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 elucidate the predictive value of norepinephrine equivalence (NEE) score on the 28-day death risk in patients with sepsis and provide evidence for its application in the diagnosis and treatment of sepsis and septic shock. METHODS: A retrospective cohort study was conducted based on the data of patients with sepsis from Medical Information Mart for Intensive Care-IV 2.2 (MIMIC-IV 2.2). The patients who received vasoactive agents within 6 hours after the diagnosis of sepsis or septic shock were enrolled, and they were divided into survival and non-survival groups based on their 28-day outcomes. The baseline characteristics, vital signs, and treatment data were collected. Multivariate Cox regression analysis was performed to identify factors influencing the 28-day death risk. Receiver operator characteristic curve (ROC curve) was drawn to analyze the predictive value of various parameters on the 28-day death risk of septic patients. Kaplan-Meier survival curve was used to evaluate cumulative survival rate in patients classified by different quantitative parameters based on the cut-off values obtained from ROC curve analysis. RESULTS: A total of 7 744 patients who met the Sepsis-3 diagnostic criteria and received vasopressor treatment within 6 hours post-diagnosis were enrolled, of which 5 997 cases survived and 1 747 died, with the 28-day mortality of 22.6%. Significant differences were observed between the two groups regarding age, gender, height, body weight, race, type of intensive care unit (ICU), acute physiology and chronic health evaluation II (APACHE II) score, sequential organ failure assessment (SOFA) score, Charlson comorbidity index (CCI) score, underlying comorbidities, and vital signs. Compared with the survival group, the non-survival group had poorer blood routine, liver and kidney function, coagulation function, blood gas analysis and other indicators. Multivariate Cox regression analysis revealed that age > 65 years old [hazard ratio (HR) = 0.892, 95% confidence interval (95%CI) was 0.801-0.994, P = 0.039] and male (HR = 0.735, 95%CI was 0.669-0.808, P < 0.001) were protective factors for 28-day death in patients with sepsis, and NEE score (HR = 1.040, 95%CI was 1.021-1.060, P < 0.001), shock index (HR = 1.840, 95%CI was 1.675-2.022, P < 0.001), APACHE II score (HR = 1.076, 95%CI was 1.069-1.083, P < 0.001), SOFA score (HR = 1.035, 95%CI was 1.015-1.056, P < 0.001), and CCI score (HR = 1.135, 95%CI was 1.115-1.155, P < 0.001) were independent risk factors for 28-day death in septic patients. ROC curve analysis showed that the area under the ROC curve (AUC) of NEE score for predicting the 28-day death risk of septic patients was 0.743 (95%CI was 0.730-0.756), which was comparable to the predictive value of APACHE II score (AUC = 0.742, 95%CI was 0.729-0.755) and ratio of mean arterial pressure (MAP)/NEE score (MAP/NEE; AUC = 0.738, 95%CI was 0.725-0.751, both P > 0.05), and better than SOFA score (AUC = 0.609, 95%CI was 0.594-0.624), CCI score (AUC = 0.658, 95%CI was 0.644-0.673), shock index (AUC = 0.613, 95%CI was 0.597-0.629) and ratio of diastolic blood pressure (DBP)/NEE score (DBP/NEE; AUC = 0.735, 95%CI was 0.721-0.748, all P < 0.05). According to the cut-off values of APACHE II and NEE scores obtained from ROC curve analysis, the patients were stratified for Kaplan-Meier survival curve analysis, and the results showed that the 28-day cumulative survival rate in the septic patients with an APACHE II score ≤ 22.5 was significantly higher than that in those with an APACHE II > 22.5 (Log-Rank test: χ² = 848.600, P < 0.001), and the 28-day cumulative survival rate in the septic patients with an NEE score ≤0.120 was significantly higher than that in those with an NEE score > 0.120 (Log-Rank test: χ² = 832.449, P < 0.001). CONCLUSIONS NEE score is an independent risk factor for 28-day death in septic patients who received vasoactive treatment within 6 hours of diagnosis and possesses significant predictive value. It can be used for severity stratification in sepsis management.
Humans ; Retrospective Studies ; Sepsis/diagnosis* ; Male ; Female ; Norepinephrine/therapeutic use* ; Middle Aged ; Aged ; Prognosis ; Predictive Value of Tests ; Shock, Septic/mortality* ; Adult ; ROC Curve ; Risk Factors ; Survival Rate ; Aged, 80 and over

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 investigate the incidence of sepsis in Xinjiang Uygur Autonomous Region and the compliance with sepsis diagnosis and treatment guidelines in intensive care unit (ICU) at different levels of hospitals, and to identify the risk factors associated with poor prognosis in patients with sepsis in this region. METHODS: A prospective cross-sectional survey was conducted in ICU of Xinjiang Uygur Autonomous Region Critical Care Medicine Alliance. The survey period was from 10:00 on January 31, 2024, to 09:59 on February 1, 2024. The patients diagnosed with sepsis admitted to the ICU during the study period were included in the analysis. Data on patient demographics, physiology, microbiology, and treatment protocols were collected, with follow-up until the 28th day after ICU admission or death. Baseline characteristics and treatment information of septic patients across different hospital levels were compared, as well as clinical data of septic patients with different 28-day outcomes. Multivariate Cox proportional hazards model was used to identify risk factors for 28-day death in septic patients. RESULTS: A total of 77 units of Xinjiang Uygur Autonomous Region Critical Care Medicine Alliance from 14 prefectures/cities in Xinjiang participated in the survey. On the survey day, 727 patients were admitted to ICU, of whom 179 (24.6%) were diagnosed with sepsis, and 64 (35.8%) died within 28 days, 115 (64.2%) survived. Among the participating institutions, 33 were tertiary hospitals (42.9%), managing 97 septic cases (54.2%), and 44 were secondary hospitals (57.1%), managing 82 septic cases (45.8%). The lactic acid monitoring rate and continuous renal replacement therapy (CRRT) rate for septic patients in tertiary hospitals were significantly higher than those in secondary hospitals [lactic acid monitoring rate: 92.8% (90/97) vs. 82.9% (68/82), CRRT rate: 17.5% (17/97) vs. 3.7% (3/82), both P < 0.05]. No statistically significant differences were observed between tertiary and secondary hospitals in length of ICU stay or 28-day mortality [length of ICU stay (days): 11.0 (16.0) vs. 10.0 (22.0), 28-day mortality: 35.1% (34/97) vs. 36.6% (30/82), both P > 0.05]. Compared with survivors, non-survivors had higher acute physiology and chronic health evaluation II (APACHE II) score, sequential organ failure assessment (SOFA) score, Charlson comorbidity index (CCI) score and lower Glasgow coma scale (GCS) score. Significant differences were noted in vital signs [heart rate, blood pressure, body temperature, pulse oxygen saturation (SpO₂)], laboratory markers [red blood cell count (RBC), white blood cell count (WBC), lymphocyte ratio (LYM%), blood urea nitrogen (BUN), total protein (TP), albumin (Alb), pH value, base excess (BE)], and monitoring, diagnosis and treatment information (invasive blood pressure monitoring, mechanical ventilation, CRRT, usage of norepinephrine). Multivariate Cox proportional hazards model indicated that body temperature [hazard ratio (HR) = 1.416, 95% confidence interval (95%CI) was 1.022-1.961, P = 0.037] and WBC (HR = 1.040, 95%CI was 1.010-1.071, P = 0.009) were independent risk factors for 28-day death in patients with sepsis. CONCLUSIONS Sepsis in Xinjiang Uygur Autonomous Region is characterized by a high mortality. In this region, tertiary hospitals demonstrate better compliance with bundled treatment strategies such as lactic acid monitoring and the usage of CRRT compared to secondary hospitals, yet they do not show significant advantages in clinical outcomes. Body temperature and WBC are independent risk factors for 28-day death in patients with sepsis in this region. However, clinicians should still consider the actual situation of patients, along with more optimal early warning indicators and comprehensive system assessments, to identify and prevent risk factors for adverse outcomes in patients.
Humans ; Sepsis/diagnosis* ; Cross-Sectional Studies ; Prospective Studies ; Risk Factors ; Intensive Care Units ; Prognosis ; China/epidemiology* ; Male ; Female ; Middle Aged ; Aged ; Proportional Hazards Models ; Incidence

OBJECTIVE: To construct and validate a prognostic prediction model for children with sepsis using the Phoenix sepsis score (PSS). METHODS: A retrospective case series study was conducted to collect clinical data of children with sepsis admitted to the pediatric intensive care unit (PICU) of the Affiliated Hospital of Guizhou Medical University from January 2022 to April 2024. The data included general information, the worst values of laboratory indicators within the first 24 hours of PICU admission, PSS score, pediatric critical illness score (PCIS), and the survival status of the children within 30 days of admission. The statistically significant indicators in univariate Logistic regression analysis were included in multivariate Logistic regression analysis to screen the risk factors affecting the prognosis of children with sepsis and construct a nomogram model. The receiver operator characteristic curve (ROC curve) was drawn to evaluate the predictive performance of the model. The Bootstrap method was used to perform 1 000 repeated sampling internal verification and draw the calibration curve of the model. RESULTS: A total of 199 children with sepsis were included, of which 32 died and 167 survived 30 days after admission. In the univariate Logistic regression analysis, shock, white blood cell count (WBC), international normalized ratio (INR), lactic acid (Lac), PSS score, and PCIS score were identified as statistically significant predictors. These variables were then included in the multivariate Logistic regression analysis, which demonstrated that shock [odds ratio (OR) = 4.258, 95% confidence interval (95%CI) was 1.049-17.288], WBC (OR = 1.124, 95%CI was 1.052-1.210), and PSS score (OR = 1.977, 95%CI was 1.298-3.012) were independent risk factors for mortality in pediatric patients with sepsis (all P < 0.05). A nomogram model was constructed based on these three risk factors, with the model equation as follows: -4.809+1.449×shock+0.682×PSS score+0.117×WBC. The calibration curve results showed that the model's predictions were highly consistent with the actual observations. The ROC curve showed that when the Youden index of the prediction model was 0.792, the sensitivity and specificity were 90.6% and 88.6%, respectively, and the area under the curve (AUC) was 0.957 (95%CI was 0.930-0.984), which was higher than the AUC of shock, WBC, and PSS score alone (0.808, 0.667, 0.908, respectively). CONCLUSIONS Shock, WBC, and PSS score have demonstrated certain predictive value for mortality in children with sepsis. The nomogram model based on the above indicators has important clinical significance for evaluating the prognosis and guiding treatment of children with sepsis.
Humans ; Sepsis/diagnosis* ; Prognosis ; Retrospective Studies ; Logistic Models ; Intensive Care Units, Pediatric ; Nomograms ; Child ; ROC Curve ; Risk Factors ; Male ; Female ; Child, Preschool ; Infant

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 correlation of hemoglobin (Hb) level with prognosis of elderly patients diagnosed as sepsis. METHODS: A retrospective cohort study was conducted. Information on the cases of elderly patients with sepsis in the Medical Information Mart for Intensive Care-IV (MIMIC-IV), including basic information, blood pressure, routine blood test results [the Hb level of a patient was defined as his/her maximum Hb level from 6 hours before admission to intensive care unit (ICU) and 24 hours after admission to ICU], blood biochemical indexes, coagulation function, vital signs, severity score and outcome indicators were extracted. The curves of Hb level vs. 28-day mortality risk were developed by using the restricted cubic spline model based on the Cox regression analysis. The patients were divided into four groups (Hb < 100 g/L, 100 g/L ≤ Hb < 130 g/L, 130 g/L ≤ Hb < 150 g/L, Hb ≥ 150 g/L groups) based on these curves. The outcome indicators of patients in each group were analyzed, and the 28-day Kaplan-Meier survival curve was drawn. Logistic regression model and Cox regression model were used to analyze the relationship between Hb level and 28-day mortality risk in different groups. RESULTS: A total of 7 473 elderly patients with sepsis were included. There was a "U" curve relationship between Hb levels within 24 hours after ICU admission and the risk of 28-day mortality in patients with sepsis. The patients with 100 g/L ≤ Hb < 130 g/L had a lower risk of 28-day mortality. When Hb level was less than 100 g/L, the risk of death decreased gradually with the increase of Hb level. When Hb level was ≥ 130 g/L, the risk of death gradually increased with the increase of Hb level. Multivariate Logistic regression analysis revealed that the mortality risks of patients with Hb < 100 g/L [odds ratio (OR) = 1.44, 95% confidence interval (95%CI) was 1.23-1.70, P < 0.001] and Hb ≥ 150 g/L (OR = 1.77, 95%CI was 1.26-2.49, P = 0.001) increased significantly in the model involving all confounding factors; the mortality risks of patients with 130 g/L ≤ Hb < 150 g/L increased, while the difference was not statistically significant (OR = 1.21, 95%CI was 0.99-1.48, P = 0.057). The multivariate Cox regression analysis suggested that the mortality risks of patients with Hb < 100 g/L [hazard ratio (HR) = 1.27, 95%CI was 1.12-1.44, P < 0.001] and Hb ≥ 150 g/L (HR = 1.49, 95%CI was 1.16-1.93, P = 0.002) increased significantly in the model involving all confounding factors; the mortality risks of patients with 130 g/L ≤ Hb < 150 g/L increased, while the difference was not statistically significant (HR = 1.17, 95%CI was 0.99-1.37, P = 0.053). Kaplan-Meier survival curve showed that the 28-day survival rate of elderly septic patients in 100 g/L ≤ Hb < 130 g/L group was significantly higher than that in Hb < 100 g/L, 130 g/L ≤ Hb < 150 g/L and Hb ≥ 150 g/L groups (85.26% vs. 77.33%, 79.81%, 74.33%; Log-Rank test: χ² = 71.850, P < 0.001). CONCLUSIONS Elderly patients with sepsis exhibited low mortality risk if their 100 g/L ≤ Hb < 130 g/L within 24 hours after admission to ICU, and both higher and lower Hb levels led to increased mortality risks.
Humans ; Male ; Female ; Aged ; Retrospective Studies ; Sepsis/diagnosis* ; Critical Care ; Intensive Care Units ; Prognosis ; Hemoglobins ; ROC Curve

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