Estimates Of Likelihood Ratios Research Articles

Accuracy of cell-free fetal DNA in detecting chromosomal anomalies in women experiencing miscarriage: systematic review and meta-analysis.

To report the diagnostic accuracy of cell-free fetal DNA (cfDNA) in detecting fetal chromosomal anomalies in women experiencing miscarriage. PubMed, MEDLINE, EMBASE and Cochrane databases were searched from inception to June 2024. The inclusion criteria were women experiencing miscarriage (defined as pregnancy loss before 20 weeks of gestation) who underwent cfDNA screening for trisomies 21, 18and13, other autosomal aneuploidies, sex-chromosome aneuploidies and/or copy-number variants (CNVs). The index test was cfDNA screening for each of the chromosomal anomalies listed. The reference standard was cytogenetic analysis of pregnancy tissue. The quality of the studies was assessed using the revised tool for the quality assessment of diagnostic accuracy studies. Summary estimates of sensitivity, specificity, positive and negative likelihood ratios, and diagnostic odds ratio were computed using the hierarchical summary receiver-operating-characteristics model. Seven studies (887 women) were included in the systematic review and meta-analysis. cfDNA had a sensitivity and specificity of 100% (95% CI, 81.5-100%) and 100% (95% CI, 99.1-100%), respectively, for trisomy 21, 100% (95% CI, 54.1-100%) and 100% (95% CI, 99.0-100%), respectively, for trisomy 18, and 88.9% (95% CI, 51.8-99.7%) and 100% (95% CI, 99.1-100%), respectively, for trisomy 13. The respective values for other autosomal trisomies were 75.8% (95% CI, 65.7-84.2%) and 99.4% (95% CI, 97.9-99.9%), while those for CNVs were 60.0% (95% CI, 14.7-94.7%) and 100% (95% CI, 97.4-100%). Failure of cfDNA testing was reported in 7.3% (95% CI, 5.7-9.2%) of cases. cfDNA has high diagnostic accuracy in detecting fetal trisomies 21, 18 and 13 in women experiencing miscarriage, while its accuracy for other autosomal aneuploidies and CNVs is only moderate. © 2024 International Society of Ultrasound in Obstetrics and Gynecology.

Performance comparison of a previously validated microhaplotype panel and a forensic STR panel for DNA mixture analysis

Short Tandem Repeats (STRs) are the most widespread markers in forensic genetics. However, STR stutter peaks can mask alleles from a minor contributor when analysing mixtures, hindering the interpretation of complex profiles. In this study we compared the performance of a previously described panel of microhaplotypes (MHs), an alternative type of forensic marker, against a standard STR kit. The parameters evaluated included: capability of determining the minimum number of contributors in the mixture; percentages of allele drop-outs and drop-ins; retrieval of alleles belonging to the minor contributor, and estimation of likelihood ratio (LR) values. In addition, the capacity of EuroForMix software to estimate each donor’s percentage of contribution was tested, as well as the impact on results when using manually, or automatically prepared libraries. The MH panel showed better performance than STRs for the detection of 2-contributor mixtures, but the lower degree of polymorphism per MH marker hindered the task of deconvolution with multiple contributors. MHs presented higher drop-in rates and lower drop-out rates, a higher capability to recover the minor contributor’s alleles and provided higher LR values than STRs, likely due to the much higher number of loci combined in the panel. Estimations of contributor ratios using EuroForMix showed promising results and marginal differences were found in these values between manually and automatically prepared libraries. Overall, results showed that the mixture detection performance of the MH panel was better or equal to the standard forensic autosomal STR panel, indicating microhaplotypes are informative markers for this purpose.

Energy Efficiency for Faster-than-Nyquist Data Transmission Using Processing Algorithms with Decision Feedback

One of the ways to increase the volume of transmitted information is to increase the bit rate above the Nyquist barrier. However, an increase in bit rate in the case of FTN (Faster-Than-Nyquist) signals leads to an increase in energy costs for receiving information on channels with limited bandwidth, for example, in Digital Video Broadcasting satellite systems like DVB-S2/S2X. It is possible to minimize energy losses by using the processing algorithm “maximum likelihood sequence estimation”. However, the computational complexity of this algorithm is extremely high, which limits its use, especially in terrestrial mobile satellite terminals. We propose a new bit-by-bit decision feedback algorithm with maximum likelihood ratio estimation of subsequent symbols in the observation interval. This algorithm provides minimal energy costs comparable to the method “maximum likelihood sequence estimation” at speeds 2–3 times higher than the Nyquist barrier. At the same time, the complexity is two orders of magnitude less. It is shown by simulation for a channel with additive noise that energy losses in relation to the potential bit error rate (BER) are less than 4.5 dB. In the presence of Rayleigh fading, the application of the proposed algorithm makes it possible to provide the processing of FTN signals for double bit rates in urban areas with energy costs equal to 12 dB when using an equalizer. We give numerical estimations of the increase in computational complexity for the proposed processing algorithm. It is shown that an increase in the bit rate by 1.5 times leads to an increase in the computational complexity by more than an order of magnitude. The same conclusion can be reformulated in another form: for the proposed algorithm, each decibel of energy gain is achieved by increasing the number of computational operations by 1.5×105. It is experimentally shown that additional energy losses due to non-ideal phase and timing synchronization are no more than 1 dB when the proposed algorithm is applied in a fading channel. The energy costs in fading channels relative to a stationary channel for twice the Nyquist rate are equal to 13.8 dB when using an equalizer.

Risk of stress cardiomyopathy associated with selective serotonin reuptake inhibitors and serotonin and norepinephrine reuptake inhibitors: a real-world pharmacovigilance analysis

Selective serotonin reuptake inhibitors (SSRIs) and serotonin and norepinephrine reuptake inhibitors (SNRIs) are reported to cause stress cardiomyopathy (SC). This study evaluated the association between SSRI/SNRI use and the occurrence of cardiomyopathy in the publicly available U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) database. Disproportionate analysis and likelihood ratio tests were used to identify risk associated with SSRIs or SNRIs and the incidence of SC, using data from between from 2012 to 2022 acquired from the FAERS database. The study identified 132 individual case safety reports (ICSRs) of SC associated with SSRIs or SNRIs. Venlafaxine (48%) and fluoxetine (27%) were the most common antidepressants of the ICSRs. Approximately 80% of SC cases were reported in females, with individuals aged 45–65 years identified as a high-risk population. Both venlafaxine (ratio-scale information component [RSIC] 2.54, 95% CI 2.06–3.04) and fluoxetine (RSIC 3.20, 95% CI 2.31–4.47) were associated with SC, with likelihood ratio estimates of 3.55 (p = 0.02) for venlafaxine and 4.82 (p = 0.008) for fluoxetine. The median time to cardiomyopathy onset was 20 days, with hospitalization reported in 48.33% of patients. Venlafaxine and fluoxetine were associated with SC risk, particularly in middle-aged women. Caution should be exercised when using SSRIs or SNRIs combined with other serotonergic medications.

Regional-specific calibration enables application of computational evidence for clinical classification of 5′ cis-regulatory variants in Mendelian disease

To date, clinical genetic testing for Mendelian disease variants has focused heavily on exonic coding and intronic gene regions. This multi-step study was undertaken to provide an evidence base for selecting and applying computational approaches for use in clinical classification of 5' cis-regulatory region variants. Curated datasets of clinically reported disease-causing 5' cis-regulatory region variants and variants from matched genomic regions in population controls were used to calibrate six bioinformatic tools as predictors of variant pathogenicity. Likelihood ratio estimates were aligned to code weights following ClinGen recommendations for application of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) classification scheme. Considering code assignment across all reference dataset variants, performance was best for CADD (81.2%) and REMM (81.5%). Optimized thresholds provided moderate evidence toward pathogenicity (CADD, REMM) and moderate (CADD) or supporting (REMM) evidence against pathogenicity. Both sensitivity and specificity of prediction were improved when further categorizing variants based on location in an EPDnew-defined promoter region. Combining predictions (CADD, REMM, and location in a promoter region) increased specificity at the expense of sensitivity. Importantly, the optimal CADD thresholds for assigning ACMG/AMP codes PP3 (≥10) and BP4 (≤8) were vastly different from recommendations for protein-coding variants (PP3≥25.3; BP4≤22.7); CADD <22.7 would incorrectly assign BP4 for >90% of reported disease-causing cis-regulatory region variants. Our results demonstrate the need to consider a tiered approach and tailored score thresholds to optimize bioinformatic impact prediction for clinical classification of 5' cis-regulatory region variants.

Direct Amortized Likelihood Ratio Estimation

We introduce a new amortized likelihood ratio estimator for likelihood-free simulation-based inference (SBI). Our estimator is simple to train and estimates the likelihood ratio using a single forward pass of the neural estimator. Our approach directly computes the likelihood ratio between two competing parameter sets which is different from the previous approach of comparing two neural network output values. We refer to our model as the direct neural ratio estimator (DNRE). As part of introducing the DNRE, we derive a corresponding Monte Carlo estimate of the posterior. We benchmark our new ratio estimator and compare to previous ratio estimators in the literature. We show that our new ratio estimator often outperforms these previous approaches. As a further contribution, we introduce a new derivative estimator for likelihood ratio estimators that enables us to compare likelihood-free Hamiltonian Monte Carlo (HMC) with random-walk Metropolis-Hastings (MH). We show that HMC is equally competitive, which has not been previously shown. Finally, we include a novel real-world application of SBI by using our neural ratio estimator to design a quadcopter. Code is available at https://github.com/SRI-CSL/dnre.

Lever sign test for anterior cruciate ligament injuries: a diagnostic meta-analysis

BackgroundSports-related ACL (anterior cruciate ligament) injuries are frequent. Successful management requires early diagnosis and treatment. One of the clinical tests used to identify ACL damage is the lever sign test. This meta-analysis aimed to assess the lever sign test's diagnostic efficacy for ACL injuries.MethodsAn extensive investigation of the Cochrane Library, Embase, and PubMed databases was conducted until April 2023. Studies assessing the lever sign test's diagnostic efficacy for ACL injuries were also included. A bivariate random-effects model was employed to acquire the pooled estimates of diagnostic odds ratios, specificity, positive and negative likelihood ratios, sensitivity, and curves of the summary receiver operating characteristic (SROC).ResultsThe meta-analysis comprised twelve investigations with a total of 1365 individuals. The lever sign test's combined sensitivity and specificity for the purpose of diagnosing injuries to the ACL were 0.810 (95% confidence interval [CI] 0.686–0.893) and 0.784 (95% CI 0.583–0.904), respectively. The positive and negative likelihood ratios were 3.148 (95% CI 1.784–5.553) and 0.210 (95% CI 0.084–0.528), respectively. The study revealed a diagnostic odds ratio of 17.656, with a 95% CI ranging from 4.800 to 64.951. The SROC curve's area was determined to be 0.912 (95% CI 0.857–0.967).ConclusionWith high specificity and sensitivity, the lever sign test is a reliable diagnostic modality for ACL injuries. However, the test should be used in combination with other diagnostic tests to increase the accuracy of the diagnosis. Further investigations are warranted to assess the clinical practicability of the lever sign test in various populations and settings.

Accuracy of the No-Biopsy Approach for the Diagnosis of Celiac Disease in Adults: A Systematic Review and Meta-Analysis

Background & AimsCurrent international guidelines recommend duodenal biopsies to confirm the diagnosis of coeliac disease in adult patients. However, growing evidence suggests that IgA anti-tissue transglutaminase (tTg) antibody levels ≥10 times the upper limit of normal (ULN) can accurately predict coeliac disease, eliminating the need for biopsy. We performed a systematic review and meta-analysis to evaluate the accuracy of the no-biopsy approach to confirm the diagnosis of coeliac disease in adults. MethodsWe systematically searched MEDLINE, EMBASE, Cochrane Library and Web of Science from January 1998 to October 2023 for studies reporting the sensitivity and specificity of IgA-tTG ≥10×ULN against duodenal biopsies (Marsh grade ≥2) in adults with suspected coeliac disease. We used a bivariate random-effects model to calculate the summary estimates of sensitivity, specificity, positive and negative likelihood ratios. The positive and negative likelihood ratios were used to calculate the positive predictive value (PPV) of the no-biopsy approach across different pre-test probabilities of coeliac disease. The methodological quality of the included studies was evaluated using the QUADAS-2 tool. This study was registered with PROSPERO, number CRD42023398812. ResultsA total of 18 studies comprising 12,103 participants from 15 countries were included. The pooled prevalence of biopsy-proven coeliac disease in the included studies was 62% (95% CI, 40% - 83%). The proportion of patients with IgA-tTG ≥10×ULN was 32% (95% CI, 24% - 40%). The summary sensitivity of IgA-tTG ≥10×ULN was 51% (95% CI, 42% - 60%), and the summary specificity was 100% (95% CI, 98% - 100%). The area under the summary receiver operating characteristic curve was 0.83 (95% CI, 0.77 – 0.89). The PPV of the no-biopsy approach to identify patients with coeliac disease was 65%, 88%, 95%, and 99% if coeliac disease prevalence was 1%, 4%, 10% and 40%, respectively. Between-study heterogeneity was moderate (I2 =30.3%), and additional sensitivity analyses did not significantly alter our findings. Only one study had a low risk of bias across all domains. ConclusionThe results of this meta-analysis suggest that selected adult patients with IgA-tTG ≥10×ULN and a moderate to high pre-test probability of coeliac disease could be diagnosed without undergoing invasive endoscopy and duodenal biopsy.

Comparison of CT and MRI in diagnosing occult hip fracture: a systematic review and meta-analysis.

To compare the diagnostic accuracy of computed tomography (CT) scans and magnetic resonance imaging (MRI) in detecting occult hip fractures. We conducted a systematic literature review and identified 12 articles involving 1,819 participants for inclusion. Data extraction and quality assessment were performed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Publication bias was assessed with the Deek funnel plot asymmetry test. We conducted a meta-analysis using a random-effects model to derive pooled estimates of sensitivity, specificity, positive and negative likelihood ratios, and the diagnostic odds ratio, along with their 95% confidence intervals. A summary receiver operating characteristic curve was generated to illustrate the overall diagnostic accuracy. The methodological quality of the included studies was high, with minimal concerns about the applicability of the tests in clinical settings. Both CT and MRI showed good diagnostic efficacy for occult hip fractures. However, MRI consistently outperformed CT, exhibiting significantly higher sensitivity, specificity, and likelihood ratios, thereby providing superior accuracy in confirming or excluding occult fractures. Meta-regression analysis revealed that sequence parameters and sample size significantly influenced the differences in sensitivity and specificity between CT and MRI. Both CT and MRI are effective modalities for detecting occult hip fractures, with MRI demonstrating greater diagnostic accuracy. This meta-analysis supports the use of MRI when higher sensitivity and specificity are required in clinical practice.

Machine Learning Models for Blood Glucose Level Prediction in Patients With Diabetes Mellitus: Systematic Review and Network Meta-Analysis.

Machine learning (ML) models provide more choices to patients with diabetes mellitus (DM) to more properly manage blood glucose (BG) levels. However, because of numerous types of ML algorithms, choosing an appropriate model is vitally important. In a systematic review and network meta-analysis, this study aimed to comprehensively assess the performance of ML models in predicting BG levels. In addition, we assessed ML models used to detect and predict adverse BG (hypoglycemia) events by calculating pooled estimates of sensitivity and specificity. PubMed, Embase, Web of Science, and Institute of Electrical and Electronics Engineers Explore databases were systematically searched for studies on predicting BG levels and predicting or detecting adverse BG events using ML models, from inception to November 2022. Studies that assessed the performance of different ML models in predicting or detecting BG levels or adverse BG events of patients with DM were included. Studies with no derivation or performance metrics of ML models were excluded. The Quality Assessment of Diagnostic Accuracy Studies tool was applied to assess the quality of included studies. Primary outcomes were the relative ranking of ML models for predicting BG levels in different prediction horizons (PHs) and pooled estimates of the sensitivity and specificity of ML models in detecting or predicting adverse BG events. In total, 46 eligible studies were included for meta-analysis. Regarding ML models for predicting BG levels, the means of the absolute root mean square error (RMSE) in a PH of 15, 30, 45, and 60 minutes were 18.88 (SD 19.71), 21.40 (SD 12.56), 21.27 (SD 5.17), and 30.01 (SD 7.23) mg/dL, respectively. The neural network model (NNM) showed the highest relative performance in different PHs. Furthermore, the pooled estimates of the positive likelihood ratio and the negative likelihood ratio of ML models were 8.3 (95% CI 5.7-12.0) and 0.31 (95% CI 0.22-0.44), respectively, for predicting hypoglycemia and 2.4 (95% CI 1.6-3.7) and 0.37 (95% CI 0.29-0.46), respectively, for detecting hypoglycemia. Statistically significant high heterogeneity was detected in all subgroups, with different sources of heterogeneity. For predicting precise BG levels, the RMSE increases with a rise in the PH, and the NNM shows the highest relative performance among all the ML models. Meanwhile, current ML models have sufficient ability to predict adverse BG events, while their ability to detect adverse BG events needs to be enhanced. PROSPERO CRD42022375250; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=375250.

Subhalo effective density slope measurements from HST strong lensing data with neural likelihood-ratio estimation

ABSTRACT Examining the properties of subhaloes with strong gravitational lensing images can shed light on the nature of dark matter. From upcoming large-scale surveys, we expect to discover orders of magnitude more strong lens systems that can be used for subhalo studies. To optimally extract information from a large number of strong lensing images, machine learning provides promising avenues for efficient analysis that is unachievable with traditional analysis methods, but application of machine learning techniques to real observations is still limited. We build upon previous work, which uses a neural likelihood-ratio estimator, to constrain the effective density slopes of subhaloes and demonstrate the feasibility of this method on real strong lensing observations. To do this, we implement significant improvements to the forward simulation pipeline and undertake careful model evaluation using simulated images. Ultimately, we use our trained model to predict the effective subhalo density slope from combining a set of strong lensing images taken by the Hubble Space Telescope. We found the subhalo slope measurement of this set of observations to be steeper than the slope predictions of cold dark matter subhaloes. Our result adds to several previous works that also measured high subhalo slopes in observations. Although a possible explanation for this is that subhaloes with steeper slopes are easier to detect due to selection effects and thus contribute to statistical bias, our result nevertheless points to the need for careful analysis of more strong lensing observations from future surveys.

New machine learning techniques for simulation-based inference: InferoStatic nets, kernel score estimation, and kernel likelihood ratio estimation

We propose an intuitive, machine-learning approach to multiparameter inference, dubbed the InferoStatic Networks (ISN) method, to model the score and likelihood ratio estimators in cases when the probability density can be sampled but not computed directly. The ISN uses a backend neural network that models a scalar function called the inferostatic potential \varphiφ. In addition, we introduce new strategies, respectively called Kernel Score Estimation (KSE) and Kernel Likelihood Ratio Estimation (KLRE), to learn the score and the likelihood ratio functions from simulated data. We illustrate the new techniques with some toy examples and compare to existing approaches in the literature. We mention en passant some new loss functions that optimally incorporate latent information from simulations into the training procedure.

Sampling distribution for single-regression Granger causality estimators

Summary The single-regression Granger–Geweke causality estimator has previously been shown to solve known problems associated with the more conventional likelihood ratio estimator; however, its sampling distribution has remained unknown. We show that, under the null hypothesis of vanishing Granger causality, the single-regression estimator converges to a generalized χ2 distribution, which is well approximated by a Γ distribution. We show that this holds too for Geweke’s spectral causality averaged over a given frequency band, and derive explicit expressions for the generalized χ2 and Γ-approximation parameters in both cases. We present a Neyman–Pearson test based on the single-regression estimators, and discuss how it may be deployed in empirical scenarios. We outline how our analysis may be extended to the conditional case, point-frequency spectral Granger causality and the important case of state-space Granger causality.

Likelihood Ratio Estimation and Validation for Discrimination of Ten Hindi Vowels in Forensic Speaker Identification

Likelihood Ratio Estimation and Validation for Discrimination of Ten Hindi Vowels in Forensic Speaker Identification

Quickest Anomaly Detection in Sensor Networks With Unlabeled Samples

The problem of quickest anomaly detection in networks with unlabeled samples is studied. At some unknown time, an anomaly emerges in the network and changes the data-generating distribution of some unknown sensor. The data vector received by the fusion center at each time step undergoes some unknown and arbitrary permutation of its entries (unlabeled samples). The goal of the fusion center is to detect the anomaly with minimal detection delay subject to false alarm constraints. With unlabeled samples, existing approaches that combines local cumulative sum (CuSum) statistics cannot be used anymore. Several major questions include whether detection is still possible without the label information, if so, what is the fundamental limit and how to achieve that. Two cases with static and dynamic anomaly are investigated, where the sensor affected by the anomaly may or may not change with time. For the two cases, practical algorithms based on the ideas of mixture likelihood ratio and/or maximum likelihood estimate are constructed. Their average detection delays and false alarm rates are theoretically characterized. Universal lower bounds on the average detection delay for a given false alarm rate are also derived, which further demonstrate the asymptotic optimality of the two algorithms.

Machine-Learning Application for a Likelihood Ratio Estimation Problem at LHC

High-energy physics is now entering a new era. Current particle experiments, such as ATLAS at the Large Hadron Collider at CERN, offer the possibility of discovering new and interesting physics’ phenomena beyond the Standard Model, the theoretical framework which describes fundamental interactions between particles. In this paper a machine-learning algorithm to deal with the estimate of the expected background processes distribution, a very demanding task for particle physics analyses, is described. A new technique is exploited, inspired by the well-known likelihood ratio estimation problem, called direct importance estimation in statistics. First, its theoretical formulation is discussed, then its performances in two ATLAS analyses are described.

Reporting likelihood ratio for casework in firearm evidence identification

Likelihood ratio (LR) plays an important role in estimating the weight of evidence in firearm evidence identifications. LR is computed from a statistical model including the distribution of the known-matching (KM or within) and known-nonmatching (KNM or between) comparison scores. Current LR procedures rely on KM/KNM scores from existing reference firearm toolmark data sets or alternatively from generating a set of test fires using multiple firearms. Both procedures may contain theoretical or practical issues which may hinder the LR procedures from reporting an unbiased LR estimation in casework. In this paper, a reference data set was established from a set of firearms, each test-fired two cartridge cases, resulting in a basic data set and a control data set. The congruent matching cells (CMC) method was used to generate CMC scores that are used to fit in the KM/KNM statistical distributions for LR estimation. In the initial test, 130 firearms from eight manufacturers were used for generating a reference data set consisting of 260 cartridge cases representing 130 KM and 8385 KNM pairwise breech face images. Test results showed that the KM and KNM distribution intersect at CMC=2, which is equivalent to LR=1 (equally to support both the prosecutor and the defense propositions). When the CMC threshold is increased to 6 or more, the LR values are higher than a million, which can provide extremely strong support to the conclusion of the same firearm (or the prosecutor's proposition) in the casework of firearm evidence identification.

388. Does This Patient Have C. difficile Infection? A Systematic Review and Meta-Analysis

Abstract Background The clinical features of Clostridioides difficile infection overlap with many conditions. Accurate and early diagnosis of C. difficile is essential for reducing morbidity and mortality. We performed a systematic review to evaluate the diagnostic utility of clinical findings associated with C. difficile. Methods We included all studies that reported clinical features of C. difficile, a valid reference standard test for confirming diagnosis of C. difficile, and a comparison among patients with a positive and negative test result. The MEDLINE, EMBASE, CINAHL, and Cochrane databases were searched up to September 2021. Meta-analyses using univariate and bivariate methods were used to determine estimates of sensitivity, specificity, and likelihood ratios (LRs). Results A total of 11,231 articles were screened and 46 were included for final analysis, enabling evaluation of 67 features for their diagnostic utility for C. difficile (10 clinical examination findings, 4 laboratory tests, 10 radiographic findings, prior exposure to 14 antibiotic types, and 29 clinical risk factors). Of the ten features identified on clinical examination, none were associated with increased likelihood of C. difficile infection. Features that increased likelihood of C. difficileinfection were stool leukocytes (LR 5.31, 95% CI 3.29-8.56), hospital admission in prior three months (LR 2.39, 95% CI 1.68-3.30), leukocytosis (LR 1.50, 95% CI 1.24-1.75), and low serum albumin (LR 1.43, 95% CI 1.06-1.97). Some clinical co-morbidities increased likelihood of C. difficile including congestive heart failure (LR 3.01, 95% CI 2.26-3.80) and end-stage renal disease (LR 3.85, 95% CI 1.73-7.57). Several radiographic findings also strongly increased the likelihood of C. difficile infection like pericolonic stranding (LR 10.72, 95% CI 9.59-11.84) and ascites (LR 2.91, 95% CI 1.76-4.80). Conclusion There is limited utility of bedside clinical examination alone in detecting or ruling out C. difficile infection. Accurate diagnosis of C. difficile infection requires a combination of thoughtful clinical assessment and interpretation of test results. However, microbiologic testing is needed for confirmation in all suspected cases. Disclosures All Authors: No reported disclosures.

Meta-DiSc 2.0: a web application for meta-analysis of diagnostic test accuracy data

BackgroundDiagnostic evidence of the accuracy of a test for identifying a target condition of interest can be estimated using systematic approaches following standardized methodologies. Statistical methods for the meta-analysis of diagnostic test accuracy (DTA) studies are relatively complex, presenting a challenge for reviewers without extensive statistical expertise. In 2006, we developed Meta-DiSc, a free user-friendly software to perform test accuracy meta-analysis. This statistical program is now widely used for performing DTA meta-analyses. We aimed to build a new version of the Meta-DiSc software to include statistical methods based on hierarchical models and an enhanced web-based interface to improve user experience.ResultsIn this article, we present the updated version, Meta-DiSc 2.0, a web-based application developed using the R Shiny package. This new version implements recommended state-of-the-art statistical models to overcome the limitations of the statistical approaches included in the previous version. Meta-DiSc 2.0 performs statistical analyses of DTA reviews using a bivariate random effects model. The application offers a thorough analysis of heterogeneity, calculating logit variance estimates of sensitivity and specificity, the bivariate I-squared, the area of the 95% prediction ellipse, and the median odds ratios for sensitivity and specificity, and facilitating subgroup and meta-regression analyses. Furthermore, univariate random effects models can be applied to meta-analyses with few studies or with non-convergent bivariate models.The application interface has an intuitive design set out in four main menus: file upload; graphical description (forest and ROC plane plots); meta-analysis (pooling of sensitivity and specificity, estimation of likelihood ratios and diagnostic odds ratio, sROC curve); and summary of findings (impact of test through downstream consequences in a hypothetical population with a given prevalence).All computational algorithms have been validated in several real datasets by comparing results obtained with STATA/SAS and MetaDTA packages.ConclusionWe have developed and validated an updated version of the Meta-DiSc software that is more accessible and statistically sound. The web application is freely available at www.metadisc.es.

Prediction of Difficult Laryngoscopy Using Ultrasound: A Systematic Review and Meta-Analysis.

Evaluate associations between ultrasound measures and difficult laryngoscopy. MEDLINE, Embase, Google Scholar, Web of Science, and the Cochrane Library were searched using MeSH terms and keywords. Studies published in English describing the use of airway ultrasound for identifying difficult laryngoscopy, with sufficient data to calculate sensitivity and specificity using 2 × 2 tables. We assigned the described indices of airway dimension to one of three domains based on methodology characteristics: anterior tissue thickness domain, anatomical position domain, and oral space domain. We then performed a bivariate random-effects meta-analysis, deriving pooled sensitivity, specificity, diagnostic odds ratio, positive likelihood ratio, and negative likelihood ratio estimates. We assessed risks of bias using Quality Assessment of Diagnostic Accuracy Studies-2 analysis. Thirty-three studies evaluating 27 unique indices were included in the meta-analysis. The ultrasound protocols of the included studies were heterogeneous. Anterior tissue thickness demonstrated a pooled sensitivity of 76% (95% CI, 71-81%), specificity of 77% (95% CI, 72-81%), and an area under the receiver operating characteristic curve (AUROC) of 0.83 (95% CI, 0.80-0.86). Anatomical position demonstrated a pooled sensitivity of 74% (95% CI, 61-84%), specificity of 86% (95% CI, 78-91%), and an AUROC of 0.87 (95% CI, 0.84-0.90). Oral space demonstrated a pooled sensitivity of 53% (95% CI, 0.36-0.69), specificity of 77% (95% CI, 0.67-0.85), and an AUROC of 0.73 (95% CI, 0.69-0.77). Airway ultrasound metrics associate with difficult laryngoscopy in three domains: anterior tissue thickness, anatomic position, and oral space. An assessment instrument combining clinical and ultrasound assessments may be an accurate screening tool for difficult laryngoscopy.