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Development of a Nomogram to Predict 28-Day Mortality of Patients With Sepsis-Induced Coagulopathy: An Analysis of the MIMIC-III Database.

ABSTRACT: 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.

SUBMITTER: Lu Z

PROVIDER: S-EPMC8056034 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectiveLung is often implicated in sepsis, resulting in acute respiratory distress syndrome (ARDS). The alveolar-arterial oxygen gradient [D(A-a)O2 ] reflects lung diffusing capacity, which is usually compromised in ARDS. But whether D(A-a)O2 impacts the prognosis of patients with sepsis remains to be explored. Our study aims to investigate the association between D(A-a)O2 and 28-day mortality in patients with sepsis using a large sample, multicenter Medical Information Mart for Intensive Care (MIMIC)-IV database.MethodsWe extracted a data of 35 010 patients with sepsis from the retrospective cohort MIMIC-IV database, by which the independent effects of D(A-a)O2 on 28-day death risk was investigated, with D(A-a)O2 as being the exposure variable and 28-day fatality being the outcome variable. Binary logistic regression and a two-piecewise linear model were employed to explore the relationship between D(A-a)O2 and the 28-day death risk after confounding factors were optimized including demographic indicators, Charlson comorbidity index (CCI), Sequential Organ Failure Assessment (SOFA) score, drug administration, and vital signs.ResultsA total of 18 933 patients were finally included in our analysis. The patients' average age was 66.67 ± 16.01 years, and the mortality at 28 days was 19.23% (3640/18933). Multivariate analysis demonstrated that each 10-mmHg rise of D(A-a)O2 was linked with a 3% increase in the probability of death at 28 days either in the unadjusted model or in adjustment for demographic variables (Odds ratio [OR]: 1.03, 95% CI: 1.02 to 1.03). But, each 10 mmHg increase in D(A-a)O2 was associated with a 3% increase of death (OR: 1.03, 95% CI: 1.023 to 1.033) in the case of adjustment for all covariants. Through smoothed curve fitting and generalized summation models, we found that non-linear relationship existed between D(A-a)O2 and the death at 28-day, which demonstrated that D(A-a)O2 had no any impacts on the prognosis of patients with sepsis when D(A-a)O2 was less than or equal to 300 mmHg, but once D(A-a)O2 exceeded 300 mmHg, however, every 10 mmHg elevation of D(A-a)O2 is accompanied by a 5% increase of the 28-day death (OR: 1.05; 95% CI:1.04 to 1.05, p < 0.0001).ConclusionOur findings suggests that D(A-a)O2 is a valuable indicator for the management of sepsis patient, and it is recommended that D(A-a)O2 be maintained less than 300 mmHg as far as possible during sepsis process.

Project description:Background and aimSepsis is a syndromic response to infection and is associated with high mortality, thus imposing a significant global burden of disease. Although low-molecular-weight heparin (LMWH) has been recommended to prevent venous thromboembolism, its anticoagulant and anti-inflammatory effects in sepsis remain controversial. Owing to the modification of the Sepsis-3 definition and diagnostic criteria, further evaluation of the efficacy and benefit population of LMWH is required.MethodsWe performed a retrospective cohort study to assess whether LMWH improved the inflammation, coagulopathy, and clinical outcomes against Sepsis-3 and to identify the target patients. All patients diagnosed with sepsis at the First Affiliated Hospital of Xi'an Jiaotong University (the largest general hospital in northwest China) from January 2016 to December 2020 were recruited and re-evaluated using Sepsis-3 criteria.ResultsAfter 1:1 propensity score matching, 88 pairs of patients were categorized into the treatment and control groups based on subcutaneous LMWH administration. Compared with the control group, a significantly lower 28-day mortality was observed in the LMWH group (26.1 vs. 42.0%, p = 0.026) with a comparable incidence of major bleeding events (6.8 vs. 8.0%, p = 0.773). Cox regression analysis showed that LMWH administration was the independent protective factor for septic patients (aHR, 0.48; 95% CI, 0.29-0.81; p = 0.006). Correspondingly, the LMWH treatment group showed a significant improvement in inflammation and coagulopathy. Further subgroup analysis showed that LMWH therapy was associated with favorable outcomes in patients younger than 60 years and diagnosed with sepsis-induced coagulopathy (SIC), ISTH overt DIC, non-septic shock, or non-diabetics and in patients included in the moderate-risk group (APACHE II score 20-35 or SOFA score 8-12).ConclusionOur study results showed that LMWH improves 28-day mortality by improving inflammatory response and coagulopathy in patients meeting Sepsis-3 criteria. The SIC and ISTH overt DIC scoring systems can better identify septic patients who are likely to benefit more from LMWH administration.

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.

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Development of a Nomogram to Predict 28-Day Mortality of Patients With Sepsis-Induced Coagulopathy: An Analysis of the MIMIC-III Database.

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