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Development of a nomogram to predict 30-day mortality of patients with sepsis-associated encephalopathy: a retrospective cohort study.

ABSTRACT: BackgroundSepsis-associated encephalopathy (SAE) is related to increased short-term mortality in patients with sepsis. We aim to establish a user-friendly nomogram for individual prediction of 30-day risk of mortality in patients with SAE.MethodsData were retrospectively retrieved from the Medical Information Mart for Intensive Care (MIMIC III) open source clinical database. SAE was defined by Glasgow Coma Score (GCS) < 15 or delirium at the presence of sepsis. Prediction model with a nomogram was constructed in the training set by logistic regression analysis and then undergone internal validation and sensitivity analysis.ResultsSAE accounted for about 50% in patients with sepsis and was independently associated with the 30-day mortality of sepsis. Variables eligible for the nomogram included patient’s age and clinical parameters on the first day of ICU admission including the GCS score, lactate, bilirubin, red blood cell distribution width (RDW), mean value of respiratory rate and temperature, and the use of vasopressor. Compared with Sequential Organ Failure Assessment (SOFA) and Logistic Organ Dysfunction System (LODS), the nomogram exhibited better discrimination with an area under the receiver operating characteristic curve (AUROC) of 0.763 (95%CI 0.736–0.791, p < 0.001) and 0.753 (95%CI 0.713–0.794, p < 0.001) in the training and validation sets, respectively. The calibration plot revealed an adequate fit of the nomogram for predicting the risk of 30-day mortality in both sets. Regarding to clinical usefulness, the DCA of the nomogram exhibited greater net benefit than SOFA and LODS in both of the training and validation sets. Besides, the nomogram exhibited acceptable discrimination, calibration, and clinical usefulness in sensitivity analysis.ConclusionsSAE is related to increased 30-day mortality of patients with sepsis. The nomogram presents excellent performance in predicting 30-day risk of mortality in SAE patients, which can be used to evaluate the prognosis of patients with SAE and may be more beneficial once specific treatments towards SAE are developed.

SUBMITTER: Yang Y

PROVIDER: S-EPMC7331133 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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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:IntroductionSepsis results from a dysregulated host response to an infection that is associated with an imbalance between pro- and anti-inflammatory cytokines. This imbalance is hypothesized to be a driver of patient mortality. Certain autoimmune diseases modulate the expression of cytokines involved in the pathophysiology of sepsis. However, the outcomes of patients with autoimmune disease who develop sepsis have not been studied in detail. The objective of this study is to determine whether patients with autoimmune diseases have different sepsis outcomes than patients without these comorbidities.MethodsUsing the Multiparameter Intelligent Monitoring in Intensive Care III database (v. 1.4) which contains retrospective clinical data for over 50,000 adult ICU stays, we compared 30-day mortality risk for sepsis patients with and without autoimmune disease. We used logistic regression models to control for known confounders, including demographics, disease severity, and immunomodulation medications. We used mediation analysis to evaluate how the chronic use of immunomodulation medications affects the relationship between autoimmune disease and 30-day mortality.ResultsOur study found a statistically significant 27.00% reduction in the 30-day mortality risk associated with autoimmune disease presence. This association was found to be the strongest (OR 0.71, 95% CI 0.54-0.93, P = 0.014) among patients with septic shock. The autoimmune disease-30-day mortality association was not mediated through the chronic use of immunomodulation medications (indirect effect OR 1.07, 95% CI 1.01-1.13, P = 0.020).ConclusionsWe demonstrated that autoimmune diseases are associated with a lower 30-day mortality risk in sepsis. Our findings suggest that autoimmune diseases affect 30-day mortality through a mechanism unrelated to the chronic use of immunomodulation medications. Since this study was conducted within a single study center, research using data from other medical centers will provide further validation.

Project description:BackgroundPredicting the risk of in-hospital mortality on admission is challenging but essential for risk stratification of patient outcomes and designing an appropriate plan-of-care, especially among transferred patients.ObjectiveDevelop a model that uses administrative and clinical data within 24 h of transfer to predict 30-day in-hospital mortality at an Academic Health Center (AHC).DesignRetrospective cohort study. We used 30 putative variables in a multiple logistic regression model in the full data set (n = 10,389) to identify 20 candidate variables obtained from the electronic medical record (EMR) within 24 h of admission that were associated with 30-day in-hospital mortality (p < 0.05). These 20 variables were tested using multiple logistic regression and area under the curve (AUC)-receiver operating characteristics (ROC) analysis to identify an optimal risk threshold score in a randomly split derivation sample (n = 5194) which was then examined in the validation sample (n = 5195).ParticipantsTen thousand three hundred eighty-nine patients greater than 18 years transferred to the Indiana University (IU)-Adult Academic Health Center (AHC) between 1/1/2016 and 12/31/2017.Main measuresSensitivity, specificity, positive predictive value, C-statistic, and risk threshold score of the model.Key resultsThe final model was strongly discriminative (C-statistic = 0.90) and had a good fit (Hosmer-Lemeshow goodness-of-fit test [X2 (8) =6.26, p = 0.62]). The positive predictive value for 30-day in-hospital death was 68%; AUC-ROC was 0.90 (95% confidence interval 0.89-0.92, p < 0.0001). We identified a risk threshold score of -2.19 that had a maximum sensitivity (79.87%) and specificity (85.24%) in the derivation and validation sample (sensitivity: 75.00%, specificity: 85.71%). In the validation sample, 34.40% (354/1029) of the patients above this threshold died compared to only 2.83% (118/4166) deaths below this threshold.ConclusionThis model can use EMR and administrative data within 24 h of transfer to predict the risk of 30-day in-hospital mortality with reasonable accuracy among seriously ill transferred patients.

Project description:BackgroundGiven the increased attention to sepsis at the population level there is a need to assess hospital performance in the care of sepsis patients using widely-available administrative data. The goal of this study was to develop an administrative risk-adjustment model suitable for profiling hospitals on their 30-day mortality rates for patients with sepsis.MethodsWe conducted a retrospective cohort study using hospital discharge data from general acute care hospitals in Pennsylvania in 2012 and 2013. We identified adult patients with sepsis as determined by validated diagnosis and procedure codes. We developed an administrative risk-adjustment model in 2012 data. We then validated this model in two ways: by examining the stability of performance assessments over time between 2012 and 2013, and by examining the stability of performance assessments in 2012 after the addition of laboratory variables measured on day one of hospital admission.ResultsIn 2012 there were 115,213 sepsis encounters in 152 hospitals. The overall unadjusted mortality rate was 18.5%. The final risk-adjustment model had good discrimination (C-statistic = 0.78) and calibration (slope and intercept of the calibration curve = 0.960 and 0.007, respectively). Based on this model, hospital-specific risk-standardized mortality rates ranged from 12.2 to 24.5%. Comparing performance assessments between years, correlation in risk-adjusted mortality rates was good (Pearson's correlation = 0.53) and only 19.7% of hospitals changed by more than one quintile in performance rankings. Comparing performance assessments after the addition of laboratory variables, correlation in risk-adjusted mortality rates was excellent (Pearson's correlation = 0.93) and only 2.6% of hospitals changed by more than one quintile in performance rankings.ConclusionsA novel claims-based risk-adjustment model demonstrated wide variation in risk-standardized 30-day sepsis mortality rates across hospitals. Individual hospitals' performance rankings were stable across years and after the addition of laboratory data. This model provides a robust way to rank hospitals on sepsis mortality while adjusting for patient risk.

Project description:BackgroundHospital mortality is high for patients with encephalopathy caused by microbial infection. Microbial infections often induce sepsis. The damage to the central nervous system (CNS) is defined as sepsis-associated encephalopathy (SAE). However, the relationship between pathogenic microorganisms and the prognosis of SAE patients is still unclear, especially gut microbiota, and there is no clinical tool to predict hospital mortality for SAE patients. The study aimed to explore the relationship between pathogenic microorganisms and the hospital mortality of SAE patients and develop a nomogram for the prediction of hospital mortality in SAE patients.MethodsThe study is a retrospective cohort study. The lasso regression model was used for data dimension reduction and feature selection. Model of hospital mortality of SAE patients was developed by multivariable Cox regression analysis. Calibration and discrimination were used to assess the performance of the nomogram. Decision curve analysis (DCA) to evaluate the clinical utility of the model.ResultsUnfortunately, the results of our study did not find intestinal infection and microorganisms of the gastrointestinal (such as: Escherichia coli) that are related to the prognosis of SAE. Lasso regression and multivariate Cox regression indicated that factors including respiratory failure, lactate, international normalized ratio (INR), albumin, SpO2, temperature, and renal replacement therapy were significantly correlated with hospital mortality. The AUC of 0.812 under the nomogram was more than that of the Simplified Acute Physiology Score (0.745), indicating excellent discrimination. DCA demonstrated that using the nomogram or including the prognostic signature score status was better than without the nomogram or using the SAPS II at predicting hospital mortality.ConclusionThe prognosis of SAE patients has nothing to do with intestinal and microbial infections. We developed a nomogram that predicts hospital mortality in patients with SAE according to clinical data. The nomogram exhibited excellent discrimination and calibration capacity, favoring its clinical utility.

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Development of a nomogram to predict 30-day mortality of patients with sepsis-associated encephalopathy: a retrospective cohort study.

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