scholarly journals Immunometabolic signatures predict risk of progression to sepsis in COVID-19

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256784
Author(s):  
Ana Sofía Herrera-Van Oostdam ◽  
Julio E. Castañeda-Delgado ◽  
Juan José Oropeza-Valdez ◽  
Juan Carlos Borrego ◽  
Joel Monárrez-Espino ◽  
...  
Keyword(s):  
Critically Ill ◽  
Area Under The Curve ◽  
Clinical Findings ◽  
Mortality Prediction ◽  
Hospital Treatment ◽  
Neutrophil Lymphocyte Ratio ◽  
Differential Correlation ◽  
Mild Disease ◽  
Diagnosis And Prognosis ◽  
Risk Of Progression

Viral sepsis has been proposed as an accurate term to describe all multisystemic dysregulations and clinical findings in severe and critically ill COVID-19 patients. The adoption of this term may help the implementation of more accurate strategies of early diagnosis, prognosis, and in-hospital treatment. We accurately quantified 110 metabolites using targeted metabolomics, and 13 cytokines/chemokines in plasma samples of 121 COVID-19 patients with different levels of severity, and 37 non-COVID-19 individuals. Analyses revealed an integrated host-dependent dysregulation of inflammatory cytokines, neutrophil activation chemokines, glycolysis, mitochondrial metabolism, amino acid metabolism, polyamine synthesis, and lipid metabolism typical of sepsis processes distinctive of a mild disease. Dysregulated metabolites and cytokines/chemokines showed differential correlation patterns in mild and critically ill patients, indicating a crosstalk between metabolism and hyperinflammation. Using multivariate analysis, powerful models for diagnosis and prognosis of COVID-19 induced sepsis were generated, as well as for mortality prediction among septic patients. A metabolite panel made of kynurenine/tryptophan ratio, IL-6, LysoPC a C18:2, and phenylalanine discriminated non-COVID-19 from sepsis patients with an area under the curve (AUC (95%CI)) of 0.991 (0.986–0.995), with sensitivity of 0.978 (0.963–0.992) and specificity of 0.920 (0.890–0.949). The panel that included C10:2, IL-6, NLR, and C5 discriminated mild patients from sepsis patients with an AUC (95%CI) of 0.965 (0.952–0.977), with sensitivity of 0.993(0.984–1.000) and specificity of 0.851 (0.815–0.887). The panel with citric acid, LysoPC a C28:1, neutrophil-lymphocyte ratio (NLR) and kynurenine/tryptophan ratio discriminated severe patients from sepsis patients with an AUC (95%CI) of 0.829 (0.800–0.858), with sensitivity of 0.738 (0.695–0.781) and specificity of 0.781 (0.735–0.827). Septic patients who survived were different from those that did not survive with a model consisting of hippuric acid, along with the presence of Type II diabetes, with an AUC (95%CI) of 0.831 (0.788–0.874), with sensitivity of 0.765 (0.697–0.832) and specificity of 0.817 (0.770–0.865).

Inflammatory biomarkers as pronostic factors of mortality in critical ILL oncology patients at ICU.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e14535-e14535
Author(s):  
Carlos Aliaga Macha ◽  
Thanya Runciman ◽  
Carlos F. Carracedo
Keyword(s):  
Prognostic Factors ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Inflammatory Markers ◽  
Area Under The Curve ◽  
Roc Curves ◽  
P Value ◽  
Predictive Values ◽  
Neutrophil Lymphocyte Ratio ◽  
Lymphocyte Ratio

e14535 Background: Inflammatory markers have been used as prognostic factors in multiple malignancies.In cancer patients, critically ill, the utility of these have limited data.The aim of our study is to determine whether neutrophil lymphocyte ratio (NLR) or lymphocyte platelet ratio(PLR) are prognostic factors for mortality in critically ill patients. Methods: We retrospectively analyzed data of 79 patients with solid tumors admitted to ICU at Sanna-Aliada Clinic between January 2018 to December 2018. Inflammatory markers results were obtained from laboratory tests performed during the first 24h of admission to ICU. Receiving operating characteristic (ROC) curves were constructed and the sensitivity, specificity, predictive values and probability indicators for the NLR and PLR. Results: A total of 79 patients were assessed, 39 women and 40 men. The average age was 60.28 years, median of 61 ( 18 to 91). 51.9% had metastatic disease. The most frequent places were lung 12 (15.2 %) and brain 9 (11,4%) . The main cause for admission to ICU was infectious disease (40.5%). The analysis of normality (Kolmogorov-Smirnov test) indicates that the variables age, hemoglobin, leukocytes, platelets, neutrophils, lymphocytes, have a normal deviation while the other variables: lactate, PCR, neutrophil to lymphocyte ratio (NLR) , Platelet to lymphocyte ratio (PLR) are not distributed normally. Regarding mortality, 44 patients were alive at 30 days (66.7%), and 30 (45.5%) were alive at 90 days. The average stay in the ICU was 8.43 days, with a median of 6, (SD 7.17, 1 to 40 days), 22.8% died in the ICU. The evaluation of PLR and NLR as a mortality marker is significant for the group of patients admitted to the ICU due to a noninfectious pathology, generating an area under the curve (AUC) of 0.706 for NLR (95% CI, 0.535 - 0.876, p-value = 0.035) and 0.767 for PLR (95% CI, 0.615-0.918; p-value = 0.006); the optimal cut point by Youden’s index for NLR was 8.29 and 267.94 for PLR (Sensitivity: 76%, Specificity: 67%). In contrast, the group with infectious pathology, the AUC was 0.47 for NLR (p = 0.78) and 0.42 for PLR (p = 0.44). The relationship of the biomarkers with stay in ICU was also evaluated, finding a statistically significant association with the lactate value (p = 0.024, Kruskal-Wallis) Conclusions: Inflammatory markers are useful as predictive markers of mortality in critically ill patients due to non-infectious causes. The lactate value serves as a predictive factor of stay in the ICU for all the patients. We suggest carrying out prospective studies to confirm the validity of our findings.

scholarly journals Prognostic biomarkers in predicting mortality in respiratory patients with ventilator-associated pneumonia

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Nermeen A. Abdelaleem ◽  
Hoda A. Makhlouf ◽  
Eman M. Nagiub ◽  
Hassan A. Bayoumi
Keyword(s):  
Sofa Score ◽  
Diagnostic Performance ◽  
Mortality Prediction ◽  
Ventilator Associated Pneumonia ◽  
Distribution Width ◽  
Mechanically Ventilated ◽  
Neutrophil Lymphocyte Ratio ◽  
Prognostic Accuracy ◽  
Severity Scores ◽  
Critical Patients

Abstract Background Ventilator-associated pneumonia (VAP) is the most common nosocomial infection. Red cell distribution width (RDW) and neutrophil-lymphocyte ratio (NLR) are prognostic factors to mortality in different diseases. The aim of this study is to evaluate prognostic efficiency RDW, NLR, and the Sequential Organ Failure Assessment (SOFA) score for mortality prediction in respiratory patients with VAP. Results One hundred thirty-six patients mechanically ventilated and developed VAP were included. Clinical characteristics and SOFA score on the day of admission and at diagnosis of VAP, RDW, and NLR were assessed and correlated to mortality. The average age of patients was 58.80 ± 10.53. These variables had a good diagnostic performance for mortality prediction AUC 0.811 for SOFA at diagnosis of VAP, 0.777 for RDW, 0.728 for NLR, and 0.840 for combined of NLR and RDW. The combination of the three parameters demonstrated excellent diagnostic performance (AUC 0.889). A positive correlation was found between SOFA at diagnosis of VAP and RDW (r = 0.446, P < 0.000) and with NLR (r = 0.220, P < 0.010). Conclusions NLR and RDW are non-specific inflammatory markers that could be calculated quickly and easily via routine hemogram examination. These markers have comparable prognostic accuracy to severity scores. Consequently, RDW and NLR are simple, yet promising markers for ICU physicians in monitoring the clinical course, assessment of organ dysfunction, and predicting mortality in mechanically ventilated patients. Therefore, this study recommends the use of blood biomarkers with the one of the simplest ICU score (SOFA score) in the rapid diagnosis of critical patients as a daily works in ICU.

scholarly journals Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Espen Jimenez-Solem ◽  
Tonny S. Petersen ◽  
Casper Hansen ◽  
Christian Hansen ◽  
Christina Lioma ◽  
...  
Keyword(s):  
Prediction Models ◽  
Severe Disease ◽  
External Validation ◽  
Area Under The Curve ◽  
Predictive Performance ◽  
Mortality Prediction ◽  
Icu Admission ◽  
Risk Of Death ◽  
Online Risk ◽  
The United Kingdom

AbstractPatients with severe COVID-19 have overwhelmed healthcare systems worldwide. We hypothesized that machine learning (ML) models could be used to predict risks at different stages of management and thereby provide insights into drivers and prognostic markers of disease progression and death. From a cohort of approx. 2.6 million citizens in Denmark, SARS-CoV-2 PCR tests were performed on subjects suspected for COVID-19 disease; 3944 cases had at least one positive test and were subjected to further analysis. SARS-CoV-2 positive cases from the United Kingdom Biobank was used for external validation. The ML models predicted the risk of death (Receiver Operation Characteristics—Area Under the Curve, ROC-AUC) of 0.906 at diagnosis, 0.818, at hospital admission and 0.721 at Intensive Care Unit (ICU) admission. Similar metrics were achieved for predicted risks of hospital and ICU admission and use of mechanical ventilation. Common risk factors, included age, body mass index and hypertension, although the top risk features shifted towards markers of shock and organ dysfunction in ICU patients. The external validation indicated fair predictive performance for mortality prediction, but suboptimal performance for predicting ICU admission. ML may be used to identify drivers of progression to more severe disease and for prognostication patients in patients with COVID-19. We provide access to an online risk calculator based on these findings.

scholarly journals Clinical risk scores for the early prediction of severe outcomes in patients hospitalized for COVID-19

2021 ◽  
Author(s):  
Walter Ageno ◽  
◽  
Chiara Cogliati ◽  
Martina Perego ◽  
Domenico Girelli ◽  
...  
Keyword(s):  
Hospital Admission ◽  
Predictive Value ◽  
Clinical Laboratory ◽  
Area Under The Curve ◽  
Orotracheal Intubation ◽  
Risk Scores ◽  
Algorithm Selection ◽  
Derivation Cohort ◽  
Neutrophil Lymphocyte Ratio ◽  
Predictive Risk

AbstractCoronavirus disease of 2019 (COVID-19) is associated with severe acute respiratory failure. Early identification of high-risk COVID-19 patients is crucial. We aimed to derive and validate a simple score for the prediction of severe outcomes. A retrospective cohort study of patients hospitalized for COVID-19 was carried out by the Italian Society of Internal Medicine. Epidemiological, clinical, laboratory, and treatment variables were collected at hospital admission at five hospitals. Three algorithm selection models were used to construct a predictive risk score: backward Selection, Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest. Severe outcome was defined as the composite of need for non-invasive ventilation, need for orotracheal intubation, or death. A total of 610 patients were included in the analysis, 313 had a severe outcome. The subset for the derivation analysis included 335 patients, the subset for the validation analysis 275 patients. The LASSO selection identified 6 variables (age, history of coronary heart disease, CRP, AST, D-dimer, and neutrophil/lymphocyte ratio) and resulted in the best performing score with an area under the curve of 0.79 in the derivation cohort and 0.80 in the validation cohort. Using a cut-off of 7 out of 13 points, sensitivity was 0.93, specificity 0.34, positive predictive value 0.59, and negative predictive value 0.82. The proposed score can identify patients at low risk for severe outcome who can be safely managed in a low-intensity setting after hospital admission for COVID-19.

scholarly journals Usefulness of sialic acid for diagnosis of sepsis in critically ill patients: a retrospective study

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Bo Yao ◽  
Wen-juan Liu ◽  
Di Liu ◽  
Jin-yan Xing ◽  
Li-juan Zhang
Keyword(s):  
Sialic Acid ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Incomplete Data ◽  
Acid Level ◽  
Operating Characteristic ◽  
Area Under The Curve ◽  
Diagnostic Efficacy ◽  
Receiver Operating Characteristic Curves ◽  
Sensitivity Specificity

Abstract Background Early diagnosis of sepsis is very important. It is necessary to find effective and adequate biomarkers in order to diagnose sepsis. In this study, we compared the value of sialic acid and procalcitonin for diagnosing sepsis. Methods Newly admitted intensive care unit patients were enrolled from January 2019 to June 2019. We retrospectively collected patient data, including presence of sepsis or not, procalcitonin level and sialic acid level. Receiver operating characteristic curves for the ability of sialic acid, procalcitonin and combination of sialic acid and procalcitonin to diagnose sepsis were carried out. Results A total of 644 patients were admitted to our department from January 2019 to June 2019. The incomplete data were found in 147 patients. Finally, 497 patients data were analyzed. The sensitivity, specificity and area under the curve for the diagnosis of sepsis with sialic acid, procalcitonin and combination of sialic acid and procalcitonin were 64.2, 78.3%, 0.763; 67.9, 84.0%, 0.816 and 75.2, 84.6%, 0.854. Moreover, sialic acid had good values for diagnosing septic patients with viral infection, with 87.5% sensitivity, 82.2% specificity, and 0.882 the area under the curve. Conclusions Compared to procalcitonin, sialic acid had a lower diagnostic efficacy for diagnosing sepsis in critically ill patients. However, the combination of sialic acid and procalcitonin had a higher diagnostic efficacy for sepsis. Moreover, sialic acid had good value for diagnosing virus-induced sepsis.

scholarly journals Short term survival of critically ill COVID-19 Egyptian patients on assisted ventilation treated by either Dexamethasone or Tocilizumab

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alaa Rashad ◽  
Sherif Mousa ◽  
Hanaa Nafady-Hego ◽  
Asmaa Nafady ◽  
Hamed Elgendy
Keyword(s):  
Critically Ill ◽  
Cox Regression ◽  
Ferritin Level ◽  
Short Term ◽  
Term Survival ◽  
Cox Regression Analysis ◽  
Neutrophil Lymphocyte Ratio ◽  
Lymphocyte Ratio ◽  
Dexamethasone Group ◽  
Short Term Survival

AbstractTocilizumab (TCZ) and Dexamethasone are used for the treatment of critically ill COVID-19 patients. We compared the short-term survival of critically ill COVID-19 patients treated with either TCZ or Dexamethasone. 109 critically ill COVID-19 patients randomly assigned to either TCZ therapy (46 patients) or pulse Dexamethasone therapy (63 patients). Age, sex, neutrophil/ lymphocyte ratio, D-dimer, ferritin level, and CT chest pattern were comparable between groups. Kaplan–Meier survival analysis showed better survival in Dexamethasone group compared with TCZ (P = 0.002), patients didn’t need vasopressor at admission (P < 0.0001), patients on non-invasive ventilation compared to patients on mechanical ventilation (P<0.0001 ), and in patients with ground glass pattern in CT chest (P<0.0001 ) compared with those who have consolidation. Cox regression analysis showed that, TCZ therapy (HR = 2.162, 95% CI, 1.144–4.087, P <0.0001) compared with Dexamethasone group, higher neutrophil/Lymphocyte ratio (HR = 2.40, CI, 1.351–4.185, P = 0.003), lower PaO2/FiO2, 2 days after treatment, (HR = 1.147, 95% CI, 1.002–1.624, P < 0.0001) independently predicted higher probability of mortality. Dexamethasone showed better survival in severe COVID-19 compared to TCZ. Considering the risk factors mentioned here is crucial when dealing with severe COVID-19 cases.Clinical trial registration No clinicalTrials.gov: Nal protocol approved by Hospital Authorities, for data collection and for participation in CT04519385 (19/08/2020).

Performance of Automated Telemetry in Diagnosing QT Prolongation in Critically Ill Patients

2021 ◽  
Vol 30 (6) ◽  
pp. 466-470
Author(s):  
Enrique Calvo-Ayala ◽  
Vince Procopio ◽  
Hayk Papukhyan ◽  
Girish B. Nair
Keyword(s):  
Critically Ill ◽  
Critically Ill Patients ◽  
Demographic Data ◽  
Area Under The Curve ◽  
Qt Prolongation ◽  
Altman Analysis ◽  
Poor Agreement ◽  
Bland Altman Analysis ◽  
Prolonged Qt ◽  
Definition Of

Background QT prolongation increases the risk of ventricular arrhythmia and is common among critically ill patients. The gold standard for QT measurement is electrocardiography. Automated measurement of corrected QT (QTc) by cardiac telemetry has been developed, but this method has not been compared with electrocardiography in critically ill patients. Objective To compare the diagnostic performance of QTc values obtained with cardiac telemetry versus electrocardiography. Methods This prospective observational study included patients admitted to intensive care who had an electrocardiogram ordered simultaneously with cardiac telemetry. Demographic data and QTc determined by electrocardiography and telemetry were recorded. Bland-Altman analysis was done, and correlation coefficient and receiver operating characteristic (ROC) coefficient were calculated. Results Fifty-one data points were obtained from 43 patients (65% men). Bland-Altman analysis revealed poor agreement between telemetry and electrocardiography and evidence of fixed and proportional bias. Area under the ROC curve for QTc determined by telemetry was 0.9 (P &lt; .001) for a definition of prolonged QT as QTc ≥ 450 milliseconds in electrocardiography (sensitivity, 88.89%; specificity, 83.33%; cutoff of 464 milliseconds used). Correlation between the 2 methods was only moderate (r = 0.6, P &lt; .001). Conclusions QTc determination by telemetry has poor agreement and moderate correlation with electrocardiography. However, telemetry has an acceptable area under the curve in ROC analysis with tolerable sensitivity and specificity depending on the cutoff used to define prolonged QT. Cardiac telemetry should be used with caution in critically ill patients.

scholarly journals COVID-IRS: A novel predictive score for risk of invasive mechanical ventilation in patients with COVID-19

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0248357
Author(s):  
José Antonio Garcia-Gordillo ◽  
Antonio Camiro-Zúñiga ◽  
Mercedes Aguilar-Soto ◽  
Dalia Cuenca ◽  
Arturo Cadena-Fernández ◽  
...  
Keyword(s):  
Mechanical Ventilation ◽  
Systemic Disease ◽  
Invasive Mechanical Ventilation ◽  
Area Under The Curve ◽  
Lactic Dehydrogenase ◽  
Total Sample ◽  
Adverse Outcomes ◽  
Predictive Score ◽  
Neutrophil Lymphocyte Ratio ◽  
Predictive Variables

Background Coronavirus disease 2019 (COVID-19) is a systemic disease that can rapidly progress into acute respiratory failure and death. Timely identification of these patients is crucial for a proper administration of health-care resources. Objective To develop a predictive score that estimates the risk of invasive mechanical ventilation (IMV) among patients with COVID-19. Study design Retrospective cohort study of 401 COVID-19 patients diagnosed from March 12, to August 10, 2020. The score development cohort comprised 211 patients (52.62% of total sample) whereas the validation cohort included 190 patients (47.38% of total sample). We divided participants according to the need of invasive mechanical ventilation (IMV) and looked for potential predictive variables. Results We developed two predictive scores, one based on Interleukin-6 (IL-6) and the other one on the Neutrophil/Lymphocyte ratio (NLR), using the following variables: respiratory rate, SpO2/FiO2 ratio and lactic dehydrogenase (LDH). The area under the curve (AUC) in the development cohort was 0.877 (0.823–0.931) using the NLR based score and 0.891 (0.843–0.939) using the IL-6 based score. When compared with other similar scores developed for the prediction of adverse outcomes in COVID-19, the COVID-IRS scores proved to be superior in the prediction of IMV. Conclusion The COVID-IRS scores accurately predict the need for mechanical ventilation in COVID-19 patients using readily available variables taken upon admission. More studies testing the applicability of COVID-IRS in other centers and populations, as well as its performance as a triage tool for COVID-19 patients are needed.

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