Browse
Submit Data
Databases
API
Help

Dataset Information

33 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

EASIX, Modified EASIX and Simplified EASIX as an Early Predictor for Intensive Care Unit Admission and Mortality in Severe COVID-19 Patients

ABSTRACT: COVID-19 receives a lot of attention due to its threat to global public health. Research is ongoing to find universal methods to assess the baseline health status of a patient to determine prognosis and management strategies. This study aims to assess the predictive potential of the EASIX (Endothelial Activation and Stress Index) and two of its modifications (mEASIX and sEASIX) in terms of the need for admission to the ICU (intensive care unit), the use of IMV (invasive mechanical ventilation) and death due to COVID-19. The medical data of 370 severely ill patients hospitalised in the COVID-19 departments of the Regional Specialist Hospital in Wroclaw (Poland), including the ICU, were analysed retrospectively. The mortality rate in the group studied was 65.7% (243 cases). In the case of all three indices, EASIX, mEASIX and sEASIX, there was a statistically significant correlation between the need for admission to the ICU (p = 0.026, p = 0.019, p = 0.001, respectively) and the risk of death (p < 0.001). In terms of the risk of death, the high values of the assessed indices (EASIX ≥ 2.36, mEASIX ≥ 704.03, sEASIX ≥ 3.81) were characterised by low sensitivity (≤40%), high specificity (approximately 90%) and low NPV (negative predictive value) (approximately 40%) with high PPV (positive predictive value) (approximately 80%). Due to the ease of implementation and the low cost of performing basic laboratory tests, the above-mentioned indices can be used as an additional, but not universal tool for the initial assessment of the health condition of patients admitted to the hospital.

SUBMITTER: Zinczuk A

PROVIDER: S-EPMC9321320 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Serum anion gap at admission as a predictor of mortality in the pediatric intensive care unit.

Project description:An accurate method to predict the mortality in the intensive care unit (ICU) patients has been required, especially in children. The aim of this study is to evaluate the value of serum anion gap (AG) for predicting mortality in pediatric ICU (PICU). We reviewed a data of 461 pediatric patients were collected on PICU admission. Corrected anion gap (cAG), the AG compensated for abnormal albumin levels, was significantly lower in survivors compared with nonsurvivors (p < 0.001). Multivariable logistic regression analysis identified the following variables as independent predictors of mortality; cAG (OR 1.110, 95% CI 1.06-1.17; p < 0.001), PIM3 [OR 7.583, 95% CI 1.81-31.78; p = 0.006], and PRISM III [OR 1.076, 95% CI 1.02-1.14; p = 0.008]. Comparing AUCs for mortality prediction, there were no statistically significant differences between cAG and other mortality prediction models; cAG 0.728, PIM2 0.779, PIM3 0.822, and PRISM III 0.808. The corporation of cAG to pre-existing mortality prediction models was significantly more accurate at predicting mortality than using any of these models alone. We concluded that cAG at ICU admission may be used to predict mortality in children, regardless of underlying etiology. And the incorporation of cAG to pre-existing mortality prediction models might improve predictability.

| S-EPMC5431089 | biostudies-literature

Circulating dipeptidyl peptidase 3 on intensive care unit admission is a predictor of organ dysfunction and mortality.

Project description:BackgroundOur aim was to investigate the prognostic potential of circulating dipeptidyl peptidase 3 (cDPP3) to predict mortality and development of organ dysfunction in a mixed intensive care unit (ICU) population, and for this reason, we analysed prospectively collected admission blood samples from adult ICU patients at four Swedish hospitals. Blood samples were stored in a biobank for later batch analysis. The association of cDPP3 levels with 30-day mortality and Sequential Organ Failure Assessment (SOFA) scores on day two was investigated before and after adjustment for the simplified acute physiology score III (SAPS-3), using multivariable (ordinal) logistic regression. The predictive power of cDPP3 was assessed using the area under the receiver operating characteristic curve (AUROC).ResultsOf 1978 included consecutive patients in 1 year (2016), 632 fulfilled the sepsis 3-criteria, 190 were admitted after cardiac arrest, and 157 because of trauma. Admission cDPP3 was independently (of SAPS-3) associated with 30-day mortality with odds ratios of 1.45 (95% confidence interval (CI) 1.28-1.64) in the entire ICU population, 1.30 (95% CI 1.08-1.57) in the sepsis subgroup and 2.28 (95% CI 1.50-3.62) in cardiac arrest. For trauma, there was no clear association. Circulating DPP3 alone was a moderate predictor of 30-day mortality with AUROCs of 0.68, 0.62, and 0.72 in the entire group, the sepsis subgroup, and the cardiac arrest subgroup, respectively. By adding cDPP3 to SAPS-3, AUROC improved for the entire group, the sepsis subgroup, and the cardiac arrest subgroup (p = 0.023).ConclusionCirculating DPP3 on admission is a SAPS-3 independent prognostic factor of day-two organ dysfunction and 30-day mortality in a mixed ICU population and needs further evaluation.

| S-EPMC8386069 | biostudies-literature

The Modified Early Warning Score as a Predictive Tool During Unplanned Surgical Intensive Care Unit Admission.

Project description:Background: The Modified Early Warning Score (MEWS) has been proposed to warn healthcare providers of potentially serious adverse events. We evaluated this scoring system during unplanned escalation of care in hospitalized surgical patients during a 1-year period. Methods: Following institutional review board approval, all consecutive, unplanned surgical admissions into the surgical intensive care unit (SICU) during 2016 were entered into this study. MEWS and patient demographics during bedside evaluation for SICU admission were extracted from electronic medical records. Logistic regression was used to analyze the association of MEWS with the incidence of future mortality. P values were set at <0.01 for statistical significance. Results: In this series of 263 consecutive patients, the incidence of mortality following unplanned escalation of care was 29.3% (confidence interval [CI] 24.1% to 35.0%), ranging from 22% to 57%, with all positive MEWS values. The association of MEWS with future mortality was not statistically significant (P=0.0107). A misclassification rate of 0.29 (CI 0.24 to 0.35) was observed with this association. Conclusion: MEWS provided no clinical benefit as an early warning system, as mortality was elevated throughout the MEWS scale in this clinical setting. The high misclassification rate indicates MEWS does not provide discriminatory support for patients at risk for mortality.

| S-EPMC7310184 | biostudies-literature

Mortality and denial of admission to an intensive care unit.

Project description: Not available

| S-EPMC3359892 | biostudies-literature

Plasma proenkephalin A 119-159 on intensive care unit admission is a predictor of organ failure and 30-day mortality.

Project description:BackgroundProenkephalin A 119-159 (penKid) has been suggested as a marker of renal failure and poor outcome. We aimed to investigate the association of penKid on ICU admission with organ dysfunction and mortality in a mixed ICU population. In this retrospective, observational study, admission penKid levels from prospectively collected blood samples of consecutive patients admitted to four Swedish ICUs were analysed. The association of penKid with day-two sequential organ failure assessment (SOFA) scores and 30-day mortality was investigated using (ordinal) logistic regression. The predictive power of penKid for 30-day mortality and dialysis was assessed using the area under the receiver operating characteristic curve (AUC).ResultsOf 1978 included patients, 632 fulfilled the sepsis 3-criteria, 190 had a cardiac arrest, and 157 had experienced trauma. Admission penKid was positively associated with 30-day mortality with an odds ratio of 1.95 (95% confidence interval 1.75-2.18, p < 0.001), and predicted 30-day mortality in the entire ICU population with an AUC of 0.71 (95% confidence interval 0.68-0.73) as well as in the sepsis, cardiac arrest and trauma subgroups (AUCs of 0.61-0.84). Correction for admission plasma creatinine revealed that penKid correlated with neurological dysfunction.ConclusionPlasma penKid on ICU admission is associated with day-two organ dysfunction and predictive of 30-day mortality in a mixed ICU-population, as well as in sepsis, cardiac arrest and trauma subgroups. In addition to being a marker of renal dysfunction, plasma penKid is associated with neurologic dysfunction in the entire ICU population, and cardiovascular dysfunction in sepsis.

| S-EPMC8286914 | biostudies-literature

Intensive care unit admission in multiple sclerosis: increased incidence and increased mortality.

Project description:ObjectiveTo compare the incidence of, and mortality after, intensive care unit (ICU) admission as well as the characteristics of critical illness in the multiple sclerosis (MS) population vs the general population.MethodsWe used population-based administrative data from the Canadian province of Manitoba for the period 1984 to 2010 and clinical data from 93% of admissions to provincial high-intensity adult ICUs. We identified 5,035 prevalent cases of MS and a cohort from the general population matched 5:1 on age, sex, and region of residence. We compared these populations using incidence rates and multivariable regression models adjusting for age, sex, comorbidity, and socioeconomic status.ResultsFrom January 2000 to October 2009, the age- and sex-standardized annual incidence of ICU admission among prevalent cohorts was 0.51% to 1.07% in the MS population and 0.34% to 0.51% in matched controls. The adjusted risk of ICU admission was higher for the MS population (hazard ratio 1.45; 95% confidence interval [CI] 1.19-1.75) than for matched controls. The MS population was more likely to be admitted for infection than the matched controls (odds ratio 1.82; 95% CI 1.10-1.32). Compared with the matched controls admitted to ICUs, 1-year mortality was higher in the MS population (relative risk 2.06; 95% CI 1.32-3.07) and was particularly elevated in patients with MS who were younger than 40 years (relative risk 3.77; 95% CI 1.45-8.11). Causes of death were MS (9.3%), infections (37.0%), and other causes (52.9%).ConclusionsCompared with the general population, the risk of ICU admission is higher in MS, and 1-year mortality after admission is higher. Greater attention to preventing infection and managing comorbidity is needed in the MS population.

| S-EPMC4118502 | biostudies-literature

Development of a machine learning model for predicting pediatric mortality in the early stages of intensive care unit admission.

Project description:The aim of this study was to develop a predictive model of pediatric mortality in the early stages of intensive care unit (ICU) admission using machine learning. Patients less than 18 years old who were admitted to ICUs at four tertiary referral hospitals were enrolled. Three hospitals were designated as the derivation cohort for machine learning model development and internal validation, and the other hospital was designated as the validation cohort for external validation. We developed a random forest (RF) model that predicts pediatric mortality within 72 h of ICU admission, evaluated its performance, and compared it with the Pediatric Index of Mortality 3 (PIM 3). The area under the receiver operating characteristic curve (AUROC) of RF model was 0.942 (95% confidence interval [CI] = 0.912-0.972) in the derivation cohort and 0.906 (95% CI = 0.900-0.912) in the validation cohort. In contrast, the AUROC of PIM 3 was 0.892 (95% CI = 0.878-0.906) in the derivation cohort and 0.845 (95% CI = 0.817-0.873) in the validation cohort. The RF model in our study showed improved predictive performance in terms of both internal and external validation and was superior even when compared to PIM 3.

| S-EPMC7806776 | biostudies-literature

Performance of the SAPS 3 admission score as a predictor of ICU mortality in a Philippine private tertiary medical center intensive care unit.

Project description:BackgroundThis study aimed to assess the performance of the Simplified Acute Physiology Score 3 (SAPS 3) as a predictor of ICU mortality in critically ill patients of different case mixes admitted to an intensive care unit.MethodsThis retrospective cohort study was performed from January 2011 to August 2013 in the intensive care unit of a private tertiary referral center in the Philippines. Predicted ICU mortality was calculated using the SAPS 3 global model. Observed versus predicted mortality rates were compared, and the standardized mortality ratio (SMR) was calculated. The discrimination and calibration characteristics of the SAPS 3 system to predict ICU mortality were assessed.ResultsA total of 2,426 patients were included. The observed ICU mortality was 277 (11.42%). The SAPS 3 global model had fair to good discrimination with an area under the receiver operating characteristic curve of 0.80 (CI 0.78-0.81). Good calibration was seen with the Hosmer-Lemeshow goodness of fit at Ĉ = 11.51 (p = 0.175). Standardized mortality ratio was 0.36 (0.26-0.81).ConclusionThe global SAPS 3 prediction model showed fair to good discrimination and good calibration in predicting mortality in our intensive care unit. Different levels of discrimination and calibration across the different subgroups analyzed suggest that overall ICU performance seemed to be affected by case mix variations.

| S-EPMC4267583 | biostudies-literature

Derivation and Validation of a Novel Cardiac Intensive Care Unit Admission Risk Score for Mortality.

Project description:Background There are no risk scores designed specifically for mortality risk prediction in unselected cardiac intensive care unit (CICU) patients. We sought to develop a novel CICU-specific risk score for prediction of hospital mortality using variables available at the time of CICU admission. Methods and Results A database of CICU patients admitted from January 1, 2007 to April 30, 2018 was divided into derivation and validation cohorts. The top 7 predictors of hospital mortality were identified using stepwise backward regression, then used to develop the Mayo CICU Admission Risk Score (M-CARS), with integer scores ranging from 0 to 10. Discrimination was assessed using area under the receiver-operator curve analysis. Calibration was assessed using the Hosmer-Lemeshow statistic. The derivation cohort included 10 004 patients and the validation cohort included 2634 patients (mean age 67.6 years, 37.7% females). Hospital mortality was 9.2%. Predictor variables included in the M-CARS were cardiac arrest, shock, respiratory failure, Braden skin score, blood urea nitrogen, anion gap and red blood cell distribution width at the time of CICU admission. The M-CARS showed a graded relationship with hospital mortality (odds ratio 1.84 for each 1-point increase in M-CARS, 95% CI 1.78-1.89). In the validation cohort, the M-CARS had an area under the receiver-operator curve of 0.86 for hospital mortality, with good calibration (P=0.21). The 47.1% of patients with M-CARS <2 had hospital mortality of 0.8%, and the 5.2% of patients with M-CARS >6 had hospital mortality of 51.6%. Conclusions Using 7 variables available at the time of CICU admission, the M-CARS can predict hospital mortality in unselected CICU patients with excellent discrimination.

| S-EPMC6755843 | biostudies-other

Early predictors of intensive care unit admission among COVID-19 patients in Qatar.

Project description:BackgroundCOVID-19 is associated with significant morbidity and mortality. This study aimed to explore the early predictors of intensive care unit (ICU) admission among patients with COVID-19.MethodsThis was a case-control study of adult patients with confirmed COVID-19. Cases were defined as patients admitted to ICU during the period February 29-May 29, 2020. For each case enrolled, one control was matched by age and gender.ResultsA total of 1,560 patients with confirmed COVID-19 were included. Each group included 780 patients with a predominant male gender (89.7%) and a median age of 49 years (interquartile range = 18). Predictors independently associated with ICU admission were cardiovascular disease (adjusted odds ratio (aOR) = 1.64, 95% confidence interval (CI): 1.16-2.32, p = 0.005), diabetes (aOR = 1.52, 95% CI: 1.08-2.13, p = 0.016), obesity (aOR = 1.46, 95% CI: 1.03-2.08, p = 0.034), lymphopenia (aOR = 2.69, 95% CI: 1.80-4.02, p < 0.001), high AST (aOR = 2.59, 95% CI: 1.53-4.36, p < 0.001), high ferritin (aOR = 1.96, 95% CI: 1.40-2.74, p < 0.001), high CRP (aOR = 4.09, 95% CI: 2.81-5.96, p < 0.001), and dyspnea (aOR = 2.50, 95% CI: 1.77-3.54, p < 0.001).ConclusionHaving cardiovascular disease, diabetes, obesity, lymphopenia, dyspnea, and increased AST, ferritin, and CRP were independent predictors for ICU admission in patients with COVID-19.

| S-EPMC10987715 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data