Sepsis prediction in critically ill patients by platelet activation markers on ICU admission: a prospective pilot study.
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SUBMITTER: Layios N
PROVIDER: S-EPMC5505890 | biostudies-other | 2017 Dec
REPOSITORIES: biostudies-other
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Sepsis prediction in critically ill patients by platelet activation markers on ICU admission: a prospective pilot study.
Layios Nathalie N Delierneux Céline C Hego Alexandre A Huart Justine J Gosset Christian C Lecut Christelle C Maes Nathalie N Geurts Pierre P Joly Arnaud A Lancellotti Patrizio P Albert Adelin A Damas Pierre P Gothot André A Oury Cécile C
Intensive care medicine experimental 20170712 1
<h4>Background</h4>Platelets have been involved in both immune surveillance and host defense against severe infection. To date, whether platelet phenotype or other hemostasis components could be associated with predisposition to sepsis in critical illness remains unknown. The aim of this work was to identify platelet markers that could predict sepsis occurrence in critically ill injured patients.<h4>Methods</h4>This single-center, prospective, observational, 7-month study was based on a cohort o ...[more]
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Project description:BackgroundRisk prediction models allow clinicians to forecast which individuals are at a higher risk for developing a particular outcome. We developed and internally validated a delirium prediction model for incident delirium parameterized to patient ICU admission acuity.MethodsThis retrospective, observational, fourteen medical-surgical ICU cohort study evaluated consecutive delirium-free adults surviving hospital stay with ICU length of stay (LOS) greater than or equal to 24 hours with both an admission APACHE II score and an admission type (e.g., elective post-surgery, emergency post-surgery, non-surgical) in whom delirium was assessed using the Intensive Care Delirium Screening Checklist (ICDSC). Risk factors included in the model were readily available in electric medical records. Least absolute shrinkage and selection operator logistic (LASSO) regression was used for model development. Discrimination was determined using area under the receiver operating characteristic curve (AUC). Internal validation was performed by cross-validation. Predictive performance was determined using measures of accuracy and clinical utility was assessed by decision-curve analysis.ResultsA total of 8,878 patients were included. Delirium incidence was 49.9% (n = 4,431). The delirium prediction model was parameterized to seven patient cohorts, admission type (3 cohorts) or mean quartile APACHE II score (4 cohorts). All parameterized cohort models were well calibrated. The AUC ranged from 0.67 to 0.78 (95% confidence intervals [CI] ranged from 0.63 to 0.79). Model accuracy varied across admission types; sensitivity ranged from 53.2% to 63.9% while specificity ranged from 69.0% to 74.6%. Across mean quartile APACHE II scores, sensitivity ranged from 58.2% to 59.7% while specificity ranged from 70.1% to 73.6%. The clinical utility of the parameterized cohort prediction model to predict and prevent incident delirium was greater than preventing incident delirium by treating all or none of the patients.ConclusionsOur results support external validation of a prediction model parameterized to patient ICU admission acuity to predict a patients' risk for ICU delirium. Classification of patients' risk for ICU delirium by admission acuity may allow for efficient initiation of prevention measures based on individual risk profiles.
| S-EPMC7437909 | biostudies-literature
Project description:ImportanceAltered heart rate variability has been associated with autonomic dysfunction in a number of disease profiles, in this work we elucidate differences in the biomarker among patients with all-cause sepsis and coronavirus disease 2019.ObjectivesTo measure heart rate variability metrics in critically ill coronavirus disease 2019 patients with comparison to all-cause critically ill sepsis patients.Design setting and participantsRetrospective analysis of coronavirus disease 2019 patients admitted to an ICU for at least 24 hours at any of Emory Healthcare ICUs between March 2020 and April 2020 up to 5 days of ICU stay. The comparison group was a cohort of all-cause sepsis patients prior to coronavirus disease 2019 pandemic.Main outcomes and measuresContinuous waveforms were captured from the patient monitor. The electrocardiogram was then analyzed for each patient over a 300 seconds observational window that was shifted by 30 seconds in each iteration from admission till discharge. A total of 23 heart rate variability metrics were extracted in each iteration. We use the Kruskal-Wallis and Steel-Dwass tests (p < 0.05) for statistical analysis and interpretations of heart rate variability multiple measures.ResultsA total of 141 critically ill coronavirus disease 2019 patients met inclusion criteria, who were compared with 208 patients with all-cause sepsis. Three nonlinear markers, including the ratio of standard deviation derived from the Poincaré plot, sample entropy, and approximate entropy and four linear features, including mode of beat-to-beat interval, acceleration capacity, deceleration capacity, and the proportion of consecutive RR intervals that differ by more than 50 ms, were all statistically significant (p < 0.05) between the coronavirus disease 2019 and all-cause sepsis cohorts. The three nonlinear features and acceleration capacity, deceleration capacity, and beat-to-beat interval (mode) were statistically significant (p < 0.05) when comparing pairwise analysis among the combinations of survivors and nonsurvivors between the coronavirus disease 2019 and sepsis cohorts. Temporal analysis of the main markers showed low variability across the 5 days of analysis compared with sepsis patients.Conclusions and relevanceIn this descriptive statistical study, heart rate variability measures were found to be statistically different across critically ill patients infected with severe acute respiratory syndrome coronavirus 2 and distinct from bacterial sepsis.
| S-EPMC8718227 | biostudies-literature
Project description:Sepsis is defined by life-threatening organ dysfunction during infection and is the leading cause of death in hospitals. During sepsis, there is a high risk that new onset of atrial fibrillation (AF) can occur, which is associated with significant morbidity and mortality. Consequently, early prediction of AF during sepsis would allow testing of interventions in the intensive care unit (ICU) to prevent AF and its severe complications. In this paper, we present a novel automated AF prediction algorithm for critically ill sepsis patients using electrocardiogram (ECG) signals. From the heart rate signal collected from 5-min ECG, feature extraction is performed using the traditional time, frequency, and nonlinear domain methods. Moreover, variable frequency complex demodulation and tunable Q-factor wavelet-transform-based time-frequency methods are applied to extract novel features from the heart rate signal. Using a selected feature subset, several machine learning classifiers, including support vector machine (SVM) and random forest (RF), were trained using only the 2001 Computers in Cardiology data set. For testing the proposed method, 50 critically ill ICU subjects from the Medical Information Mart for Intensive Care (MIMIC) III database were used in this study. Using distinct and independent testing data from MIMIC III, the SVM achieved 80% sensitivity, 100% specificity, 90% accuracy, 100% positive predictive value, and 83.33% negative predictive value for predicting AF immediately prior to the onset of AF, while the RF achieved 88% AF prediction accuracy. When we analyzed how much in advance we can predict AF events in critically ill sepsis patients, the algorithm achieved 80% accuracy for predicting AF events 10 min early. Our algorithm outperformed a state-of-the-art method for predicting AF in ICU patients, further demonstrating the efficacy of our proposed method. The annotations of patients' AF transition information will be made publicly available for other investigators. Our algorithm to predict AF onset is applicable for any ECG modality including patch electrodes and wearables, including Holter, loop recorder, and implantable devices.
| S-EPMC8391773 | biostudies-literature
Project description:Background: Systemic inflammation is a whole body reaction that can have an infection-positive (i.e. sepsis) or infection-negative origin. It is important to distinguish between septic and non-septic presentations early and reliably, because this has significant therapeutic implications for critically ill patients. We hypothesized that a molecular classifier based on a small number of RNAs expressed in peripheral blood could be discovered that would: 1) determine which patients with systemic inflammation had sepsis; 2) be robust across independent patient cohorts; 3) be insensitive to disease severity; and 4) provide diagnostic utility. The overall goal of this study was to identify and validate such a molecular classifier. Methods and Findings: We conducted an observational, non-interventional study of adult patients recruited from tertiary intensive care units (ICU). Biomarker discovery was conducted with an Australian cohort (n = 105) consisting of sepsis patients and post -surgical patients with infection-negative systemic inflammation. Using this cohort, a four-gene classifier consisting of a combination of CEACAM4, LAMP1, PLA2G7 and PLAC8 RNA biomarkers was identified. This classifier, designated SeptiCyte® Lab, was externally validated using RT-qPCR and receiver operating characteristic (ROC) curve analysis in five cohorts (n = 345) from the Netherlands. Cohort 1 (n=59) consisted of unambiguous septic cases and infection-negative systemic inflammation controls; SeptiCyte® Lab gave an area under curve (AUC) of 0.96 (95% CI: 0.91-1.00). ROC analysis of a more heterogeneous group of patients (Cohorts 2-5; 249 patients after excluding 37 patients with infection likelihood possible) gave an AUC of 0.89 (95% CI: 0.85-0.93). Disease severity, as measured by Sequential Organ Failure Assessment (SOFA) score or the Acute Physiology and Chronic Health Evaluation (APACHE) IV score, was not a significant confounding variable. The diagnostic utility o f SeptiCyte® Lab was evaluated by comparison to various clinical and laboratory parameters that would be available to a clinician within 24 hours of ICU admission. SeptiCyte® Lab was significantly better at differentiating sepsis from infection-negative systemic inflammation than all tested parameters, both singly and in various logistic combinations. SeptiCyte® Lab more than halved the diagnostic error rate compared to PCT in all tested cohorts or cohort combinations. Conclusions: SeptiCyte® Lab is a rapid molecular assay that may be clinically useful in the management of ICU patients with systemic inflammation. SIRS and Sepsis ICU patients, admission samples Retrospective, mutli-site sutdy using retrospective physician adjudication as a comparator
2015-10-24 | E-GEOD-74224 | biostudies-arrayexpress
Project description:Objective: It is unclear whether the host response of gram-positive sepsis differs from gram-negative sepsis at a transcriptome level. Using microarray technology, we compared the gene-expression profiles of gram-positive sepsis and gram-negative sepsis in critically ill patients. Design: A prospective cross-sectional study. Setting: A 20-bed general intensive care unit of a tertiary referral hospital. Patients: Seventy-two patients admitted to the intensive care unit. Interventions: Intravenous blood was collected for leukocyte separation and RNA extraction. Microarray experiements were then performed examing the expression level of 19,232 genes in each sample. Measurements and Main Results: There was no difference in the expression profile between gram-positive and gram-negative sepsis. The finding remained unchanged even when genes with lower expression level were included or after statistical stringency was lowered. There were, however, ninety-four genes differentially expressed between sepsis and control patients. These genes included those involved in immune regulation, inflammation and mitochondrial function. Hierarchical cluster analysis confirmed that the difference in gene expression profile existed between sepsis and control patients, but not between gram-positive and gram-negative patients. Conclusion: Gram-positive and gram-negative sepsis share a common host response at a transcriptome level. These findings support the hypothesis that the septic response is non-specific and is designed to provide a more general response that can be elicited by a wide range of different micro-organisms. The study included seventy-two critically ill patients admitted to the intensive care unit (ICU) of Nepean Hospital, Sydney, Australia. Of these, fifty-five patients were diagnosed to have sepsis, as confirmed by microbiological culture. The remaining seventeen patients did not have sepsis and were therefore used as controls. The study was approved by the hospital ethics committee and informed consent was obtained from all patients or their relatives. Patient Samples. Whole blood was taken from each patient on admission to ICU. Neutrophils were separated from whole blood using density-gradient separation with Ficoll-PaqueP P(Amersham). Subsequent neutrophil RNA extraction was performed using guanidinium thiocyanate (Ambion). Microarray Experiment. The neutrophil RNA was converted to cDNA, fluorescently labeled and hybridized to its complimentary sequences on the microarray (Invitrogen). The fluorescent signals on each micrroarray were captured using the GenePix 4000B laser scanner (Axon Instruments). Expression level of each gene was represented by the intensity of its fluorescent signal. Data Extraction. All signal intensity values were processed using background-subtraction method. Prior to analysis, all values were log-transformed and normalized by fitting a print-tip group Lowess curve. Normalization minimizes bias due to dye chemistry, signal intensity or location of a gene on the array. It ensures the detection of genes that are truly differentially expressed, instead of those caused by experimental artifacts or variation in the hybridization process. After normalization, genes that had more than 50% of data missing were removed. We then selected genes that had at least 80% of the data showing two-fold changes from the geneâs median values. After filtering, 1617 genes were available for further analysis.
2010-05-25 | E-GEOD-6535 | biostudies-arrayexpress
Project description:The surprise question, "Would I be surprised if this patient died in the next 12 months?" is a tool to identify patients at high risk of death in the next year. Especially in the situation of an ICU admission, it is important to recognize patients who could and could not have the benefits of an intensive treatment in the ICU department.Design and settingA single-center, prospective, observational cohort study was conducted between April 2013 and April 2018, in ICU Gelre hospitals, location Apeldoorn.PatientsA total of 3,140 patients were included (57% male) with a mean age of 63.5 years. Seven-hundred thirteen patients (23%) died within 1 year.InterventionsThe physician answered three different surprise question's with either "yes" or "no": "I expect that the patient is going to survive the ICU admission" (surprise question 1), "I expect that the patient is going to survive the hospital stay" (surprise question 2), and "I expect that the patient is going to survive one year after ICU admission" (surprise question 3). We tested positive and negative predicted values of the surprise questions, the mean accuracy of the surprise questions, and kappa statistics.Measurements and main resultsThe positive and negative predictive values of the surprise questions for ICU admission, hospital admission, and 1-year survival were, respectively, 64%/94%, 59%/92%, and 60%/86%. Accordingly, the mean accuracy and kappa statistics were 93% (95% CI, 92-94%), κ equals to 0.43, 89% (95% CI, 88-90%), κ equals to 0.40, and 81% (95% CI, 80-82%), κ equals to 0.43.ConclusionsThe frequently overlooked simple and cheap surprise question is probably an useful tool to evaluate the prognosis of acutely admitted critically ill patients.
| S-EPMC7803926 | biostudies-literature
Project description:Platelet mitochondrial respiratory chain enzymes (that produce energy) are variably inhibited during human sepsis. Whether these changes occur even during other acute critical illness or are associated with impaired platelet aggregation and secretion (that consume energy) is not known. The aims of this study were firstly to compare platelet mitochondrial respiratory chain enzymes activity between patients with sepsis and those with cardiogenic shock, and secondly to study the relationship between platelet mitochondrial respiratory chain enzymes activity and platelet responsiveness to (exogenous) agonists in patients with sepsis.This was a prospective, observational, case-control study. Platelets were isolated from venous blood of 16 patients with severe sepsis or septic shock (free from antiplatelet drugs) and 16 others with cardiogenic shock, within 48 hours from admission to Intensive Care. Platelet mitochondrial respiratory chain enzymes activity was measured with spectrophotometry and expressed relative to citrate synthase activity, a marker of mitochondrial density. Platelet aggregation and secretion in response to adenosine di-phosphate (ADP), collagen, U46619 and thrombin receptor activating peptide were measured with lumiaggregometry only in patients with sepsis. In total, 16 healthy volunteers acted as controls for both spectrophotometry and lumiaggregometry.Platelets of patients with sepsis or cardiogenic shock similarly had lower mitochondrial nicotinamide adenine dinucleotide dehydrogenase (NADH) (P?<?0.001), complex I (P?=?0.006), complex I and III (P?<?0.001) and complex IV (P?<?0.001) activity than those of controls. Platelets of patients with sepsis were generally hypo-responsive to exogenous agonists, both in terms of maximal aggregation (P?<?0.001) and secretion (P?<?0.05). Lower mitochondrial NADH (R (2) 0.36; P?<?0.001), complex I (R (2) 0.38; P?<?0.001), complex I and III (R (2) 0.27; P?=?0.002) and complex IV (R (2) 0.43; P?<?0.001) activity was associated with lower first wave of aggregation with ADP.Several platelet mitochondrial respiratory chain enzymes are similarly inhibited during human sepsis and cardiogenic shock. In patients with sepsis, mitochondrial dysfunction is associated with general platelet hypo-responsiveness to exogenous agonists.ClinicalTrials.gov NCT00541827 . Registered 8 October 2007.
| S-EPMC4338849 | biostudies-literature
Project description:OBJECTIVES:ICU admission delays can negatively affect patient outcomes, but emergency department volume and boarding times may also affect these decisions and associated patient outcomes. We sought to investigate the effect of emergency department and ICU capacity strain on ICU admission decisions and to examine the effect of emergency department boarding time of critically ill patients on in-hospital mortality. DESIGN:A retrospective cohort study. SETTING:Single academic tertiary care hospital. PATIENTS:Adult critically ill emergency department patients for whom a consult for medical ICU admission was requested, over a 21-month period. INTERVENTIONS:None. MEASUREMENTS AND MAIN RESULTS:Patient data, including severity of illness (Mortality Probability Model III on Admission), outcomes of mortality and persistent organ dysfunction, and hourly census reports for the emergency department, for all ICUs and all adult wards were compiled. A total of 854 emergency department requests for ICU admission were logged, with 455 (53.3%) as "accept" and 399 (46.7%) as "deny" cases, with median emergency department boarding times 4.2 hours (interquartile range, 2.8-6.3?hr) and 11.7 hours (3.2-20.3?hr) and similar rates of persistent organ dysfunction and/or death 41.5% and 44.6%, respectively. Those accepted were younger (mean ± SD, 61?±?17 vs 65?±?18 yr) and more severely ill (median Mortality Probability Model III on Admission score, 15.3% [7.0-29.5%] vs 13.4% [6.3-25.2%]) than those denied admission. In the multivariable model, a full medical ICU was the only hospital-level factor significantly associated with a lower probability of ICU acceptance (odds ratio, 0.55 [95% CI, 0.37-0.81]). Using propensity score analysis to account for imbalances in baseline characteristics between those accepted or denied for ICU admission, longer emergency department boarding time after consult was associated with higher odds of mortality and persistent organ dysfunction (odds ratio, 1.77 [1.07-2.95]/log10 hour increase). CONCLUSIONS:ICU admission decisions for critically ill emergency department patients are affected by medical ICU bed availability, though higher emergency department volume and other ICU occupancy did not play a role. Prolonged emergency department boarding times were associated with worse patient outcomes, suggesting a need for improved throughput and targeted care for patients awaiting ICU admission.
| S-EPMC5899025 | biostudies-literature
Project description:Endotheliopathy is suggested to be an important feature of COVID-19 in hospitalized patients. To determine whether endotheliopathy is involved in COVID-19-associated mortality, markers of endothelial damage were assessed in critically ill COVID-19 patients upon intensive care unit (ICU) admission. Thirty-eight critically ill COVID-19 patients were included in this observational study, 10 of whom died in the ICU. Endothelial biomarkers, including soluble (s)E-selectin, sP-selectin, angiopoietin 1 and 2 (Ang-1 and Ang-2, respectively), soluble intercellular adhesion molecule 1 (sICAM-1), vascular endothelial growth factor (VEGF), soluble vascular endothelial (VE)-cadherin, and von Willebrand factor (vWf), were measured upon ICU admission. The ICU cohort was subsequently divided into survivors and non-survivors; Kaplan-Meier analysis was used to explore associations between biomarkers and survival, while receiver operating characteristic (ROC) curves were generated to determine their potential prognostic value. sE-selectin, sP-selectin, Ang-2, and sICAM-1 were significantly elevated in ICU non-survivors compared to survivors, and also associated with a higher mortality probability in the Kaplan-Meier analysis. The prognostic values of sE-selectin, Ang-2, and sICAM-1 from the generated ROC curves were greater than 0.85. Hence, we conclude that in our cohort, ICU non-survivors had higher levels of specific endothelial markers compared to survivors. Elevated levels of these markers upon ICU admission could possibly predict mortality in COVID-19.
| S-EPMC7832393 | biostudies-literature
Project description:Background: Systemic inflammation is a whole body reaction that can have an infection-positive (i.e. sepsis) or infection-negative origin. It is important to distinguish between septic and non-septic presentations early and reliably, because this has significant therapeutic implications for critically ill patients. We hypothesized that a molecular classifier based on a small number of RNAs expressed in peripheral blood could be discovered that would: 1) determine which patients with systemic inflammation had sepsis; 2) be robust across independent patient cohorts; 3) be insensitive to disease severity; and 4) provide diagnostic utility. The overall goal of this study was to identify and validate such a molecular classifier. Methods and Findings: We conducted an observational, non-interventional study of adult patients recruited from tertiary intensive care units (ICU). Biomarker discovery was conducted with an Australian cohort (n = 105) consisting of sepsis patients and post -surgical patients with infection-negative systemic inflammation. Using this cohort, a four-gene classifier consisting of a combination of CEACAM4, LAMP1, PLA2G7 and PLAC8 RNA biomarkers was identified. This classifier, designated SeptiCyte® Lab, was externally validated using RT-qPCR and receiver operating characteristic (ROC) curve analysis in five cohorts (n = 345) from the Netherlands. Cohort 1 (n=59) consisted of unambiguous septic cases and infection-negative systemic inflammation controls; SeptiCyte® Lab gave an area under curve (AUC) of 0.96 (95% CI: 0.91-1.00). ROC analysis of a more heterogeneous group of patients (Cohorts 2-5; 249 patients after excluding 37 patients with infection likelihood possible) gave an AUC of 0.89 (95% CI: 0.85-0.93). Disease severity, as measured by Sequential Organ Failure Assessment (SOFA) score or the Acute Physiology and Chronic Health Evaluation (APACHE) IV score, was not a significant confounding variable. The diagnostic utility o f SeptiCyte® Lab was evaluated by comparison to various clinical and laboratory parameters that would be available to a clinician within 24 hours of ICU admission. SeptiCyte® Lab was significantly better at differentiating sepsis from infection-negative systemic inflammation than all tested parameters, both singly and in various logistic combinations. SeptiCyte® Lab more than halved the diagnostic error rate compared to PCT in all tested cohorts or cohort combinations. Conclusions: SeptiCyte® Lab is a rapid molecular assay that may be clinically useful in the management of ICU patients with systemic inflammation. SIRS and Sepsis ICU patients, admission samples
2015-10-24 | GSE74224 | GEO