Biomarker combination and SOFA score for the prediction of mortality in sepsis and septic shock

Song, Jae-Ju; Moon, Sungwoo; Park, Dae Won; Cho, Han Jin; Kim, Joo Yeong; Park, Jong Hak; Hyung, Jae

doi:10.1097/md.0000000000020495

Cited by 40 publications

(31 citation statements)

References 24 publications

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“…It should be pointed out that our results are also consistent with recent prospective studies, according to the Sepsis-3 definitions [34][35][36][37]. In contrast to our study, Hu et al reported that PCT is a moderate predictor of 28-day mortality in patients with sepsis and septic shock.…”

Section: Plos Onesupporting

confidence: 92%

Prognostic value of plasma pentraxin 3 levels in patients with septic shock admitted to intensive care

et al. 2020

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Objective To evaluate the usefulness of a new marker, pentraxin, as a prognostic marker in septic shock patients. Materials and methods Single-centre prospective observational study that included all consecutive patients 18 years or older who were admitted to the intensive care unit (ICU) with septic shock. Serum levels of procalcitonin (PCT), C-reactive protein (CRP) and pentraxin (PTX3) were measured on ICU admission. Results Seventy-five septic shock patients were included in the study. The best predictors of in-hospital mortality were the severity scores: SAPS II (AUC = 0.81), SOFA (AUC = 0.79) and APACHE II (AUC = 0.73). The ROC curve for PTX3 (ng/mL) yielded an AUC of 0.70, higher than the AUC for PCT (0.43) and CRP (0.48), but lower than lactate (0.79). Adding PTX3 to the logistic model increased the predictive capacity in relation to SAPS II, SOFA and APACHE II for in-hospital mortality (AUC 0.814, 0.795, and 0.741, respectively). In crude regression models, significant associations were found between in-hospital mortality and PTX3. This positive association increased after adjusting for age, sex and immunosuppression: adjusted OR T3 for PTX3 = 7.83, 95% CI 1.35–45.49, linear P trend = 0.024. Conclusion Our results support the prognostic value of a single determination of plasma PTX3 as a predictor of hospital mortality in septic shock patients.

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Section: Plos Onesupporting

confidence: 92%

Prognostic value of plasma pentraxin 3 levels in patients with septic shock admitted to intensive care

et al. 2020

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“…In a recent, prospective, observational study including 547 ICU patients (42.4% with infections), a PTX3 cut off similar to that identified in our study was reported to predict mortality: PTX3 serum level above the median cohort value of 20.9 ng/mL was independently associated to 28-day mortality when adjusted for age, sex, chronic diseases, and immunosuppression (HR 1.87, 95% CI 1.41-2.48) 51 . In another recent paper conducted on 281 sepsis patients, serum PTX3 >26 ng/mL was associated to mortality 52 . Taken together, these findings and our results suggest that circulating PTX3 levels ten-fold above the normal value reflect a severe systemic inflammatory involvement with ominous outcome.…”

Section: Discussionmentioning

confidence: 94%

Macrophage expression and prognostic significance of the long pentraxin PTX3 in COVID-19

Brunetta

Folci

Bottazzi

et al. 2020

Preprint

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PTX3 is an essential component of humoral innate immunity, involved in resistance to selected pathogens and in the regulation of inflammation. PTX3 plasma levels are associated with poor outcome in systemic inflammatory conditions and vascular pathology. The present study was designed to assess expression and significance of PTX3 in COVID-19. By bioinformatics analysis of public databases PTX3 expression was detected in lung respiratory cell lines exposed to SARS-CoV-2. By analysis at single cell level of COVID-19 circulating mononuclear cells, we found that PTX3 was selectively expressed by monocytes among circulating leukocytes. Moreover, in lung bronchoalveolar lavage fluid, single cell analysis revealed selective expression of PTX3 in neutrophils and macrophages, which play a major role in the pathogenesis of the disease. By immunohistochemistry, PTX3 was expressed by lung myelomocytic cells, type 2 pneumocytes and vascular endothelial cells. PTX3 plasma levels were determined by ELISA in 96 consecutive patients with a laboratory-confirmed diagnosis of COVID-19. Higher PTX3 plasma levels were observed in 52 (54.2%) patients admitted in ICU (median 21.0ng/mL, IQT 15.5-46.3 vs 12.4ng/mL IQT 6.1-20.2 in ward patients; p=0.0017) and in 22 (23%) patients died by 28 days (39.8ng/mL, IQT 20.2-75.7 vs 15.7ng/mL, IQT 8.2-21.6 in survivors; p=0.0001). After determining an optimal PTX3 cut-off for the primary outcome, the Kaplan-Meier curve showed an increased mortality in patients with PTX3>22.25ng/mL (Log-rank tests p<0.0001). In Cox regression model, PTX3>22.25ng/mL showed an adjusted Hazard Ratio (aHR) of 7.6 (95%CI2.45-23.76) in predicting mortality. Performing a multivariate logistic regression including all inflammatory markers (PTX3, ferritin, D-Dimer, IL-6, and CRP), PTX3 was the only marker significantly associated with death (aHR 1.13;95%CI1.02-1.24; p=0.021). The results reported here suggest that circulating and lung myelomonocytic cells are a major source of PTX3 and that PTX3 plasma levels can serve as a strong prognostic indicator of short-term mortality in COVID-19.

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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%

“…Some sensitive serum markers, such as Ang-2, PCT, interleukin-6, pentraxin 3, etc. [1,4,5], have been widely used to facilitate sepsis prognosis, however, their prognostic values are limited, not only rarely available but often lack of sensitivity or specificity. On the other hand, traditional prediction models based on small sample data such as logistic regression analysis and scoring systems including acute physiology and chronic health evaluation-II (APHACHE-II), Simplified acute physiology score-II (SAPS-II) and etc.…”

mentioning

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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Biomarker combination and SOFA score for the prediction of mortality in sepsis and septic shock

Cited by 40 publications

References 24 publications

Prognostic value of plasma pentraxin 3 levels in patients with septic shock admitted to intensive care

Prognostic value of plasma pentraxin 3 levels in patients with septic shock admitted to intensive care

Macrophage expression and prognostic significance of the long pentraxin PTX3 in COVID-19

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

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