A mildly relativistic wide-angle outflow in the neutron-star merger event GW170817.

GW170817 was the first gravitational-wave detection of a binary neutron-star merger. It was accompanied by radiation across the electromagnetic spectrum and localized to the galaxy NGC 4993 at a distance of 40 megaparsecs. It has been proposed that the observed γ-ray, X-ray and radio emission is due to an ultra-relativistic jet being launched during the merger (and successfully breaking out of the surrounding material), directed away from our line of sight (off-axis). The presence of such a jet is predicted from models that posit neutron-star mergers as the drivers of short hard-γ-ray bursts. Here we report that the radio light curve of GW170817 has no direct signature of the afterglow of an off-axis jet. Although we cannot completely rule out the existence of a jet directed away from the line of sight, the observed γ-ray emission could not have originated from such a jet. Instead, the radio data require the existence of a mildly relativistic wide-angle outflow moving towards us. This outflow could be the high-velocity tail of the neutron-rich material that was ejected dynamically during the merger, or a cocoon of material that breaks out when a jet launched during the merger transfers its energy to the dynamical ejecta. Because the cocoon model explains the radio light curve of GW170817, as well as the γ-ray and X-ray emission (and possibly also the ultraviolet and optical emission), it is the model that is most consistent with the observational data. Cocoons may be a ubiquitous phenomenon produced in neutron-star mergers, giving rise to a hitherto unidentified population of radio, ultraviolet, X-ray and γ-ray transients in the local Universe.

Evolution of the sheep industry and genetic research in the United States: opportunities for convergence in the twenty-first century.

The continuous development and application of technology for genetic improvement is a key element for advancing sheep production in the United States. The US sheep industry has contracted over time but appears to be at a juncture where a greater utilization of technology can facilitate industry expansion to new markets and address inefficiencies in traditional production practices. Significant transformations include the increased value of lamb in relation to wool, and a downtrend in large-scale operations but a simultaneous rise in small flocks. Additionally, popularity of hair breeds not requiring shearing has surged, particularly in semi-arid and subtropical US environments. A variety of domestically developed composite breeds and newly established technological approaches are now widely available for the sheep industry to use as it navigates these ongoing transformations. These genetic resources can also address long-targeted areas of improvement such as growth, reproduction and parasite resistance. Moderate progress in production efficiency has been achieved by producers who have employed estimated breeding values, but widespread adoption of this technology has been limited. Genomic marker panels have recently shown promise for reducing disease susceptibility, identifying parentage and providing a foundation for marker-assisted selection. As the ovine genome is further explored and genomic assemblies are improved, the sheep research community in the USA can capitalize on new-found information to develop and apply genetic technologies to improve the production efficiency and profitability of the sheep industry.

Numbers of close contacts of individuals infected with SARS-CoV-2 and their association with government intervention strategies.

BACKGROUND: Contact tracing is conducted with the primary purpose of interrupting transmission from individuals who are likely to be infectious to others. Secondary analyses of data on the numbers of close contacts of confirmed cases could also: provide an early signal of increases in contact patterns that might precede larger than expected case numbers; evaluate the impact of government interventions on the number of contacts of confirmed cases; or provide data information on contact rates between age cohorts for the purpose of epidemiological modelling. We analysed data from 140,204 close contacts of 39,861 cases in Ireland from 1st May to 1st December 2020. RESULTS: Negative binomial regression models highlighted greater numbers of contacts within specific population demographics, after correcting for temporal associations. Separate segmented regression models of the number of cases over time and the average number of contacts per case indicated that a breakpoint indicating a rapid decrease in the number of contacts per case in October 2020 preceded a breakpoint indicating a reduction in the number of cases by 11 days. CONCLUSIONS: We found that the number of contacts per infected case was overdispersed, the mean varied considerable over time and was temporally associated with government interventions. Analysis of the reported number of contacts per individual in contact tracing data may be a useful early indicator of changes in behaviour in response to, or indeed despite, government restrictions. This study provides useful information for triangulating assumptions regarding the contact mixing rates between different age cohorts for epidemiological modelling.

Application of machine-learning methods to milk mid-infrared spectra for discrimination of cow milk from pasture or total mixed ration diets.

The prevalence of "grass-fed" labeled food products on the market has increased in recent years, often commanding a premium price. To date, the majority of methods used for the authentication of grass-fed source products are driven by auditing and inspection of farm records. As such, the ability to verify grass-fed source claims to ensure consumer confidence will be important in the future. Mid-infrared (MIR) spectroscopy is widely used in the dairy industry as a rapid method for the routine monitoring of individual herd milk composition and quality. Further harnessing the data from individual spectra offers a promising and readily implementable strategy to authenticate the milk source at both farm and processor levels. Herein, a comprehensive comparison of the robustness, specificity, and accuracy of 11 machine-learning statistical analysis methods were tested for the discrimination of grass-fed versus non-grass-fed milks based on the MIR spectra of 4,320 milk samples collected from cows on pasture or indoor total mixed ration-based feeding systems over a 3-yr period. Linear discriminant analysis and partial least squares discriminant analysis (PLS-DA) were demonstrated to offer the greatest level of accuracy for the prediction of cow diet from MIR spectra. Parsimonious strategies for the selection of the most discriminating wavelengths within the spectra are also highlighted.

Predicting cow milk quality traits from routinely available milk spectra using statistical machine learning methods.

Numerous statistical machine learning methods suitable for application to highly correlated features, as those that exist for spectral data, could potentially improve prediction performance over the commonly used partial least squares approach. Milk samples from 622 individual cows with known detailed protein composition and technological trait data accompanied by mid-infrared spectra were available to assess the predictive ability of different regression and classification algorithms. The regression-based approaches were partial least squares regression (PLSR), ridge regression (RR), least absolute shrinkage and selection operator (LASSO), elastic net, principal component regression, projection pursuit regression, spike and slab regression, random forests, boosting decision trees, neural networks (NN), and a post-hoc approach of model averaging (MA). Several classification methods (i.e., partial least squares discriminant analysis (PLSDA), random forests, boosting decision trees, and support vector machines (SVM)) were also used after stratifying the traits of interest into categories. In the regression analyses, MA was the best prediction method for 6 of the 14 traits investigated [curd firmness at 60 min, αS1-casein (CN), αS2-CN, κ-CN, α-lactalbumin, and ß-lactoglobulin B], whereas NN and RR were the best algorithms for 3 traits each (rennet coagulation time, curd-firming time, and heat stability, and curd firmness at 30 min, ß-CN, and ß-lactoglobulin A, respectively), PLSR was best for pH, and LASSO was best for CN micelle size. When traits were divided into 2 classes, SVM had the greatest accuracy for the majority of the traits investigated. Although the well-established PLSR-based method performed competitively, the application of statistical machine learning methods for regression analyses reduced the root mean square error compared with PLSR from between 0.18% (κ-CN) to 3.67% (heat stability). The use of modern statistical machine learning methods for trait prediction from mid-infrared spectroscopy may improve the prediction accuracy for some traits.

New Author Guidelines for Displaying Data and Reporting Data Analysis and Statistical Methods in Experimental Biology.

The American Society for Pharmacology and Experimental Therapeutics has revised the Instructions to Authors for Drug Metabolism and Disposition, Journal of Pharmacology and Experimental Therapeutics, and Molecular Pharmacology These revisions relate to data analysis (including statistical analysis) and reporting but do not tell investigators how to design and perform their experiments. Their overall focus is on greater granularity in the description of what has been done and found. Key recommendations include the need to differentiate between preplanned, hypothesis-testing, and exploratory experiments or studies; explanations of whether key elements of study design, such as sample size and choice of specific statistical tests, had been specified before any data were obtained or adapted thereafter; and explanations of whether any outliers (data points or entire experiments) were eliminated and when the rules for doing so had been defined. Variability should be described by S.D. or interquartile range, and precision should be described by confidence intervals; S.E. should not be used. P values should be used sparingly; in most cases, reporting differences or ratios (effect sizes) with their confidence intervals will be preferred. Depiction of data in figures should provide as much granularity as possible, e.g., by replacing bar graphs with scatter plots wherever feasible and violin or box-and-whisker plots when not. This editorial explains the revisions and the underlying scientific rationale. We believe that these revised guidelines will lead to a less biased and more transparent reporting of research findings.

New Author Guidelines for Displaying Data and Reporting Data Analysis and Statistical Methods in Experimental Biology.

The American Society for Pharmacology and Experimental Therapeutics has revised the Instructions to Authors for Drug Metabolism and Disposition, Journal of Pharmacology and Experimental Therapeutics, and Molecular Pharmacology These revisions relate to data analysis (including statistical analysis) and reporting but do not tell investigators how to design and perform their experiments. Their overall focus is on greater granularity in the description of what has been done and found. Key recommendations include the need to differentiate between preplanned, hypothesis-testing, and exploratory experiments or studies; explanations of whether key elements of study design, such as sample size and choice of specific statistical tests, had been specified before any data were obtained or adapted thereafter; and explanation of whether any outliers (data points or entire experiments) were eliminated and when the rules for doing so had been defined. Variability should be described by S.D. or interquartile range, and precision should be described by confidence intervals; S.E. should not be used. P values should be used sparingly; in most cases, reporting differences or ratios (effect sizes) with their confidence intervals will be preferred. Depiction of data in figures should provide as much granularity as possible, e.g., by replacing bar graphs with scatter plots wherever feasible and violin or box-and-whisker plots when not. This editorial explains the revisions and the underlying scientific rationale. We believe that these revised guidelines will lead to a less biased and more transparent reporting of research findings.

New Author Guidelines for Displaying Data and Reporting Data Analysis and Statistical Methods in Experimental Biology.

The American Society for Pharmacology and Experimental Therapeutics has revised the Instructions to Authors for Drug Metabolism and Disposition, Journal of Pharmacology and Experimental Therapeutics, and Molecular Pharmacology These revisions relate to data analysis (including statistical analysis) and reporting but do not tell investigators how to design and perform their experiments. Their overall focus is on greater granularity in the description of what has been done and found. Key recommendations include the need to differentiate between preplanned, hypothesis-testing, and exploratory experiments or studies; explanations of whether key elements of study design, such as sample size and choice of specific statistical tests, had been specified before any data were obtained or adapted thereafter; and explanations of whether any outliers (data points or entire experiments) were eliminated and when the rules for doing so had been defined. Variability should be described by S.D. or interquartile range, and precision should be described by confidence intervals; S.E. should not be used. P values should be used sparingly; in most cases, reporting differences or ratios (effect sizes) with their confidence intervals will be preferred. Depiction of data in figures should provide as much granularity as possible, e.g., by replacing bar graphs with scatter plots wherever feasible and violin or box-and-whisker plots when not. This editorial explains the revisions and the underlying scientific rationale. We believe that these revised guidelines will lead to a less biased and more transparent reporting of research findings.

Genome-wide association study of milk production traits in a crossbred dairy sheep population using three statistical models.

Milk production is one of the most important characteristics of dairy sheep, and the identification of genes affecting milk production traits is critical to understanding the genetics and improve milk production in future generations. Three statistical techniques, namely GWAS, ridge-regression BLUP and BayesC π , were used to identify SNPs in significant association with three milk production traits (milk yield, fat yield and protein yield) in a crossbred dairy sheep population. The results suggested that chromosomes 1, 3, 4, 5, 7 and 11 were likely to harbor genes important to milk production because these chromosomes had the greatest top-100-SNP variance contributions on the three milk production traits. The GWAS analysis identified between 74 and 288 genome-wide significant SNP (P < 0.05) whereas the BayesCπ model revealed between six and 63 SNPs, each with >95% posterior probability of inclusion as having a non-zero association effect on at least one of the three milk production traits. Positional candidate genes for milk production in sheep were searched, based on the sheep genomic assembly OAR version 3.1, such as those which map position coincided with or was located within 0.1 Mbp of a genome-wide suggestive or significant SNP. These identified SNPs and candidate genes supported some previous findings and also added new information about genetic markers for genetic improvement of lactation in dairy sheep, but keeping in mind that the majority of these positional candidate genes are not necessarily true causative loci for these traits and future validations are thus necessary.

A risk calculator to inform the need for a prostate biopsy: a rapid access clinic cohort.

BACKGROUND: Prostate cancer (PCa) represents a significant healthcare problem. The critical clinical question is the need for a biopsy. Accurate risk stratification of patients before a biopsy can allow for individualised risk stratification thus improving clinical decision making. This study aims to build a risk calculator to inform the need for a prostate biopsy. METHODS: Using the clinical information of 4801 patients an Irish Prostate Cancer Risk Calculator (IPRC) for diagnosis of PCa and high grade (Gleason ≥7) was created using a binary regression model including age, digital rectal examination, family history of PCa, negative prior biopsy and Prostate-specific antigen (PSA) level as risk factors. The discrimination ability of the risk calculator is internally validated using cross validation to reduce overfitting, and its performance compared with PSA and the American risk calculator (PCPT), Prostate Biopsy Collaborative Group (PBCG) and European risk calculator (ERSPC) using various performance outcome summaries. In a subgroup of 2970 patients, prostate volume was included. Separate risk calculators including the prostate volume (IPRCv) for the diagnosis of PCa (and high-grade PCa) was created. RESULTS: IPRC area under the curve (AUC) for the prediction of PCa and high-grade PCa was 0.6741 (95% CI, 0.6591 to 0.6890) and 0.7214 (95% CI, 0.7018 to 0.7409) respectively. This significantly outperforms the predictive ability of cancer detection for PSA (0.5948), PCPT (0.6304), PBCG (0.6528) and ERSPC (0.6502) risk calculators; and also, for detecting high-grade cancer for PSA (0.6623) and PCPT (0.6804) but there was no significant improvement for PBCG (0.7185) and ERSPC (0.7140). The inclusion of prostate volume into the risk calculator significantly improved the AUC for cancer detection (AUC = 0.7298; 95% CI, 0.7119 to 0.7478), but not for high-grade cancer (AUC = 0.7256; 95% CI, 0.7017 to 0.7495). The risk calculator also demonstrated an increased net benefit on decision curve analysis. CONCLUSION: The risk calculator developed has advantages over prior risk stratification of prostate cancer patients before the biopsy. It will reduce the number of men requiring a biopsy and their exposure to its side effects. The interactive tools developed are beneficial to translate the risk calculator into practice and allows for clarity in the clinical recommendations.

Glycosylation in Indolent, Significant and Aggressive Prostate Cancer by Automated High-Throughput N-Glycan Profiling.

The diagnosis and treatment of prostate cancer (PCa) is a major health-care concern worldwide. This cancer can manifest itself in many distinct forms and the transition from clinically indolent PCa to the more invasive aggressive form remains poorly understood. It is now universally accepted that glycan expression patterns change with the cellular modifications that accompany the onset of tumorigenesis. The aim of this study was to investigate if differential glycosylation patterns could distinguish between indolent, significant, and aggressive PCa. Whole serum N-glycan profiling was carried out on 117 prostate cancer patients' serum using our automated, high-throughput analysis platform for glycan-profiling which utilizes ultra-performance liquid chromatography (UPLC) to obtain high resolution separation of N-linked glycans released from the serum glycoproteins. We observed increases in hybrid, oligomannose, and biantennary digalactosylated monosialylated glycans (M5A1G1S1, M8, and A2G2S1), bisecting glycans (A2B, A2(6)BG1) and monoantennary glycans (A1), and decreases in triantennary trigalactosylated trisialylated glycans with and without core fucose (A3G3S3 and FA3G3S3) with PCa progression from indolent through significant and aggressive disease. These changes give us an insight into the disease pathogenesis and identify potential biomarkers for monitoring the PCa progression, however these need further confirmation studies.

Discovery and Contribution of Nontypeable Haemophilus influenzae NTHI1441 to Human Respiratory Epithelial Cell Invasion.

Nontypeable Haemophilus influenzae (NTHi) is the primary cause of bacterially induced acute exacerbations of chronic obstructive pulmonary disease (COPD). NTHi adheres to and invades host respiratory epithelial cells as a means to persist in the lower airways of adults with COPD. Therefore, we mined the genomes of NTHi strains isolated from the airways of adults with COPD to identify novel proteins to investigate their role in adherence and invasion of human respiratory epithelial cells. An isogenic knockout mutant of the open reading frame NTHI1441 showed a 76.6% ± 5.5% reduction in invasion of human bronchial and alveolar epithelial cells at 1, 3, and 6 h postinfection. Decreased invasion of the NTHI1441 mutant was independent of either intracellular survival or adherence to cells. NTHI1441 is conserved among NTHi genomes. Results of whole-bacterial-cell enzyme-linked immunosorbent assay (ELISA) and flow cytometry experiments identified that NTHI1441 has epitopes expressed on the bacterial cell surface. Adults with COPD develop increased serum IgG against NTHI1441 after experiencing an exacerbation with NTHi. This study reveals NTHI1441 as a novel NTHi virulence factor expressed during infection of the COPD lower airways that contributes to invasion of host respiratory epithelial cells. The role in host cell invasion, conservation among strains, and expression of surface-exposed epitopes suggest that NTHI1441 is a potential target for preventative and therapeutic interventions for disease caused by NTHi.

Investigation of thermally induced damage to surrounding nerve tissue when using curettage and cementation of long bone tumours, modelled in cadaveric porcine femurs.

INTRODUCTION: Curettage with cement augmentation is a technique used in the treatment of bone tumours. Thermal energy released during the cement polymerisation process can damage surrounding tissues. This study aims to record temperature changes at various sites on and around bone during the cementing process. We hypothesised that adjacent structures, such as the radial nerve, may be threatened by this process in the clinical setting. MATERIALS AND METHODS: Using 18 porcine femurs as a model of the human humerus, we used thermocouples and a thermal imaging camera to measure changes in temperature during the cementing process. Fractures were created in nine samples to establish whether a discontinuity of the cortex had an effect on thermal conduction. RESULTS: Significantly higher temperatures were recorded in samples with a fracture compared to those without a fracture. The site overlying the centre of the cement bolus (hypothetical site of the radial nerve) demonstrated higher temperatures than all other sites on the same cortex. When considering the radial nerve site, over half the samples demonstrated temperatures exceeding 47 °C for over a minute. When a threshold of 50 °C for more than 30 s was considered, three samples without a fracture exceeded this value compared to two with a fracture. CONCLUSION: The temperatures recorded were sufficient to cause damage to neural tissue. Limiting thermal exposure to soft tissues is recommended. Increased attention is required when using larger cement boluses, or where bone quality is poor or a fracture, iatrogenic or preexisting, is present.

Systematic Fuel Cavity Asymmetries in Directly Driven Inertial Confinement Fusion Implosions.

We present narrow-band self-emission x-ray images from a titanium tracer layer placed at the fuel-shell interface in 60-laser-beam implosion experiments at the OMEGA facility. The images are acquired during deceleration with inferred convergences of ∼9-14. Novel here is that a systematically observed asymmetry of the emission is linked, using full sphere 3D implosion modeling, to performance-limiting low mode asymmetry of the drive.

Particulate and non-particulate steroids in spinal epidurals: a systematic review and meta-analysis.

BACKGROUND: Steroids in transforaminal epidural injections are widely used to ease radicular pain in both cervical and lumbar radiculopathy. Concerns have been articulated about the use of particulate steroids for this intervention, as a number of case reports have been published linking them with post procedural paralysis, possibly due to spinal ischaemia secondary to a steroid particulate embolism. Non-particulate, or soluble steroids, are mooted as an alternative; however, their effectiveness relative to particulate steroids has not been conclusively proven. STUDY DESIGN: We review the evidence in the published literature regarding the efficacy of non-particulate steroids in epidural injections compared to particulate steroids, and synthesise it to gauge the qualitative outcomes from level one evidence (visual analogue scales, numerical pain scores and Oswestry Disability Index) from baseline to specified follow up. METHODS: The PRISMA guidelines were utilised for this review. An internet search was performed to collate the available literature from medical databases PubMed, EMBASE, Web of Science and the Cochrane library. We used a broad search term [epidural (and) steroid] to ensure a wide capture of articles. No limitations in terms of language or date of publication were implemented. The reference lists of articles included for full text review were searched for any additional primary or review publications. RESULTS: Four online libraries were searched, with a combined total of 11,353 titles reviewed, not excluding duplicates. Post title abstract and full text review, nine articles were identified as suitable for inclusion for qualitative synthesis. Four of these were suitable for quantitative synthesis, with a total of 300 participants, 147 in the particulate group and 153 in the non-particulate group. Using a random effects model, the pooled standard mean difference of VAS score diminution was not significant between groups (0.31 in favour of particulates, 95 % CI -0.68 to 1.30). From our qualitative synthesis, there was a trend for greater improvement in pain scores within the particulate group. The type of steroid used did not appear to have an effect on the disability score given by patients. CONCLUSION: Particulate steroids are not demonstrably better in relieving pain compared to their non-particulate counterparts. In view of the concerns over the safety profile of particulate steroids, it may be prudent to switch to non particulates, or at the very least the dangers and alternatives should be flagged with the patient group as part of a shared decision making process.

Structure and clinical correlates of obsessive-compulsive symptoms in a large sample of children and adolescents: a factor analytic study across five nations.

The underlying structure of obsessive-compulsive disorder (OCD) remains to be confirmed in child and adolescent populations. In this paper we report the first factor analytic study of individual OCD items from Children's Yale-Brown Obsessive Compulsive Scale (CY-BOCS). OCD symptoms were assessed using the CY-BOCS symptom checklist in a sample of 854 patients with OCD (7-18 years of age) recruited from clinics in five countries. Pooled data were subjected to exploratory and confirmatory factor analysis (CFA) to identify the optimal factor structure. Various models were tested for age and gender subgroups. Also, the invariance of the solution across age and gender was tested and associations with demographic and clinical factors were explored. A three-factor model provided the best-fit solution. It consisted of the following factors: (1) harm/sexual, (2) symmetry/hoarding, (3) contamination/cleaning. The factor structure was invariant for age and gender across subgroups. Factor one was significantly correlated with anxiety, and factor two with depression and anxiety. Factor three was negatively correlated with tic disorder and attention-deficit/hyperactivity disorder (ADHD). Females had higher scores on factor two than males. The OCD symptom structure in children and adolescents is consistent across age and gender and similar to results from recent child and adolescents although hoarding may not be a separate factor. Our three-factor structure is almost identical to that seen in early studies on adults. Common mental disorders had specific patterns of associations with the different factors.

Late-Time Mixing Sensitivity to Initial Broadband Surface Roughness in High-Energy-Density Shear Layers.

Using a large volume high-energy-density fluid shear experiment (8.5 cm^{3}) at the National Ignition Facility, we have demonstrated for the first time the ability to significantly alter the evolution of a supersonic sheared mixing layer by controlling the initial conditions of that layer. By altering the initial surface roughness of the tracer foil, we demonstrate the ability to transition the shear mixing layer from a highly ordered system of coherent structures to a randomly ordered system with a faster growing mix layer, indicative of strong mixing in the layer at a temperature of several tens of electron volts and at near solid density. Simulations using a turbulent-mix model show good agreement with the experimental results and poor agreement without turbulent mix.

Improving multivariable prostate cancer risk assessment using the Prostate Health Index.

OBJECTIVES: To analyse the clinical utility of a prediction model incorporating both clinical information and a novel biomarker, p2PSA, in order to inform the decision for prostate biopsy in an Irish cohort of men referred for prostate cancer assessment. PATIENTS AND METHODS: Serum isolated from 250 men from three tertiary referral centres with pre-biopsy blood draws was analysed for total prostate-specific antigen (PSA), free PSA (fPSA) and p2PSA. From this, the Prostate Health Index (PHI) score was calculated (PHI = (p2PSA/fPSA)*√tPSA). The men's clinical information was used to derive their risk according to the Prostate Cancer Prevention Trial (PCPT) risk model. Two clinical prediction models were created via multivariable regression consisting of age, family history, abnormality on digital rectal examination, previous negative biopsy and either PSA or PHI score, respectively. Calibration plots, receiver-operating characteristic (ROC) curves and decision curves were generated to assess the performance of the three models. RESULTS: The PSA model and PHI model were both well calibrated in this cohort, with the PHI model showing the best correlation between predicted probabilities and actual outcome. The areas under the ROC curve for the PHI model, PSA model and PCPT model were 0.77, 0.71 and 0.69, respectively, for the prediction of prostate cancer (PCa) and 0.79, 0.72 and 0.72, respectively, for the prediction of high grade PCa. Decision-curve analysis showed a superior net benefit of the PHI model over both the PSA model and the PCPT risk model in the diagnosis of PCa and high grade PCa over the entire range of risk probabilities. CONCLUSION: A logical and standardized approach to the use of clinical risk factors can allow more accurate risk stratification of men under investigation for PCa. The measurement of p2PSA and the integration of this biomarker into a clinical prediction model can further increase the accuracy of risk stratification, helping to better inform the decision for prostate biopsy in a referral population.

European Randomised Study of Screening for Prostate Cancer (ERSPC) risk calculators significantly outperform the Prostate Cancer Prevention Trial (PCPT) 2.0 in the prediction of prostate cancer: a multi-institutional study.

OBJECTIVE: To analyse the performance of the Prostate Cancer Prevention Trial Risk Calculator (PCPT-RC) and two iterations of the European Randomised Study of Screening for Prostate Cancer (ERSPC) Risk Calculator, one of which incorporates prostate volume (ERSPC-RC) and the other of which incorporates prostate volume and the prostate health index (PHI) in a referral population (ERSPC-PHI). PATIENTS AND METHODS: The risk of prostate cancer (PCa) and significant PCa (Gleason score ≥7) in 2001 patients from six tertiary referral centres was calculated according to the PCPT-RC and ERSPC-RC formulae. The calculators' predictions were analysed using the area under the receiver-operating characteristic curve (AUC), calibration plots, Hosmer-Lemeshow test for goodness of fit and decision-curve analysis. In a subset of 222 patients for whom the PHI score was available, each patient's risk was calculated as per the ERSPC-RC and ERSPC-PHI risk calculators. RESULTS: The ERSPC-RC outperformed the PCPT-RC in the prediction of PCa, with an AUC of 0.71 compared with 0.64, and also outperformed the PCPT-RC in the prediction of significant PCa (P<0.001), with an AUC of 0.74 compared with 0.69. The ERSPC-RC was found to have improved calibration in this cohort and was associated with a greater net benefit on decision-curve analysis for both PCa and significant PCa. The performance of the ERSPC-RC was further improved through the addition of the PHI score in a subset of 222 patients. The AUCs of the ERSPC-PHI were 0.76 and 0.78 for PCa and significant PCa prediction, respectively, in comparison with AUC values of 0.72 in the prediction of both PCa and significant PCa for the ERSPC-RC (P = 0.12 and P = 0.04, respectively). The ERSPC-PHI risk calculator was well calibrated in this cohort and had an increase in net benefit over that of the ERSPC-RC. CONCLUSIONS: The performance of the risk calculators in the present cohort shows that the ERSPC-RC is a superior tool in the prediction of PCa; however the performance of the ERSPC-RC in this population does not yet warrant its use in clinical practice. The incorporation of the PHI score into the ERSPC-PHI risk calculator allowed each patient's risk to be more accurately quantified. Individual patient risk calculation using the ERSPC-PHI risk calculator can be undertaken in order to allow a systematic approach to patient risk stratification and to aid in the diagnosis of PCa.