scholarly journals Efficacy of Monocyte Distribution Width in the Early Diagnosis of Sepsis: A Diagnostic Meta-Analysis

2021 ◽  
Author(s):  
Jiahao Chen ◽  
Qiang Guo
Keyword(s):  
Early Diagnosis ◽  
Large Scale ◽  
Meta Analysis ◽  
Delayed Diagnosis ◽  
Area Under The Curve ◽  
High Sensitivity ◽  
Inexpensive Method ◽  
Diagnostic Efficacy ◽  
Distribution Width ◽  
Non Invasive

Abstract Background: Delayed diagnosis of sepsis urgently requires a fast, convenient, and inexpensive method to improve the early diagnosis of sepsis. Increasing evidence showed that monocyte distribution width (MDW) could be used as a non-invasive biomarker with high sensitivity and specificity for the early diagnosis of sepsis. However, the accuracy and reliability of its diagnosis are still controversial in different studies. Method: A meta-analysis of all available studies regarding the association between MDW and the diagnosis of sepsis was performed to systematically evaluate the diagnostic efficacy of MDW in the prediction of sepsis. Results: The estimated results of all eight studies are as follows: sensitivity, 0.84 (95% CI 0.77, 0.90); specificity, 0.68 (95% CI 0.54, 0.80); PLR, 2.7 (95% CI 1.8, 4.1); NLR, 0.23 (95% CI 0.15, 0.35); DOR is 12 (95% CI 5, 25). The corresponding overall area under the curve is 0.85 (95% CI 0.82, 0.88). Conclusion: In conclusion, this meta-analysis demonstrates that MDW has high accuracy in distinguishing patients with sepsis from healthy controls for early diagnosis of sepsis. However, large-scale prospective studies and joint diagnosis with other indicators are urgently required to confirm our findings and their utilization for routine clinical diagnosis in the future.

scholarly journals Abnormally Expressed Circular RNAs as Novel Non-Invasive Biomarkers for Hepatocellular Carcinoma: A Pair-wise Meta-Analysis

2019 ◽  
Author(s):  
Yanlin Jiang ◽  
Mengmeng Shang ◽  
Shizhen Dong ◽  
Menglu Chen ◽  
Xiaoli Wang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Meta Analysis ◽  
Area Under The Curve ◽  
Circular Rnas ◽  
Sample Type ◽  
Analysis Model ◽  
Diagnostic Efficacy ◽  
Non Invasive ◽  
Trim And Fill ◽  
Sensitivity Specificity

Abstract Background: In the recent literature, dysregulated circular RNAs (circRNAs) have been extensively investigated in hepatocellular carcinoma (HCC). This study strives to evaluate the diagnostic as well as the predictive value of abnormally expressed circRNAs in HCC. Methods: Eligible studies were sourced from PubMed, EMBASE, and CNKI online databases. Data on patients’ clinical characteristics, including diagnostic efficacy and overall survival (OS) were extracted. The diagnostic and prognostic parameters were respectively synthesized using the bivariate meta-analysis model and multivariate Cox hazard regression analysis based on STATA 12.0. The trim and fill approach was employed to evaluate the impacts of publication bias. Results: A sum of 21 eligible types of research was incorporated. The pooled sensitivity, specificity and area under the curve (AUC) of abnormally expressed circRNAs in distinguishing HCC from non-cancer controls were estimated to be 0.78 (95% confidence interval (CI): 0.69–0.85), 0.80 (95% CI: 0.74–0.86) and 0.86, respectively. Survival analyses expressed that the down-regulated circRNA expression signature correlated perfectly with HCC survival (hazard ratio (HR) = 0.42, 95% CI: 0.19–0.91, p = 0.028; I2 = 92.7%; p = 0.000), whereas the HCC cases with high circRNA levels had significantly poorer prognoses than those of patients with low circRNA levels (HR = 2.22, 95% CI: 1.50–3.30, p = 0.000; I2 = 91%; p = 0.000). Moreover, abnormally expressed circRNAs were intimately linked with tumor size, differentiation grade, microvascular invasion, metastasis, TNM stage, and serum AFP level in patients with HCC. Stratified analysis based on sample type, control source, and expression status also yielded robust results. Conclusions: Abnormally expressed circRNA signatures show immense potential as novel non-invasive biomarker(s) in complementing HCC diagnosis and prognosis.

scholarly journals Analytical Validation and Clinical Application of Rapid Serological Tests for SARS-CoV-2 Suitable for Large-Scale Screening

Diagnostics ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 869
Author(s):  
Amedeo De Nicolò ◽  
Valeria Avataneo ◽  
Jessica Cusato ◽  
Alice Palermiti ◽  
Jacopo Mula ◽  
...  
Keyword(s):  
Early Diagnosis ◽  
Real Time Pcr ◽  
Large Scale ◽  
High Sensitivity ◽  
Analytical Validation ◽  
Pcr Assay ◽  
Serological Tests ◽  
Flow Test ◽  
Lateral Flow Test ◽  
Large Scale Screening

Recently, large-scale screening for COVID-19 has presented a major challenge, limiting timely countermeasures. Therefore, the application of suitable rapid serological tests could provide useful information, however, little evidence regarding their robustness is currently available. In this work, we evaluated and compared the analytical performance of a rapid lateral-flow test (LFA) and a fast semiquantitative fluorescent immunoassay (FIA) for anti-nucleocapsid (anti-NC) antibodies, with the reverse transcriptase real-time PCR assay as the reference. In 222 patients, LFA showed poor sensitivity (55.9%) within two weeks from PCR, while later testing was more reliable (sensitivity of 85.7% and specificity of 93.1%). Moreover, in a subset of 100 patients, FIA showed high sensitivity (89.1%) and specificity (94.1%) after two weeks from PCR. The coupled application for the screening of 183 patients showed satisfactory concordance (K = 0.858). In conclusion, rapid serological tests were largely not useful for early diagnosis, but they showed good performance in later stages of infection. These could be useful for back-tracing and/or to identify potentially immune subjects.

Inflammatory and Hematological Indices as Simple, Practical Severity Predictors of Microdysfunction Following Coronary Intervention: A Systematic Review and Meta-Analysis

Angiology ◽  
2020 ◽  
Vol 71 (4) ◽  
pp. 349-359 ◽  
Author(s):  
Enyuan Zhang ◽  
Mingdong Gao ◽  
Jing Gao ◽  
Jianyong Xiao ◽  
Xiaowei Li ◽  
...  
Keyword(s):  
Meta Analysis ◽  
High Sensitivity ◽  
Coronary Intervention ◽  
Pooled Analysis ◽  
Slow Flow ◽  
Distribution Width ◽  
Hematological Indices ◽  
Lymphocyte Ratio ◽  
Reactive Protein ◽  
No Reflow

C-reactive protein (CRP) and high-sensitivity CRP (hsCRP), along with a series of hematological indices, platelet to lymphocyte ratio (PLR), neutrophil to lymphocyte ratio (NLR), mean platelet volume (MPV), platelet distribution width (PDW), and red blood cell distribution width (RDW), are regarded to be related to the incidence of no-reflow or slow flow. Clinical studies were retrieved from the electronic databases of PubMed, EMBASE, Google Scholar, Clinical Trials, and science direct from their inception to August 24, 2019. A total of 21 studies involving 7403 patients were included in the meta-analysis. Pooled analysis results revealed patients with higher hsCRP (odds ratio [OR] = 1.03, 95% confidence interval [CI], 1.01-1.05, P = .006), hsCRP (OR = 1.04, 95% CI: 1.0-1.08, P = .012), NLR (OR = 1.23, 95% CI: 1.11-1.37, P < .0001), PLR (OR = 1.13, 95% CI: 1.07-1.20, P < .0001), and MPV (OR = 2.13, 95% CI: 1.57-2.90, P < .0001) all exhibited significantly higher no-reflow incidence, but there was no significant association between no-reflow risk and RDW or PDW. Patients with higher CRP/hsCRP also performed higher rate of slow flow (OR = 1.06, 95% CI: 1.01-1.11, P = .018). Preangiographic CRP/hsCRP could independently predict no-reflow and slow flow. Moreover, some hematological indices are associated with no-flow.

scholarly journals A routine blood test-associated predictive model and application for tuberculosis diagnosis: a retrospective cohort study from northwest China

2019 ◽  
Vol 47 (7) ◽  
pp. 2993-3007 ◽  
Author(s):  
Kui Li ◽  
Sheng-Xi Liu ◽  
Cai-Yong Yang ◽  
Zi-Cheng Jiang ◽  
Jun Liu ◽  
...  
Keyword(s):  
Area Under The Curve ◽  
High Sensitivity ◽  
Threshold Value ◽  
Northwest China ◽  
Optimal Threshold ◽  
Accuracy Rate ◽  
Distribution Width ◽  
Routine Blood ◽  
Resistant Tuberculosis ◽  
Diagnostic Values

Objectives This study aimed to use the results of routine blood tests and relevant parameters to construct models for the prediction of active tuberculosis (ATB) and drug-resistant tuberculosis (DRTB) and to assess the diagnostic values of these models. Methods We performed logistic regression analysis to generate models of plateletcrit-albumin scoring (PAS) and platelet distribution width-treatment-sputum scoring (PTS). Area under the curve (AUC) analysis was used to analyze the diagnostic values of these curves. Finally, we performed model validation and application assessment. Results In the training cohort, for the PAS model, the AUC for diagnosing ATB was 0.902, sensitivity was 82.75%, specificity was 82.20%, accuracy rate was 81.00%, and optimal threshold value was 0.199. For the PTS model, the AUC for diagnosing DRTB was 0.700, sensitivity was 63.64%, specificity was 73.53%, accuracy rate was 89.00%, and optimal threshold value was −2.202. These two models showed significant differences in the AUC analysis, compared with single-factor models. Results in the validation cohort were similar. Conclusions The PAS model had high sensitivity and specificity for the diagnosis of ATB, and the PTS model had strong predictive potential for the diagnosis of DRTB.

Diagnostic and prognostic values of circular RNAs for lung cancer: a meta-analysis

2020 ◽  
pp. postgradmedj-2019-137178
Author(s):  
Qian Yang ◽  
Lizhen Chen ◽  
Li Yang ◽  
Yuanshuai Huang
Keyword(s):  
Lung Cancer ◽  
Cancer Patients ◽  
Meta Analysis ◽  
Area Under The Curve ◽  
High Sensitivity ◽  
Cochrane Library ◽  
Clinicopathological Parameters ◽  
Circular Rnas ◽  
Lung Cancer Patients ◽  
Diagnostic Values

Circular RNAs (circRNAs) may serve as potential biomarkers for patients with lung cancer. The aim of this meta-analysis was to analyse the diagnostic, prognostic and clinicopathological values of circRNAs in lung cancer patients. A systematic search of PubMed, Embase, Web of Science, Scopus and the Cochrane Library databases was performed for relevant articles from inception to 29 January 2020. Pooled parameters including sensitivity, specificity and area under the curve (AUC) were used to assess the diagnostic performance, HRs and 95% CIs were used to evaluate overall survival (OS) and ORs were used to estimate clinicopathological parameters. 52 studies from 45 articles were enrolled in this study, including 17 on diagnosis and 35 on prognosis. For diagnostic values, circRNAs could discriminate lung cancer patients from the controls, with AUC of 0.83 (95% CI: 0.79 to 0.86), a relatively high sensitivity of 0.77 (95% CI: 0.73 to 0.81) and specificity of 0.75 (95% CI: 0.71 to 0.79). For prognostic significances, overexpression of 23 upregulated circRNAs was relevant to a poor prognosis (OS: HR=2.21, 95% CI: 1.96 to 2.49, p<0.001), and overexpression of 9 downregulated circRNAs was correlated with a favourable prognosis (OS: HR=0.62, 95% CI: 0.53 to 0.73, p<0.001). As for clinicopathological parameters, high expression of 23 upregulated circRNAs was associated with unfavourable clinicopathological features while 9 downregulated circRNAs proved the contrary. In conclusion, this study confirmed that circRNAs might serve as important biomarkers for diagnostic and prognostic values of lung cancer.

scholarly journals Non-invasive measurement of fasciculation frequency demonstrates diagnostic accuracy in amyotrophic lateral sclerosis

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Arina Tamborska ◽  
James Bashford ◽  
Aidan Wickham ◽  
Raquel Iniesta ◽  
Urooba Masood ◽  
...  
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Confidence Interval ◽  
Biceps Brachii ◽  
Delayed Diagnosis ◽  
Area Under The Curve ◽  
Motor Neuropathy ◽  
Healthy Individuals ◽  
Non Invasive ◽  
Lateral Sclerosis ◽  
Invasive Measurement

Abstract Delayed diagnosis of amyotrophic lateral sclerosis prevents early entry into clinical trials at a time when neuroprotective therapies would be most effective. Fasciculations are an early hallmark of amyotrophic lateral sclerosis, preceding muscle weakness and atrophy. To assess the potential diagnostic utility of fasciculations measured by high-density surface electromyography, we carried out 30-min biceps brachii recordings in 39 patients with amyotrophic lateral sclerosis, 7 patients with benign fasciculation syndrome, 1 patient with multifocal motor neuropathy and 17 healthy individuals. We employed the surface potential quantification engine to compute fasciculation frequency, fasciculation amplitude and inter-fasciculation interval. Inter-group comparison was assessed by Welch’s analysis of variance. Logistic regression, receiver operating characteristic curves and decision trees discerned the diagnostic performance of these measures. Fasciculation frequency, median fasciculation amplitude and proportion of inter-fasciculation intervals &lt;100 ms showed significant differences between the groups. In the best-fit regression model, increasing fasciculation frequency and median fasciculation amplitude were independently associated with the diagnosis of amyotrophic lateral sclerosis. Fasciculation frequency was the single best measure predictive of the disease, with an area under the curve of 0.89 (95% confidence interval 0.81–0.98). The cut-off of more than 14 fasciculation potentials per minute achieved 80% sensitivity (95% confidence interval 63–90%) and 96% specificity (95% confidence interval 78–100%). In conclusion, non-invasive measurement of fasciculation frequency at a single time-point reliably distinguished amyotrophic lateral sclerosis from its mimicking conditions and healthy individuals, warranting further research into its diagnostic applications.

scholarly journals Clinically Correlated MicroRNAs in the Diagnosis of Non-Small Cell Lung Cancer: A Systematic Review and Meta-Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Min Jiang ◽  
Xuelian Li ◽  
Xiaowei Quan ◽  
Xiaoying Li ◽  
Baosen Zhou
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Meta Analysis ◽  
Area Under The Curve ◽  
Diagnostic Value ◽  
Small Cell ◽  
Diagnostic Tools ◽  
Small Cell Lung ◽  
Diagnostic Efficacy

(1) Background. Non-small cell lung cancer (NSCLC) has a high mortality rate. MiRNAs have been found to be diagnostic biomarkers for NSCLC. However, controversial results exist. We conducted this meta-analysis to evaluate the diagnostic value of miRNAs for NSCLC.(2) Methods. Databases and reference lists were searched. Pooled sensitivity (SEN), specificity (SPE), and area under the curve (AUC) were applied to examine the general diagnostic efficacy, and subgroup analysis was also performed.(3) Results. Pooled SEN, SPE, and AUC were 85%, 88%, and 0.93, respectively, for 71 studies. Multiple miRNAs (AUC: 0.96) obtained higher diagnostic value than single miRNA (AUC: 0.86), and the same result was found for Caucasian population (AUC: 0.97) when compared with Asian (AUC: 0.91) and Caucasian/African population (AUC: 0.92). MiRNA had higher diagnostic efficacy when participants contained both smokers and nonsmokers (AUC is 0.95 for imbalanced group and 0.91 for balanced group) than when containing only smokers (AUC: 0.90). Meanwhile, AUC was 0.91 for both miR-21 and miR-210.(4) Conclusions. Multiple miRNAs such as miR-21 and miR-210 could be used as diagnostic tools for NSCLC, especially for the Caucasian and nonsmoking NSCLC.

scholarly journals Identifying small-effect genetic associations overlooked by the conventional fixed-effect model in a large-scale meta-analysis of coronary artery disease

2019 ◽  
Author(s):  
Lerato E Magosi ◽  
Anuj Goel ◽  
Jemma C Hopewell ◽  
Martin Farrall
Keyword(s):  
Random Effects ◽  
Complex Traits ◽  
Large Scale ◽  
Meta Analysis ◽  
Genetic Effect ◽  
High Sensitivity ◽  
Fixed Effect Model ◽  
Fixed Effect ◽  
Supplementary Information ◽  
Genetic Associations

Abstract Motivation Common small-effect genetic variants that contribute to human complex traits and disease are typically identified using traditional fixed-effect (FE) meta-analysis methods. However, the power to detect genetic associations under FE models deteriorates with increasing heterogeneity, so that some small-effect heterogeneous loci might go undetected. A modified random-effects meta-analysis approach (RE2) was previously developed that is more powerful than traditional fixed and random-effects methods at detecting small-effect heterogeneous genetic associations, the method was updated (RE2C) to identify small-effect heterogeneous variants overlooked by traditional fixed-effect meta-analysis. Here, we re-appraise a large-scale meta-analysis of coronary disease with RE2C to search for small-effect genetic signals potentially masked by heterogeneity in a FE meta-analysis. Results Our application of RE2C suggests a high sensitivity but low specificity of this approach for discovering small-effect heterogeneous genetic associations. We recommend that reports of small-effect heterogeneous loci discovered with RE2C are accompanied by forest plots and standardized predicted random-effects statistics to reveal the distribution of genetic effect estimates across component studies of meta-analyses, highlighting overly influential outlier studies with the potential to inflate genetic signals. Availability and implementation Scripts to calculate standardized predicted random-effects statistics and generate forest plots are available in the getspres R package entitled from https://magosil86.github.io/getspres/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Hyperglycemia Identification Using ECG in Deep Learning Era

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6263
Author(s):  
Renato Cordeiro ◽  
Nima Karimian ◽  
Younghee Park
Keyword(s):  
Deep Learning ◽  
Blood Glucose Concentration ◽  
Area Under The Curve ◽  
High Sensitivity ◽  
Physiological Signals ◽  
Ecg Signals ◽  
Non Invasive ◽  
Learning Classifier ◽  
Electrocardiogram Ecg ◽  
Smart Wearable

A growing number of smart wearable biosensors are operating in the medical IoT environment and those that capture physiological signals have received special attention. Electrocardiogram (ECG) is one of the physiological signals used in the cardiovascular and medical fields that has encouraged researchers to discover new non-invasive methods to diagnose hyperglycemia as a personal variable. Over the years, researchers have proposed different techniques to detect hyperglycemia using ECG. In this paper, we propose a novel deep learning architecture that can identify hyperglycemia using heartbeats from ECG signals. In addition, we introduce a new fiducial feature extraction technique that improves the performance of the deep learning classifier. We evaluate the proposed method with ECG data from 1119 different subjects to assess the efficiency of hyperglycemia detection of the proposed work. The result indicates that the proposed algorithm is effective in detecting hyperglycemia with a 94.53% area under the curve (AUC), 87.57% sensitivity, and 85.04% specificity. That performance represents an relative improvement of 53% versus the best model found in the literature. The high sensitivity and specificity achieved by the 10-layer deep neural network proposed in this work provide an excellent indication that ECG possesses intrinsic information that can indicate the level of blood glucose concentration.

scholarly journals Non-Invasive Preoperative Imaging Differential Diagnosis of Intracranial Hemangiopericytoma and Angiomatous Meningioma: A Novel Developed and Validated Multiparametric MRI-Based Clini-Radiomic Model

2022 ◽  
Vol 11 ◽  
Author(s):  
Yanghua Fan ◽  
Panpan Liu ◽  
Yiping Li ◽  
Feng Liu ◽  
Yu He ◽  
...  
Keyword(s):  
Differential Diagnosis ◽  
Clinical Features ◽  
Area Under The Curve ◽  
High Sensitivity ◽  
Recursive Feature Elimination ◽  
Preoperative Imaging ◽  
Multivariable Logistic Regression Analysis ◽  
Clinical Model ◽  
Non Invasive ◽  
Model Combining

BackgroundAccurate preoperative differentiation of intracranial hemangiopericytoma and angiomatous meningioma can greatly assist operation plan making and prognosis prediction. In this study, a clini-radiomic model combining radiomic and clinical features was used to distinguish intracranial hemangiopericytoma and hemangioma meningioma preoperatively.MethodsA total of 147 patients with intracranial hemangiopericytoma and 73 patients with angiomatous meningioma from the Tiantan Hospital were retrospectively reviewed and randomly assigned to training and validation sets. Radiomic features were extracted from MR images, the elastic net and recursive feature elimination algorithms were applied to select radiomic features for constructing a fusion radiomic model. Subsequently, multivariable logistic regression analysis was used to construct a clinical model, then a clini-radiomic model incorporating the fusion radiomic model and clinical features was constructed for individual predictions. The calibration, discriminating capacity, and clinical usefulness were also evaluated.ResultsSix significant radiomic features were selected to construct a fusion radiomic model that achieved an area under the curve (AUC) value of 0.900 and 0.900 in the training and validation sets, respectively. A clini-radiomic model that incorporated the radiomic model and clinical features was constructed and showed good discrimination and calibration, with an AUC of 0.920 in the training set and 0.910 in the validation set. The analysis of the decision curve showed that the fusion radiomic model and clini-radiomic model were clinically useful.ConclusionsOur clini-radiomic model showed great performance and high sensitivity in the differential diagnosis of intracranial hemangiopericytoma and angiomatous meningioma, and could contribute to non-invasive development of individualized diagnosis and treatment for these patients.

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