Early goal-directed and lactate-guided therapy in adult patients with severe sepsis and septic shock: a meta-analysis of randomized controlled trials

Ding, Xian Fei; Yang, Z. X.; Xu, Zhen; Li, Li Feng; Yuan, Bo; Guo, Li; Wang, Le Xin; Zhu, Xi

doi:10.1186/s12967-018-1700-7

Cited by 21 publications

(13 citation statements)

References 33 publications

(100 reference statements)

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“…Lactate and AG are typical metabolic indicators. Patients with a normal lactate level alone should not be excluded life-threatening sepsis, and with high AG levels regardless of lactate levels, have high rates of mortality and should also be considered for early, aggressive therapy [ 40 ]. However, Liu and Velissaris et al [ 41 , 42 ] clearly pointed out that plasma lactate were associated with poor outcomes in patients with sepsis and predicted mortality.…”

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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“…11,14 Moreover, levels of serum lactate serve as indicators for occult shock and help in monitoring the response to therapy. 15 Here, patients with acute respiratory failure due to severe pneumonia with pneumogenic sepsis received high-flow VV ECMO support in combination with immunomodulatory therapy. Data from a historical control group who received high-flow VV ECMO without CytoSorb therapy were retrospectively analyzed.…”

Section: Discussionmentioning

confidence: 99%

Combined Use of CytoSorb and ECMO in Patients with Severe Pneumogenic Sepsis

Akil

Ziegeler

Reichelt

et al. 2020

Thorac Cardiovasc Surg

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Background High morbidity and mortality are frequently reported in intensive care patients suffering from severe sepsis with systemic inflammation. With the development of severe respiratory failure, extracorporeal membrane oxygenation (ECMO) is often required. In this study, cytokine adsorption therapy in combination with ECMO is applied in patients with acute respiratory distress syndrome (ARDS) due to severe pneumogenic sepsis. The efficacy of this therapy is evaluated compared with a historical cohort without hemoadsorption therapy. Methods Between January and May 2018, combined high-flow venovenous ECMO and CytoSorb therapy (CytoSorb filter connected to ECMO circuit) was applied in patients (n = 13) with pneumogenic sepsis and ARDS. These patients were prospectively included (CytoSorb group). Data from patients (n = 7) with pneumogenic sepsis and ECMO therapy were retrospectively analyzed (control group). Results All patients survived in the CytoSorb group, where the 30-day mortality rate reached 57% in the control group. After CytoSorb therapy, we instantly observed a significant reduction in procalcitonin (PCT) and C-reactive protein (CRP) levels compared with the control group. Within 48 hours, the initial high doses of catecholamine could be weaned off only in the CytoSorb group. Conclusions Our results indicate that CytoSorb in combination with ECMO is an effective therapy to prevent escalation of sepsis with rapid weaning off high-dose catecholamine infusions and quick reduction in PCT and CRP levels. Optimal timing of immunomodulatory therapy and impact on ECMO-related inflammation still need to be furtherly investigated.

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“…49 Meta-analysis of studies using decreases in lactate as a clinical endpoint showed improved survival both in hyperlactatemic patients as in patients with severe sepsis and septic shock. 50,51…”

Section: Clearance Of Lactatementioning

confidence: 99%