30 year experience of index case identification and outcomes of cascade testing in high-risk breast and colorectal cancer predisposition genes

It is 30 years since the first diagnostic cancer predisposition gene (CPG) test in the Manchester Centre for Genomic Medicine (MCGM), providing opportunities for cancer prevention, early detection and targeted treatments in index cases and at-risk family members. Here, we present time trends (1990–2020) of identification of index cases with a germline CPG variant and numbers of subsequent cascade tests, for 15 high-risk breast and gastro-intestinal tract cancer-associated CPGs: BRCA1, BRCA2, PALB2, PTEN, TP53, APC, BMPR1a, CDH1, MLH1, MSH2, MSH6, PMS2, SMAD4, STK11 and MUTYH. We recorded 2082 positive index case diagnostic screening tests, generating 3216 positive and 3140 negative family cascade (non-index) tests. This is equivalent to an average of 3.05 subsequent cascade tests per positive diagnostic index test, with 1.54 positive and 1.51 negative non-index tests per family. The CPGs with the highest numbers of non-index positive cases identified on cascade testing were BRCA1/2 (n = 1999) and the mismatch repair CPGs associated with Lynch Syndrome (n = 731). These data are important for service provision and health economic assessment of CPG diagnostic testing, in terms of cancer prevention and early detection strategies, and identifying those likely to benefit from targeted treatment strategies.

Inflammatory Biomarkers Are Inaccurate Indicators of Bacterial Infection on Admission in Patients With Acute Exacerbation of Chronic Obstructive Pulmonary Disease—A Systematic Review and Diagnostic Accuracy Network Meta-Analysis

Introduction: The value of inflammatory biomarkers in the diagnosis of bacterial infection induced acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is currently unclear. Our objective was to investigate the diagnostic accuracy of on-admission inflammatory biomarkers in differentiating bacterial origin in AECOPD.Methods: Systematic literature search was performed to include cross-sectional studies on AECOPD patients with microbiological culture results as gold standard, and at least one on-admission inflammatory biomarker determined from serum: C-reactive protein (CRP), procalcitonin (PCT), neutrophil/lymphocyte ratio, eosinophil percentage, CD64index; or sputum: neutrophil elastase, tumor necrosis factor alfa, interleukin-1-beta (IL-1b), interleukin-8, sputum color, as index tests. We ranked index tests by superiority indices in a network meta-analysis and also calculated pooled sensitivity and specificity.Results: Altogether, 21 eligible articles reported data on 2,608 AECOPD patients (44% bacterial). Out of the 14 index tests, sputum IL-1b showed the highest diagnostic performance with a pooled sensitivity of 74% (CI: 26–97%) and specificity of 65% (CI: 19–93%). Pooled sensitivity for CRP and PCT were: 67% (CI: 54–77%) and 54% (CI: 39–69%); specificity 62% (CI: 52–71%) and 71% (CI: 59–79%), respectively.Conclusion: Admission inflammatory biomarkers are inaccurate indicators of bacterial infection in AECOPD.Systematic Review Registration:https://www.crd.york.ac.uk/prospero/#myprospero, identifier: 42020161301.

The Effects of Rock Index Tests on Prediction of Tensile Strength of Granitic Samples: A Neuro-Fuzzy Intelligent System

Rock tensile strength (TS) is an essential parameter for designing structures in rock-based projects such as tunnels, dams, and foundations. During the preliminary phase of geotechnical projects, rock TS can be determined through laboratory works, i.e., Brazilian tensile strength (BTS) test. However, this approach is often restricted by laborious and costly procedures. Hence, this study attempts to estimate the BTS values of rock by employing three non-destructive rock index tests. BTS predictive models were developed using 127 granitic rock samples. Since the simple regression analysis did not yield a meaningful result, the development of models that integrate multiple input parameters were considered to improve the prediction accuracy. The effects of non-destructive rock index tests were examined through the use of multiple linear regression (MLR) and adaptive neuro-fuzzy inference system (ANFIS) approaches. Different strategies and scenarios were implemented during modelling of MLR and ANFIS approaches, where the focus was to consider the most important parameters of these techniques. As a result, and according to background and behaviour of the ANFIS (or neuro-fuzzy) model, the predicted values obtained by this intelligent methodology are closer to the actual BTS compared to MLR which works based on linear statistical rules. For instance, in terms of system error and a-20 index, values of (0.84 and 1.20) and (0.96 and 0.80) were obtained for evaluation parts of ANFIS and MLR techniques, which revealed that the ANFIS model outperforms the MLR in forecasting BTS values. In addition, the same results were obtained through ranking systems by the authors. The neuro-fuzzy developed in this study is a strong technique in terms of prediction capacity and it can be used in the other rock-based projects for solving relevant problems.

A new combination testing methodology to identify accurate and economical point-of-care testing strategies

Background: Quick, cheap and accurate point-of-care testing is urgently needed to enable frequent, large-scale testing to contain COVID-19. Lateral flow tests for antigen and antibody detection are an obvious candidate for use in community-wide testing, because they are quick and cheap relative to lab-processed tests. However, their low accuracy has limited their adoption. We develop a new methodology to increase the diagnostic accuracy of a combination of cheap, quick and inaccurate index tests with correlated or discordant outcomes, and illustrate its performance on commercially available lateral flow immunoassays (LFIAs) for Sars-CoV-2 antibody detection. Methods and Findings: We analyze laboratory test outcomes of 300 serum samples from health care workers detected with PCR-confirmed SARS-Cov-2 infection at least 21 days prior to sample collection, and 500 pre-pandemic serum samples, from a national seroprevalence survey, tested using eight LFIAs (Abbott, Biosure/Mologic, Orientgene-Menarini, Fortress, Biopanda I, Biopanda II, SureScreen and Wondfo) and Hybrid DABA as reference test. For each of 14 two-test combinations (e.g., Abbott, Fortress) and 16 three-test combinations (e.g., Abbott, Fortress, Biosure/Mologic) used on at least 100 positive and 100 negative samples, we classify an outcome sequence -- e.g., (+,-) for (Abbott, Fortress) -- as positive if its combination positive predictive value (CPPV) exceeds a given threshold, set between 0 and 1. Our main outcome measures are the sensitivity and specificity of different classification rules for classifying the outcomes of a combination test. We define testing possibility frontiers which represent sensitivity and false positive rates for different thresholds. The envelope of frontiers further enables test selection. The eight index tests individually meet neither the UK Medicines and Healthcare Products Regulatory Agency's 98% sensitivity and 98% specificity criterion, nor the US Center for Disease Control's 99.5% specificity criterion. Among these eight tests, the highest single-test LFIA specificity is 99.4% (with a sensitivity of 65.2%) and the highest single-test LFIA sensitivity is 93.4% (with a specificity of 97.4%). Using our methodology, a two-test combination meets the UK Medicines and Healthcare Products Regulatory Agency's criterion, achieving sensitivity of 98.4% and specificity of 98.0%. While two-test combinations meeting the US Center for Disease Control's 99.5% specificity criterion have sensitivity below 83.6%, a three-test combination delivers a specificity of 99.6% and a sensitivity of 95.8%. Conclusions: Current CDC guidelines suggest combining tests, noting that 'performance of orthogonal testing algorithms has not been systematically evaluated' and highlighting discordant outcomes. Our methodology combines available LFIAs to meet desired accuracy criteria, by identifying testing possibility frontiers which encompass benchmarks, enabling cost savings. Our methodology applies equally to antigen testing and can greatly expand testing capacity through combining less accurate tests, especially for use cases needing quick, accurate tests, e.g., entry to public spaces such as airports, nursing homes or hospitals.

Cerebrospinal Fluid and Plasma Procalcitonin for the Diagnosis of Neonatal Bacterial Meningitis

Objective: To determine accuracy of cerebro-spinal fluid (CSF) procalcitonin (PCT) to diagnose neonatal bacterial meningitis (NBM) among septic neonates and compare with other index tests. Design: Prospective, cross-sectional, single-gate study Setting: Level-3 neonatal unit Patients: Neonates with suspected sepsis undergoing lumbar puncture Index tests: CSF PCT, leukocyte count and biochemistry; plasma PCT and CSF:plasma PCT ratio Reference standards ″Definite meningitis″ defined by positive CSF culture and/or gram stain and/or broad-based primer 16S rDNA polymerase chain reaction. ″Definite or probable meningitis″ defined by definite meningitis or probable meningitis (based on cytochemistry cut-offs). Results: Of 216 eligible neonates, 18 had ″definite meningitis″ and 37 ″definite or probable meningitis″. Median (Q1, Q3) CSF PCT level was higher in ″definite meningitis″ compared to ″no definite meningitis″ [0.429 (0.123, 1.300) vs. 0.181 (0.119, 0.286) ng/ml respectively, p=0.028]. Likewise, it was higher in ″definite or probable meningitis″ compared to no meningitis [0.245 (0.136, 0.675) vs. 0.170 (0.116, 0.28), p=0.01]. The area under ROC curve (AUC) of CSF PCT level for definite meningitis was 0.656 and for ″definite or probable meningitis″ 0.635. Paired comparisons of AUC of CSF PCT with other index tests were not significant. Based on a priori cut-off of 0.2 ng/ml, CSF PCT level had a sensitivity (95% CI) of 67% (50, 80), specificity 58% (54, 61), LR+ 1.6 (1.1, 2.0) and LR- 0.6 (0.3, 0.9). Conclusions: Higher values of CSF PCT are associated with NBM. CSF PCT cannot reliably discriminate between meningitis and no meningitis and is not superior to other CSF tests.

Screening Methods for Diagnosing Cystic Fibrosis-Related Diabetes: A Network Meta-Analysis of Diagnostic Accuracy Studies

Background: Cystic fibrosis-related diabetes (CFRD) has become more common due to higher life expectancy with cystic fibrosis. Early recognition and prompt treatment of CFRD leads to improved outcomes. Methods: We performed a network meta-analysis (NMA) in order to identify the most valuable diagnostic metrics for diagnosing CFRD out of available screening tools (index test), using the oral glucose tolerance test as a reference standard. Pooled sensitivity (Se), specificity (Sp), and superiority indices were calculated and used to rank the index tests. Results: A total of 31 articles with 25 index tests were eligible for inclusion. Two-day, continuous glucose monitoring (CGM) ranked the highest (Se: 86% Sp: 76%), followed by glucose measurement from blood capillary samples (Se: 70%, Sp: 82%) and three-day CGM (Se: 96%, Sp: 56%). When we compared the CGM of different durations, two-day CGM performed best (Se: 88%, Sp: 80%), followed by three-day (Se: 96%, Sp: 59%) and six-day CGM (Se: 66%, Sp: 79%). Conclusions: Considering its overall performance ranking, as well as the high sensitivity, two-day CGM appears to be a promising screening test for CFRD.