood pressure index, the HR at admission to ICU [odds ratio (OR) = 0.992, 95% confidence interval (95%CI) was 0.988-0.997] and the maximum HR (OR = 1.006, 95%CI was 1.001-1.011), maximum MAP index (OR = 1.620, 95%CI was 1.244-2.126), mean diastolic index (OR = 0.283, 95%CI was 0.091-0.856), median systolic index (OR = 2.149, 95%CI was 0.805-4.461), median diastolic index (OR = 3.986, 95%CI was 1.376-11.758) were selected (all P < 0.1). There were 14 other variables with P < 0.05, including age, Elixhauser comorbidity score, continuous renal replacement therapy (CRRT), use of ventilator, sedation and analgesia, norepinephrine, norepinephrine, highest serum creatinine (SCr), maximum blood urea nitrogen (BUN), highest prothrombin time (PT), highest activated partial thromboplastin time (APTT), lowest platelet count (PLT), highest white blood cell count (WBC), minimum hemoglobin (Hb). The ROC curve showed that the area under the curve (AUC) of Model 1 and Model 2 were 0.769 and 0.637, respectively, indicating that model 1 had higher prediction accuracy. The PRC curve showed that the AUC of Model 1 and Model 2 were 0.381 and 0.240, respectively, indicating that Model 1 had a better effect. The DCA curve showed that when the threshold was 0-0.8 (the probability of death was 0-80%), the net benefit rate of Model 1 was higher than that of Model 2. The calibration curve showed that the prediction effect of the nomogram model developed according to Model 1 was in good agreement with the actual outcome. The Bootstrap verification results showed that the nomogram model was consistent with the above results and had good prediction effects. CONCLUSIONS The nomogram model constructed has good prediction effects on the 28-day prognosis in sepsis patients, and the blood pressure indexes are important predictors in the model.
Humans ; Cohort Studies ; Retrospective Studies ; Blood Pressure ; Intensive Care Units ; ROC Curve ; Sepsis/diagnosis* ; Prognosis ; Critical Care ; Norepinephrine

OBJECTIVES: To study the value of heparin-binding protein (HBP) in the diagnosis of severe infection in children. METHODS: This study was a prospective observational study. The medical data of children who were admitted to the pediatric intensive care unit due to infection from January 2019 to January 2020 were collected. According to the diagnostic criteria for severe sepsis and sepsis, the children were divided into a severe sepsis group with 49 children, a sepsis group with 82 children, and a non-severe infection group with 33 children. The three groups were compared in terms of related biomarkers such as plasma HBP, serum C-reactive protein, serum procalcitonin, and platelet count. The receiver operating characteristic (ROC) curve was plotted to investigate the value of plasma HBP level in the diagnosis of severe infection (including severe sepsis and sepsis). RESULTS: The severe sepsis and sepsis groups had a significantly higher plasma HBP level on admission than the non-severe infection group (P<0.05). Compared with the sepsis and non-severe groups, the severe sepsis group had significantly higher serum levels of C-reactive protein and procalcitonin and a significantly lower platelet count (P<0.05). Plasma HBP level had an area under the ROC curve of 0.590 in determining severe infection, with a sensitivity of 38.0% and a specificity of 82.4% (P<0.05). CONCLUSIONS There is an increase in plasma HBP level in children with severe infection, and plasma HBP level has a lower sensitivity but a higher specificity in the diagnosis of severe infection and can thus be used as one of the markers for the judgment of severe infection in children.
Antimicrobial Cationic Peptides ; Biomarkers ; Blood Proteins ; C-Reactive Protein/analysis* ; Child ; Humans ; Procalcitonin ; Prospective Studies ; ROC Curve ; Sepsis/diagnosis*

To determine expression levels of glial fibrillary acidic protein in patients of sepsis-associated encephalopathy (SAE) and its clinical significance.
 Methods: Patients, admitted to intensive care units and diagnosed as sepsis, were recruited to our study from October 2016 to August 2018 in the Third Xiangya Hospital, Central South University. SAE is defined as a brain dysfunction secondary to sepsis and without evidence of a primary central nervous system infection or encephalopathy due to other reasons. The SAE group and non-SAE group were classed by Confusion Assessment Method for the ICU (CAM-ICU) score. We measured the levels of serum GFAP, S100β and neuron-specific enolase (NSE) within 24 hours after diagnosis of sepsis, and compared the patients' general clinical data, ICU stay time, 28-day and 180-day mortality.
 Results: Among 152 enrolled patients, 58 and 94 were assigned to the SAE group and the non-SAE group, respectively. There were a significantly higher Sequential Organ Failure Assessment (SOFA) scores, 28-day mortality rate, as well as 180-day mortality rate in the SAE group (all P<0.001). The levels of GFAP, NSE and S100β in the SAE group were significantly higher than those in the non-SAE group (all P<0.001). The diagnostic values of GFAP was 0.67 μg/L, with sensitivity at 75.9% and specificity at 77.7%. Area under the receiver operating characteristic curve (AUROC) of GFAP, NSE and S100β were 0.803, 0.795 and 0.750, respectively. Pearson analysis showed that serum GFAP level was positively correlated with Acute Physiology and Chronic Health Evaluation II (APACHE II) score, but it was negatively correlated with Glasgow Coma Scale (GCS) score, 28-day survival rate and 180-day survival rate.
 Conclusion: The level of serum GFAP is significantly increased in SAE, which shows certain correlation with incidence, severity and prognosis of the disease.
APACHE ; Glial Fibrillary Acidic Protein ; blood ; Humans ; Intensive Care Units ; Organ Dysfunction Scores ; Prognosis ; ROC Curve ; Sepsis ; Sepsis-Associated Encephalopathy ; diagnosis