The dynamic change of serum S100B levels from day 1 to day 3 is more associated with sepsis-associated encephalopathy

Long, Wenyong; Feng, Qing; Ai, Meilin; Deng, Songyun; Liu, Zhiyong; Huang, Li; Ai, Yuhang; Zhang, Lina

doi:10.1038/s41598-020-64200-3

Cited by 36 publications

(27 citation statements)

References 43 publications

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“…Logistic regression analysis as one of the classic regression analyses is widely used to test the association between sepsis and mortality. For instance, through the logistic regression analysis, Vivien et al [ 24 ] observed an association between mortality at day 28 and the tidal volume indexed on ideal body weight (VTIBW) in pre-hospital mechanically ventilated patients with septic shock; Wu et al [ 25 ] revealed that dynamic changes of serum S100B levels from day 3 to 1 were more associated with mortality than those on day 1 in patients with sepsis; Oud et al [ 26 ] indicated that sepsis was associated with most of the short-term deaths among ICU patients with SLE despite its relatively low mortality; Song et al [ 5 ] revealed that combined biomarkers approach showed good performance in predicting 28-day all-cause mortality among patients diagnosed with either sepsis or septic shock according to the sepsis-3 definition, however, the differences might not be statistically proven. Furthemore, some studies [ 27 , 28 ] found conventional logistic regression had a relatively low indicator of performance as measured by AUCs for ROC curves or showed higher prediction error and worsen performance compared to some novel techniques.…”

Section: Discussionmentioning

confidence: 99%

Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost

et al. 2020

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Background Sepsis is a significant cause of mortality in-hospital, especially in ICU patients. Early prediction of sepsis is essential, as prompt and appropriate treatment can improve survival outcomes. Machine learning methods are flexible prediction algorithms with potential advantages over conventional regression and scoring system. The aims of this study were to develop a machine learning approach using XGboost to predict the 30-days mortality for MIMIC-III Patients with sepsis-3 and to determine whether such model performs better than traditional prediction models. Methods Using the MIMIC-III v1.4, we identified patients with sepsis-3. The data was split into two groups based on death or survival within 30 days and variables, selected based on clinical significance and availability by stepwise analysis, were displayed and compared between groups. Three predictive models including conventional logistic regression model, SAPS-II score prediction model and XGBoost algorithm model were constructed by R software. Then, the performances of the three models were tested and compared by AUCs of the receiver operating characteristic curves and decision curve analysis. At last, nomogram and clinical impact curve were used to validate the model. Results A total of 4559 sepsis-3 patients are included in the study, in which, 889 patients were death and 3670 survival within 30 days, respectively. According to the results of AUCs (0.819 [95% CI 0.800–0.838], 0.797 [95% CI 0.781–0.813] and 0.857 [95% CI 0.839–0.876]) and decision curve analysis for the three models, the XGboost model performs best. The risk nomogram and clinical impact curve verify that the XGboost model possesses significant predictive value. Conclusions Using machine learning technique by XGboost, more significant prediction model can be built. This XGboost model may prove clinically useful and assist clinicians in tailoring precise management and therapy for the patients with sepsis-3.

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Section: Discussionmentioning

confidence: 99%

Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost

et al. 2020

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“…Sepsis is caused by dysregulated immune responses to the systemic infections, which leads to multiple organ dysfunction and is a potentially lethal complication in the ICU ( 1 – 3 , 28 ). Thus, early identification of the risk of death from sepsis is important for the timely and effective management and intervention.…”

Section: Discussionmentioning

confidence: 99%

“…Sepsis is a potentially life-threatening condition that emerges when the host response to infections exceeds normal limits and impairs vital organ functions ( 1 – 3 ). Sepsis is still one of the leading causes of death in the world, as a heavy burden and colossal menace to the critical patients ( 3 , 4 ).…”

Section: Introductionmentioning

confidence: 99%

Effects of Neutrophil-to-Lymphocyte Ratio Combined With Interleukin-6 in Predicting 28-Day Mortality in Patients With Sepsis

et al. 2021

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BackgroundThe current study aimed to evaluate the relationship between the neutrophil-to-lymphocyte ratio (NLR) combined with interleukin (IL)-6 on admission day and the 28-day mortality of septic patients.Material and MethodsWe conducted an observational retrospective study. Patients with presumed sepsis were included. We observed the correlation of studied biomarkers (NLR, IL-6, PCT, and CRP) and the severity scores (APACHE II and SOFA scores) by plotting scatter plots. The relationships of the studied biomarkers and 28-day mortality were evaluated by using Cox regression model, receiver-operating characteristic (ROC) curve, and reclassification analysis.ResultsA total of 264 patients diagnosed with sepsis were enrolled. It was revealed that IL-6 had the strongest correlation with both APACHE II and SOFA scores, followed by the NLR and PCT, and there was no obvious correlation between CRP and the illness severity. NLR and IL-6 were independent predictors of the 28-day mortality in septic patients in the Cox regression model [NLR, odds ratio 1.281 (95% CI 1.159–1.414), P < 0.001; IL-6, odds ratio 1.017 (95% CI 1.005–1.028), P=0.004]. The area under the ROC curve (AUC) of NLR, IL-6 and NLR plus IL-6 (NLR_IL-6) was 0.776, 0.849, and 0.904, respectively.ConclusionOur study showed that the levels of NLR and IL-6 were significantly higher in the deceased patients with sepsis. NLR and IL-6 appeared to be independent predictors of 28-day mortality in septic patients. Moreover, NLR combined with IL-6 could dramatically enhance the prediction value of 28-day mortality.

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“…The detection ranges used in these studies vary between concentrations from pg/mL to ng/mL. This aim to different clinical uses of biosensors, since S100B has been studied as a marker of damage in the central nervous system for other illness as Alzheimer’s disease [ 10 , 11 , 12 ], stroke [ 13 ], spinal trauma [ 14 ], and sepsis-associated encephalopathy [ 15 ] so that its use is not limited to TBI.…”

Section: Introductionmentioning

confidence: 99%

Electrochemical Immunosensor for the Quantification of S100B at Clinically Relevant Levels Using a Cysteamine Modified Surface

Rodríguez

Burgos-Flórez

Posada

et al. 2021

Sensors

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Neuronal damage secondary to traumatic brain injury (TBI) is a rapidly evolving condition, which requires therapeutic decisions based on the timely identification of clinical deterioration. Changes in S100B biomarker levels are associated with TBI severity and patient outcome. The S100B quantification is often difficult since standard immunoassays are time-consuming, costly, and require extensive expertise. A zero-length cross-linking approach on a cysteamine self-assembled monolayer (SAM) was performed to immobilize anti-S100B monoclonal antibodies onto both planar (AuEs) and interdigitated (AuIDEs) gold electrodes via carbonyl-bond. Surface characterization was performed by atomic force microscopy (AFM) and specular-reflectance FTIR for each functionalization step. Biosensor response was studied using the change in charge-transfer resistance (Rct) from electrochemical impedance spectroscopy (EIS) in potassium ferrocyanide, with [S100B] ranging 10–1000 pg/mL. A single-frequency analysis for capacitances was also performed in AuIDEs. Full factorial designs were applied to assess biosensor sensitivity, specificity, and limit-of-detection (LOD). Higher Rct values were found with increased S100B concentration in both platforms. LODs were 18 pg/mL(AuES) and 6 pg/mL(AuIDEs). AuIDEs provide a simpler manufacturing protocol, with reduced fabrication time and possibly costs, simpler electrochemical response analysis, and could be used for single-frequency analysis for monitoring capacitance changes related to S100B levels.

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The dynamic change of serum S100B levels from day 1 to day 3 is more associated with sepsis-associated encephalopathy

Cited by 36 publications

References 43 publications

Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost

Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost

Effects of Neutrophil-to-Lymphocyte Ratio Combined With Interleukin-6 in Predicting 28-Day Mortality in Patients With Sepsis

Electrochemical Immunosensor for the Quantification of S100B at Clinically Relevant Levels Using a Cysteamine Modified Surface

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