scholarly journals Complementary Keratoconus Indices Based on Topographical Interpretation of Biomechanical Waveform Parameters: A Supplement to Established Keratoconus Indices

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Susanne Goebels ◽  
Timo Eppig ◽  
Stefan Wagenpfeil ◽  
Alan Cayless ◽  
Berthold Seitz ◽  
...  
Keyword(s):  
Area Under Curve ◽  
Operating Characteristic ◽  
Prediction Models ◽  
Roc Curves ◽  
Screening Performance ◽  
New Models ◽  
Classification Index ◽  
Biomechanical Measurement ◽  
Topographic Measurement ◽  
Gold Standards

Purpose. To build new models with the Ocular Response Analyzer (ORA) waveform parameters to create new indices analogous to established topographic keratoconus indices. Method. Biomechanical, tomographic, and topographic measurements of 505 eyes from the Homburger Keratoconus Centre were included. Thirty-seven waveform parameters (WF) were derived from the biomechanical measurement with the ORA. Area under curve (ROC, receiver operating characteristic) was used to quantify the screening performance. A logistic regression analysis was used to create two new keratoconus prediction models based on these waveform parameters to resample the clinically established keratoconus indices from Pentacam and TMS-5. Results. ROC curves show the best results for the waveform parameters p1area, p2area, h1, h2, dive1, mslew1, aspect1, aplhf, and dslope1. The new keratoconus prediction model to resample the Pentacam topographic keratoconus index (TKC) was WFTKC = −4.068 + 0.002 × p2area − 0.005 × dive1 − 0.01 × h1 − 2.501 × aplhf, which achieves a sensitivity of 90.3% and specificity of 89.4%; to resample the TMS-5 keratoconus classification index (KCI) it was WFKCI = −3.606 + 0.002 × p2area, which achieves a sensitivity of 75.4% and a specificity of 81.8%. Conclusion. In addition to the biomechanically provided Keratoconus Index two new indices which were based on the topographic gold standards (either Pentacam or TMS-5) were created. Of course, these do not replace the original topographic measurement.

scholarly journals Variable Selection Method in Prediction Models: Application in Periodontology

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Paul Tramini ◽  
Jean-Christophe Chazel ◽  
Isabelle Calas-Bennasar ◽  
Philippe Gibert ◽  
Nicolas Molinari
Keyword(s):  
Fatty Acids ◽  
Fatty Acid ◽  
Operating Characteristic ◽  
Prediction Models ◽  
Periodontal Diseases ◽  
Roc Curves ◽  
Oral Diseases ◽  
Predictive Variables ◽  
Diagnosis And Prognosis ◽  
Severe Periodontitis

The aim of this study, applied in the field of periodontal diseases, was first to analyze the fatty acid levels in two groups of patients and then to propose a method for selecting the most relevant predictors. Two groups of patients, 29 with moderate or severe periodontitis and 27 who served as controls, were clinically examined, and their fatty acids in serum were measured by gas chromatography. The levels of these 12 fatty acids were the variables of the analysis. Logistic regression, together with the area under the receiver operating characteristic (ROC) curves, allowed determining a composite score which led to a subset of the most relevant covariables. The fatty acid levels differed significantly between the 2 groups in multivariate analysis (P=0.03) and the best logistic model was obtained with only 3 predictive variables: arachidonic acid, linoleic acid, and DHA. Fatty acid levels in serum of patients were significantly different according to the presence of moderate or severe periodontitis. By taking into account the comparison of ROC curves, our approach could optimize the choice of variables in multivariate analyses and could better fit it with diagnosis and prognosis of oral diseases in dental research.

scholarly journals Reflection on modern methods: Revisiting the area under the ROC Curve

2020 ◽  
Vol 49 (4) ◽  
pp. 1397-1403 ◽  
Author(s):  
A Cecile J W Janssens ◽  
Forike K Martens
Keyword(s):  
Prediction Model ◽  
Roc Curve ◽  
Operating Characteristic ◽  
Prediction Models ◽  
Disease Risk ◽  
Roc Curves ◽  
Discriminative Ability ◽  
Small Set ◽  
Intuitive Interpretation ◽  
Underlying Risk

Abstract The area under the receiver operating characteristic (ROC) curve (AUC) is commonly used for assessing the discriminative ability of prediction models even though the measure is criticized for being clinically irrelevant and lacking an intuitive interpretation. Every tutorial explains how the coordinates of the ROC curve are obtained from the risk distributions of diseased and non-diseased individuals, but it has not become common sense that therewith the ROC plot is just another way of presenting these risk distributions. We show how the ROC curve is an alternative way to present risk distributions of diseased and non-diseased individuals and how the shape of the ROC curve informs about the overlap of the risk distributions. For example, ROC curves are rounded when the prediction model included variables with similar effect on disease risk and have an angle when, for example, one binary risk factor has a stronger effect; and ROC curves are stepped rather than smooth when the sample size or incidence is low, when the prediction model is based on a relatively small set of categorical predictors. This alternative perspective on the ROC plot invalidates most purported limitations of the AUC and attributes others to the underlying risk distributions. AUC is a measure of the discriminative ability of prediction models. The assessment of prediction models should be supplemented with other metrics to assess their clinical utility.

scholarly journals Deregulated Expression of Long Non-Coding RNAs UCA1, NEAT1 and BDNF-As in Multiple Myeloma

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 2073-2073
Author(s):  
Lenka Sedlarikova ◽  
Barbora Gromesova ◽  
Jana Filipova ◽  
Veronika Kubaczkova ◽  
Lenka Radova ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Research Funding ◽  
Area Under Curve ◽  
Operating Characteristic ◽  
Hematological Malignancies ◽  
Roc Curves ◽  
Pcr Array ◽  
P Values ◽  
Expression Levels ◽  
Non Coding Rna

Abstract Introduction. Multiple myeloma (MM) is the second most common hematological malignancy in the world. It is characterized by increasing rate of various genetic mutations and dysregulated pathways. This work aims to find out if this phenomenon is also reflected in dysregulation of the so-called long non-coding RNA molecules (lncRNA). These molecules are over 200 nt long and primarily localized in the nucleus. It seems increasingly obvious that lncRNAs play a crucial role in human diseases and during hematopoiesis. Presumably, lncRNA affect hematological malignancies including MM by regulating the expression of oncogenes, tumor suppressor and key factors involved in hematopoiesis. We identified a disease-specific cellular lncRNA signature using a cohort of MM patients in comparison to healthy donors (HD). Methods . Fifty CD138+ samples obtained from newly diagnosed MM patients and HD were evaluated for this study. Total RNA was extracted from MM cells using miRNeasy Mini Kit or miRNeasy Micro Kit (all Qiagen) according to the manufacturer's instructions. Concentration and purity of RNA were determined spectrophotometrically by NanoDrop ND-1000 (Thermo Scientific, USA). Screening analysis of 83 lncRNA was performed on 6 MM patients and 6 HD using RT2 lncRNA PCR Array - Human lncRNA Finder (Qiagen). Significantly deregulated lncRNAs between MM vs HD were validated by qPCR using relative quantification approach 2-ΔCt on a larger cohort of patients and HD. Briefly, High-capacity cDNA reverse transcription kit (Applied Biosystem, USA), was used to synthesize cDNA from 200 ng RNA according to the manufacturer's recommendations. Expression levels of ZFAS1, UCA1, BDNF-AS, NEAT1 and FAS-AS1 were detected by RT-qPCR using TaqMan non-coding RNA assay (ZFAS1: Hs01379985_m1, FAS-AS1: Hs04233476_s1, BDNF-AS: Hs01010228_m1, UCA1: Hs01909129_s1, NEAT1: Hs03453535_s1), expression level of GAPDH using Human GAPD (GAPDH) Endogenous Control (VIC®/MGB probe, primer limited) and TaqMan Gene Expression Master Mix (all Applied Biosystem, USA). qPCR was performed using the Applied Biosystem 7500 Sequence Detection System. Analysis of the RT-qPCR data was performed using SDS version 2.0.1 software (Applied Biosystem, USA). Expression data from lncRNAs profiling were statistically evaluated in the environment of statistical language R by use of Bioconductor package and LIMMA approach combined with hierarchical clustering (HCL). P values were adjusted according to Bonferroni correction for multiple comparisons. Statistical differences between lncRNAs expression levels in MM patients and HD were evaluated by non-parametric Mann-Whitney U test. Receiver Operating Characteristic (ROC) analysis was used to calculate specificity and sensitivity of each lncRNA. P values <0.05 were considered significant. Results.RT2 lncRNA PCR Array profiling revealed 27 deregulated lncRNAs (all p<0.01) between MM patients and HD. ZFAS1, UCA1, BDNF-AS, NEAT1 and FAS-AS1 expression was further verified on a larger cohort of MM and HD samples. As the difference in ZFAS1 and FAS-AS1 expression between MM and HD was not significant (p=0.7; p=0.2, respectively), it was excluded from further analyses. UCA1 was significantly down-regulated (p<0.0001), NEAT1 and BDNF-AS were up-regulated in MM samples when compared with HD (both p<0.00000001). To discriminate MM from HD, receiver operating characteristic (ROC) curve was calculated. It revealed sensitivity of 100% (95%CI: 91.6 - 100.0) and specificity of 100% (95%CI: 63.1 - 100.0), and area under curve (AUC) = 1.000 for both, NEAT1 and BDNF-AS lncRNA expression and sensitivity of 95.24% (95%CI: 83.8 - 99.4) and specificity of 75.00% (95%CI: 34.9 - 96.8), and area under curve (AUC) = 0.905 for UCA1 expression levels (Fig. 1). We suppose that these dysregulated lncRNAs could have a biological relevance in MM since BDNF-AS is an antisense RNA for BDNF, a significant stimulating factor of osteoclasts in MM, NEAT1 and UCA1 were described as dysregulated in several types of cancer, including hematological malignancies. Further validations are currently ongoing. Conclusions. Altogether, our first observations demonstrate that cellular lncRNA UCA1, NEAT1 and BDNF-AS may be involved in pathophysiological processes occurring in MM cells and prompt further studies in this field. Grant support: AZV 15-29508A Figure 1 ROC curves for dysregulated lncRNAs: A) BDNF-AS, B) NEAT1, C) UCA1. Figure 1. ROC curves for dysregulated lncRNAs: A) BDNF-AS, B) NEAT1, C) UCA1. Disclosures Hajek: Onyx: Consultancy; Amgen: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria, Research Funding; BMS: Honoraria; Novartis: Research Funding.

Use of Receiver Operating Characteristic (ROC) Curves to Evaluate Computer Confidence Threshold and Clinical Performance in the Diagnosis of Appendicitis

1978 ◽  
Vol 17 (03) ◽  
pp. 157-161 ◽  
Author(s):  
F. T. De Dombal ◽  
Jane C. Horrocks
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Clinical Performance ◽  
Roc Curves ◽  
Final Diagnosis ◽  
Confidence Threshold ◽  
Computer Aided ◽  
Computer Confidence ◽  
Overall Performance ◽  
Receiver Operating

This paper uses simple receiver operating characteristic (ROC) curves (i) to study the effect of varying computer confidence of threshold levels and (ii) to evaluate clinical performance in the diagnosis of acute appendicitis. Over 1300 patients presenting to five centres with abdominal pain of short duration were studied in varying detail. Clinical and computer-aided diagnostic predictions were compared with the »final« diagnosis. From these studies it is concluded the simplistic setting of a 50/50 confidence threshold for the computer program is as »good« as any other. The proximity of a computer-aided system changed clinical behaviour patterns; a higher overall performance level was achieved and clinicians performance levels became associated with the »mildly conservative« end of the computers ROC curve. Prior forecasts of over-confidence or ultra-caution amongst clinicians using the computer-aided system have not been fulfilled.

scholarly journals MicroRNAs-1299, -126-3p and -30e-3p as Potential Diagnostic Biomarkers for Prediabetes

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 949
Author(s):  
Cecil J. Weale ◽  
Don M. Matshazi ◽  
Saarah F. G. Davids ◽  
Shanel Raghubeer ◽  
Rajiv T. Erasmus ◽  
...  
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Predictive Ability ◽  
Roc Curves ◽  
Tolerance Test ◽  
Cross Sectional Study ◽  
Oral Glucose ◽  
Cross Sectional ◽  
Receiver Operating

This cross-sectional study investigated the association of miR-1299, -126-3p and -30e-3p with and their diagnostic capability for dysglycaemia in 1273 (men, n = 345) South Africans, aged >20 years. Glycaemic status was assessed by oral glucose tolerance test (OGTT). Whole blood microRNA (miRNA) expressions were assessed using TaqMan-based reverse transcription quantitative-PCR (RT-qPCR). Receiver operating characteristic (ROC) curves assessed the ability of each miRNA to discriminate dysglycaemia, while multivariable logistic regression analyses linked expression with dysglycaemia. In all, 207 (16.2%) and 94 (7.4%) participants had prediabetes and type 2 diabetes mellitus (T2DM), respectively. All three miRNAs were significantly highly expressed in individuals with prediabetes compared to normotolerant patients, p < 0.001. miR-30e-3p and miR-126-3p were also significantly more expressed in T2DM versus normotolerant patients, p < 0.001. In multivariable logistic regressions, the three miRNAs were consistently and continuously associated with prediabetes, while only miR-126-3p was associated with T2DM. The ROC analysis indicated all three miRNAs had a significant overall predictive ability to diagnose prediabetes, diabetes and the combination of both (dysglycaemia), with the area under the receiver operating characteristic curve (AUC) being significantly higher for miR-126-3p in prediabetes. For prediabetes diagnosis, miR-126-3p (AUC = 0.760) outperformed HbA1c (AUC = 0.695), p = 0.042. These results suggest that miR-1299, -126-3p and -30e-3p are associated with prediabetes, and measuring miR-126-3p could potentially contribute to diabetes risk screening strategies.

scholarly journals Development and validation of prediction models for mechanical ventilation, renal replacement therapy, and readmission in COVID-19 patients

2021 ◽  
Author(s):  
Victor Alfonso Rodriguez ◽  
Shreyas Bhave ◽  
Ruijun Chen ◽  
Chao Pang ◽  
George Hripcsak ◽  
...  
Keyword(s):  
At Risk ◽  
Mechanical Ventilation ◽  
Renal Replacement Therapy ◽  
Receiver Operating Characteristic ◽  
Replacement Therapy ◽  
Operating Characteristic ◽  
Prediction Models ◽  
External Validation ◽  
Electronic Health Record Data ◽  
Receiver Operating

Abstract Objective Coronavirus disease 2019 (COVID-19) patients are at risk for resource-intensive outcomes including mechanical ventilation (MV), renal replacement therapy (RRT), and readmission. Accurate outcome prognostication could facilitate hospital resource allocation. We develop and validate predictive models for each outcome using retrospective electronic health record data for COVID-19 patients treated between March 2 and May 6, 2020. Materials and Methods For each outcome, we trained 3 classes of prediction models using clinical data for a cohort of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2)–positive patients (n = 2256). Cross-validation was used to select the best-performing models per the areas under the receiver-operating characteristic and precision-recall curves. Models were validated using a held-out cohort (n = 855). We measured each model’s calibration and evaluated feature importances to interpret model output. Results The predictive performance for our selected models on the held-out cohort was as follows: area under the receiver-operating characteristic curve—MV 0.743 (95% CI, 0.682-0.812), RRT 0.847 (95% CI, 0.772-0.936), readmission 0.871 (95% CI, 0.830-0.917); area under the precision-recall curve—MV 0.137 (95% CI, 0.047-0.175), RRT 0.325 (95% CI, 0.117-0.497), readmission 0.504 (95% CI, 0.388-0.604). Predictions were well calibrated, and the most important features within each model were consistent with clinical intuition. Discussion Our models produce performant, well-calibrated, and interpretable predictions for COVID-19 patients at risk for the target outcomes. They demonstrate the potential to accurately estimate outcome prognosis in resource-constrained care sites managing COVID-19 patients. Conclusions We develop and validate prognostic models targeting MV, RRT, and readmission for hospitalized COVID-19 patients which produce accurate, interpretable predictions. Additional external validation studies are needed to further verify the generalizability of our results.

scholarly journals Sonographic Cervical Shortening after Labor Induction is a Predictor of Vaginal Delivery

2016 ◽  
Vol 38 (12) ◽  
pp. 585-588 ◽  
Author(s):  
Ugo Indraccolo ◽  
Gennaro Scutiero ◽  
Pantaleo Greco
Keyword(s):  
Logistic Regression ◽  
Vaginal Delivery ◽  
Regression Models ◽  
Operating Characteristic ◽  
Roc Curves ◽  
Cervical Length ◽  
Labor Induction ◽  
Logistic Regression Models ◽  
Independent Variables ◽  
Sonographic Evaluation

Objective Analyzing if the sonographic evaluation of the cervix (cervical shortening) is a prognostic marker for vaginal delivery. Methods Women who underwent labor induction by using dinoprostone were enrolled. Before the induction and three hours after it, the cervical length was measured by ultrasonography to obtain the cervical shortening. The cervical shortening was introduced in logistic regression models among independent variables and for calculating receiver operating characteristic (ROC) curves. Results Each centimeter in the cervical shortening increases the odds of vaginal delivery in 24.4% within 6 hours; in 16.1% within 24 hours; and in 10.5% within 48 hours. The best predictions for vaginal delivery are achieved for births within 6 and 24 hours, while the cervical shortening poorly predicts vaginal delivery within 48 hours. Conclusion The greater the cervical shortening 3 hours after labor induction, the higher the likelihood of vaginal delivery within 6, 24 and 48 hours.

Receiver operating characteristic (ROC) analysis of neurons in the cat's lateral geniculate nucleus during tonic and burst response mode

1995 ◽  
Vol 12 (4) ◽  
pp. 723-741 ◽  
Author(s):  
W. Guido ◽  
S.-M. Lu ◽  
J.W. Vaughan ◽  
Dwayne W. Godwin ◽  
S. Murray Sherman
Keyword(s):  
Visual Stimulus ◽  
Action Potentials ◽  
Operating Characteristic ◽  
Visual Stimuli ◽  
Roc Curves ◽  
Response Mode ◽  
Burst Mode ◽  
Low Threshold Spike ◽  
Low Threshold ◽  
Roc Area

AbstractRelay cells of the lateral geniculate nucleus respond to visual stimuli in one of two modes: burst and tonic. The burst mode depends on the activation of a voltage-dependent, Ca2+ conductance underlying the low threshold spike. This conductance is inactivated at depolarized membrane potentials, but when activated from hyperpolarized levels, it leads to a large, triangular, nearly all-or-none depolarization. Typically, riding its crest is a high-frequency barrage of action potentials. Low threshold spikes thus provide a nonlinear amplification allowing hyperpolarized relay neurons to respond to depolarizing inputs, including retinal EPSPs. In contrast, the tonic mode is characterized by a steady stream of unitary action potentials that more linearly reflects the visual stimulus. In this study, we tested possible differences in detection between response modes of 103 geniculate neurons by constructing receiver operating characteristic (ROC) curves for responses to visual stimuli (drifting sine-wave gratings and flashing spots). Detectability was determined from the ROC curves by computing the area under each curve, known as the ROC area. Most cells switched between modes during recording, evidently due to small shifts in membrane potential that affected the activation state of the low threshold spike. We found that the more often a cell responded in burst mode, the larger its ROC area. This was true for responses to optimal and nonoptimal visual stimuli, the latter including nonoptimal spatial frequencies and low stimulus contrasts. The larger ROC areas associated with burst mode were due to a reduced spontaneous activity and roughly equivalent level of visually evoked response when compared to tonic mode. We performed a within-cell analysis on a subset of 22 cells that switched modes during recording. Every cell, whether tested with a low contrast or high contrast visual stimulus exhibited a larger ROC area during its burst response mode than during its tonic mode. We conclude that burst responses better support signal detection than do tonic responses. Thus, burst responses, while less linear and perhaps less useful in providing a detailed analysis of visual stimuli, improve target detection. The tonic mode, with its more linear response, seems better suited for signal analysis rather than signal detection.

scholarly journals Diagnostic performances of manual and automated reticulocyte parameters in anaemic cats

2017 ◽  
Vol 20 (2) ◽  
pp. 122-127 ◽  
Author(s):  
Saverio Paltrinieri ◽  
Marco Fossati ◽  
Valentina Menaballi
Keyword(s):  
Receiver Operating Characteristic ◽  
Sensitivity And Specificity ◽  
Operating Characteristic ◽  
Positive Likelihood Ratio ◽  
Roc Curves ◽  
Blood Samples ◽  
Production Index ◽  
Instrumental Measurement ◽  
Sensitivity Specificity ◽  
Reticulocyte Production

Objectives The objective of this study was to evaluate the diagnostic performances of manual and instrumental measurement of reticulocyte percentage (Ret%), reticulocyte number (Ret#) and reticulocyte production index (RPI) to differentiate regenerative anaemia (RA) from non-regenerative anaemia (NRA) in cats. Methods Data from 106 blood samples from anaemic cats with manual counts (n = 74; 68 NRA, six RA) or instrumental counts of reticulocytes (n = 32; 25 NRA, seven RA) collected between 1995 and 2013 were retrospectively analysed. Sensitivity, specificity and positive likelihood ratio (LR+) were calculated using either cut-offs reported in the literature or cut-offs determined from receiver operating characteristic (ROC) curves. Results All the reticulocyte parameters were significantly higher in cats with RA than in cats with NRA. All the ROC curves were significantly different ( P <0.001) from the line of no discrimination, without significant differences between the three parameters. Using the cut-offs published in literature, the Ret% (cut-off: 0.5%) was sensitive (100%) but not specific (<75%), the RPI (cut-off: 1.0) was specific (>92%) but not sensitive (<15%), and the Ret# (cut-off: 50 × 10³/µl) had a sensitivity and specificity >80% and the highest LR+ (manual count: 14; instrumental count: 6). For all the parameters, sensitivity and specificity approached 100% using the cut-offs determined by the ROC curves. These cut-offs were higher than those reported in the literature for Ret% (manual: 1.70%; instrumental: 3.06%), lower for RPI (manual: 0.39; instrumental: 0.59) and variably different, depending on the method (manual: 41 × 10³/µl; instrumental: 57 × 10³/µl), for Ret#. Using these cut-offs, the RPI had the highest LR+ (manual: 22.7; instrumental: 12.5). Conclusions and relevance This study indicated that all the reticulocyte parameters may confirm regeneration when the pretest probability is high, while when this probability is moderate, RA should be identified using the RPI providing that cut-offs <1.0 are used.

Sign in / Sign up

Export Citation Format

Share Document