Thiol/Disulfide Homeostasis as an Early Biomarker to Differentiate Sepsis from Pneumonia in Intensive Care Units

2020 ◽  
Vol 23 ◽  
Author(s):  
Esra Cakir ◽  
Gamze Gok ◽  
Ozcan Erel ◽  
Isil Ozkocak Turan
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Early Period ◽  
Sepsis Group ◽  
Group Index ◽  
Total Thiol ◽  
Potential Biomarker ◽  
Early Biomarker ◽  
Index 2 ◽  
Research Period

Background: It is possible that patients with pneumonia also may have sepsis and the separation of these two clinical entities may induce some trouble to clinicians Objective: In order to separate a patient with pneumonia and a patient with sepsis, we qualify thiol/disulfide homeostasis as a potential biomarker. Method: This study designed between February 2018 – February 2019 prospectively. All patients in the intensive care unit with pneumonia and sepsis were enrolled in the study. At the time of hospitalization, thiol/disulfide homeostasis was measured. Patients diagnosed with sepsis and pneumonia were compared, in regards to thiol/disulfide homeostasis. Conclusion: In this study, we showed that thiol/disulfide homeostasis can be used as new markers in the early period in order to separate patients with sepsis and patients with pneumonia. Results: During research period, 103 patients with sepsis and 120 patients with pneumonia were enrolled into the study. When we compared native-thiol, total-thiol, and disulfide levels in both sepsis and pneumonia patients, we had similar results (p>0.05). In sepsis group, index-1 (disulfide/native thiol ratio) and index-2 (disulfide/total thiol ratio) were found out to be statistically higher than the pneumonia group, and index-3 (native thiol/total thiol ratio) was statistically lower than the pneumonia group (p=0.020, p= 0.021, p=0.021, respectively).

scholarly journals Procalcitonin in patients with influenza A (H1N1) infection and acute respiratory failure

2011 ◽  
Vol 9 (1) ◽  
pp. 52-55
Author(s):  
Péricles Almeida Delfino Duarte ◽  
Carla Sakuma de Oliveira Bredt ◽  
Gerson Luís Bredt Jr ◽  
Amaury César Jorge ◽  
Alisson Venazzi ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Respiratory Failure ◽  
Hospital Mortality ◽  
Acute Respiratory Failure ◽  
Influenza A ◽  
Laboratory Data ◽  
Sepsis Group ◽  
Influenza A H1n1 ◽  
Low Serum

ABSTRACT Objective: To verify serum procalcitonin levels of patients with acute respiratory failure secondary to influenza A (H1N1) upon their admission to the Intensive Care Unit and to compare these results to values found in patients with sepsis and trauma admitted to the same unit. Methods: Analysis of records of patients infected with influenza A (H1N1) and respiratory failure admitted to the General Intensive Care Unit during in a period of 60 days. The values of serum procalcitonin and clinical and laboratory data were compared to those of all patients admitted with sepsis or trauma in the previous year. Results: Among patients with influenza A (H1N1) (n = 16), the median serum procalcitonin level upon admission was 0.11 ng/mL, lower than in the sepsis group (p < 0.001) and slightly lower than in trauma patients. Although the mean values were low, serum procalcitonin was a strong predictor of hospital mortality in patients with influenza A (H1N1). Conclusion: Patients with influenza A (H1N1) with severe acute respiratory failure presented with low serum procalcitonin values upon admission, although their serum levels are predictors of hospital mortality. The kinetics study of this biomarker may be a useful tool in the management of this group of patients.

scholarly journals Quantification of Volatile Acetone Oligomers Using Ion-Mobility Spectrometry

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Tobias Hüppe ◽  
Dominik Lorenz ◽  
Felix Maurer ◽  
Tobias Fink ◽  
Ramona Klumpp ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Critically Ill ◽  
Ion Mobility Spectrometry ◽  
Ion Mobility ◽  
Critically Ill Patients ◽  
Ambient Air ◽  
Coefficient Of Determination ◽  
Acetone Concentration ◽  
Potential Biomarker

Background. Volatile acetone is a potential biomarker that is elevated in various disease states. Measuring acetone in exhaled breath is complicated by the fact that the molecule might be present as both monomers and dimers, but in inconsistent ratios. Ignoring the molecular form leads to incorrect measured concentrations. Our first goal was to evaluate the monomer-dimer ratio in ambient air, critically ill patients, and rats. Our second goal was to confirm the accuracy of the combined (monomer and dimer) analysis by comparison to a reference calibration system. Methods. Volatile acetone intensities from exhaled air of ten intubated, critically ill patients, and ten ventilated Sprague-Dawley rats were recorded using ion-mobility spectrometry. Acetone concentrations in ambient air in an intensive care unit and in a laboratory were determined over 24 hours. The calibration reference was pure acetone vaporized by a gas generator at concentrations from 5 to 45 ppbv (parts per billion by volume). Results. Acetone concentrations in ambient laboratory air were only slightly greater (5.6 ppbv; 95% CI 5.1–6.2) than in ambient air in an intensive care unit (5.1 ppbv; 95% CI 4.4–5.5; p < 0.001 ). Exhaled acetone concentrations were only slightly greater in rats (10.3 ppbv; 95% CI 9.7–10.9) than in critically ill patients (9.5 ppbv; 95% CI 7.9–11.1; p < 0.001 ). Vaporization yielded acetone monomers (1.3–5.3 mV) and dimers (1.4–621 mV). Acetone concentrations (ppbv) and corresponding acetone monomer and dimer intensities (mV) revealed a high coefficient of determination (R2 = 0.96). The calibration curve for acetone concentration (ppbv) and total acetone (monomers added to twice the dimers; mV) was described by the exponential growth 3-parameter model, with an R2 = 0.98. Conclusion. The ratio of acetone monomer and dimer is inconsistent and varies in ambient air from place-to-place and across individual humans and rats. Monomers and dimers must therefore be considered when quantifying acetone. Combining the two accurately assesses total volatile acetone.

scholarly journals A Comparison of miRNAs with Proinflammatory Cytokines and Procalcitonin in the Diagnosis, Treatment, Follow Up and Prognosis of Sepsis in the Intensive Care Unit

2021 ◽  
Author(s):  
Semiha Orhan ◽  
Kemal Yetıs Gulsoy ◽  
Esra Orenlili Yaylagul ◽  
Halit Bugra Koca ◽  
Lutfi Yavuz ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Proinflammatory Cytokines ◽  
Interleukin 1 ◽  
Interleukin 10 ◽  
Sepsis Group ◽  
Control Group ◽  
Dependent Manner ◽  
Serum Samples

Abstract Background: The development of sepsis, the low efficacy of antibiotics used, long-term antibiotic use, and the development of resistance to antibiotics are significant problems in patients in intensive care units. The use of biological markers is promising for the diagnosis and treatment of sepsis. In this study, proinflammatory cytokines, procalcitonin and four miRNA expressions were analyzed in a time-dependent manner in a patient group and control group, and the correlations between them were examined.Material and Method: The study included 30 patients in the intensive care unit who were diagnosed with sepsis and applied with SOFA and APACHE-2 scoring and 30 control subjects. Serum samples were taken at 24 hours, 72 hours, and on the 7th day. Analyses according to time were made of interleukin-1 beta, interleukin-6, interleukin-10, TNF-alpha, procalcitonin and four miRNAs (miR-122, miR-146a, miR-150, and miR-223) in the collected samples and comparisons were made between the patients and the control group. Results: At 24 hours, a decrease was observed in the miRNA-146a, miRNA-150, and miRNA-122 values and an increase in the miRNA-223 values in the sepsis group compared to the control group. At 72 hours, a decrease was observed in the miRNA-146a, miRNA-150, miRNA-122, and miRNA-223 values in the sepsis group compared to the control group.Conclusion: When procalcitonin and inflammatory cytokines were compared with selected miRNAs in the diagnosis, treatment follow-up, and prognosis of sepsis in the intensive care unit, a correlation between procalcitonin levels, proinflammatory cytokines and miRNA-150, miRNA-146a, and miRNA-223 was found.

scholarly journals Prediction of intensive care unit mortality based on missing events

2021 ◽  
Author(s):  
Tatsuma Shoji ◽  
Hiroshi Yonekura ◽  
Yoshiharu Sato ◽  
yohei Kawashiki
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Missing Values ◽  
Integrated Model ◽  
Mortality Prediction ◽  
Sepsis Group ◽  
Admission Diagnosis ◽  
Complementary Subset ◽  
Imputation Approach

Abstract BackgroundThe increasing availability of electronic health records has made it possible to construct and implement models for predicting intensive care unit (ICU) mortality using machine learning. However, the algorithms used are not clearly described, and the performance of the model remains low owing to several missing values, which is unavoidable in big databases.MethodsWe developed an algorithm for subgrouping patients based on missing event patterns using the Philips eICU Research Institute (eRI) database as an example. The eRI database contains data associated with 200,859 ICU admissions from many hospitals (>400) and is freely available. We then constructed a model for each subgroup using random forest classifiers and integrated the models. Finally, we compared the performance of the integrated model with the Acute Physiology and Chronic Health Evaluation (APACHE) scoring system, one of the best known predictors of patient mortality, and the imputation approach-based model.ResultsSubgrouping and patient mortality prediction were separately performed on two groups: the sepsis group (the ICU admission diagnosis of which is sepsis) and the non-sepsis group (a complementary subset of the sepsis group). The subgrouping algorithm identified a unique, clinically interpretable missing event patterns and divided the sepsis and non-sepsis groups into five and seven subgroups, respectively. The integrated model, which comprises five models for the sepsis group or seven models for the non-sepsis group, greatly outperformed the APACHE IV or IVa, with an area under the receiver operating characteristic (AUROC) of 0.91 (95% confidence interval 0.89–0.92) compared with 0.79 (0.76–0.81) for the APACHE system in the sepsis group and an AUROC of 0.90 (0.89–0.91) compared with 0.86 (0.85–0.87) in the non-sepsis group. Moreover, our model outperformed the imputation approach-based model, which had an AUROC of 0.85 (0.83–0.87) and 0.87 (0.86–0.88) in the sepsis and non-sepsis groups, respectively.ConclusionsWe developed a method to predict patient mortality based on missing event patterns. Our method more accurately predicts patient mortality than others. Our results indicate that subgrouping, based on missing event patterns, instead of imputation is essential and effective for machine learning against patient heterogeneity.Trial registrationNot applicable.

scholarly journals The alteration of renal function in sepsis and septic shock patients in pediatric intensive care unit

2011 ◽  
Vol 51 (2) ◽  
pp. 89
Author(s):  
Feiby Julianto ◽  
Adrian Umboh ◽  
Suryadi Tatura
Keyword(s):  
Intensive Care Unit ◽  
Septic Shock ◽  
Renal Function ◽  
Intensive Care ◽  
Mortality Rate ◽  
Correlation Coefficient ◽  
Pediatric Intensive Care Unit ◽  
Pediatric Intensive Care ◽  
Sepsis Group ◽  
Sepsis And Septic Shock

Background Sepsis is a commonly seen emergency case in the pediatric intensive care unit.1 Severe sepsis mortality rate in developed country andin developing country such as Indonesia are 9% and 50-70%, respectively. Furthennore, the mortality rate in septic shock is 80%.2 Several researches documented increasing rate of acute kidney injury (AKI) incidence correlated 'With sepsis. Clinical intervention identification may decrease AKI and sepsis incidence.Objective To identify the correlation between incidence of AKI in sepsis and in septic shock patients who was treated in pediatric intensive care unit (PICU).Methods A cross sectional study was perfonned in 37 patients diagnosed as sepsis according ACCP/SCCM criteria for children aged 1 month to 13 years. The study was conducted in Pediatric Department, Prof. Dr. R.D. Kandou hospital from April 2009 to June 2009.Results From 37 sepsis patients, 27 were boys and 10 were girls. In the sepsis group (n=27) 10 had AKI, and in the septic shock group (n= 10) had AKI. Phi correlation coefficient applied to statistically analyzed sepsis in correlation with AKI (creatinin serum and GFR). Significant Phi correlation coefficient was (r=0.117; P> 0.05)Conclusions The study concludes that there is no correlation of renal function impainnent Mth sepsis and septic shock.

scholarly journals Prediction of intensive care unit mortality based on missing events

2021 ◽  
Author(s):  
Tatsuma Shoji ◽  
Hiroshi Yonekura ◽  
Sato Yoshiharu ◽  
Yohei Kawasaki
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Missing Values ◽  
Integrated Model ◽  
Mortality Prediction ◽  
Sepsis Group ◽  
Admission Diagnosis ◽  
Complementary Subset ◽  
Imputation Approach

AbstractBackgroundThe increasing availability of electronic health records has made it possible to construct and implement models for predicting intensive care unit (ICU) mortality using machine learning. However, the algorithms used are not clearly described, and the performance of the model remains low owing to several missing values, which is unavoidable in big databases.MethodsWe developed an algorithm for subgrouping patients based on missing event patterns using the Philips eICU Research Institute (eRI) database as an example. The eRI database contains data associated with 200,859 ICU admissions from many hospitals (>400) and is freely available. We then constructed a model for each subgroup using random forest classifiers and integrated the models. Finally, we compared the performance of the integrated model with the Acute Physiology and Chronic Health Evaluation (APACHE) scoring system, one of the best known predictors of patient mortality, and the imputation approach-based model.ResultsSubgrouping and patient mortality prediction were separately performed on two groups: the sepsis group (the ICU admission diagnosis of which is sepsis) and the non-sepsis group (a complementary subset of the sepsis group). The subgrouping algorithm identified a unique, clinically interpretable missing event patterns and divided the sepsis and non-sepsis groups into five and seven subgroups, respectively. The integrated model, which comprises five models for the sepsis group or seven models for the non-sepsis group, greatly outperformed the APACHE IV or IVa, with an area under the receiver operating characteristic (AUROC) of 0.91 (95% confidence interval 0.89–0.92) compared with 0.79 (0.76–0.81) for the APACHE system in the sepsis group and an AUROC of 0.90 (0.89–0.91) compared with 0.86 (0.85–0.87) in the non-sepsis group. Moreover, our model outperformed the imputation approach-based model, which had an AUROC of 0.85 (0.83–0.87) and 0.87 (0.86–0.88) in the sepsis and non-sepsis groups, respectively.ConclusionsWe developed a method to predict patient mortality based on missing event patterns. Our method more accurately predicts patient mortality than others. Our results indicate that subgrouping, based on missing event patterns, instead of imputation is essential and effective for machine learning against patient heterogeneity.Trial registrationNot applicable.

scholarly journals Neutrophil-Lymphocyte Ratio as a Potential Biomarker for Delirium in the Intensive Care Unit

2021 ◽  
Vol 12 ◽  
Author(s):  
Chai Lee Seo ◽  
Jin Young Park ◽  
Jaesub Park ◽  
Hesun Erin Kim ◽  
Jaehwa Cho ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Early Detection ◽  
Inflammatory Markers ◽  
Inflammatory Marker ◽  
Neutrophil Lymphocyte Ratio ◽  
Lymphocyte Ratio ◽  
Icu Admission ◽  
Potential Biomarker ◽  
Significant Difference

Background: Recognition and early detection of delirium in the intensive care unit (ICU) is essential to improve ICU outcomes. To date, neutrophil-lymphocyte ratio (NLR), one of inflammatory markers, has been proposed as a potential biomarker for brain disorders related to neuroinflammation. This study aimed to investigate whether NLR could be utilized in early detection of delirium in the ICU.Methods: Of 10,144 patients who admitted to the ICU, 1,112 delirium patients (DE) were included in the current study. To compare among inflammatory markers, NLR, C-reactive protein (CRP), and white blood cell (WBC) counts were obtained: the mean NLR, CRP levels, and WBC counts between the initial day of ICU admission and the day of initial delirium onset within DE were examined. The inflammatory marker of 1,272 non-delirium patients (ND) were also comparatively measured as a supplement. Further comparisons included a subgroup analysis based on delirium subtypes (non-hypoactive vs. hypoactive) or admission types (elective vs. emergent).Results: The NLR and CRP levels in DE increased on the day of delirium onset compared to the initial admission day. ND also showed increased CRP levels on the sixth day (the closest day to average delirium onset day among DE) of ICU admission compared to baseline, while NLR in ND did not show significant difference over time. In further analyses, the CRP level of the non-hypoactive group was more increased than that of the hypoactive group during the delirium onset. NLR, however, was more significantly increased in patients with elective admission than in those with emergent admission.Conclusion: Elevation of NLR was more closely linked to the onset of delirium compared to other inflammatory markers, indicating that NLR may play a role in early detection of delirium.

scholarly journals Efficacy of Tocilizumab in COVID-19: Single-Center Experience

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Safak Kaya ◽  
Seyhmus Kavak
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Early Period ◽  
Cytokine Release ◽  
Cytokine Release Syndrome ◽  
Survival Group ◽  
Survivor Group ◽  
Group 2 ◽  
D Dimer ◽  
Group 1

Background. Cytokine release syndrome can be observed during the course of COVID-19. Tocilizumab is used for treating this highly fatal syndrome. We think that the starting time of tocilizumab is important. In this article, we aimed to discuss the efficacy of tocilizumab and to review the necessity of starting it in the early period and the laboratory values that guide us in determining the time of this early period. Methods. This retrospective study includes a total of 308 patients with a diagnosis of COVID-19 who were treated with tocilizumab, who were hospitalized in the University of Health Sciences, Gazi Yaşargil Training and Research Hospital between July 2020 and December 2020. The data of the patients were recorded on the day of hospitalization, the day of taking tocilizumab (day 0), and the 1st day, 3rd day, 7th day, and 14th day after taking tocilizumab. Data included age, gender, underlying diseases, where the patient was followed, duration of symptoms before admission to the hospital, duration of oxygen demand before tocilizumab, fever, saturation, and laboratory values. Patients were divided into the mortality group (group 1) and the survival group (group 2), and all data were compared. Results. The study consisted of 308 COVID-19 patients divided into two groups: the mortality group (group 1, n = 135 ) and the survival group (group 2, n = 173 ). The median age of the patients was 60 (min–max: 50-70) years, 75.3% ( n = 232 ) were male, and 56.8% had at least one comorbidity. While 88.9% of group 1 was in the intensive care unit, 26.6% of group 2 received tocilizumab while in the intensive care unit, and there was a statistically significant difference. Median SpO2 values and lymphocyte counts were significantly lower in group 1 than in group 2, both on the day of hospitalization and on the day of the first dose of tocilizumab treatment ( p < 0.001 for both). C-reactive protein, d-dimer, and alanine aminotransferase values were higher in the mortal group on the first day of hospitalization, and this was significant ( p = 0.021 , p = 0.001 , and p = 0.036 , respectively). In our study, d-dimer was 766.5 ng/mL in the survivor group and 988.5 ng/mL in the mortal group. In our patient group, the mean lymphocyte count was 700 × 10 3 / m m 3 in the group that survived the first day of TCZ and 500 × 10 3 / m m 3 in the mortal group. In addition, the CRP value was 135.5 mg/L in the survivor group and 169 mg/L in the mortal group. There was no difference between ferritin values. Conclusions. Tocilizumab is still among the COVID-19 treatment options and appears to be effective. But the start time is important. In order to increase its effectiveness, it may be important to know a cut-off value of the laboratory findings required for the diagnosis of cytokine release syndrome. Further studies are needed for this.

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