This study aims to investigate the mechanism by which resveratrol(RES) alleviates cerebral vascular endothelial damage in sepsis-associated encephalopathy(SAE) through network pharmacology and animal experiments. By using network pharmacology, the study identified common targets and genes associated with RES and SAE and constructed a protein-protein interaction( PPI) network. Gene Ontology(GO) analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analysis were performed to pinpoint key signaling pathways, followed by molecular docking validation. In the animal experiments, a cecum ligation and puncture(CLP) method was employed to induce SAE in mice. The mice were randomly assigned to the sham group, CLP group, and medium-dose and high-dose groups of RES. The sham group underwent open surgery without CLP, and the CLP group received an intraperitoneal injection of 0. 9% sodium chloride solution after surgery. The medium-dose and high-dose groups of RES were injected intraperitoneally with 40 mg·kg-1 and 60 mg·kg~(-1) of RES after modeling, respectively, and samples were collected 12 hours later. Neurological function scores were assessed, and the wet-dry weight ratio of brain tissue was detected. Serum superoxide dismutase(SOD), catalase( CAT) activity, and malondialdehyde( MDA) content were measured by oxidative stress kit. Histopathological changes in brain tissue were examined using hematoxylin-eosin(HE) staining. Transmission electron microscopy was employed to evaluate tight cell junctions and mitochondrial ultrastructure changes in cerebral vascular endothelium. Western blot analysis was performed to detect the expression of zonula occludens1( ZO-1), occludin, claudins-5, optic atrophy 1( OPA1), mitofusin 2(Mfn2), dynamin-related protein 1(Drp1), fission 1(Fis1), and hypoxia-inducible factor-1α(HIF-1α). Network pharmacology identified 76 intersecting targets for RES and SAE, with the top five core targets being EGFR, PTGS2, ESR1, HIF-1α, and APP. GO enrichment analysis showed that RES participated in the SAE mechanism through oxidative stress reaction. KEGG enrichment analysis indicated that RES participated in SAE therapy through HIF-1α, Rap1, and other signaling pathways. Molecular docking results showed favorable docking activity between RES and key targets such as HIF-1α. Animal experiment results demonstrated that compared to the sham group, the CLP group exhibited reduced nervous reflexes, decreased water content in brain tissue, as well as serum SOD and CAT activity, and increased MDA content. In addition, the CLP group exhibited disrupted tight junctions in cerebral vascular endothelium and abnormal mitochondrial morphology. The protein expression levels of Drp1, Fis1, and HIF-1α in brain tissue were increased, while those of ZO-1, occludin, claudin-5, Mfn2, and OPA1 were decreased. In contrast, the medium-dose and high-dose groups of RES showed improved neurological function, increased water content in brain tissue and SOD and CAT activity, and decreased MDA content. Cell morphology in brain tissue, tight junctions between endothelial cells, and mitochondrial structure were improved. The protein expressions of Drp1, Fis1, and HIF-1α were decreased, while those of ZO-1, occludin, claudin-5, Mfn2, and OPA1 were increased. This study suggested that RES could ameliorate cerebrovascular endothelial barrier function and maintain mitochondrial homeostasis by inhibiting oxidative stress after SAE damage, potentially through modulation of the HIF-1α signaling pathway.
Animals ; Mice ; Network Pharmacology ; Resveratrol/administration & dosage* ; Male ; Sepsis-Associated Encephalopathy/genetics* ; Signal Transduction/drug effects* ; Hypoxia-Inducible Factor 1, alpha Subunit/genetics* ; Endothelium, Vascular/metabolism* ; Molecular Docking Simulation ; Protein Interaction Maps/drug effects* ; Humans ; Sepsis/complications* ; Oxidative Stress/drug effects*

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

OBJECTIVE: To analyze the factors influencing the development of refeeding syndrome (RFS) in patients with sepsis and its impact on clinical prognosis. METHODS: A retrospective case-control study method was used to collect the clinical data of patients with sepsis admitted to the intensive care unit (ICU) of Qingdao Municipal Hospital from December 2018 to December 2023. The patients were divided into RFS and non-RFS groups according to whether RFS occurred, and the basic data, nutritional status and assessment scale, laboratory indicators, nutritional intake, medical history and prognosis were compared between the two groups. Binary multifactorial Logistic regression analysis was used to screen the influencing factors of the occurrence of RFS in patients with sepsis. RESULTS: A total of 544 patients with sepsis were finally enrolled, of whom 250 did not develop RFS and 294 developed RFS, with an incidence of 54.0%. Compared with the non-RFS group, the patients in the RFS group had lower body mass index (BMI), albumin, prealbumin, baseline electrolytes (serum phosphorus, serum potassium, and serum magnesium), creatinine-height index, and protein intake, and had higher nutritional risk screening 2002 (NRS2002) score, sequential organ failure assessment (SOFA) score, calorie intake, and the proportions of feedings during the 48 hours of ICU admission, history of diabetes and septic shock. Binary multifactorial Logistic regression analysis showed that BMI [odds ratio (OR) = 0.910, 95% confidence interval (95%CI) was 0.857-0.947, P < 0.001], SOFA score (OR = 1.166, 95%CI was 1.085-1.254, P < 0.001), albumin (OR = 0.946, 95%CI was 0.902-0.991, P = 0.019), baseline serum phosphorus (OR = 0.343, 95%CI was 0.171-0.689, P = 0.003), baseline serum potassium (OR = 0.531, 95%CI was 0.377-0.746, P < 0.001), creatinine-height index (OR = 0.891, 95%CI was 0.819-0.970, P = 0.008), caloric intake (OR = 1.108, 95%CI was 1.043-1.178, P = 0.001), protein intake (OR = 0.107, 95%CI was 0.044-0.260, P < 0.001), and feedings during the 48 hours of ICU admission (OR = 0.592, 95%CI was 0.359-0.977, P = 0.040) and septic shock (OR = 0.538, 95%CI was 0.300-0.963, P = 0.037) were independent influence factors on the occurrence of RFS in septic patients. Of the 544 patients, 267 died at 28 days, with a mortality of 49.1%. The 28-day mortality of patients in the RFS group was significantly higher than that in the non-RFS group [54.4% (160/294) vs. 42.8% (107/250); χ² = 7.302, P = 0.007]. 544 patients had a length of ICU stay of 20 (17, 24) days. The patients in the RFS group had a significantly longer length of ICU stay than that in the non-RFS group [days: 20 (17, 25) vs. 19 (17, 23); Z = -2.312, P = 0.021]. CONCLUSIONS The incidence of RFS in septic patients is high. Factors influencing the occurrence of RFS in septic patients include BMI, SOFA score, albumin, baseline serum phosphorus, baseline serum potassium, caloric intake, protein intake, feeding within 48 hours of ICU admission, and septic shock. RFS prolongs the length of ICU stay and increases the 28-day mortality in patients with sepsis.
Humans ; Retrospective Studies ; Sepsis/complications* ; Prognosis ; Refeeding Syndrome/etiology* ; Case-Control Studies ; Intensive Care Units ; Male ; Nutritional Status ; Female ; Risk Factors ; Middle Aged ; Logistic Models ; Body Mass Index ; Aged

OBJECTIVE: To explore the relationship between serum sodium level and the risk of delirium in patients with sepsis. METHODS: Based on the Medical Information Mart for Intensive Care-IV (MIMIC-IV), adult patients with sepsis in the intensive care unit (ICU) were enrolled. The serum sodium level prior to the onset of sepsis during hospitalization was used as the exposure variable. Delirium was assessed using the ICU-confusion assessment method (ICU-CAM) as the primary outcome. Patients were divided into delirium and non-delirium groups based on the occurrence of delirium. The relationship between serum sodium level and delirium risk was described using restricted cubic spline (RCS) to determine the optimal reference range for serum sodium. Logistic regression analysis was used to evaluate the effect of blood sodium levels on delirium in sepsis patients. Subgroup analyses were performed to explore potential interactions and further validate the robustness of the results. Receiver operator characteristic curve (ROC curve) analysis was performed to assess the predictive value of serum sodium level for delirium occurrence in patients with sepsis. RESULTS: A total of 13 889 patients with sepsis were included, of which 4 831 experienced delirium. The maximum and mean serum sodium values were significantly higher in the delirium group compared to the non-delirium group, while there were no statistically significant differences in terms of initial and minimum serum sodium values between the two groups. Compared with the non-delirium group, the delirium group had a higher mortality and longer hospital stay. The RCS curve showed that a "U"-shaped relationship between serum sodium level and delirium risk in patients with sepsis, with the optimal reference range for average serum sodium was 135.3-141.3 mmol/L. Group based on this reference range, compared to the group with 135.3 mmol/L ≤ serum sodium ≤ 141.3 mmol/L, the delirium incidence and mortality were significantly higher, and the hospital stay was longer in the groups with serum sodium < 135.3 mmol/L and serum sodium ≥ 141.3 mmol/L [delirium incidence: 36.92%, 40.88% vs. 31.22%; 28-day mortality: 23.08%, 20.15% vs. 13.39%; 90-day mortality: 30.75%, 24.81% vs. 18.26%; in-hospital mortality: 19.53%, 17.48% vs. 11.61%; ICU mortality: 14.35%, 14.05% vs. 9.00%; hospital length of stay (days): 10.1 (6.1, 17.7), 9.4 (5.4, 17.0) vs. 8.9 (5.5, 15.4), length of ICU stay (days): 3.7 (2.1, 7.1), 4.0 (2.1, 8.9) vs. 3.2 (1.9, 6.8); all P < 0.01]. Logistic regression analysis showed that, in the initial model and each factor-adjusted models, compared to the reference group with 135.3 mmol/L ≤ serum sodium < 141.3 mmol/L, serum sodium < 135.3 mmol/L increased the risk of delirium in septic patients by 21% to 29% [odds ratio (OR) was 1.21-1.29, all P < 0.01], while serum sodium ≥ 141.3 mmol/L increased the delirium risk by 28%-52% (OR was 1.28-1.52, all P < 0.01). Subgroup analyses based on gender, age, race, diuretic use, and sequential organ failure assessment (SOFA) score revealed there was no significant interactions between subgroup variables and serum sodium, and the results supported that both serum sodium < 135.3 mmol/L and serum sodium ≥ 141.3 mmol/L were risk factors for delirium in septic patients. ROC curve analysis showed that the area under the curve (AUC) for predicting delirium in septic patients based on serum sodium was 0.614, with a cut-off value of 139.5 mmol/L yielding a specificity of 67.5% and sensitivity of 50.9%. CONCLUSIONS The risk of delirium in patients with sepsis is associated with serum sodium level in a "U"-shaped manner. Both high and low serum sodium levels are associated with increased risk of delirium, higher all-cause mortality, and prolonged hospital stays in patients with sepsis. Abnormal serum sodium levels may have predictive value for sepsis-associated delirium and could serve as an early biomarker for identifying delirium in septic patients, although further validation is needed.
Humans ; Delirium/etiology* ; Sepsis/complications* ; Sodium/blood* ; Intensive Care Units ; Risk Factors ; Male ; Middle Aged ; Female ; Aged ; Logistic Models ; Adult

OBJECTIVE: To observe the protective effect of Fisetin on sepsis-associated brain injury and explore its possible mechanism from the perspective of ferroptosis. METHODS: Sprague-Dawley (SD) rats (6-8-week-old male) were randomly divided into three groups: sham operation group (Sham group), colonic ligation and puncture (CLP) induced sepsis model group (CLP group) and Fisetin preprocessing group (CLP+Fisetin group), with 18 rats in each group (12 for observing survival rate and 6 for indicator testing). The CLP+Fisetin group was given Fisetin solution 50 mg×kg^-1×d^-1 by gavage continuously for 5 days before CLP, with dimethyl sulfoxide (DMSO) as the solute, while Sham group and CLP group were given the same dose of DMSO. The model was established at 2 hours after the last gavage. The general condition of each group of rats were observed, and the 10-day mortality were record. The behavioral testing (new object recognition experiment, elevated cross maze experiment) were performed after 7 days of modeling. After 24 hours of modeling, nerve reflex scoring was performed, and then the rats were euthanized and brain tissue was collected. The pathological changes of brain tissue were observed under a microscope by hematoxylin-eosin (HE) staining, the deposition of iron ion in brain tissue was observed by Prussian blue staining. The content of iron in brain tissue was determined by tissue iron kit, and the content of malondialdehyde (MDA) in brain tissue was determined by colorimetry. The expressions of tumor necrosis factor-α (TNF-α), neuron damage marker S100β, nuclear factor E2-related factor 2 (Nrf2), heme oxygenases-1 (HO-1) and glutathione peroxidase 4 (GPX4) were detected by Western blotting. RESULTS: On day 10 post-operation, 12, 3, and 7 animals survived in the Sham group, CLP group, and CLP+Fisetin group, respectively. Compared with the Sham group, rats in the CLP group showed significantly decreased nerve reflex score, new object discrimination index and open arm dwell time. HE staining showed arranged disorderly of neuronal cells, cytoplasm deep staining, nuclear condensation, unclear structures, neuron loss, and significant inflammation in the hippocampus in the hippocampus. Prussian blue staining showed iron ion deposition in the brain tissue. The contents of iron and MDA in brain tissue were elevated, and the expressions of TNF-α and S100β were up-regulated, while the expressions of Nrf2, HO-1, and GPX4 were down-regulated. Compared with the CLP group, the CLP+Fisetin group showed significantly increased neurological reflex score (7.33±1.15 vs. 4.67±1.53), improved new object discrimination index (0.44±0.02 vs. 0.32±0.04), and longer open arm dwell time (minutes: 78.33±9.29 vs. 41.15±9.64). Neuronal cells in the hippocampus were more organized, with less cytoplasmic staining, nuclear condensation, reduced neuronal loss, and fewer inflammatory cells. Iron ion deposition was reduced, and the contents of iron ions and MDA in brain tissue were decreased [iron ion (μg/g): 151.27±14.90 vs. 224.69±17.64, MDA (μmol/g): 470.0±44.3 vs. 709.3±65.4]. The expressions of TNF-α and S100β were significantly decreased (TNF-α/GAPDH: 0.651±0.060 vs. 0.896±0.022, S100β/GAPDH: 0.685±0.032 vs. 0.902±0.014), while the expressions of Nrf2, HO-1, and GPX4 were significantly increased (Nrf2/GAPDH: 0.708±0.108 vs. 0.316±0.112, HO-1/GAPDH: 0.694±0.022 vs. 0.538±0.024, GPX4/GAPDH: 0.620±0.170 vs. 0.317±0.039). All differences were statistically significant (all P < 0.05). CONCLUSION Fisetin pretreatment can inhibit ferroptosis and reduce sepsis-associated brain injury by Nrf2/HO-1/GPX4 pathway.
Animals ; Ferroptosis/drug effects* ; Rats, Sprague-Dawley ; NF-E2-Related Factor 2/metabolism* ; Sepsis/complications* ; Male ; Rats ; Phospholipid Hydroperoxide Glutathione Peroxidase ; Neurons/drug effects* ; Signal Transduction ; Brain Injuries/metabolism* ; Flavonols ; Flavonoids/pharmacology* ; Heme Oxygenase-1/metabolism* ; Heme Oxygenase (Decyclizing)

OBJECTIVE: To evaluate the prognosis of septic right ventricular dysfunction (SRVD) based on bedside ultrasound and explore its risk factors. METHODS: A prospective observational study was conducted involving septic and septic shock patients admitted to the intensive care unit (ICU) of the General Hospital of Ningxia Medical University from February 2021 to January 2022. Tricuspid annular plane systolic excursion (TAPSE) was measured by M-mode ultrasound within 24 hours after ICU admission. According to the results of TAPSE, the subjects were divided into SRVD group (TAPSE < 16 mm) and non-SRVD group (TAPSE ≥ 16 mm). The gender, age, occurrence of septic shock, underlying diseases, source of patients, acute physiology and chronic health evaluation II (APACHE II) score, sequential organ failure assessment (SOFA) score, maximal body temperature within 24 hours after ICU admission, location and number of infections, duration of mechanical ventilation, and 28-day mortality were collected. Hemodynamic parameters, organ function indexes, oxygen therapy parameters and arterial blood gas analysis indexes were recorded within 24 hours after ICU admission. The differences of the above indexes between the two groups were compared. Binary multivariate Logistic regression analysis was used to screen out the independent risk factors for SRVD, and a nomogram of SRVD risk factors was drawn. RESULTS: 116 patients with sepsis and septic shock were enrolled, of which 24 (20.7%) had SRVD and 92 (79.3%) had no SRVD. Compared with the non-SRVD group, the patients in the SRVD group had higher emergency transfer and infection site ≥ 2 ratio, APACHE II score, SOFA score, higher cardiac troponin I (cTnI), myoglobin (Mb), MB isoenzyme of creatine kinase (CK-MB), N-terminal pro-brain natriuretic peptide (NT-proBNP), serum creatinine (SCr), arterial blood lactic acid (Lac) and lower left ventricular ejection fraction (LVEF), platelet count (PLT) within 24 hours after ICU admission, and higher proportion of norepinephrine application and continuous renal replacement therapy (CRRT). Binary multivariate Logistic regression analysis showed that LVEF [odds ratio (OR) = 0.918, 95% confidence interval (95%CI) was 0.851-0.991, P = 0.028], PLT (OR = 0.990, 95%CI was 0.981-0.999, P = 0.035), SCr (OR = 1.008, 95%CI was 1.001-1.016, P = 0.025), and the usage of norepinephrine (OR = 15.198, 95%CI was 1.541-149.907, P = 0.020) were independent risk factors for SRVD in patients with sepsis and septic shock. Based on the above four independent risk factors, a nomogram of SRVD risk factors was drawn. The results showed that the score was 64 when LVEF was 0.50, 18 when SCr was 100 μmol/L, 85 when PLT was 100×10⁹/L, and 39 when norepinephrine was used. When the total score reached 253, the risk of SRVD was 88%. Compared with non-SRVD group, the duration of mechanical ventilation in SRVD group was slightly longer [hours: 80.0 (28.5, 170.0) vs. 47.0 (10.0, 135.0), P > 0.05], and the 28-day mortality was significantly higher [41.7% (10/24) vs. 21.7% (20/92), P < 0.05]. CONCLUSIONS Patients with sepsis may have right ventricular dysfunction, impaired renal function and increased mortality in the early stage. The decrease in LVEF and PLT, the increase in SCr and the application of norepinephrine are independent risk factors for SRVD in patients with sepsis.
Humans ; Prognosis ; Ventricular Dysfunction, Right/diagnostic imaging* ; Risk Factors ; Prospective Studies ; Intensive Care Units ; Shock, Septic ; Male ; Ultrasonography ; Female ; Sepsis/complications* ; Middle Aged ; Point-of-Care Systems ; Aged ; Logistic Models ; APACHE

OBJECTIVE: To develop and validate a nomogram model for predicting 30-day mortality among elderly patients with sepsis-associated liver dysfunction (SALD), to identify high-risk patients and improve prognosis. METHODS: A retrospective cohort study was conducted using data extracted from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database for elderly patients with SALD who were first admitted to the intensive care unit (ICU) of Beth Israel Deaconess Medical Center between 2008 and 2019, including basic characteristics, severity scores, underlying diseases, infection foci, 24-hour vital signs, initial laboratory indicators, 24-hour complications, and prognosis related indicators. Patients were randomly assigned to training group and validation group in a ratio of 7 : 3. The training group used the LASSO regression analysis, as well as multivariate Logistic regression analysis to screen for independent risk factors for 30-day mortality. A nomogram prediction model was constructed, and receiver operator characteristic curve (ROC curve), calibration curves, and decision curve analysis (DCA) were used to evaluate the model, and validate the model using the validation cohort. RESULTS: A total of 630 elderly patients with SLAD were included in the study, including 441 in the training group and 189 in the validation group. Oxford acute severity of illness score (OASIS) for training group [odds ratio (OR) = 1.060, 95% confidence interval (95%CI) was 1.034-1.086], 24-hour pulse oxygen saturation (SpO₂; OR = 0.876, 95%CI was 0.797-0.962), initial mean corpuscular volume (MCV; OR = 1.043, 95%CI was 1.009-1.077), initial red blood cell distribution width (RDW; OR = 1.237, 95%CI was 1.123-1.362), initial blood glucose (OR = 1.008, 95%CI was 1.004-1.013), and initial aspartate aminotransferase (AST; OR = 1.000, 95%CI was 1.000-1.001) were independent risk factors for 30-day mortality in patients (all P < 0.05). Based on the above variables, a nomogram model was constructed, and the ROC curve showed that the area under the curve (AUC) of the model in the training group was 0.757 (95%CI was 0.712-0.803), with a sensitivity of 65.05% and a specificity of 74.90%; the AUC of the model in the validation group was 0.712 (95%CI was 0.631-0.792), with a sensitivity of 58.67% and a specificity of 81.58%. The calibration curves of the training and validation groups show that both the fitted curves were close to the standard curves. The Hosmer-Lemeshow test: the training group (χ ² = 6.729, P = 0.566), the validation group (χ ² = 13.889, P = 0.085), indicating that the model can fit the observed data well. The DCA curve shows that when the threshold probability of the training group was 16% to 94% and the threshold probability of the validation group was 27% to 99%, the net benefit of the model was good. CONCLUSIONS OASIS, 24-hour SpO₂, initial MCV, initial RDW, initial blood glucose and initial AST are independent risk factors for 30-day mortality in elderly patients with SALD. The nomogram based on these six variables demonstrates good predictive performance.
Humans ; Sepsis/complications* ; Retrospective Studies ; Nomograms ; Aged ; Prognosis ; Risk Factors ; Liver Diseases/mortality* ; Intensive Care Units ; ROC Curve ; Male ; Female ; Logistic Models

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

Sepsis-associated encephalopathy (SAE) is a common complication of sepsis, referring to a diffuse brain dysfunction caused by sepsis in the absence of direct central nervous system (CNS) infection. SAE occurs in up to 70% of patients with sepsis. Globally, the annual incidence of sepsis ranges from 30.0 to 48.9 million cases, resulting in approximately 11 million deaths per year, which accounts for 20% of all global mortalities. SAE is identified as an independent risk factor contributing to the increased mortality rate among these patients. Early diagnosis of SAE and related cerebral protection interventions hold significant clinical importance. Currently, the main indicators of brain function for sepsis patients include Glasgow coma score (GCS), confusion assessment method for the intensive care unit (CAM-ICU), electroencephalogram (EEG), brain CT or magnetic resonance imaging (MRI) and other related imaging changes, which have the problems of low sensitivity, poor specificity, and non-objective evaluation of the results of the diagnosis of SAE. This article focuses on the latest progress in the pathogenesis of SAE and systematically reviews potential biomarkers related to the onset of SAE from multiple aspects, including inflammatory markers, endothelial and neuronal injury markers, and metabolic markers. This will provide new insights for the clinical diagnosis and treatment of SAE.
Humans ; Sepsis-Associated Encephalopathy/therapy* ; Biomarkers ; Sepsis/complications* ; Magnetic Resonance Imaging ; Electroencephalography ; Brain Diseases/etiology*

Sepsis-associated encephalopathy (SAE) is one of the complications of sepsis, causes cognitive dysfunction ranging from mild attention deficits to progression into coma, which severely impairs patients' ability to live and mental health, and increases the long-term disability and mortality rates. Although the clinical attention to SAE has been increasing in recent years, effective interventions to improve cognitive dysfunction in sepsis survivors are still in the preclinical stage. The pathogenesis of SAE is numerous and complex, and mitochondrial dysfunction, as one of the key pathogenic mechanisms, plays a role in the cognitive development process through oxidative stress imbalance, energy metabolism disorders, and activation of apoptosis signaling pathway. The present review systematically integrates the recent studies on mitochondrial dysfunction in the development of cognitive disorders. This review systematically integrates the cutting-edge research results in recent years, discusses the mitochondrial structural disruption, mitochondrial kinetic abnormalities, respiratory chain dysfunction, and comprehensively comprehends the research progress of mitochondria-targeted antioxidant, mitochondrial autophagy activator, mitochondrial biosynthesis modifier and other novel intervention strategies in improving cognitive function of SAE patients, with the aim of providing theoretical basis for the breakthrough of the current status of clinical treatment of SAE and the targeting of mitochondria for treatment. The aim is to provide theoretical basis for breaking through the status of SAE clinical treatment and targeting mitochondrial therapy.
Humans ; Sepsis-Associated Encephalopathy/metabolism* ; Mitochondria/metabolism* ; Sepsis/complications* ; Oxidative Stress ; Cognitive Dysfunction ; Autophagy