Mortality Risk Profiles for Sepsis: A Novel Longitudinal and Multivariable Approach.
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ABSTRACT:
SUBMITTER: Liaw PC
PROVIDER: S-EPMC7063956 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:This data article features supplementary figures and tables related to the article "Differential Multivariable risk prediction of appropriate shock vs. competing mortality - a prospective cohort study to estimate benefits from implantable cardioverter defibrillator therapy" (Bergau et al., 2018) [1]. The figures show the clinical study CONSORT graph (data that show the number of patients not-analyzable as well as a distribution of patients by outcomes) and the correlation scatter plot for risk scores of appropriate shock vs. mortality (data that show the calculated score values of the two scores plotted against each other). The tables show the results for the univariate Cox regressions for prediction of mortality and appropriate shock. For further information, please see Bergau et al. (2018) [1].
| S-EPMC6262164 | biostudies-literature
Project description:BackgroundExisting scoring systems have limitations in predicting the in-hospital mortality of adult sepsis patients. We aimed to develop and validate a novel risk score for predicting the in-hospital mortality of adult sepsis patients.MethodsThe clinical data of 1,335 adult sepsis inpatients were retrospectively analyzed. Enrolled patients were randomly divided into a modeling group and a validation group at a 3:2 ratio. The modeling group (n=801) was used to develop the risk score by univariate and multivariate logistic regression analyses. The score's performance was validated in the validation group (n=534). We classified patients into four risk levels according to the novel risk score.ResultsAge, central vein catheterization, mechanical ventilation, vasopressin, Charlson comorbidity index (CCI), respiratory rate (RR), heart rate (HR), Glasgow coma scale (GCS) score, platelet (PLT), hematocrit (HCT), aspartate aminotransferase (AST), and activated partial thrombin time (APTT) were independent risk factors for in-hospital death in adult sepsis patients. Continuous variables were converted into classified variables to develop the risk score, with a total score of 39 points. Adult sepsis patients with low, lower medium, higher medium, and high risk levels had in-hospital mortality rates of 9.8%, 24.7%, 55.8%, and 83.5%, respectively.ConclusionsCompared with the Acute Physiology and Chronic Health Evaluation II scoring system (APACHE II) and the Modified Early Warning Score (MEWS), the novel risk score showed good predictive performance for in-hospital mortality in adult sepsis patients.
| S-EPMC9372666 | biostudies-literature
Project description:BackgroundPrognostic biomarker research neonatal sepsis is lacking. We assessed the utility of a validated pediatric prognostic tool called PERSEVERE II that uses decision tree methodology to predict mortality at discharge in neonates who experienced sepsis.MethodsProspective study in a dual-center cohort of neonates with sepsis admitted between June 2020 and December 2021. Biomarker analysis was done on serum samples obtained at the time of evaluation for the event.ResultsIn a cohort of 59 neonates with a mortality rate of 15.3%, PERSEVERE II was 67% sensitive and 59% specific for mortality, p 0.27. Amongst PERSEVERE II biomarkers, IL-8 showed good prognostic performance for mortality prediction with a cutoff of 300 pg/mL (sensitivity 100%, specificity 65%, negative predictive value 100%, AUC 0.87, p 0.0003). We derived a new decision tree that is neonate specific (nPERSEVERE) with improved performance compared to IL-8 (sensitivity 100%, specificity 86%, negative predictive value 100%, AUC 0.95, p < 0.0001).ConclusionsIL-8 and nPERSEVERE demonstrated good prognostic performance in a small cohort of neonates with sepsis. Moving toward precision medicine in sepsis, our study proposes an important tool for clinical trial prognostic enrichment that needs to be validated in larger studies.ImpactPrognostic and predictive biomarker research is lacking in the newborn intensive care unit. Biomarkers can be used at the time of evaluation for neonatal sepsis (blood culture acquisition) to identify neonates with high baseline mortality risk. Stratification is an important step toward precision medicine in neonatal sepsis.
| S-EPMC10261505 | biostudies-literature
Project description:In Coronavirus disease 2019 (COVID-19), early identification of patients with a high risk of mortality can significantly improve triage, bed allocation, timely management, and possibly, outcome. The study objective is to develop and validate individualized mortality risk scores based on the anonymized clinical and laboratory data at admission and determine the probability of Deaths at 7 and 28 days. Data of 1393 admitted patients (Expired-8.54%) was collected from six Apollo Hospital centers (from April to July 2020) using a standardized template and electronic medical records. 63 Clinical and Laboratory parameters were studied based on the patient's initial clinical state at admission and laboratory parameters within the first 24 h. The Machine Learning (ML) modelling was performed using eXtreme Gradient Boosting (XGB) Algorithm. 'Time to event' using Cox Proportional Hazard Model was used and combined with XGB Algorithm. The prospective validation cohort was selected of 977 patients (Expired-8.3%) from six centers from July to October 2020. The Clinical API for the Algorithm is http://20.44.39.47/covid19v2/page1.php being used prospectively. Out of the 63 clinical and laboratory parameters, Age [adjusted hazard ratio (HR) 2.31; 95% CI 1.52-3.53], Male Gender (HR 1.72, 95% CI 1.06-2.85), Respiratory Distress (HR 1.79, 95% CI 1.32-2.53), Diabetes Mellitus (HR 1.21, 95% CI 0.83-1.77), Chronic Kidney Disease (HR 3.04, 95% CI 1.72-5.38), Coronary Artery Disease (HR 1.56, 95% CI - 0.91 to 2.69), respiratory rate > 24/min (HR 1.54, 95% CI 1.03-2.3), oxygen saturation below 90% (HR 2.84, 95% CI 1.87-4.3), Lymphocyte% in DLC (HR 1.99, 95% CI 1.23-2.32), INR (HR 1.71, 95% CI 1.31-2.13), LDH (HR 4.02, 95% CI 2.66-6.07) and Ferritin (HR 2.48, 95% CI 1.32-4.74) were found to be significant. The performance parameters of the current model is at AUC ROC Score of 0.8685 and Accuracy Score of 96.89. The validation cohort had the AUC of 0.782 and Accuracy of 0.93. The model for Mortality Risk Prediction provides insight into the COVID Clinical and Laboratory Parameters at admission. It is one of the early studies, reflecting on 'time to event' at the admission, accurately predicting patient outcomes.
| S-EPMC8211710 | biostudies-literature
Project description:Background and Objectives: Acute kidney injury (AKI) that results from ischemia is a common clinical syndrome and correlates with high morbidity and mortality among hospitalized patients. However, a clinical tool to predict mortality risk of ischemic AKI is not available. In this study, we aimed to develop and validate models to predict the 30-day and 1-year mortality risk of hospitalized patients with ischemic AKI. Methods A total of 1,836 admissions with ischemic AKI were recruited from 277,898 inpatients admitted to three affiliated tertiary general hospitals of Central South University in China between January 2015 and December 2015. Patients in the final analysis were followed up for 1 year. Study patients were randomly divided in a 7:3 ratio to form the training cohort and validation cohort. Multivariable regression analyses were used for developing mortality prediction models. Results Hepatorenal syndrome, shock, central nervous system failure, Charlson comorbidity index (≥2 points), mechanical ventilation, renal function at discharge were independent risk factors for 30-day mortality after ischemic AKI, while malignancy, sepsis, heart failure, liver failure, Charlson comorbidity index (≥2 points), mechanical ventilation, and renal function at discharge were predictors for 1-year mortality. The area under the receiver operating characteristic curves (AUROCs) of 30-day prediction model were 0.878 (95% confidence interval (CI): 0.849-0.908) in the training cohort and 0.867 (95% CI: 0.820–0.913) in the validation cohort. The AUROCs of the 1-year mortality prediction in the training and validation cohort were 0.803 (95% CI: 0.772–0.834) and 0.788 (95% CI: 0.741–0.835), respectively. Conclusion Our easily applied prediction models can effectively identify individuals at high mortality risk within 30 days or 1 year in hospitalized patients with ischemic AKI. It can guide the optimal clinical management to minimize mortality after an episode of ischemic AKI.
| S-EPMC9420861 | biostudies-literature
Project description:BACKGROUND:High-risk patients are most vulnerable during transitions of care. Due to the high burden of resource allocation for such patients, we propose that segmentation of this heterogeneous population into distinct subgroups will enable improved healthcare resource planning. In this study, we segmented a high-risk population with the aim to identify and characterize a patient subgroup with the highest 30-day and 90-day hospital readmission and mortality. METHODS:We extracted data from our transitional care program (TCP), a Hospital-to-Home program launched by the Singapore Ministry of Health, from June to November 2018. Latent class analysis (LCA) was used to determine the optimal number and characteristics of latent subgroups, assessed based on model fit and clinical interpretability. Regression analysis was performed to assess the association of class membership on 30- and 90-day all-cause readmission and mortality. RESULTS:Among 752 patients, a 3-class best fit model was selected: Class 1 "Frail, cognitively impaired and physically dependent", Class 2 "Pre-frail, but largely physically independent" and Class 3 "Physically independent". The 3 classes have distinct demographics, medical and socioeconomic characteristics (p < 0.05), 30- and 90-day readmission (p < 0.05) and mortality (p < 0.01). Class 1 patients have the highest age-adjusted 90-day readmission (OR = 2.04, 95%CI: 1.21-3.46, p = 0.008), 30- (OR = 6.92, 95%CI: 1.76-27.21, p = 0.006) and 90-day mortality (OR = 11.51, 95%CI: 4.57-29.02, p < 0.001). CONCLUSIONS:We identified a subgroup with the highest readmission and mortality risk amongst high-risk patients. We also found a lack of interventions in our TCP that specifically addresses increased frailty and poor cognition, which are prominent features in this subgroup. These findings will help to inform future program modifications and strengthen existing transitional healthcare structures currently utilized in this patient cohort.
| S-EPMC6894210 | biostudies-literature
Project description:BACKGROUND:Increasingly, molecular measurements from multiple studies are pooled to identify risk scores, with only partial overlap of measurements available from different studies. Univariate analyses of such markers have routinely been performed in such settings using meta-analysis techniques in genome-wide association studies for identifying genetic risk scores. In contrast, multivariable techniques such as regularized regression, which might potentially be more powerful, are hampered by only partial overlap of available markers even when the pooling of individual level data is feasible for analysis. This cannot easily be addressed at a preprocessing level, as quality criteria in the different studies may result in differential availability of markers - even after imputation. METHODS:Motivated by data from the InterLymph Consortium on risk factors for non-Hodgkin lymphoma, which exhibits these challenges, we adapted a regularized regression approach, componentwise boosting, for dealing with partial overlap in SNPs. This synthesis regression approach is combined with resampling to determine stable sets of single nucleotide polymorphisms, which could feed into a genetic risk score. The proposed approach is contrasted with univariate analyses, an application of the lasso, and with an analysis that discards studies causing the partial overlap. The question of statistical significance is faced with an approach called stability selection. RESULTS:Using an excerpt of the data from the InterLymph Consortium on two specific subtypes of non-Hodgkin lymphoma, it is shown that componentwise boosting can take into account all applicable information from different SNPs, irrespective of whether they are covered by all investigated studies and for all individuals in the single studies. The results indicate increased power, even when studies that would be discarded in a complete case analysis only comprise a small proportion of individuals. CONCLUSIONS:Given the observed gains in power, the proposed approach can be recommended more generally whenever there is only partial overlap of molecular measurements obtained from pooled studies and/or missing data in single studies. A corresponding software implementation is available upon request. TRIAL REGISTRATION:All involved studies have provided signed GWAS data submission certifications to the U.S. National Institute of Health and have been retrospectively registered.
| S-EPMC6642584 | biostudies-literature
Project description:ObjectiveSepsis is the leading cause of in-hospital mortality in the United States (US). Quality improvement initiatives for improving sepsis care depend on accurate estimates of sepsis mortality. While hospital 30-day risk-standardized mortality rates have been published for patients hospitalized with acute myocardial infarction, heart failure, and pneumonia, risk-standardized mortality rates for sepsis have not been well characterized. We aimed to construct a sepsis risk-standardized mortality rate map for the United States, to illustrate disparities in sepsis care across the country.MethodsThis cross-sectional study included adults from the US Nationwide Inpatient Sample who were hospitalized with sepsis between 1 January 2010 and 30 December 2011. Hospital-level risk-standardized mortality rates were calculated using hierarchical logistic modelling, and were risk-adjusted with predicted mortality derived from (1) the Sepsis Risk Prediction Score, a logistic regression model, and (2) gradient-boosted decision trees, a supervised machine learning (ML) algorithm.ResultsAmong 1,739,033 adults hospitalized with sepsis, 50% were female, and the median age was 71 years (interquartile range: 58-81). The national median risk-standardized mortality rate for sepsis was 18.4% (interquartile range: 17.0, 21.0) by the boosted tree model, which had better discrimination than the Sepsis Risk Prediction Score model (C-statistic 0.87 and 0.78, respectively). The highest risk-standardized mortality rates were found in Wyoming, North Dakota, and Mississippi, while the lowest were found in Arizona, Colorado, and Michigan.ConclusionsWide variation exists in sepsis risk-standardized mortality rates across states, representing opportunities for improvement in sepsis care. This represents the first map of state-level variation of risk-standardized mortality rates in sepsis.
| S-EPMC8796072 | biostudies-literature
Project description:The COVID-19 pandemic is impressively challenging the healthcare system. Several prognostic models have been validated but few of them are implemented in daily practice. The objective of the study was to validate a machine-learning risk prediction model using easy-to-obtain parameters to help to identify patients with COVID-19 who are at higher risk of death. The training cohort included all patients admitted to Fondazione Policlinico Gemelli with COVID-19 from March 5, 2020, to November 5, 2020. Afterward, the model was tested on all patients admitted to the same hospital with COVID-19 from November 6, 2020, to February 5, 2021. The primary outcome was in-hospital case-fatality risk. The out-of-sample performance of the model was estimated from the training set in terms of Area under the Receiving Operator Curve (AUROC) and classification matrix statistics by averaging the results of fivefold cross validation repeated 3-times and comparing the results with those obtained on the test set. An explanation analysis of the model, based on the SHapley Additive exPlanations (SHAP), is also presented. To assess the subsequent time evolution, the change in paO2/FiO2 (P/F) at 48 h after the baseline measurement was plotted against its baseline value. Among the 921 patients included in the training cohort, 120 died (13%). Variables selected for the model were age, platelet count, SpO2, blood urea nitrogen (BUN), hemoglobin, C-reactive protein, neutrophil count, and sodium. The results of the fivefold cross-validation repeated 3-times gave AUROC of 0.87, and statistics of the classification matrix to the Youden index as follows: sensitivity 0.840, specificity 0.774, negative predictive value 0.971. Then, the model was tested on a new population (n = 1463) in which the case-fatality rate was 22.6%. The test model showed AUROC 0.818, sensitivity 0.813, specificity 0.650, negative predictive value 0.922. Considering the first quartile of the predicted risk score (low-risk score group), the case-fatality rate was 1.6%, 17.8% in the second and third quartile (high-risk score group) and 53.5% in the fourth quartile (very high-risk score group). The three risk score groups showed good discrimination for the P/F value at admission, and a positive correlation was found for the low-risk class to P/F at 48 h after admission (adjusted R-squared = 0.48). We developed a predictive model of death for people with SARS-CoV-2 infection by including only easy-to-obtain variables (abnormal blood count, BUN, C-reactive protein, sodium and lower SpO2). It demonstrated good accuracy and high power of discrimination. The simplicity of the model makes the risk prediction applicable for patients in the Emergency Department, or during hospitalization. Although it is reasonable to assume that the model is also applicable in not-hospitalized persons, only appropriate studies can assess the accuracy of the model also for persons at home.
| S-EPMC8551240 | biostudies-literature
Project description:ObjectivePrior studies have concentrated on the acute short-term outcomes of sepsis, with little focus on its long-term consequences. The objective of this study was to characterise long-term mortality following a sepsis event.DesignPopulation-based data from the 30 239 community-dwelling individuals in the REasons for Geographic and Racial Differences in Stroke (REGARDS) cohort.Setting usa participantsCommunity-dwelling adults ?45 years of age. Sepsis was defined as hospitalisation or emergency department treatment for a serious infection with the presence of ?2 systemic inflammatory response syndrome criteria.Outcomes6-year all-cause mortality. The analysis utilised a time-varying Cox model adjusted for participant's age, demographic factors, health behaviours and chronic medical conditions.ResultsThe participants were observed for a median of 6.1 years (IQR 4.5-7.1). During this period, 975 individuals experienced a sepsis event. Sepsis hospital mortality was 8.9%. One-year, 2-year and 5-year all-cause mortality among individuals with sepsis were 23%, 28.8% and 43.8%, respectively, compared with death rates of 1%, 2.6% and 8.3% among those who never developed sepsis. On multivariable analysis, the association of sepsis with increased all-cause mortality persisted for up to 5 years, after adjustment for confounders; year 0.00-1.00, adjusted HR (aHR) 13.07 (95% CI 10.63 to 16.06); year 1.01-2.00 aHR 2.64 (1.85 to 3.77); year 2.01-3.00 aHR 2.18 (1.43 to 3.33); year 3.01-4.00 aHR 1.97 (1.19 to 3.25); year 4.01-5.00 aHR 2.08 (1.14 to 3.79); year 5.01+ aHR 1.41 (0.67 to 2.98).ConclusionsIndividuals with sepsis exhibited increased rates of death for up to 5 years after the illness event, even after accounting for comorbidities. Sepsis is independently associated with increased risk of mortality well after hospital treatment.
| S-EPMC3902401 | biostudies-literature