Dataset Information

Development and validation of immune dysfunction score to predict 28-day mortality of sepsis patients.

ABSTRACT:

Background

Sepsis-induced immune dysfunction ranging from cytokines storm to immunoparalysis impacts outcomes. Monitoring immune dysfunction enables better risk stratification and mortality prediction and is mandatory before widely application of immunoadjuvant therapies. We aimed to develop and validate a scoring system according to patients' immune dysfunction status for 28-day mortality prediction.

Methods

A prospective observational study from a cohort of adult sepsis patients admitted to ICU between August 2013 and June 2016 at Kaohsiung Chang Gung Memorial Hospital in Taiwan. We evaluated immune dysfunction status through measurement of baseline plasma Cytokine levels, Monocyte human leukocyte-DR expression by flow cytometry, and stimulated immune response using post LPS stimulated cytokine elevation ratio. An immune dysfunction score was created for 28-day mortality prediction and was validated.

Results

A total of 151 patients were enrolled. Data of the first consecutive 106 septic patients comprised the training cohort, and of other 45 patients comprised the validation cohort. Among the 106 patients, 21 died and 85 were still alive on day 28 after ICU admission. (mortality rate, 19.8%). Independent predictive factors revealed via multivariate logistic regression analysis included segmented neutrophil-to-monocyte ratio, granulocyte-colony stimulating factor, interleukin-10, and monocyte human leukocyte antigen-antigen D-related levels, all of which were selected to construct the score, which predicted 28-day mortality with area under the curve of 0.853 and 0.789 in the training and validation cohorts, respectively.

Conclusions

The immune dysfunction scoring system developed here included plasma granulocyte-colony stimulating factor level, interleukin-10 level, serum segmented neutrophil-to-monocyte ratio, and monocyte human leukocyte antigen-antigen D-related expression appears valid and reproducible for predicting 28-day mortality.

SUBMITTER: Fang WF

PROVIDER: S-EPMC5658156 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Publications

Development and validation of immune dysfunction score to predict 28-day mortality of sepsis patients.

Fang Wen-Feng WF Douglas Ivor S IS Chen Yu-Mu YM Lin Chiung-Yu CY Kao Hsu-Ching HC Fang Ying-Tang YT Huang Chi-Han CH Chang Ya-Ting YT Huang Kuo-Tung KT Wang Yi-His YH Wang Chin-Chou CC Lin Meng-Chih MC

PloS one 20171026 10

<h4>Background</h4>Sepsis-induced immune dysfunction ranging from cytokines storm to immunoparalysis impacts outcomes. Monitoring immune dysfunction enables better risk stratification and mortality prediction and is mandatory before widely application of immunoadjuvant therapies. We aimed to develop and validate a scoring system according to patients' immune dysfunction status for 28-day mortality prediction.<h4>Methods</h4>A prospective observational study from a cohort of adult sepsis patients ...[more]

PMID: 29073262

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Project description:PURPOSE:Sepsis is a common acute life-threatening condition that emergency physicians routinely face. Diagnostic options within the Emergency Department (ED) are limited due to lack of infrastructure, consequently limiting the use of invasive hemodynamic monitoring or imaging tests. The mortality rate due to sepsis can be assessed via multiple scoring systems, for example, mortality in emergency department sepsis (MEDS) score and sepsis patient evaluation in the emergency department (SPEED) score, both of which quantify the variation of mortality rates according to clinical findings, laboratory data, or therapeutic interventions. This study aims to improve the management processes of sepsis patients by comparing SPEED score and MEDS score for predicting the 28-day mortality in cases of emergency sepsis. METHODS:The study is a cross-sectional, prospective study including 61 sepsis patients in ED in Suez Canal University Hospital, Egypt, from August 2017 to June 2018. Patients were selected by two steps: (1) suspected septic patients presenting with at least one of the following abnormal clinical findings: (a) body temperature higher than 38 °C or lower than 36 °C, (b) heart rate higher than 90 beats/min, (c) hyperventilation evidenced by respiratory rate higher than 20 breaths/min or PaCO2 lower than 32 mmHg, and (d) white blood cell count higher than 12,000/?L or lower than 4000/?L; (2) confirmed septic patients with at least a 2-point increase from the baseline total sequential organ failure assessment (SOFA) score following infection. Other inclusion criteria included adult patients with an age ?18 years regardless of gender and those who had either systemic inflammatory response syndrome or suspected/confirmed infection. Patients were shortly follow-up for the 28-day mortality. Each patient was subject to SPEED score and MEDS score and then the results were compared to detect which of them was more effective in predicting outcome. The receiver operating characteristic curves were also done for MEDS and SPEED scores. RESULTS:Among the 61 patients, 41 died with the mortality rate of 67.2%. The mortality rate increased with a higher SPEED and MEDS scores. Both SPEED and MEDS scores revealed significant difference between the survivors and nonsurvivors (p = 0.004 and p < 0.001, respectively), indicating that both the two systems are effective in predicting the 28-day mortality of sepsis patients. Thereafter, the receiver operating characteristic curves were plotted, which showed that SPEED was better than the MEDS score when applied to the complete study population with an area under the curve being 0.87 (0.788-0.963) as compared with 0.75 (0.634-0.876) for MEDS. Logistic regression analysis revealed that the best fitting predictor of 28-day mortality for sepsis patients was the SPEED scoring system. For every one unit increase in SPEED score, the odds of 28-day mortality increased by 37%. CONCLUSION:SPEED score is more useful and accurate than MEDS score in predicting the 28-day mortality among sepsis patients. Therefore SPEED rather than MEDS should be more widely used in the ED for sepsis patients.

Project description:BackgroundThe sequential organ failure assessment (SOFA) score has poor discriminative ability for death in severely or critically ill patients with Coronavirus disease 2019 (COVID-19) requiring intensive care unit (ICU) admission. Our aim was to create a new score powered to predict 28-day mortality.MethodsRetrospective, observational, bicentric cohort study including 425 patients with COVID-19 pneumonia, acute respiratory failure and SOFA score ≥ 2 requiring ICU admission for ≥72 h. Factors with independent predictive value for 28-day mortality were identified after stepwise Cox proportional hazards (PH) regression. Based on the regression coefficients, an equation was computed representing the COVID-SOFA score. Discriminative ability was tested using receiver operating characteristic (ROC) analysis, concordance statistics and precision-recall curves. This score was internally validated.ResultsMedian (Q1-Q3) age for the whole sample was 64 [55-72], with 290 (68.2%) of patients being male. The 28-day mortality was 54.58%. After stepwise Cox PH regression, age, neutrophil-to-lymphocyte ratio (NLR) and SOFA score remained in the final model. The following equation was computed: COVID-SOFA score = 10 × [0.037 × Age + 0.347 × ln(NLR) + 0.16 × SOFA]. Harrell's C-index for the COVID-SOFA score was higher than the SOFA score alone for 28-day mortality (0.697 [95% CI; 0.662-0.731] versus 0.639 [95% CI: 0.605-0.672]). Subsequently, the prediction error rate was improved up to 16.06%. Area under the ROC (AUROC) was significantly higher for the COVID-SOFA score compared with the SOFA score for 28-day mortality: 0.796 [95% CI: 0.755-0.833] versus 0.699 [95% CI: 0.653-0.742, p < 0.001]. Better predictive value was observed with repeated measurement at 48 h after ICU admission.ConclusionsThe COVID-SOFA score is better than the SOFA score alone for 28-day mortality prediction. Improvement in predictive value seen with measurements at 48 h after ICU admission suggests that the COVID-SOFA score can be used in a repetitive manner. External validation is required to support these results.

Project description:ObjectiveSepsis-induced coagulopathy (SIC) is extremely common in individuals with sepsis, significantly associated with poor outcomes. This study attempted to develop an interpretable and generalizable machine learning (ML) model for early predicting the risk of 28-day death in patients with SIC.MethodsIn this retrospective cohort study, we extracted SIC patients from the Medical Information Mart for Intensive Care III (MIMIC-III), MIMIC-IV, and eICU-CRD database according to Toshiaki Iba's scale. And the overlapping in the MIMIC-IV was excluded for this study. Afterward, only the MIMIC-III cohort was randomly divided into the training set, and the internal validation set according to the ratio of 7:3, while the MIMIC-IV and eICU-CRD databases were considered the external validation sets. The predictive factors for 28-day mortality of SIC patients were determined using recursive feature elimination combined with tenfold cross-validation (RFECV). Then, we constructed models using ML algorithms. Multiple metrics were used for evaluation of performance of the models, including the area under the receiver operating characteristic curve (AUROC), area under the precision recall curve (AUPRC), accuracy, sensitivity, specificity, negative predictive value, positive predictive value, recall, and F1 score. Finally, Shapley Additive Explanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME) were employed to provide a reasonable interpretation for the prediction results.ResultsA total of 3280, 2798, and 1668 SIC patients were screened from MIMIC-III, MIMIC-IV, and eICU-CRD databases, respectively. Seventeen features were selected to construct ML prediction models. XGBoost had the best performance in predicting the 28-day mortality of SIC patients, with AUC of 0.828, 0.913 and 0.923, the AUPRC of 0.807, 0.796 and 0.921, the accuracy of 0.785, 0.885 and 0.891, the F1 scores were 0.63, 0.69 and 0.70 in MIMIC-III (internal validation set), MIMIC-IV, and eICU-CRD databases. The importance ranking and SHAP analyses showed that initial SOFA score, red blood cell distribution width (RDW), and age were the top three critical features in the XGBoost model.ConclusionsWe developed an optimal and explainable ML model to predict the risk of 28-day death of SIC patients 28-day death risk. Compared with conventional scoring systems, the XGBoost model performed better. The model established will have the potential to improve the level of clinical practice for SIC patients.

Project description:Background & aimsEndothelial injury and dysfunction play a detrimental role in the pathogenesis of infections. Endothelium-related molecules have been reported as potential diagnostic and/or prognostic biomarkers of infection. The prognostic value of these biomarkers in patients with cirrhosis and infections remains elusive.MethodsIn this study, we investigated the performance of key soluble endothelial injury biomarkers, including intercellular adhesion molecule 1 (ICAM1), von Willebrand factor (vWF), vascular endothelial growth factor receptor 1 (VEGFR1), and angiopoietin 1 and 2 (Ang1, 2) as mortality predictors in patients with cirrhosis and severe COVID-19 or bacterial sepsis.ResultsA total of 66 hospitalized patients (admitted to the COVID-19 ward or liver intensive care unit [ICU]) were included. Twenty-two patients had COVID-19 alone, while 20 patients had cirrhosis plus COVID-19. Twenty-four patients had cirrhosis plus bacterial sepsis. Among patients with cirrhosis, the most common aetiology of liver disease was alcohol. ICAM1 was increased (p = 0.003) while VEGFR1 (p <0.0001) and Ang1 (p <0.0001) were reduced in patients with COVID-19 and cirrhosis, compared to patients with COVID-19 alone. Endothelial biomarker levels did not differ significantly between patients with cirrhosis and severe COVID-19 or bacterial sepsis in the ICU. In these patients, ICAM1 levels significantly and independently predicted mortality (hazard ratio 3.24; 95% CI 1.19-8.86) along with model for end-stage liver disease (MELD) score, renal and coagulation failures. The AUC for ICAM1 was 0.74, MELD was 0.60 and combined ICAM1 and MELD was 0.70. ICAM1 also positively correlated with the composite organ failure scores recorded 3-5 days post ICU admission (CLIF-OF and SOFA) in this subgroup of patients.ConclusionThe study indicates that in patients with cirrhosis, elevated plasma ICAM1 serves as an independent predictor of severe COVID-19- or sepsis-associated 28-day mortality.Lay summaryBacterial sepsis and COVID-19 lead to increased mortality in patients with cirrhosis. In this study, we demonstrate that high plasma levels of ICAM1, an endothelial injury biomarker, is one of the important factors predicting mortality in critically ill cirrhotic patients with severe COVID-19 or bacterial sepsis.

Project description:Background: Sepsis-induced coagulopathy (SIC) is a common cause for inducing poor prognosis of critically ill patients in intensive care unit (ICU). However, currently there are no tools specifically designed for assessing short-term mortality in SIC patients. This study aimed to develop a practical nomogram to predict the risk of 28-day mortality in SIC patients. Methods: In this retrospective cohort study, we extracted patients from the Medical Information Mart for Intensive Care III (MIMIC-III) database. Sepsis was defined based on Sepsis 3.0 criteria and SIC based on Toshiaki Iba's criteria. Kaplan-Meier curves were plotted to compare the short survival time between SIC and non-SIC patients. Afterward, only SIC cohort was randomly divided into training or validation set. We employed univariate logistic regression and stepwise multivariate analysis to select predictive features. The proposed nomogram was developed based on multivariate logistic regression model, and the discrimination and calibration were verified by internal validation. We then compared model discrimination with other traditional severity scores and machine learning models. Results: 9432 sepsis patients in MIMIC III were enrolled, in which 3280 (34.8%) patients were diagnosed as SIC during the first ICU admission. SIC was independently associated with the 7- and 28-day mortality of ICU patients. K-M curve indicated a significant difference in 7-day (Log-Rank: P < 0.001 and P = 0.017) and 28-day survival (Log-Rank: P < 0.001 and P < 0.001) between SIC and non-SIC groups whether the propensity score match (PSM) was balanced or not. For nomogram development, a total of thirteen variables of 3,280 SIC patients were enrolled. When predicted the risk of 28-day mortality, the nomogram performed a good discrimination in training and validation sets (AUROC: 0.78 and 0.81). The AUROC values were 0.80, 0.81, 0.71, 0.70, 0.74, and 0.60 for random forest, support vector machine, sequential organ failure assessment (SOFA) score, logistic organ dysfunction score (LODS), simplified acute physiology II score (SAPS II) and SIC score, respectively, in validation set. And the nomogram calibration slope was 0.91, the Brier value was 0.15. As presented by the decision curve analyses, the nomogram always obtained more net benefit when compared with other severity scores. Conclusions: SIC is independently related to the short-term mortality of ICU patients. The nomogram achieved an optimal prediction of 28-day mortality in SIC patient, which can lead to a better prognostics assessment. However, the discriminative ability of the nomogram requires validation in external cohorts to further improve generalizability.

Project description:BackgroundEarly and accurate identification of septic patients at high risk for ICU mortality can help clinicians make optimal clinical decisions and improve the patients' outcomes. This study aimed to develop and validate (internally and externally) a mortality prediction score for sepsis following admission in the ICU.MethodsWe extracted data retrospectively regarding adult septic patients from one teaching hospital in Wenzhou, China and a large multi-center critical care database from the USA. Demographic data, vital signs, laboratory values, comorbidities, and clinical outcomes were collected. The primary outcome was ICU mortality. Through multivariable logistic regression, a mortality prediction score for sepsis was developed and validated.ResultsFour thousand two hundred and thirty six patients in the development cohort and 8359 patients in three validation cohorts. The Prediction of Sepsis Mortality in ICU (POSMI) score included age ≥ 50 years, temperature < 37 °C, Respiratory rate > 35 breaths/min, MAP ≤ 50 mmHg, SpO2 < 90%, albumin ≤ 2 g/dL, bilirubin ≥ 0.8 mg/dL, lactate ≥ 4.2 mmol/L, BUN ≥ 21 mg/dL, mechanical ventilation, hepatic failure and metastatic cancer. In addition, the area under the receiver operating characteristic curve (AUC) for the development cohort was 0.831 (95% CI, 0.813-0.850) while the AUCs ranged from 0.798 to 0.829 in the three validation cohorts. Moreover, the POSMI score had a higher AUC than both the SOFA and APACHE IV scores. Notably, the Hosmer-Lemeshow (H-L) goodness-of-fit test results and calibration curves suggested good calibration in the development and validation cohorts. Additionally, the POSMI score still exhibited excellent discrimination and calibration following sensitivity analysis. With regard to clinical usefulness, the decision curve analysis (DCA) of POSMI showed a higher net benefit than SOFA and APACHE IV in the development cohort.ConclusionPOSMI was validated to be an effective tool for predicting mortality in ICU patients with sepsis.

Dataset Information

Development and validation of immune dysfunction score to predict 28-day mortality of sepsis patients.

Background

Methods

Results

Conclusions

Publications

Development and validation of immune dysfunction score to predict 28-day mortality of sepsis patients.

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