Objective To evaluate the effects and the clinical significances of liquid resuscitation on blood gas analysis， acid-base balance， electrolytes， acute physiology and chronic health evaluationsⅡ（APACHEⅡ） score of patients with septic shock， and then to analyze the relations between serum chlorine （Cl－） level and APACHEⅡscore and the volume of liquid resuscitation. Methods According to the target of resuscitation （centre venous pressure 8-12mm Hg and mean arterial pressure≥65mm Hg）， 21 patients with septic shock received enough fluid for resuscitation during 24h . The results of blood gas analysis， acid-base balance， electrolytes， and APACHE Ⅱ score were compared between pre-resuscitation and 24h post-resuscitation by self-controlled prospective study. The relationships of the level of serum Cl－ and APACHEⅡ score with the volume of liquid used in resuscitation were analyzed . Results The mean resus-citation duration was （18.09±4.57） h， and the volume of liquid during 24 h resuscitation was 5 320-11 028mL with mean volume of （7 775±1 735） mL in 21 patients with septic shock. Serum sodium （Na＋， mmol/L） and Cl－（mmol/L）levels of post-resuscitation were significant higher than those of pre-resuscitation （Na＋：138.71±5.67 versus 135.62±7.23， P=0.024；Cl－：109.10±4.90 versus 101.67±8.59， P=0.000）. Compared with the levels of pre-resuscitation， the blood pH value， hematocrit （Hct，%）， anion gap （AG， mmol/L）， lactic acid （mmol/L）， and APACHE Ⅱscore significantly decreased （pH：7.31±0.05 versus 7.37±0.06， P=0.000；Hct：28.48±2.56 versus 32.76±9.19， P=0.049；AG：8.33±3.45 versus 14.17±8.83， P=0.004；lactic acid：1.66±0.89 versus 2.96±1.23， P=0.001；APACHEⅡ：10.90±3.73 versus 17.24±4.06， P=0.000） after 24h resuscitation. The correlation analysis showed that the level of serum Cl- was positively correlated with the volume of liquid used in resuscitation （r=0.717，P＜0.01）. However， there was no correlation between APACHEⅡscore and the volume of liquid used in resuscitation （P＞0.05）. Conclusions The target of liquid resuscitation in patients with septic shock should be cautiously determined， including control of the volume of crystal liquid for resuscitation， in order to avoid acid-base imbalance or hyperchloraemia. At the same time， the change in internal environment should be monitored. An optimistic fluid resuscitation to decrease APACHE Ⅱ score in patients with septic shock is unrelated to the volume of liquid resuscitation.
Objective To investigate the value of pulse indicator continuous cardiac output ( PiCCO) monitoring in the treatment of septic shock.Methods Patients with septic shock were selected in intensive care unit ( ICU) . After initial empirical resuscitating and using vasoactive drugs, the patients with circulation instability were connected with the PiCCO temperature probe to monitor hemodynamics and to resuscitate in the target of intrathoracic blood volume index ( ITBVI) , cardiac index ( CI) , extravascular lung water index ( EVLWI) . Hemodynamic parameters, oxygen metabolic variability and 24h-fluid management after 0h ( before) , 8h, 24h, the rate of implementing resuscitation goals, oxygen metabolic variability and fluid resuscitation at different times in the guidance of PiCCO parameters were compared. The data of age, APACHEⅡ score, central venous pressure ( CVP) , CI, ITBVI, mean arterial pressure ( MAP) , systemic vascular resistance index ( SVRI) and EVLWI after 0h and 24h were substituted into the regression equation by the multiple linear regression, to determine the indexes which would affect the 28-day prognosis. Results A total of 80 patients with septic shock were recruited in the study. Comparing fluid resuscitation at different times in the guidance of PiCCO,MAP( 73.6 ±13.4 and 75.1 ±10.2 mm Hg) , ITBVI ( 843.5 ±168.9 and 891.5 ±232.9 mL/m2 ) and CI ( 3.2 ±1.1 and 3.9 ±0. 4 L· min-1 · m-2 ) on 8h and 24h were significantly higher than that at 0h ( 69.1 ±21.4 mm Hg, 781.2±146.7 mL/m2 and 2.7 ±1.5 L·min-1·m-2 ) , and Lac( 2.0 ±1.4 and 1.1 ±1.0 mmol /L) and SVRI ( 1 624. 2 ±301. 7 and 1 543.6 ±435.4 d·s·m2·cm-5 ) were declined than that at 0h( 3.1 ±2.4 mmol /L and 1 796.2 ±399.1 d·s·m2 ·cm-5 ) ( Plt;0.05) . The rate of implementing resuscitation goals at 8h ( 64.7% ) and 24h ( 66.9% ) were significantly higher than that at 0h ( 55.7% ) ( Plt;0.05) , but there was no significant difference between 8h and 24h ( Pgt;0.05) . All of the patients were divided into a survival group ( n=54) and a death group ( n=26) . The rate of implementing resuscitation goals at 0h and 24h in the survival group ( 57.1% and 71.3% ) were significantly higher than that of the death group( 28.6% and 39.3% ) . By the prognosis on 28-day as the dependent variability in the multiple linear regression, multiple linear regression equation were established, and there was significantly difference ( F=55.03, Plt;0.05) . By the layer-wise screening, equation was fitted, both the CI ( R=0.431) and ITBVI ( R=0.627) at beginning and EVLWI ( R= 0.305) at 24h were determined to influence the 28-day prognosis. Conclusions The fluid resuscitation under the guidance of PiCCO can achieve the goal better and improve the prognosis. CI, ITBVI and EVLWI were useful goaldirectors for the prognosis evaluation in critical ill patients.
Objective To identify potential hub genes and key pathways in the early period of septic shock via bioinformatics analysis. MethodsThe gene expression profile GSE110487 dataset was downloaded from the Gene Expression Omnibus database. Differentially expressed genes were identified by using DESeq2 package of R project. Then Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses were constructed to investigated pathways and biological processes using clusterProfiler package. Subsequently, protein-protein interaction (PPI) network was mapped using ggnetwork package and the molecular complex detection (MCODE) analysis was implemented to further investigate the interactions of differentially expressed genes using Cytoscape software. Results A total of 468 differentially expressed genes were identified in septic shock patients with different responses who accepted early supportive hemodynamic therapy, including 255 upregulated genes and 213 downregulated genes. The results of GO and the KEGG pathway enrichment analysis indicated that these up-regulated genes were highly associated with the immune-related biological processes, and the down-regulated genes are involved in biological processes related to organonitrogen compound, multicellular organismal process, ion transport. Finally, a total of 23 hub genes were identified based on PPI and the subcluster analysis through MCODE software plugin in Cytoscape, which included 19 upregulated hub genes, such as CD28, CD3D, CD8B, CD8A, CD160, CXCR6, CCR3, CCR8, CCR9, TLR3, EOMES, GZMB, PTGDR2, CXCL8, GZMA, FASLG, GPR18, PRF1, IDO1, and additional 4 downregulated hub genes, such as CNR1, GPER1, TMIGD3, GRM2. KEGG pathway enrichment analysis and GO functional annotation showed that differentially expressed genes were primarily associated with the items related to cytokine-cytokine receptor interaction, natural killer cell mediated cytotoxicity, hematopoietic cell lineage, T cell receptor signaling pathway, phospholipase D signaling pathway, cell adhesion molecules, viral protein interaction with cytokine and cytokine receptor, primary immunodeficiency, graft-versus-host disease, type 1 diabetes mellitus. Conclusions Some lymphocytes such as T cells and natural killer cells, cytokines and chemokines participate in the immune process, which plays an important role in the early treatment of septic shock, and CD160, CNR1, GPER1, and GRM2 may be considered as new biomarkers.
ObjectiveTo explore the influence of norepinephrine on the prediction of fluid responsiveness by passive leg raising (PLR) during septic shock. MethodsForty-six septic shock patients in intensive care unit of Nanjing Drum Tower Hospital were prospectively observed from September to November 2012. Among which 36 septic shock patients were enrolled with a positive PLR test (defined by an increase in stroke volume index ≥10%). A PLR test was performed at baseline (PLR1). A second PLR test (PLR2) was performed at returning to supine position for 10 min and the dose of norepinephrine was increased to maintain MAP ≥65 mmHg for 20 min. The changes of heart rate(HR),mean arterial pressure(MAP),central venous pressure(CVP),cardiac index(CI),stroke volume index(SVI),index of systemic vascular resistance(SVRI),global end-diastolic volume index(GEDVI),and cardiac function index(CFI) were monitored by transpulmonary thermodilution technique (PiCCO). ResultsPLR1 significantly increased SVI by (20.54±9.63)%,CI by (20.57±9.89)%,MAP by (7.64±5.77)%,and CVP by (25.83±23.39)%. As the dose of norepinephrine increased,SVI was increased by (16.97±9.06)%,CI by (16.78±8.39)%,GEDVI by (9.08±4.47)%,MAP by (28.07±12.48)%,and CVP by (7.86±8.52)%. PLR2 increased SVI by (13.74±8.79)%,CI by (13.79±9.08)%,MAP by (2.93±5.06)%,and CVP by (13.36±14.74)%. The PLR2 and the dose increase of norepinephrine augmented SVI to a significantly lesser extent than the PLR1 performed at baseline (both P<0.05). However,SVI increased by <10% in 6 patients while the baseline PLR was positive in these patients. ConclusionIn septic patients with a positive PLR at baseline,norepinephrine increases cardiac preload and cardiac output and influences the fluid responsiveness.
Objective To evaluate the predicting effect of quick Sequential Organ Failure Assessment (qSOFA) on septic shock, and investigate the probability of improving the predicting effect. Methods Patients with sepsis diagnosed in Emergency Department from July 2015 to June 2016 were enrolled. They were divided into shock group and non-shock group based on whether or not they had septic shock during 72 hours after admission. The multivariate logistic regression analysis was used to find out the independent risk factors affecting the incidence of septic shock. Receiver operating characteristic (ROC) curve was used to analyze those risk factors. Modified Early Warning Score (MEWS), Mortality in Emergency Department Sepsis Score (MEDS), Sequential Organ Failure Assessment (SOFA), Acute Physiology and Chronic HealthEvaluation (APACHE)Ⅱ and qSOFA were also compared with ROC curve analysis. The possibility of improvement of qSOFA predicting effect was discussed. Results A total of 821 patients were enrolled, with 108 in septic shock group and 713 in non-septic shock. The result of multivariate logistic regression analysis indicated that respiratory rate, systolic blood pressure, pH value, oxygenation index, lactate, albumin, Glasgow Coma Score and procalcitonin were the independent risk factors (P<0.05). The result of ROC analysis showed that the area under curve (AUC) of pH value, lactate and procalcitonin was 0.695, 0.678 and 0.694, respectively. Lactate had the highest value of specificity (0.868), positive predictive value (0.356) and positive likelihood ratio (3.644), while the sensitivity (0.889) and negative predictive value (0.961) of procalcitonin were the highest. MEWS, MEDS, SOFA, APACHEⅡ and qSOFA were compared with ROC. SOFA had the best predicting effect with the statistical results of AUC (0.833), sensitivity (0.835), specificity (0.435), positive predictive value (0.971), negative predictive value (0.971), and positive likelihood ratio (5.048); and MEWS had the highest negative likelihood ratio (0.581). qSOFA did not show a best predicting value. Conclusion qSOFA is not the best choice to predict the possibility of septic shock, but its predicting value might be improved when combined with pH value, lactate and procalcitonin.
ObjectiveTo systematically review the efficacy of pulse indicating continuous cardiac output (PICCO) monitoring for guiding the treatment of patients with septic shock.MethodsDatabases including PubMed, The Cochrane Library, EMbase, Web of Science, CBM, WanFang Data, VIP and CNKI were electronically searched from inception to February 2017 to collect randomized controlled trials (RCTs) about PICCO monitoring on treatment guidance of patients with septic shock. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies. Then meta-analysis was performed using Stata 12.0 software.ResultsA total of 20 RCTs involving 1 253 patients were included. The results of meta-analysis showed: compared with central venous pressure (CVP) measurements, the treatment of sepsis bundles informed by PICCO could significantly shorten the length of intensive care unit (ICU) stay (MD=–2.74, 95%CI –3.40 to –2.09, P<0.001), reduce the ICU mortality (RR=0.49, 95%CI 0.36 to 0.67, P<0.001) and 28-day mortality (RR=0.61, 95%CI 0.43 to 0.87, P=0.006).ConclusionCurrent evidence shows the PICCO monitoring can significantly improve the prognosis of septic shock. Due to limited and quantity quality of the included studies, more high-quality studies are needed to verify above conclusion.
ObjectiveTo explore the effects of metoprolol on hemodynamics of early septic shock patients with myocardial injury. MethodsWe prospectively recruited 22 septic shock patients with myocardial injury, who were admitted to the ICU of Xiaolan Hospital during March 2014 and February 2015.The metoprolol was injected through central venous catheter to reduce heart rate by 20% from baseline and maintain for 6h.Hemodynamic and oxygen metabolic parameters were collected to establish database. ResultsHeart rate decreased significantly to (98±18), (95±16) and (92±18) beat/min respectively at 1h, 3h and 6h post-dosing, compared with (125±28) beat/min at pre-dosing (P < 0.05).Cardiac index decreased significantly to (3.2±1.5), (3.3±1.9) and (3.3±1.6) L·min-1·m-2 respectively at 1h, 3h and 6h post-dosing, compared with (3.9±2.5) L·min-1·m-2 at pre-dosing (P < 0.05).The mean blood pressure, central venous pressure, pulmonary artery wedge pressure, pulmonary vascular resistance index, systemic vascular resistance index and stroke volume index showed no significant changes between pre-dosing and post-dosing (all P > 0.05). Lactate concentration decreased significantly to (9.8±4.1) and (8.1±3.6)mmol/L respectively at 3h and 6h post-dosing, compared with (13.4±5.2)mmol/L at pre-dosing (all P < 0.05), but mixed venous oxygen saturation showed no significant changes (P > 0.05). ConclusionMetoprolol may reduce heart rate and cardiac output in septic shock patients with myocardial injury, without obvious adverse effects on circulatory function and systemic perfusion.
ObjectiveTo explore the value of inferior vena cava inspiratory collapsibility (ΔIVC) in guiding septic shock resuscitation with early goal-directed therapy (EGDT).MethodsA single center, randomized controlled trial was conducted at an 812-bed hospital in Mianyang, Sichuan. Adult patients with early septic shock in the intensive care unit were assessed and treated at defined intervals over 6 h using an ΔIVC-guided resuscitation protocol or an EGDT protocol. Feasibility outcomes were fluid balance and norepinephrine administration. The primary clinical outcomes were in-hospital mortality rate, 90-day survival rate. Secondary outcomes included incidence of acute kidney injury and consumption of health resources.ResultsSixty-eight patients with septic shock were enrolled in this study. Baseline characteristics were similar between the two groups. The ΔIVC-guided septic shock resuscitation group was lower than the EGDT group in the ICU 24 h fluid replacement (L): 3.8 (4.0, 5.3) vs. 4.7 (4.0, 6.6), 72 h liquid positive balance (L): 0.2 (–0.65, 1.2) vs. 2.5 (0.0, 4.1), intensive care unit length of stay (d): 7.5 (5.0, 14.0) vs. 15.0 (7.0, 21.5), mechanical ventilation cumulative time (d): 3.0 (0.0, 7.0) vs. 7.5 (2.2, 12.0), ICU costs (ten thousand yuan): 3.4 (2.1, 5.9) vs. 8.6 (4.2, 16.5), bedside blood purification treatment costs (ten thousand yuan): 2.3 (1.1, 3.3) vs. 6.8 (2.1, 10.0) (P<0.05). No difference was observed in the incidence of acute kidney injury (P > 0.05), in-hospital mortality and 90-day survival between the two groups (log-rank χ2=0.35, P>0.05).ConclusionsAmong patients with septic shock, a ΔIVC-guided septic shock resuscitation, compared with EGDT, did not reduce in-hospital mortality. It might prevent the risk of over resuscitation, shorten the duration of mechanical ventilation, and lead to a better utilization of intensive care unit resources.
Objective To provide a comprehensive overview of model performance and predictive efficacy of machine learning techniques to predict septic shock in children, in order to target and improve the quality and predictive power of models for future studies. MethodsTo systematically review all studies in four databases (PubMed, Embase, Web of Science, ScienceDirect, CNKI, WanFang Data) on machine learning prediction of septic shock in children before April 1, 2024. Two investigators independently conducted literature screening, literature data extraction and bias assessment, and conducted a systematic review of basic information, research data, study design and prediction models. Model discrimination, which area under the curve (AUC), was pooled using a random-effects model and meta-analysis was performed. Subgroup analyses were performed according to sample sizes, machine learning models, types of predictors, number of predictors, etc. And publication bias and sensitivity analyses were performed for the included literature. Results A total of 11 studies were included, of which 2 were at low risk of bias, 7 were at unknown risk of bias, and 2 were at high risk of bias. The data used in the included studies included both public and non-public electronic medical record databases, and the machine learning models used included logistic regression, random forest, support vector machine, and XGBoost, etc. The predictive models constructed based on different databases appeared to have different results in terms of the characteristic variables, so identifying the key variables of the predictive models requires further validation on other datasets. Meta-analysis showed the pooled AUC of 0.812 (95%CI 0.763 to 0.860, P<0.001), and further subgroup analyses showed that larger sample sizes (≥1 000) and predictor variable types significantly improved the predictive effect of the model, and the difference in AUC was statistically significant (95%CI not overlapping). The funnel plot showed that there was publication bias in the study, and when the extreme AUC values were excluded, the meta-analysis yielded a total AUC of 0.815 (95%CI 0.769 to 0.861, P<0.001), indicating that the extreme AUC values were insensitive. ConclusionMachine learning technology has shown some potential in predicting septic shock in children, but the quality of existing research needs to be strengthened, and future research work should improve the quality of research and improve the prediction effect of the model by expanding the sample size.
ObjectiveTo investigate the clinical value of quick sequential organ failure assessment (qSOFA) score in predicting the outcome of patients with septic shock. MethodsWe collected the clinical data of 170 patients with septic shock treated in the Emergency Intensive Care Unit between January 2013 and January 2014. According to the 28-day outcomes of the patients, they were recorded as survival group and non-survival group. We calculated the qSOFA score, acute physiology and chronic health evaluation (APACHE)Ⅱ score on patients' admission. Using receiver operating characteristic (ROC) curve, we analyzed the qSOFA score, the effect of APACHE Ⅱ score in predicting the 28-day prognosis for patients with septic shock. The correlation between qSOFA score and APACHEⅡ score was also assessed. ResultsThe qSOFA and APACHEⅡ scores in non-survivors were higher than those in the survivors. According to ROC curve analysis, the area under the curve for qSOFA score and APACHE Ⅱ score was 0.666 and 0.791, respectively. For qSOFA score with 2 cut-off points to evaluate the prognosis of septic shock, the sensitivity was 62.7%, specificity was 61.1%, positive predictive value was 56.0%, negative predictive value was 67.4%, positive likelihood ratio was 1.61, and negative likelihood ratio was 0.61. For the APACHEⅡ score with 24 cut-off points to evaluate the prognosis of septic shock, the sensitivity was 70.7%, specificity was 80%, positive predictive value was 73.6%, negative predictive value was 67.3%, positive likelihood ratio was 3.54, and negative likelihood ratio was 0.37. The correlation coefficient of qSOFA score and APACHE Ⅱ score was 0.499. ConclusionThe qSOFA score is useful to evaluate the prognosis of the patients with septic shock early in Emergency Department.