scholarly journals Evaluation of six methylation markers derived from genome-wide screens for detection of cervical precancer and cancer

Epigenomics ◽  
2020 ◽  
Vol 12 (18) ◽  
pp. 1569-1578
Author(s):  
Stèfanie Dick ◽  
Lisanne Verhoef ◽  
Lise MA De Strooper ◽  
Iuliana Ciocănea-Teodorescu ◽  
G Bea A Wisman ◽  
...  
Keyword(s):  
Human Papillomavirus ◽  
High Performance ◽  
Operating Characteristic ◽  
Cross Validation ◽  
Area Under The Curve ◽  
Intraepithelial Neoplasia ◽  
Receiver Operating Characteristic Curves ◽  
Genome Wide ◽  
Cervical Precancer ◽  
Leave One Out

Aim: To evaluate the triage performance of six host-cell DNA methylation markers derived from two genome-wide discovery screens for detection of cervical precancer (cervical intraepithelial neoplasia 3 [CIN]) and cancer. Materials & methods: Human papillomavirus-positive cervical scrapes of controls (≤CIN1; n = 352) and women diagnosed with CIN3 (n = 175) or cervical cancer (n = 50) were analyzed for methylation of ASCL1, LHX8, ST6GALNAC5, GHSR, SST and ZIC1. Results: Methylation levels increased significantly with disease severity (all markers p < 0.001). Three markers ( ASCL1, LHX8, ZIC1) showed receiver operating characteristic curves with area under the curve >0.800 after leave-one-out cross-validation. Bi-marker panel ASCL1/LHX8 had highest area under the curve (0.882), and detected 83.4% of CIN3 and all cervical cancers at specificity of 82.4%. Conclusion: All six methylation markers showed an equivalent, high performance for the triage of human papillomavirus-positive women using cervical scrapes with complementarity between markers.

scholarly journals Prioritizing Disease-Related Microbes Based on the Topological Properties of a Comprehensive Network

2021 ◽  
Vol 12 ◽  
Author(s):  
Haixiu Yang ◽  
Fan Tong ◽  
Changlu Qi ◽  
Ping Wang ◽  
Jiangyu Li ◽  
...  
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Cross Validation ◽  
Area Under The Curve ◽  
Disease Pathogenesis ◽  
Physiological Processes ◽  
Disease Associations ◽  
Inflammatory Bowel ◽  
Leave One Out ◽  
Insight Into

Many microbes are parasitic within the human body, engaging in various physiological processes and playing an important role in human diseases. The discovery of new microbe–disease associations aids our understanding of disease pathogenesis. Computational methods can be applied in such investigations, thereby avoiding the time-consuming and laborious nature of experimental methods. In this study, we constructed a comprehensive microbe–disease network by integrating known microbe–disease associations from three large-scale databases (Peryton, Disbiome, and gutMDisorder), and extended the random walk with restart to the network for prioritizing unknown microbe–disease associations. The area under the curve values of the leave-one-out cross-validation and the fivefold cross-validation exceeded 0.9370 and 0.9366, respectively, indicating the high performance of this method. Despite being widely studied diseases, in case studies of inflammatory bowel disease, asthma, and obesity, some prioritized disease-related microbes were validated by recent literature. This suggested that our method is effective at prioritizing novel disease-related microbes and may offer further insight into disease pathogenesis.

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.

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.

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.

scholarly journals Performance of the Kidney Failure Risk Equation by Disease Etiology in Advanced CKD

2020 ◽  
Vol 15 (10) ◽  
pp. 1424-1432
Author(s):  
Gregory L. Hundemer ◽  
Navdeep Tangri ◽  
Manish M. Sood ◽  
Tim Ramsay ◽  
Ann Bugeja ◽  
...  
Keyword(s):  
Kidney Disease ◽  
Confidence Interval ◽  
Kidney Failure ◽  
Operating Characteristic ◽  
Area Under The Curve ◽  
Polycystic Kidney ◽  
Receiver Operating Characteristic Curves ◽  
Time Points ◽  
Risk Equation ◽  
Failure Risk

Background and objectivesThe kidney failure risk equation is a clinical tool commonly used for prediction of progression from CKD to kidney failure. The kidney failure risk equation’s accuracy in advanced CKD and whether this varies by CKD etiology remains unknown. This study examined the kidney failure risk equation’s discrimination and calibration at 2 and 5 years among a large tertiary care population with advanced CKD from heterogeneous etiologies.Design, setting, participants, & measurementsThis retrospective cohort study included 1293 patients with advanced CKD (median eGFR 15 ml/min per 1.73 m2) referred to the Ottawa Hospital Multi-Care Kidney Clinic between 2010 and 2016, with follow-up clinical data available through 2018. Four-variable kidney failure risk equation scores for 2- and 5-year risks of progression to kidney failure (defined as dialysis or kidney transplantation) were calculated upon initial referral and correlated with the subsequent observed kidney failure incidence within these time frames. Receiver operating characteristic curves and calibration plots were used to measure the discrimination and calibration of the kidney failure risk equation both in the overall advanced CKD population and by CKD etiology: diabetic kidney disease, hypertensive nephrosclerosis, GN, polycystic kidney disease, and other. Pairwise comparisons of the receiver operating characteristic curves by CKD etiology were performed to compare kidney failure risk equation discrimination.ResultsThe kidney failure risk equation provided adequate to excellent discrimination in identifying patients with CKD likely to progress to kidney failure at the 2- and 5-year time points both overall (2-year area under the curve, 0.83; 95% confidence interval, 0.81 to 0.85; 5-year area under the curve, 0.81; 95% confidence interval, 0.77 to 0.84) and across CKD etiologies. The kidney failure risk equation displayed adequate calibration at the 2- and 5-year time points both overall and across CKD etiologies (Hosmer–Lemeshow P≥0.05); however, the predicted risks of kidney failure were higher than the observed risks across CKD etiologies with the exception of polycystic kidney disease.ConclusionsThe kidney failure risk equation provides adequate discrimination and calibration in advanced CKD and across CKD etiologies.

Prediction of regaining consciousness despite an early epileptiform EEG after cardiac arrest

Neurology ◽  
2020 ◽  
Vol 94 (16) ◽  
pp. e1675-e1683 ◽  
Author(s):  
Giuseppina Barbella ◽  
Jong Woo Lee ◽  
Vincent Alvarez ◽  
Jan Novy ◽  
Mauro Oddo ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
Operating Characteristic ◽  
Neuronal Damage ◽  
Cerebral Performance Category ◽  
Neuron Specific Enolase ◽  
Area Under The Curve ◽  
Performance Category ◽  
Receiver Operating Characteristic Curves ◽  
Normal Background ◽  
Serum Neuron Specific Enolase

ObjectiveAfter cardiac arrest (CA), epileptiform EEG, occurring in about 1/3 of patients, often but not invariably heralds poor prognosis. We tested the hypothesis that a combination of specific EEG features identifies patients who may regain consciousness despite early epileptiform patterns.MethodsWe retrospectively analyzed a registry of comatose patients post-CA (2 Swiss centers), including those with epileptiform EEG. Background and epileptiform features in EEGs 12–36 hours or 36–72 hours from CA were scored according to the American Clinical Neurophysiology Society nomenclature. Best Cerebral Performance Category (CPC) score within 3 months (CPC 1–3 vs 4–5) was the primary outcome. Significant EEG variables were combined in a score assessed with receiver operating characteristic curves, and independently validated in a US cohort; its correlation with serum neuron-specific enolase (NSE) was also tested.ResultsOf 488 patients, 107 (21.9%) had epileptiform EEG <72 hours; 18 (17%) reached CPC 1–3. EEG 12–36 hours background continuity ≥50%, absence of epileptiform abnormalities (p < 0.00001 each), 12–36 and 36–72 hours reactivity (p < 0.0001 each), 36–72 hours normal background amplitude (p = 0.0004), and stimulus-induced discharges (p = 0.0001) correlated with favorable outcome. The combined 6-point score cutoff ≥2 was 100% sensitive (95% confidence interval [CI], 78%–100%) and 70% specific (95% CI, 59%–80%) for CPC 1–3 (area under the curve [AUC], 0.98; 95% CI, 0.94–1.00). Increasing score correlated with NSE (ρ = −0.46, p = 0.0001). In the validation cohort (41 patients), the score was 100% sensitive (95% CI, 60%–100%) and 88% specific (95% CI, 73%–97%) for CPC 1–3 (AUC, 0.96; 95% CI, 0.91–1.00).ConclusionPrognostic value of early epileptiform EEG after CA can be estimated combining timing, continuity, reactivity, and amplitude features in a score that correlates with neuronal damage.

Video-Polysomnographic Assessment for the Diagnosis of Disorders of Arousal in Children

Neurology ◽  
2020 ◽  
Vol 96 (1) ◽  
pp. e121-e130
Author(s):  
Régis Lopez ◽  
Christine Laganière ◽  
Sofiène Chenini ◽  
Anna Laura Rassu ◽  
Elisa Evangelista ◽  
...  
Keyword(s):  
Operating Characteristic ◽  
Slow Wave Sleep ◽  
Area Under The Curve ◽  
Class Iii ◽  
Receiver Operating Characteristic Curves ◽  
Arousal Index ◽  
Fragmentation Index ◽  
Increased Sensitivity ◽  
Disorders Of Arousal

ObjectivesTo highlight the slow-wave sleep (SWS) fragmentation and validate the video-polysomnographic (vPSG) criteria and cutoffs for the diagnosis of disorders of arousal (DOA) in children, as already reported in adults.MethodsOne hundred children (66 boys, 11.0 ± 3.3 years) with frequent episodes of DOA and 50 nonparasomniac children (32 boys, 10.9 ± 3.9 years) underwent vPSG recording to quantify SWS characteristics (number of N3 sleep interruptions, fragmentation index, slow/mixed and fast arousal ratios, and indexes per hour) and associated behaviors. We compared SWS characteristics in the 2 groups and defined the optimal cutoff values for the diagnosis of DOA using receiver operating characteristic curves.ResultsPatients with DOA had higher amounts of N3 and REM sleep, number of N3 interruptions, SWS fragmentation, and slow/mixed arousal indexes than controls. The highest area under the curve (AUC) values were obtained for SWS fragmentation and slow/mixed arousal indexes with satisfactory classification performances (AUC 0.80, 95% confidence interval [CI] 0.73–0.87; AUC 0.82, 95% CI 0.75–0.89). SWS fragmentation index cutoff value of 4.1/h reached a sensitivity of 65.0% and a specificity of 84.0%. Slow/mixed arousal index cutoff of 3.8/h reached a sensitivity of 69.0% and a specificity of 82.0%. At least one parasomniac episode was recorded in 63.0% of patients and none of the controls. Combining behavioral component by vPSG increased sensitivity of both biomarkers to 83% and 89%, respectively.ConclusionsWe confirmed that SWS fragmentation and slow/mixed arousal indexes are 2 relevant biomarkers for the diagnosis of DOA in children, with different cutoffs obtained than those validated in adults.Classification of EvidenceThis study provides Class III evidence that SWS fragmentation and slow/mixed arousal indexes on vPSG accurately identify children with DOA.

scholarly journals Epidemiological and Clinical Predictors of COVID-19

2020 ◽  
Vol 71 (15) ◽  
pp. 786-792 ◽  
Author(s):  
Yinxiaohe Sun ◽  
Vanessa Koh ◽  
Kalisvar Marimuthu ◽  
Oon Tek Ng ◽  
Barnaby Young ◽  
...  
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Cross Validation ◽  
Prediction Models ◽  
Tertiary Care ◽  
Laboratory Data ◽  
Care Hospital ◽  
Laboratory Test Results ◽  
Leave One Out ◽  
Receiver Operating

Abstract Background Rapid identification of COVID-19 cases, which is crucial to outbreak containment efforts, is challenging due to the lack of pathognomonic symptoms and in settings with limited capacity for specialized nucleic acid–based reverse transcription polymerase chain reaction (PCR) testing. Methods This retrospective case-control study involves subjects (7–98 years) presenting at the designated national outbreak screening center and tertiary care hospital in Singapore for SARS-CoV-2 testing from 26 January to 16 February 2020. COVID-19 status was confirmed by PCR testing of sputum, nasopharyngeal swabs, or throat swabs. Demographic, clinical, laboratory, and exposure-risk variables ascertainable at presentation were analyzed to develop an algorithm for estimating the risk of COVID-19. Model development used Akaike’s information criterion in a stepwise fashion to build logistic regression models, which were then translated into prediction scores. Performance was measured using receiver operating characteristic curves, adjusting for overconfidence using leave-one-out cross-validation. Results The study population included 788 subjects, of whom 54 (6.9%) were SARS-CoV-2 positive and 734 (93.1%) were SARS-CoV-2 negative. The median age was 34 years, and 407 (51.7%) were female. Using leave-one-out cross-validation, all the models incorporating clinical tests (models 1, 2, and 3) performed well with areas under the receiver operating characteristic curve (AUCs) of 0.91, 0.88, and 0.88, respectively. In comparison, model 4 had an AUC of 0.65. Conclusions Rapidly ascertainable clinical and laboratory data could identify individuals at high risk of COVID-19 and enable prioritization of PCR testing and containment efforts. Basic laboratory test results were crucial to prediction models.

scholarly journals Assessment Tools for Identifying Functional Limitations Associated With Functional Ankle Instability

2008 ◽  
Vol 43 (1) ◽  
pp. 44-50 ◽  
Author(s):  
Scott E. Ross ◽  
Kevin M. Guskiewicz ◽  
Michael T. Gross ◽  
Bing Yu
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Assessment Tool ◽  
Ankle Instability ◽  
Functional Limitations ◽  
Area Under The Curve ◽  
Assessment Tools ◽  
Functional Ankle Instability ◽  
Receiver Operating Characteristic Curves ◽  
Time To Stabilization

Abstract Context: Assessment tools should identify functional limitations associated with functional ankle instability (FAI) by discriminating unstable from stable ankles. Objective: To identify assessment tools that discriminated FAI from stable ankles and determine the most accurate assessment tool for discriminating between FAI and stable ankles. Design: Case-control study. Setting: Research laboratory. Patients or Other Participants: Fifteen individuals with FAI and 15 healthy individuals; participants with unilateral FAI reported “giving-way” sensations and ankle sprains, whereas healthy participants did not. Intervention(s): Participants answered 12 questions on the Ankle Joint Functional Assessment Tool (AJFAT). They also performed a single-leg jump landing, which required them to jump to half their maximum jump height, land on a single leg, and stabilize quickly on a force plate. Main Outcome Measure(s): Receiver operating characteristic curves determined cutoff scores for discriminating between ankle groups for AJFAT total score and resultant vector (RV) time to stabilization. Accuracy values for discriminating between groups were determined by calculating the area under the receiver operating characteristic curves. Results: The cutoff score for discriminating between FAI and stable ankles was ≥26 (sensitivity  =  1, specificity  =  1) and ≥1.58 seconds (sensitivity  =  0.67, specificity  =  0.73) for the AJFAT total score and RV time to stabilization, respectively. The area under the curve for the AJFAT was 1.0 (asymptotic significance &lt;.05), whereas the RV time to stabilization had an area under the curve of 0.72 (asymptotic significance &lt;.05). Conclusions: The AJFAT was an excellent assessment tool for discriminating between ankle groups, whereas RV time to stabilization was a fair assessment tool. Although both assessments discriminated between ankle groups, the AJFAT more accurately discriminated between groups than the RV time to stabilization did. Future researchers should confirm these findings using a prospective research design.

scholarly journals A semi-automated measurement technique for the assessment of radiolucency

2014 ◽  
Vol 11 (96) ◽  
pp. 20140303 ◽  
Author(s):  
E. C. Pegg ◽  
B. J. L. Kendrick ◽  
H. G. Pandit ◽  
H. S. Gill ◽  
D. W. Murray
Keyword(s):  
Quantitative Assessment ◽  
Operating Characteristic ◽  
Area Under The Curve ◽  
Imaging Algorithm ◽  
Receiver Operating Characteristic Curves ◽  
Automated Method ◽  
Automated Imaging ◽  
Kappa Value ◽  
Patients At Risk ◽  
Total Agreement

The assessment of radiolucency around an implant is qualitative, poorly defined and has low agreement between clinicians. Accurate and repeatable assessment of radiolucency is essential to prevent misdiagnosis, minimize cases of unnecessary revision, and to correctly monitor and treat patients at risk of loosening and implant failure. The purpose of this study was to examine whether a semi-automated imaging algorithm could improve repeatability and enable quantitative assessment of radiolucency. Six surgeons assessed 38 radiographs of knees after unicompartmental knee arthroplasty for radiolucency, and results were compared with assessments made by the semi-automated program. Large variation was found between the surgeon results, with total agreement in only 9.4% of zones and a kappa value of 0.602; whereas the automated program had total agreement in 81.6% of zones and a kappa value of 0.802. The software had a ‘fair to excellent’ prediction of the presence or the absence of radiolucency, where the area under the curve of the receiver operating characteristic curves was 0.82 on average. The software predicted radiolucency equally well for cemented and cementless implants ( p = 0.996). The identification of radiolucency using an automated method is feasible and these results indicate that it could aid the definition and quantification of radiolucency.

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