Updated Thresholds Research Articles

Lower FIB-4 threshold in patients with diabetes improves diagnostic accuracy of the test in a Hispanic population.

Non-invasive tests (NITs) can be used to estimate the severity of fibrosis in patients with nonalcoholic fatty liver disease (NAFLD) but their diagnostic accuracy is variable. Hispanic patients are at increased risk of NAFLD and diabetes. We evaluate the diagnostic performance of the fibrosis index based on 4 factors (FIB-4) in a population of Hispanic patients who underwent vibration-controlled transient elastography (VCTE). A total of 1,524 patients underwent VCTE at University of California, Los Angeles from July 18, 2019 to June 7, 2022. Ultimately 110 patients were identified as Hispanic, with confirmed NAFLD. Sensitivity, specificity, positive predictive value and negative predictive value of FIB-4 threshold ≥1.3 were calculated. Logistic regression models were used to determine updated thresholds for patients with and without diabetes based on Youden's index. Of the 110 patients, the majority (65%) were female. Prevalence of diabetes was higher in the group with clinically significant fibrosis (76% vs. 36%, P<0.001). Using a FIB-4 threshold ≥1.3 to predict clinically significant fibrosis (F2-F4 on VCTE), area under the receiver operating characteristic (AUROC) was 0.74. By incorporating diabetes status, AUROC was 0.81 when employing a FIB-4 threshold of ≥1.0 in patients with diabetes and ≥1.5 in patients without diabetes. Using a FIB-4 threshold of ≥1.0 in patients with diabetes and ≥1.5 in patients without diabetes improves the diagnostic performance of the test. The new FIB-4 including diabetes status will lead to improved screening in patients who are at risk of clinically significant fibrosis.

LiverRisk score: An accurate, cost-effective tool to predict fibrosis, liver-related, and diabetes-related mortality in the general population

Noninvasive and early assessment of liver fibrosis is of great significance and is challenging. We aimed to evaluate the predictive performance and cost-effectiveness of the LiverRisk score for liver fibrosis and liver-related and diabetes-related mortality in the general population. The general population from the NHANES 2017-March 2020, NHANES 1999-2018, and UK Biobank 2006-2010 were included in the cross-sectional cohort (n= 3,770), along with the NHANES follow-up cohort (n= 25,317) and the UK Biobank follow-up cohort (n= 17,259). The cost-effectiveness analysis was performed using TreeAge Pro software. Liver stiffness measurements ≥10kPa were defined as compensated advanced chronic liver disease (cACLD). Compared to conventional scores, the LiverRisk score had significantly better accuracy and calibration in predicting liver fibrosis, with an area under the receiver operating characteristic curve (AUC) of 0.76 (0.72-0.79) for cACLD. According to the updated thresholds of LiverRisk score (6 and 10), we reclassified the population into three groups: low, medium, and high risk. The AUCs of LiverRisk score for predicting liver-related and diabetes-related mortality at 5, 10, and 15 years were all above 0.8, with better performance than the Fibrosis-4 score. Furthermore, compared to the low-risk group, the medium-risk and high-risk groups in the two follow-up cohorts had a significantly higher risk of liver-related and diabetes-related mortality. Finally, the cost-effectiveness analysis showed that the incremental cost-effectiveness ratio for LiverRisk score compared to FIB-4 was USD $18,170 per additional quality-adjusted life-year (QALY) gained, below the willingness-to-pay threshold of $50,000/QALY. The LiverRisk score is an accurate, cost-effective tool to predict liver fibrosis and liver-related and diabetes-related mortality in the general population. The National Natural Science Foundation of China (nos. 82330060, 92059202, and 92359304); the Key Research and Development Program of Jiangsu Province (BE2023767a); the Fundamental Research Fund of Southeast University (3290002303A2); Changjiang Scholars Talent Cultivation Project of Zhongda Hospital of Southeast University (2023YJXYYRCPY03); and the Research Personnel Cultivation Program of Zhongda Hospital Southeast University (CZXM-GSP-RC125).

Updating the Dermal Sensitisation Thresholds using an expanded dataset and an in silico expert system

The Dermal Sensitisation Thresholds (DST) are Thresholds of Toxicological Concern, which can be used to justify exposure-based waiving when conducting a skin sensitisation risk assessment. This study aimed to update the published DST values by expanding the size of the Local Lymph Node Assay dataset upon which they are based, whilst assigning chemical reactivity using an in silico expert system (Derek Nexus). The potency values within the expanded dataset fitted a similar gamma distribution to that observed for the original dataset. Derek Nexus was used to classify the sensitisation activity of the 1152 chemicals in the expanded dataset and to predict which chemicals belonged to a High Potency Category (HPC). This two-step classification led to three updated thresholds: a non-reactive DST of 710 μg/cm2 (based on 79 sensitisers), a reactive (non-HPC) DST of 73 μg/cm2 (based on 331 sensitisers) and an HPC DST of 1.0 μg/cm2 (based on 146 sensitisers). Despite the dataset containing twice as many sensitisers, these values are similar to the previously published thresholds, highlighting their robustness and increasing confidence in their use. By classifying reactivity in silico the updated DSTs can be applied within a skin sensitisation risk assessment in a reproducible, scalable and accessible manner.

Association of Pediatric Obstructive Sleep Apnea With Elevated Blood Pressure and Orthostatic Hypertension in Adolescence

Although pediatric guidelines have delineated updated thresholds for elevated blood pressure (eBP) in youth and adult guidelines have recognized obstructive sleep apnea (OSA) as an established risk factor for eBP, the relative association of pediatric OSA with adolescent eBP remains unexplored. To assess the association of pediatric OSA with eBP and its orthostatic reactivity in adolescence. At baseline of this population-based cohort study (Penn State Child Cohort) in 2000-2005, a random sample of 700 children aged 5 to 12 years from the general population was studied. A total of 421 participants (60.1%) were followed up in 2010-2013 after 7.4 years as adolescents (ages, 12-23 years). Data analyses were conducted from July 6 to October 29, 2020. Outcomes were the apnea-hypopnea index (AHI) score, ascertained via polysomnography conducted in a laboratory; eBP measured in the seated position identified using guideline-recommended pediatric criteria; orthostatic hyperreactivity identified with BP assessed in the supine and standing positions; and visceral adipose tissue assessed via dual-energy x-ray absorptiometry. Among the 421 participants (mean [SD] age at follow-up, 16.5 [2.3] years), 227 (53.9%) were male and 92 (21.9%) were racial/ethnic minorities. A persistent AHI of 2 or more since childhood was longitudinally associated with adolescent eBP (odds ratio [OR], 2.9; 95% CI 1.1-7.5), while a remitted AHI of 2 or more was not (OR, 0.9; 95% CI 0.3-2.6). Adolescent OSA was associated with eBP in a dose-response manner; however, the association of an AHI of 2 to less than 5 among adolescents was nonsignificant (OR, 1.5; 95% CI, 0.9-2.6) and that of an AHI of 5 or more was approximately 2-fold (OR, 2.3; 95% CI, 1.1-4.9) after adjusting for visceral adipose tissue. An AHI of 5 or more (OR, 3.1; 95% CI, 1.2-8.5), but not between 2 and less than 5 (OR, 1.3; 95% CI, 0.6-3.0), was associated with orthostatic hyperreactivity among adolescents even after adjusting for visceral adipose tissue. Childhood OSA was not associated with adolescent eBP in female participants, while the risk of OSA and eBP was greater in male participants. The results of this cohort study suggest that childhood OSA is associated with adolescent hypertension only if it persists during this developmental period. Visceral adiposity explains a large extent of, but not all, the risk of hypertension associated with adolescent OSA, which is greater in male individuals.

A Fully Distributed Protocol with an Event-Triggered Communication Strategy for Second-Order Multi-Agent Systems Consensus with Nonlinear Dynamics

This paper presents the communication strategy for second-order multi-agent systems with nonlinear dynamics. To address the problem of the scarcity of communication channel resources and get rid of using continuous signals among the followers in lead-follower multi-agent systems, a novel event-triggered communication mechanism is proposed in this paper. To avoid employing the centralized information that depends on the Laplacian matrix spectrum, a network protocol with updated coupling gains is proposed, as well as an event-triggered strategy with updated thresholds. To eliminate the ill effects of inter-node communicating noise, relative positions are employed by the protocol instead of absolute positions. By a Lyapunov–Krasovskii functional, it is rigorously proven that the leader-following consensus of MASs is achieved without Zeno behavior, under the control of the proposed protocol with an event-triggered mechanism communication. The effectiveness of the proposed protocol is verified through numerical examples.

Basal and stimulated calcitonin for the diagnosis of medullary thyroid cancer: updated thresholds and safety assessment

Basal and stimulated calcitonin for the diagnosis of medullary thyroid cancer: updated thresholds and safety assessment

Comments on “Flash Flood Verification: Pondering Precipitation Proxies”

AbstractNew operational tools for monitoring flash flooding based on radar quantitative precipitation estimates (QPEs) have become available to U.S. National Weather Service forecasters. Herman and Schumacher examined QPE exceedance thresholds for several tools and compared them to each other, to flash flood reports (FFRs), and to flash flood warnings. The Next Generation Radar network has been updated with dual-polarization capabilities since the publication of Herman and Schumacher, which has changed the characteristics of the derived QPEs. Updated thresholds on Multi-Radar Multi-Sensor version 12 products that are associated to FFRs are provided and thus can be used as guidance by the operational forecasting community and other end-users of the products.

Basal and stimulated calcitonin for the diagnosis of medullary thyroid cancer: updated thresholds and safety assessment

PurposeReliable cut-offs for basal (bCT) and calcium stimulated calcitonin (casCT) are needed for an early and accurate diagnosis of medullary thyroid cancer (MTC).Patients and methodsFifty-four new patients with nodular goiter were enrolled and analysed together with those previously published by our group for a total of 135 cases. bCT and casCT were measured by a highly sensitive method and the results compared with histological findings. In a subgroup of patients, cardiac rhythm was recorded before and during the calcium test.ResultsIn both females (F) and males (M), there was a significant correlation between tumor size and bCT levels (P < 0.001). The receiver operating characteristic plot analyses showed that, for bCT, the new cut-off points able to separate non-MTC from MTC patients were > 30 (F) and > 34 pg/mL (M), whereas the best casCT thresholds were > 79 (F) and > 466 pg/mL (M). bCT was shown to harbour a high accuracy, though some cases were diagnosed only upon stimulation test. Importantly, combining bCT, below or above the cut-offs, with casCT above the cut-offs, all the MTC cases were correctly identified. A reversible sinus bradycardia was observed in 9% of cases during the test.ConclusionsRefined cut-offs for bCT and casCT in patients with nodular goiter are reported. Sensitive bCT was shown to have a high accuracy, but the combination with casCT data was needed to identify all MTC cases. The reliability and safety of calcium test strongly favour the routine use of CT determination in nodular thyroid disease.

A novel event-triggered communication strategy for second-order multiagent systems

This paper concentrates on the design of an event-triggered communication strategy for secondary multi-agent systems. Many existing results in this field usually require centralized information depending on the Laplacian matrices. In a real application, however, such the constraint is conservative. To address it, this paper proposes an event-triggered sampling mechanism for interagent communication with updated thresholds. Accordingly, consensus protocols with adaptive coupling control gains are proposed for both a fixed topology and switching topologies. It is proven that the proposed method guarantees the leader-following consensus for second-order multiagent systems with an arbitrarily connected topology and even with jointly connected switching topologies. The effectiveness of the proposed scheme is verified by simulation examples.

A Novel Adaptive Conditional Probability-Based Predicting Model for User’s Personality Traits

With the pervasive increase in social media use, the explosion of users’ generated data provides a potentially very rich source of information, which plays an important role in helping online researchers understand user’s behaviors deeply. Since user’s personality traits are the driving force of user’s behaviors, hence, in this paper, along with social network features, we first extract linguistic features, emotional statistical features, and topic features from user’s Facebook status updates, followed by quantifying importance of features via Kendall correlation coefficient. And then, on the basis of weighted features and dynamic updated thresholds of personality traits, we deploy a novel adaptive conditional probability-based predicting model which considers prior knowledge of correlations between user’s personality traits to predict user’s Big Five personality traits. In the experimental work, we explore the existence of correlations between user’s personality traits which provides a better theoretical support for our proposed method. Moreover, on the same Facebook dataset, compared to other methods, our method can achieve anF1-measure of 80.6% when taking into account correlations between user’s personality traits, and there is an impressive improvement of 5.8% over other approaches.

Updated thresholds for serum alanine aminotransferase level in a large‐scale population study composed of 34 346 subjects

The sensitivity of current upper limit of normal (ULN) of serum alanine aminotransferase (ALT) levels for detecting chronic liver disease has been challenged recently. To identify modulating factors for serum ALT levels and to refine its ULN threshold. We enrolled 34 346 consecutive subjects who completed the health check-up at Taipei Veterans General Hospital from 2002 to 2009. ULN was set for healthy ALT level to the 95th percentile of the reference healthy population. A group of 21 282 subjects were used as a training set to define an ULN with the highest sensitivity; afterwards, this ULN was validated in another set of 13 064 subjects. A reference healthy population was selected from the training set after excluding subjects with any abnormalities in independent risk factors associated with elevated serum ALT level (>40 IU/L) by multivariate analysis like body mass index, waist circumference, glucose, cholesterol, high-density lipoprotein-cholesterol, triglyceride, hepatitis B virus surface antigen, anti-hepatitis C virus antibody and fatty liver. The new ULN of serum ALT level defined as the 95% percentile in the healthy population were 21 IU/L and 17 IU/L for men and women respectively. These cut-off values had the highest Youden's index and areas under the corresponding receiver operating curves among four widely applied thresholds in both the training and validation sets. The suggested threshold of upper limit of normal provides better discrimination between healthy and unhealthy status. Viral hepatitis, metabolic syndrome and fatty liver are the major risk factors of elevated serum alanine aminotransferase levels.

The use of global synthetic data for pre-launch tuning of the VIIRS cloud mask algorithm

A methodology is presented to perform pre-launch tuning of thresholds used in the Visible Infrared Imager/Radiometer Suite (VIIRS) cloud mask (VCM) algorithm. The approach relies upon several data sources, including global synthetic data (GSD), Moderate Resolution Imaging Spectroradiometer (MODIS) and VIIRS relative spectral responses (RSRs) and MODIS top-of-atmosphere (TOA) radiance, reflectance and brightness temperature data. The GSD are used first to derive cloud cover distributions, that is, 0%, 50% and 100%, at VIIRS moderate resolution (800 m) for each VCM cloud detection test, based on inputs to the radiative transfer models. These cloud distributions are then used to update the values of the low, mid and high cloud-free confidence thresholds in the VCM algorithm. The approach is demonstrated by using MODIS RSRs with the GSD to set these thresholds and then analysing granules of MODIS data with the updated VCM. Performance is quantified through comparisons with manually generated cloud masks created from the MODIS imagery. The performance of the tuned VCM with MODIS data improved substantially for all major background conditions. The probability of correct typing (PCT) improved nearly 4% over the ocean to 97.5% and nearly 20% over snow-covered surfaces to 95.1%. The PCT values over land improved from 87.1% to 93.4% and over desert from 87.2% to 93.9%. The process was then repeated using VIIRS RSRs and the updated thresholds were forwarded to the National Polar-Orbiting Operational Satellite System (NPOESS) Preparatory Project (NPP) ground segment for incorporation into the operational system. It is concluded that GSD are invaluable for the pre-launch tuning of the VCM algorithm, which is now expected to exceed system requirements soon after the launch of the NPP satellite.

Deriving Bayesian Classifiers from Flight Data to Enhance Aircraft Diagnosis Models

Online fault diagnosis is critical for detecting the onset and hence the mitigation of adverse events that arise in complex systems, such as aircraft and industrial processes. A typical fault diagnosis system consists of: (1) a reference model that provides a mathematical representation for various diagnostic monitors that provide partial evidence towards active failure modes, and (2) a reasoning algorithm that combines set-covering and probabilistic computation to establish fault candidates and their rankings. However, for complex systems reference models are typically incomplete, and simplify- ing assumptions are made to make the reasoning algorithms tractable. Incompleteness in the reference models can take several forms, such as absence of discriminating evidence, and errors and incompleteness in the mapping between evidence and failure modes. Inaccuracies in the reasoning algorithm arise from the use of simplified noise models and independence assumptions about the evidence and the faults. Recently, data mining approaches have been proposed to help mitigate some of the problems with the reference models and reasoning schemes. This paper describes a Tree Augmented Na ̈ıve Bayesian Classifier (TAN) that forms the basis for systematically extending aircraft diagnosis reference models using flight data from systems operating with and without faults. The performance of the TAN models is investigated by comparing them against an expert supplied reference model. The results demonstrate that the generated TAN structures can be used by human experts to identify improvements to the reference model, by adding (1) new causal links that relate evidence to faults, and different pieces of evidence, and (2) updated thresholds and new monitors that facilitate the derivation of more precise evidence from the sensor data. A case study shows that this improves overall reasoner performance.

Updated thresholds for alanine aminotransferase do not exclude significant histological disease in chronic hepatitis C

Histological changes in hepatitis C virus (HCV)-infected patients with persistently normal alanine aminotransferase (PNALT) have not been evaluated for updated upper limits of normal (ULN; ≤ 19/30 U/L for females/males). We assessed significant fibrosis (≥ F2, METAVIR) in patients with PNALT and persistently elevated alanine aminotransferase (PEALT). Nine hundred and twenty consecutive, unselected HCV patients were stratified into four groups: Group I: (n = 124) PNALT within the updated ULN [0.5 × ULN (corresponding to ≤ 19 U/L) for females; 0.75 × ULN (corresponding to ≤ 30 U/L) for males]; Group II (n = 173): PNALT ≤ 1 × ULN but greater than Group I; Group III (n = 313): PEALT 1-2 × ULN; and Group IV (n = 310): PEALT > 2 × ULN. PNALT was defined as ≥ 3 determinations within the normal range over ≥ 6 months. Advanced ≥ F3 and ≥ F2 fibrosis increased incrementally across Groups I; II; III; and IV: 24.2 and 45.2%; 25.4 and 56.1%; 36.1 and 64.2%; and 50 and 77.1% respectively (P<0.0001 for both). Multivariable logistic regression analysis identified age [odds ratio (OR), 1.05; 95% confidence intervals (CI): 1.02-1.08; P<0.0001], alanine aminotransferase (ALT) groups (OR 1.38; 95% CI: 1.03-1.83; P = 0.030), presence of moderate-severe steatosis (OR 2.70; 95% CI: 1.19-6.15; P = 0.018) and ≥ A2 necroinflammation (OR 17.9; 95% CI: 8.88-36.20; P < 0.0001) as independent predictors of ≥ F2 fibrosis. Updated ULN for ALT were better at excluding ≥ F2 fibrosis compared with traditional ULN (90.6 vs. 74.2%, P = 0.0041) but less specific (20.8 vs. 44%, P = 0.0007) with similar positive/negative predictive values. HCV patients with 'updated' normal ALT have the lowest prevalence of significant fibrosis, although utilizing these levels without resorting to biopsy would miss significant fibrosis in almost one-half of such patients.

Noise Smoothing for Nonlinear Time Series Using Wavelet Soft Threshold

In this letter, a new threshold algorithm based on wavelet analysis is applied to smooth noise for a nonlinear time series. By detailing the signals decomposed onto different scales, we smooth the details by using the updated thresholds to different characters of a noisy nonlinear signal. This method is an improvement of Donoho's wavelet methods to nonlinear signals. The approach has been successfully applied to smoothing the noisy chaotic time series generated by the Lorenz system as well as the observed annual runoff of Yellow River. For the nonlinear dynamical system, an attempt is made to analyze the noise reduced data by using multiresolution analysis, i.e., the false nearest neighbors, correlation integral, and autocorrelation function, to verify the proposed noise smoothing algorithm