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 Evaluation of alpha-l-fucosidase for the diagnosis of hepatocellular carcinoma based on meta-analysis

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Lei Xi ◽  
Chunqing Yang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Operating Characteristic ◽  
Meta Analysis ◽  
Area Under The Curve ◽  
Diagnostic Odds Ratio ◽  
Diagnostic Value ◽  
Receiver Operating Characteristic Curves ◽  
Sensitivity Specificity ◽  
Review Manager

AbstractObjectivesThe main aim of the present study was to assess the diagnostic value of alpha-l-fucosidase (AFU) for hepatocellular carcinoma (HCC).MethodsStudies that explored the diagnostic value of AFU in HCC were searched in EMBASE, SCI, and PUBMED. The sensitivity, specificity, and DOR about the accuracy of serum AFU in the diagnosis of HCC were pooled. The methodological quality of each article was evaluated with QUADAS-2 (quality assessment for studies of diagnostic accuracy 2). Receiver operating characteristic curves (ROC) analysis was performed. Statistical analysis was conducted by using Review Manager 5 and Open Meta-analyst.ResultsEighteen studies were selected in this study. The pooled estimates for AFU vs. α-fetoprotein (AFP) in the diagnosis of HCC in 18 studies were as follows: sensitivity of 0.7352 (0.6827, 0.7818) vs. 0.7501 (0.6725, 0.8144), and specificity of 0.7681 (0.6946, 0.8283) vs. 0.8208 (0.7586, 0.8697), diagnostic odds ratio (DOR) of 7.974(5.302, 11.993) vs. 13.401 (8.359, 21.483), area under the curve (AUC) of 0.7968 vs. 0.8451, respectively.ConclusionsAFU is comparable to AFP for the diagnosis of HCC.

Cytomorphometric Neutrophil and Monocyte Markers May Strengthen the Diagnosis of Sepsis

2016 ◽  
Vol 33 (12) ◽  
pp. 656-662
Author(s):  
Joy Mammen ◽  
Jui Choudhuri ◽  
Joshua Paul ◽  
Thomas Isaiah Sudarsan ◽  
T. Josephine ◽  
...  
Keyword(s):  
Critically Ill ◽  
Critically Ill Patients ◽  
Blood Donors ◽  
Area Under The Curve ◽  
Standard Test ◽  
Suspected Sepsis ◽  
Predictive Values ◽  
Reactive Protein ◽  
Sensitivity Specificity

Background: The diagnosis of sepsis is challenging in the absence of a gold standard test. Recent studies have explored the role of neutrophil and monocyte volume, conductivity, and scatter (VCS), derived from automated hematology analyzers, in diagnosing sepsis. We assessed the diagnostic accuracy of VCS parameters in critically ill patients with sepsis. Methodology: In this prospective study, VCS parameters, procalcitonin, and C-reactive protein (CRP) were assessed in patients with proven sepsis (cases) and 2 control groups (intensive care unit [ICU] patients without sepsis and healthy blood donors). The diagnostic property of each test was explored by calculating sensitivity, specificity, negative and positive predictive values, and area under the curve (AUC). Results: The study included 65 patients with sepsis, 58 nonseptic ICU controls, and 98 blood donors. Procalcitonin and CRP were not significantly different ( P > .06) between patients with sepsis and nonseptic patients. Mean (95% confidence interval [CI]) neutrophil volume (MNV) was significantly higher ( P < .001) in patients with sepsis (165.5; 95%CI 161.6-169.4) than in nonseptic (157.3; 95%CI 154.6-160.1) patients and donors (148.9; 95%CI 147.9-150). A similar pattern was seen with mean monocyte volume (MMoV). Neutrophil and monocyte conductivity and scatter parameters were variably associated. The AUC was highest for MMoV (0.74) and lowest for CRP (0.62). Among all parameters, MNV and MMoV had the highest specificity of 85% and 80%, respectively. Conclusion: In critically ill patients with suspected sepsis, VCS parameters may help strengthen the diagnostic probability of sepsis. Future studies may explore the role of serial monitoring of VCS to track response to antimicrobial therapy.

scholarly journals Elevated D-Dimer Levels Predict a Poor Outcome in Critically Ill Patients

2020 ◽  
Vol 26 ◽  
pp. 107602962097308
Author(s):  
Yuhuko Ichkawa ◽  
Hideo Wada ◽  
Minoru Ezaki ◽  
Motoko Tanaka ◽  
Shinya Hiromori ◽  
...  
Keyword(s):  
Critically Ill ◽  
Critically Ill Patients ◽  
Operating Characteristic ◽  
Receiver Operating Characteristic Analysis ◽  
Area Under The Curve ◽  
Underlying Disease ◽  
Characteristic Analysis ◽  
Poor Outcome ◽  
Underlying Diseases ◽  
D Dimer

D-dimer is a biomarker of thrombosis and recently been considered to predict a poor outcome in patients with infectious diseases. Plasma D-dimer levels were measured in critically ill patients to examine their relationship with the poor outcome. The plasma D-dimer levels were markedly higher in the patients with various underlying disease especially venous thromboembolism in comparison to those without severe underlying diseases. The plasma D-dimer levels in non-survivors were significantly higher than those in survivors. In a receiver operating characteristic analysis, the area under the curve was high for the disseminated intravascular coagulation (DIC) score, the D-dimer value, and the prothrombin time-international normalize ratio (PT-INR). Adequate cut-off values for predicting the outcome were 3 as follows: DIC score, 3 points; D-dimer, 4.2 mg/L; and PT-INR, 1.08. D-dimer, which is a biomarker for thrombosis, is increased in various underlying diseases and predicts a poor outcome.

scholarly journals The diagnostic ability of procalcitonin to differentiate Gram-negative bacteria from Gram-positive bacteria and fungal bloodstream infections in critically ill patients

2019 ◽  
Vol 17 ◽  
pp. 205873921984146
Author(s):  
Di Wang ◽  
Xiaogen Tao ◽  
Wei Guo ◽  
Haihua Liu ◽  
Shaohui Cheng ◽  
...  
Keyword(s):  
Critically Ill ◽  
Critically Ill Patients ◽  
Operating Characteristic ◽  
Bloodstream Infections ◽  
Gram Negative Bacteria ◽  
Diagnostic Ability ◽  
Gram Positive ◽  
Gram Negative ◽  
Receiver Operating Characteristic Curves ◽  
Gram Positive Bacteria

Bloodstream infection (BSI) is a severe infectious complication in critically ill patients. This study was aimed to investigate the diagnostic accuracy of procalcitonin (PCT) to differentiate Gram-negative bacteria (Gram-neg) from Gram-positive bacteria (Gram-pos) and fungal BSI. PCT and other inflammation markers of monomicrobial BSI patients were retrospectively collected and compared between patients with Gram-neg, Gram-pos, or fungal BSI. The differential diagnosis performance of PCT was evaluated by receiver operating characteristic curves (ROC). The area under curve (AUC) of PCT for differentiating Gram-neg BSI from Gram-pos BSI was 0.95 with an optimal cut-off value of 4.15 ng/mL, a sensitivity of 82.05%, and a specificity of 96.15%. AUC of PCT for differentiating Gram-neg BSI from fungal BSI was 0.92 with an optimal cut-off value of 3.13 ng/mL, a sensitivity of 84.62%, and a specificity of 88.89%. Serum PCT concentration can be used to differentiate Gram-neg from Gram-pos and fungal BSI.

Impact of Bedside Tracheostomy on Procalcitonin Kinetics in Critically Ill Patients

2017 ◽  
Vol 34 (3) ◽  
pp. 245-251 ◽  
Author(s):  
Koji Sato ◽  
Masaki Okajima ◽  
Toru Noda ◽  
Takumi Taniguchi
Keyword(s):  
Critically Ill ◽  
Critically Ill Patients ◽  
Operating Characteristic ◽  
University Hospital ◽  
Septic Complication ◽  
C Reactive Protein ◽  
Septic Complications ◽  
Receiver Operating Characteristic Curves ◽  
Japanese University ◽  
Reactive Protein

Background: Critically ill patients are particularly vulnerable to invasive procedures and complications; however, tracheostomy is frequently performed in the intensive care unit (ICU). We analyzed the effects of tracheostomy on procalcitonin (PCT) kinetics and investigated whether PCT could reliably predict septic complications after tracheostomy. Methods: We retrospectively identified 134 patients who underwent bedside tracheostomy during their ICU stay at a Japanese university hospital from October 2010 to December 2015. We extracted PCT data from the day of the procedure (day 0) to postoperative day 2 and defined alert PCT as a PCT level ≥0.5 ng/mL, which had not decreased from the previous day. We divided patients into the following groups: nonevent, aseptic complication, and septic complication. Results: Twelve (9.2%) patients developed acute aseptic complications, and 12 (9.2%) patients developed septic complications. In the nonevent group, the PCT value decreased continuously in the initial PCT ≥ 0.5 ng/mL subgroup ( P < .001, P <.001 for trend). In contrast, significant changes were not observed in the initial PCT < 0.5 ng/mL subgroup. Significant differences and an upward trend in alert PCT incidence rate existed between the groups ( P < .001, P < .001 for trend): nonevent group, 5.5%; aseptic complication group, 41.7%; and septic complication group, 66.7%. In a multivariate linear regression model, septic complications were independently associated with PCT change at postoperative days 1 and 2 (adjusted β = 3.58, P < .001; adjusted β = 9.84, P < .001, respectively). Procalcitonin predicted septic complications more accurately than C-reactive protein, with the area under the receiver operating characteristic curves of 0.8 versus 0.63 ( P = .058) and 0.91 versus 0.69 ( P = .036) at postoperative days 1 and 2, respectively. Conclusion: Our results demonstrated that PCT was not elevated after uncomplicated surgical tracheostomy in critically ill patients.

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.

A Model Based on Artificial Intelligence Algorithm for Monitoring Recurrence of HCC after Hepatectomy

2021 ◽  
pp. 000313482110635
Author(s):  
Li-Yue Sun ◽  
Qing Ouyang ◽  
Wen-Jian Cen ◽  
Fang Wang ◽  
Wen-Ting Tang ◽  
...  
Keyword(s):  
Operating Characteristic ◽  
Validation Cohort ◽  
Tnm Staging ◽  
Resonance Imaging ◽  
Diagnostic Efficacy ◽  
Receiver Operating Characteristic Curves ◽  
Dynamic Variations ◽  
Included Patient ◽  
Artificial Intelligence Algorithm ◽  
Adaboost Classifier

Background There is no satisfactory indicator for monitoring recurrence after resection of hepatocellular carcinoma (HCC). This retrospective study aimed to design and validate an HCC monitor recurrence (HMR) model for patients without metastasis after hepatectomy. Methods A training cohort was recruited from 1179 patients with HCC without metastasis after hepatectomy between February 2012 and December 2015. An HMR model was developed using an AdaBoost classifier algorithm. The factors included patient age, TNM staging, tumor size, and pre/postoperative dynamic variations of alpha-fetoprotein (AFP). The diagnostic efficacy of the model was evaluated based on the area under the receiver operating characteristic curves (AUCs). The model was validated using a cohort of 695 patients. Results In preoperative patients with positive or negative AFP, the AUC of the validation cohort in the HMR model was .8877, which indicated better diagnostic efficacy than that of serum AFP (AUC, .7348). The HMR model predicted recurrence earlier than computed tomography/magnetic resonance imaging did by 191.58 ± 165 days. In addition, the HMR model can predict the prognosis of patients with HCC after resection. Conclusions The HMR model established in this study is more accurate than serum AFP for monitoring recurrence after hepatectomy for HCC and can be used for real-time monitoring of the postoperative status in patients with HCC without metastasis.

scholarly journals Value and Diagnostic Efficacy of Fetal Morphology Assessment Using Ultrasound in A Poor-Resource Setting

Diagnostics ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 109 ◽  
Author(s):  
Ukweh ◽  
Ugbem ◽  
Okeke ◽  
Ekpo
Keyword(s):  
Predictive Value ◽  
Performance Metrics ◽  
Area Under The Curve ◽  
Youden Index ◽  
Likelihood Ratios ◽  
Diagnostic Efficacy ◽  
Predictive Values ◽  
Resource Setting ◽  
Post Test ◽  
Sensitivity Specificity

Background: Ultrasound is operator-dependent, and its value and efficacy in fetal morphology assessment in a low-resource setting is poorly understood. We assessed the value and efficacy of fetal morphology ultrasound assessment in a Nigerian setting. Materials and Methods: We surveyed fetal morphology ultrasound performed across five facilities and followed-up each fetus to ascertain the outcome. Fetuses were surveyed in the second trimester (18th–22nd weeks) using the International Society of Ultrasound in Obstetrics and Gynecology (ISUOG) guideline. Clinical and surgical reports were used as references to assess the diagnostic efficacy of ultrasound in livebirths, and autopsy reports to confirm anomalies in terminated pregnancies, spontaneous abortions, intrauterine fetal deaths, and still births. We calculated sensitivity, specificity, positive and negative predictive values, Area under the curve (AUC), Youden index, likelihood ratios, and post-test probabilities. Results: In total, 6520 fetuses of women aged 15–46 years (mean = 31.7 years) were surveyed. The overall sensitivity, specificity, and AUC were 77.1 (95% CI: 68–84.6), 99.5 (95% CI: 99.3–99.7), and 88.3 (95% CI: 83.7–92.2), respectively. Other performance metrics were: positive predictive value, 72.4 (95% CI: 64.7–79.0), negative predictive value, 99.6 (95% CI: 99.5–99.7), and Youden index (77.1%). Abnormality prevalence was 1.67% (95% CI: 1.37–2.01), and the positive and negative likelihood ratios were 254 (95% CI: 107.7–221.4) and 0.23 (95% CI: 0.16–0.33), respectively. The post-test probability for positive test was 72% (95% CI: 65–79). Conclusion: Fetal morphology assessment is valuable in a poor economics setting, however, the variation in the diagnostic efficacy across facilities and the limitations associated with the detection of circulatory system anomalies need to be addressed.

scholarly journals Galectin-3 in Critically Ill Patients with Sepsis and/or Trauma: A Good Predictor of Outcome or Not?

2018 ◽  
Vol 0 (0) ◽  
Author(s):  
Jasna Jevdjic ◽  
Maja Surbatovic ◽  
Snezana Milosavljevic ◽  
Goran Rondovic ◽  
Ivan Stanojevic ◽  
...  
Keyword(s):  
Severe Sepsis ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Good Predictor ◽  
Injury Severity ◽  
Area Under The Curve ◽  
University Hospital ◽  
Youden Index ◽  
Lethal Outcome ◽  
Galectin 3

Abstract Severe sepsis and/or trauma complicated with multiple organ dysfunction syndrome are leading causes of death in critically ill patients. The aim of this prospective, observational, single centre study was to assess the prognostic value of galectin-3 regarding outcome in critically ill patients with severe trauma and/or severe sepsis. The outcome measure was hospital mortality. In total, 75 critically ill patients who were admitted to the intensive care unit of the tertiary university hospital were enrolled in a prospective observational study. Blood samples were collected upon fulfilling Sepsis-3 criteria and for a traumatized Injury Severity Score > 25 points. Levels of galectin-3 were significantly higher in nonsurvivors on the day of enrolment - Day 1 (p<0.05). On Day 1, the area under the curve (AUC) for the galectin-3 for lethal outcome was 0.602. At a cut-off level of 262.82 ng/mL, the sensitivity was 53%, and the specificity was 69.7%, which was objectively determined by a Youden index of 0.20. The discriminative power of galectin-3 in predicting outcome was statistically significant. Galectin-3 on Day 1 is a fairly good predictor of lethal outcome.

Considerations on the region of interest in the ROC space

2021 ◽  
pp. 096228022110605
Author(s):  
Luigi Lavazza ◽  
Sandro Morasca
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
False Positive Rate ◽  
Area Under The Curve ◽  
Region Of Interest ◽  
True Positive Rate ◽  
True Positive ◽  
Receiver Operating Characteristic Curves ◽  
Positive Rate ◽  
Receiver Operating

Receiver Operating Characteristic curves have been widely used to represent the performance of diagnostic tests. The corresponding area under the curve, widely used to evaluate their performance quantitatively, has been criticized in several respects. Several proposals have been introduced to improve area under the curve by taking into account only specific regions of the Receiver Operating Characteristic space, that is, the plane to which Receiver Operating Characteristic curves belong. For instance, a region of interest can be delimited by setting specific thresholds for the true positive rate or the false positive rate. Different ways of setting the borders of the region of interest may result in completely different, even opposing, evaluations. In this paper, we present a method to define a region of interest in a rigorous and objective way, and compute a partial area under the curve that can be used to evaluate the performance of diagnostic tests. The method was originally conceived in the Software Engineering domain to evaluate the performance of methods that estimate the defectiveness of software modules. We compare this method with previous proposals. Our method allows the definition of regions of interest by setting acceptability thresholds on any kind of performance metric, and not just false positive rate and true positive rate: for instance, the region of interest can be determined by imposing that [Formula: see text] (also known as the Matthews Correlation Coefficient) is above a given threshold. We also show how to delimit the region of interest corresponding to acceptable costs, whenever the individual cost of false positives and false negatives is known. Finally, we demonstrate the effectiveness of the method by applying it to the Wisconsin Breast Cancer Data. We provide Python and R packages supporting the presented method.

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