Development and validation of two nomograms for predicting overall survival and cancer-specific survival in gastric cancer patients with liver metastases: A retrospective cohort study from SEER database

BackgroundGastric cancer is heterogeneous and aggressive, especially with liver metastasis. This study aims to develop two nomograms to predict the overall survival (OS) and cancer-specific survival (CSS) of gastric cancer with liver metastasis (GCLM) patients.MethodsFrom January 2000 to December 2018, a total of 1936 GCLM patients were selected from the Surveillance, Epidemiology, and End Results Program (SEER) database. They were further divided into a training cohort and a validation cohort, with the OS and CSS serving as the study's endpoints. The correlation analyses were used to determine the relationship between the variables. The univariate and multivariate Cox analyses were used to confirm the independent prognostic factors. To discriminate and calibrate the nomogram, calibration curves and the area under the time-dependent receiver operating characteristic curve (time-dependent AUC) were used. DCA curves were used to examine the accuracy and clinical benefits. The clinical utility of the nomogram and the AJCC Stage System was compared using net reclassification improvement (NRI) and integrated differentiation improvement (IDI) (IDI). Finally, the nomogram and the AJCC Stage System risk stratifications were compared.ResultsThere was no collinearity among the variables that were screened. The results of multivariate Cox regression analysis showed that six variables (bone metastasis, lung metastasis, surgery, chemotherapy, grade, age) and five variables (lung metastasis, surgery, chemotherapy, grade, N stage) were identified to establish the nomogram for OS and CSS, respectively. The calibration curves, time-dependent AUC curves, and DCA revealed that both nomograms had pleasant predictive power. Furthermore, NRI and IDI confirmed that the nomogram outperformed the AJCC Stage System.ConclusionBoth nomograms had satisfactory accuracy and were validated to assist clinicians in evaluating the prognosis of GCLM patients.     

Developing Genetic Epidemiological Models to Predict Risk for Nasopharyngeal Carcinoma in High-Risk Population of China

To date, the only established model for assessing risk for nasopharyngeal carcinoma (NPC) relies on the sero-status of the Epstein-Barr virus (EBV). By contrast, the risk assessment models proposed here include environmental risk factors, family history of NPC, and information on genetic variants. The models were developed using epidemiological and genetic data from a large case-control study, which included 1,387 subjects with NPC and 1,459 controls of Cantonese origin. The predictive accuracy of the models were then assessed by calculating the area under the receiver-operating characteristic curves (AUC). To compare the discriminatory improvement of models with and without genetic information, we estimated the net reclassification improvement (NRI) and integrated discrimination index (IDI). Well-established environmental risk factors for NPC include consumption of salted fish and preserved vegetables and cigarette smoking (in pack years). The environmental model alone shows modest discriminatory ability (AUC = 0.68; 95% CI: 0.66, 0.70), which is only slightly increased by the addition of data on family history of NPC (AUC = 0.70; 95% CI: 0.68, 0.72). With the addition of data on genetic variants, however, our model’s discriminatory ability rises to 0.74 (95% CI: 0.72, 0.76). The improvements in NRI and IDI also suggest the potential usefulness of considering genetic variants when screening for NPC in endemic areas. If these findings are confirmed in larger cohort and population-based case-control studies, use of the new models to analyse data from NPC-endemic areas could well lead to earlier detection of NPC.     

Statistical methods for model discrimination. Applications to gating kinetics and permeation of the acetylcholine receptor channel.

Methods are described for discrimination of models of the gating kinetics and permeation of single ionic channels. Both maximum likelihood and regression procedures are discussed. In simple situations, where models are nested, standard hypothesis tests can be used. More commonly, however, non-nested models are of interest, and several procedures are described for model discrimination in these cases, including Monte Carlo methods, which allow the comparison of models at significance levels of choice. As an illustration, the methods are applied to single-channel data from acetylcholine receptor channels.     

Performance of reclassification statistics in comparing risk prediction models

Concerns have been raised about the use of traditional measures of model fit in evaluating risk prediction models for clinical use, and reclassification tables have been suggested as an alternative means of assessing the clinical utility of a model. Several measures based on the table have been proposed, including the reclassification calibration (RC) statistic, the net reclassification improvement (NRI), and the integrated discrimination improvement (IDI), but the performance of these in practical settings has not been fully examined. We used simulations to estimate the type I error and power for these statistics in a number of scenarios, as well as the impact of the number and type of categories, when adding a new marker to an established or reference model. The type I error was found to be reasonable in most settings, and power was highest for the IDI, which was similar to the test of association. The relative power of the RC statistic, a test of calibration, and the NRI, a test of discrimination, varied depending on the model assumptions. These tools provide unique but complementary information.     

Applications of binary segmentation to the estimation of quantal response curves and spatial intensity

This paper explores the use of binary segmentation procedures in two applications. The first application is concerned with the estimation of nonparametric quantal response curves. With Bernoulli data and an assumed monotone increasing curve, this gives rise a change-point model where the change points are determined using a sequence of nested hypothesis tests of whether a change point exists. The second application concerns cluster identification and inference for spatial data where the shape of the clusters and the number of clusters is unknown. The procedure involves a sequence of nested hypothesis tests of a single cluster versus a pair of distinct clusters. Examples of both applications are provided.     

A consumer's guide to nestedness analysis

Nestedness analysis has become increasingly popular in the study of biogeographic patterns of species occurrence. Nested patterns are those in which the species composition of small assemblages is a nested subset of larger assemblages. For species interaction networks such as plant–pollinator webs, nestedness analysis has also proven a valuable tool for revealing ecological and evolutionary constraints. Despite this popularity, there has been substantial controversy in the literature over the best methods to define and quantify nestedness, and how to test for patterns of nestedness against an appropriate statistical null hypothesis. Here we review this rapidly developing literature and provide suggestions and guidelines for proper analyses. We focus on the logic and the performance of different metrics and the proper choice of null models for statistical inference. We observe that traditional 'gap-counting' metrics are biased towards species loss among columns (occupied sites) and that many metrics are not invariant to basic matrix properties. The study of nestedness should be combined with an appropriate gradient analysis to infer possible causes of the observed presence–absence sequence. In our view, statistical inference should be based on a null model in which row and columns sums are fixed. Under this model, only a relatively small number of published empirical matrices are significantly nested. We call for a critical reassessment of previous studies that have used biased metrics and unconstrained null models for statistical inference.     

Model selection in non-nested hidden Markov models for ion channel gating

An important task in the application of Markov models to the analysis of ion channel data is the determination of the correct gating scheme of the ion channel under investigation. Some prior knowledge from other experiments can reduce significantly the number of possible models. If these models are standard statistical procedures nested like likelihood ratio testing, provide reliable selection methods. In the case of non-nested models, information criteria like AIC, BIC, etc., are used. However, it is not known if any of these criteria provide a reliable selection method and which is the best one in the context of ion channel gating. We provide an alternative approach to model selection in the case of non-nested models with an equal number of open and closed states. The models to choose from are embedded in a properly defined general model. Therefore, we circumvent the problems of model selection in the non-nested case and can apply model selection procedures for nested models.     

Directional biases in phylogenetic structure quantification: a Mediterranean case study

Recent years have seen an increasing effort to incorporate phylogenetic hypotheses to the study of community assembly processes. The incorporation of such evolutionary information has been eased by the emergence of specialized software for the automatic estimation of partially resolved supertrees based on published phylogenies. Despite this growing interest in the use of phylogenies in ecological research, very few studies have attempted to quantify the potential biases related to the use of partially resolved phylogenies and to branch length accuracy, and no work has examined how tree shape may affect inference of community phylogenetic metrics. In this study, we tested the influence of phylogenetic resolution and branch length information on the quantification of phylogenetic structure, and also explored the impact of tree shape (stemminess) on the loss of accuracy in phylogenetic structure quantification due to phylogenetic resolution. For this purpose, we used 9 sets of phylogenetic hypotheses of varying resolution and branch lengths to calculate three indices of phylogenetic structure: the mean phylogenetic distance (NRI), the mean nearest taxon distance (NTI) and phylogenetic diversity (stdPD) metrics. The NRI metric was the less sensitive to phylogenetic resolution, stdPD showed an intermediate sensitivity, and NTI was the most sensitive one; NRI was also less sensitive to branch length accuracy than NTI and stdPD, the degree of sensitivity being strongly dependent on the dating method and the sample size. Directional biases were generally towards type II errors. Interestingly, we detected that tree shape influenced the accuracy loss derived from the lack of phylogenetic resolution, particularly for NRI and stdPD. We conclude that well‐resolved molecular phylogenies with accurate branch length information are needed to identify the underlying phylogenetic structure of communities, and also that sensitivity of phylogenetic structure measures to low phylogenetic resolution can strongly vary depending on phylogenetic tree shape.     

Adiponectin Provides Additional Information to Conventional Cardiovascular Risk Factors for Assessing the Risk of Atherosclerosis in Both Genders

Background

This study evaluated the relation between adiponectin and atherosclerosis in both genders, and investigated whether adiponectin provides useful additional information for assessing the risk of atherosclerosis.

Methods

We measured serum adiponectin levels and other cardiovascular risk factors in 1033 subjects (454 men, 579 women) from the Korean Genomic Rural Cohort study. Carotid intima–media-thickness (CIMT) was used as measure of atherosclerosis. Odds ratios (ORs) with 95% confidence intervals (95% CI) were calculated using multiple logistic regression, and receiver operating characteristic curves (ROC), the category-free net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were calculated.

Results

After adjustment for conventional cardiovascular risk factors, such as age, waist circumference, smoking history, low-density and high-density lipoprotein cholesterol, triglycerides, systolic blood pressure and insulin resistance, the ORs (95%CI) of the third tertile adiponectin group were 0.42 (0.25–0.72) in men and 0.47 (0.29–0.75) in women. The area under the curve (AUC) on the ROC analysis increased significantly by 0.025 in men and 0.022 in women when adiponectin was added to the logistic model of conventional cardiovascular risk factors (AUC in men: 0.655 to 0.680, p = 0.038; AUC in women: 0.654 to 0.676, p = 0.041). The NRI was 0.32 (95%CI: 0.13–0.50, p<0.001), and the IDI was 0.03 (95%CI: 0.01–0.04, p<0.001) for men. For women, the category-free NRI was 0.18 (95%CI: 0.02–0.34, p = 0.031) and the IDI was 0.003 (95%CI: −0.002–0.008, p = 0.189).

Conclusion

Adiponectin and atherosclerosis were significantly related in both genders, and these relationships were independent of conventional cardiovascular risk factors. Furthermore, adiponectin provided additional information to conventional cardiovascular risk factors regarding the risk of atherosclerosis.     

Circulating Progenitor Cell Count for Cardiovascular Risk Stratification: A Pooled Analysis

Background

Circulating progenitor cells (CPC) contribute to the homeostasis of the vessel wall, and a reduced CPC count predicts cardiovascular morbidity and mortality. We tested the hypothesis that CPC count improves cardiovascular risk stratification and that this is modulated by low-grade inflammation.

Methodology/Principal Findings

We pooled data from 4 longitudinal studies, including a total of 1,057 patients having CPC determined and major adverse cardiovascular events (MACE) collected. We recorded cardiovascular risk factors and high-sensitive C-reactive protein (hsCRP) level. Risk estimates were derived from Cox proportional hazard analyses. CPC count and/or hsCRP level were added to a reference model including age, sex, cardiovascular risk factors, prevalent CVD, chronic renal failure (CRF) and medications. The sample was composed of high-risk individuals, as 76.3% had prevalent CVD and 31.6% had CRF. There were 331 (31.3%) incident MACE during an average 1.7±1.1 year follow-up time. CPC count was independently associated with incident MACE even after correction for hsCRP. According to C-statistics, models including CPC yielded a non-significant improvement in accuracy of MACE prediction. However, the integrated discrimination improvement index (IDI) showed better performance of models including CPC compared to the reference model and models including hsCRP in identifying MACE. CPC count also yielded significant net reclassification improvements (NRI) for CV death, non-fatal AMI and other CV events. The effect of CPC was independent of hsCRP, but there was a significant more-than-additive interaction between low CPC count and raised hsCRP level in predicting incident MACE.

Conclusions/Significance

In high risk individuals, a reduced CPC count helps identifying more patients at higher risk of MACE over the short term, especially in combination with a raised hsCRP level.     

Isotopic Diversity Indices: How Sensitive to Food Web Structure?

Recently revisited, the concept of niche ecology has lead to the formalisation of functional and trophic niches using stable isotope ratios. Isotopic diversity indices (IDI) derived from a set of measures assessing the dispersion/distribution of points in the δ-space were recently suggested and increasingly used in the literature. However, three main critics emerge from the use of these IDI: 1) they fail to account for the isotopic sources overlap, 2) some indices are highly sensitive to the number of species and/or the presence of rare species, and 3) the lack of standardization prevents any spatial and temporal comparisons. Using simulations we investigated the ability of six commonly used IDI to discriminate among different trophic food web structures, with a focus on the first two critics. We tested the sensitivity of the IDI to five food web structures along a gradient of sources overlap, varying from two distinct food chains with differentiated sources to two superimposed food chains sharing two sources. For each of the food web structure we varied the number of species (from 10 to 100 species) and the type of species feeding behaviour (i.e. random or selective feeding). Values of IDI were generally larger in food webs with distinct basal sources and tended to decrease as the superimposition of the food chains increased. This was more pronounced when species displayed food preferences in comparison to food webs where species fed randomly on any prey. The number of species composing the food web also had strong effects on the metrics, including those that were supposedly less sensitive to small sample size. In all cases, computing IDI on food webs with low numbers of species always increases the uncertainty of the metrics. A threshold of ∼20 species was detected above which several metrics can be safely used.     

Experimental implementation of a polyenergetic statistical reconstruction algorithm for a commercial fan-beam CT scanner

PurposeTo present a framework for characterizing the data needed to implement a polyenergetic model-based statistical reconstruction algorithm, Alternating Minimization (AM), on a commercial fan-beam CT scanner and a novel method for assessing the accuracy of the commissioned data model.MethodsThe X-ray spectra for three tube potentials on the Philips Brilliance CT scanner were estimated by fitting a semi-empirical X-ray spectrum model to transmission measurements. Spectral variations due to the bowtie filter were computationally modeled. Eight homogeneous cylinders of PMMA, Teflon and water with varying diameters were scanned at each energy. Central-axis scatter was measured for each cylinder using a beam-stop technique. AM reconstruction with a single-basis object-model matched to the scanned cylinder's composition allows assessment of the accuracy of the AM algorithm's polyenergetic data model. Filtered-backprojection (FBP) was also performed to compare consistency metrics such as uniformity and object-size dependence.ResultsThe spectrum model fit measured transmission curves with residual root-mean-square-error of 1.20%–1.34% for the three scanning energies. The estimated spectrum and scatter data supported polyenergetic AM reconstruction of the test cylinders to within 0.5% of expected in the matched object-model reconstruction test. In comparison to FBP, polyenergetic AM exhibited better uniformity and less object-size dependence.ConclusionsReconstruction using a matched object-model illustrate that the polyenergetic AM algorithm's data model was commissioned to within 0.5% of an expected ground truth. These results support ongoing and future research with polyenergetic AM reconstruction of commercial fan-beam CT data for quantitative CT applications.     

Plasma B-type Natriuretic Peptide as a Predictor of Cardiovascular Events in Subjects with Atrial Fibrillation: A Community-Based Study

Objectives

Atrial fibrillation (AF) is a significant public health issue due to its high prevalence in the general population, and is associated with an increased risk of cardiovascular (CV) events including systemic thrombo-embolism, heart failure, and coronary artery disease. The relationship between plasma B-type natriuretic peptide (BNP) and CV risk in real world AF subjects remains unknown.

Methods

The subject of the study (n = 228; mean age = 69 years) was unselected individuals with AF in a community-based population (n = 15,394; AF prevalence rate = 1.5%). The CV event free rate within each BNP tertile was estimated, and Cox regression analysis was performed to examine the relative risk of the onset of CV events among the tertiles. The prognostic ability of BNP was compared to an established risk score for embolic events (CHADS2 score). In addition, to determine the usefulness of BNP as a predictor in addition to CHADS2 score, we calculated Net Reclassification Improvement (NRI) and Integrated Discrimination Improvement (IDI) indices.

Results

During the follow-up period 58 subjects experienced CV events (52 per 1,000 person-years). The event-free ratio was significantly lower in the highest tertile (p < 0.02). After adjustment for established CV risk factors, the hazard ratio (HR) of the highest tertile was significantly higher than that of the lowest tertile (HR = 2.38; p < 0.02). The predictive abilities of plasma BNP in terms of sensitivity and specificity for general CV events were comparable to those of CHADS2 score. Adding BNP to the CHADS2 score only model improved the NRI (0.319; p < 0.05) and the IDI (0.046; p < 0.05).

Conclusion

Plasma BNP is a valuable biomarker both singly or in combination with an established scoring system for assessing general CV risk including stroke, heart failure and acute coronary syndrome in real-world AF subjects.     

A unified procedure for meta-analytic evaluation of surrogate end points in randomized clinical trials

The meta-analytic approach to evaluating surrogate end points assesses the predictiveness of treatment effect on the surrogate toward treatment effect on the clinical end point based on multiple clinical trials. Definition and estimation of the correlation of treatment effects were developed in linear mixed models and later extended to binary or failure time outcomes on a case-by-case basis. In a general regression setting that covers nonnormal outcomes, we discuss in this paper several metrics that are useful in the meta-analytic evaluation of surrogacy. We propose a unified 3-step procedure to assess these metrics in settings with binary end points, time-to-event outcomes, or repeated measures. First, the joint distribution of estimated treatment effects is ascertained by an estimating equation approach; second, the restricted maximum likelihood method is used to estimate the means and the variance components of the random treatment effects; finally, confidence intervals are constructed by a parametric bootstrap procedure. The proposed method is evaluated by simulations and applications to 2 clinical trials.     

Fish‐based multimetric index for evaluating land use effects on large neotropical reservoirs

Because of their importance to human well‐being and ecosystem services, it is important to have cost‐effective methods for assessing reservoir ecological condition or health. To do so, we constructed a preliminary reservoir fish assemblage index (RFAI) to assess the effect of predominantly agricultural land use on two oligotrophic neotropical reservoirs that differed in disturbance level. We first determined the disturbance levels around each reservoir through use of an integrated disturbance index (IDI). The IDI was calculated from the combination of land use metrics at local (Local Disturbance Index ‐ LDI) and buffer scales (Buffer Disturbance Index ‐ BDI). Afterwards, we employed the IDI to assess the response of candidate metrics to environmental disturbance in the RFAI construction. The final RFAI included three metrics selected by range, responsiveness, and redundancy tests. Metrics were scored continuously, and the final RFAI was divided in four quality classes: very poor, poor, acceptable and good. The results showed a clear disturbance gradient in both reservoirs with the IDI values varying between 0.07 and 1.33. However, the IDI values were greater around Volta Grande reservoir because of the predominance of agriculture land use (at local and buffer scales). The RFAI scores in Nova Ponte reservoir were significantly higher than in Volta Grande. All the sites scored as very poor or poor were in Volta Grande and all the sites scored as good were in Nova Ponte. In addition, RFAI scores in the reservoir river arms did not differ from those in the reservoir main body, indicating that it was applicable to both habitat types. Finally, our RFAI scores indicated no significant seasonal difference in the two reservoirs; however, the rainy season produced a greater range and more low scores, particularly in Volta Grande. We related this result to the harmful effect of agriculture and the surface runoff of fertilizers, herbicides, and pesticides. Thus, the RFAI was effective in detecting agricultural impacts at the site scale, even in oligotrophic reservoirs.     

Performance comparison of SNP detection tools with illumina exome sequencing data—an assessment using both family pedigree information and sample-matched SNP array data

To apply exome-seq-derived variants in the clinical setting, there is an urgent need to identify the best variant caller(s) from a large collection of available options. We have used an Illumina exome-seq dataset as a benchmark, with two validation scenarios—family pedigree information and SNP array data for the same samples, permitting global high-throughput cross-validation, to evaluate the quality of SNP calls derived from several popular variant discovery tools from both the open-source and commercial communities using a set of designated quality metrics. To the best of our knowledge, this is the first large-scale performance comparison of exome-seq variant discovery tools using high-throughput validation with both Mendelian inheritance checking and SNP array data, which allows us to gain insights into the accuracy of SNP calling through such high-throughput validation in an unprecedented way, whereas the previously reported comparison studies have only assessed concordance of these tools without directly assessing the quality of the derived SNPs. More importantly, the main purpose of our study was to establish a reusable procedure that applies high-throughput validation to compare the quality of SNP discovery tools with a focus on exome-seq, which can be used to compare any forthcoming tool(s) of interest.     

On nestedness analyses: rethinking matrix temperature and anti-nestedness

The analysis of nested structures in sets of species assemblages across different sites or in networks of interspecific interactions has become common practice in ecological studies. Although new analyses and metrics have been proposed, few studies have scrutinized the concepts that subtend nestedness analysis. We note two important conceptual problems that can lead to terminological inconsistencies and flawed interpretations. First, the thermodynamic analogy that underlies the most common metric of nestedness, matrix temperature, is flawed and has led some authors to incorrect interpretations. Second, the term "anti-nestedness" is a potential source of confusion and inconsistencies. We review four concepts for anti-nestedness and examine how distinct they are. "Anti-nested" matrices, i.e. less nested than expected by chance, may result from different ecological processes and show distinct structural patterns. Thus, there is no single unequivocal opposite of nestedness to be represented as "anti-nestedness". A more profitable approach is to designate and test each distinct non-nested pattern according to its specific assumptions and mechanistic hypotheses.     

A fish-based multimetric index for Brazilian savanna streams

Multimetric indices (MMI) have been widely used to assess ecosystem conditions because they are low-cost, employ a rapid field method, and can incorporate various biological metrics at different levels of biological organization. Our objective was to create a fish-based multimetric index applicable to all streams of the Brazilian savanna biome (Cerrado), the second largest biome in Brazil and deemed a global biodiversity hotspot. We evaluated 156 sites in two river basins (Paraná and São Francisco) and selected metrics capable of distinguishing stream-sites across a gradient of anthropogenic disturbances. We employed two different MMI approaches to determine if an MMI based on natural variation-adjusted metrics performed better than one based on unadjusted metrics. In addition, we assessed the performance of the two final MMIs and their sensitivity to anthropogenic pressures at local (LDI), catchment (CDI) and both scales integrated (IDI). Finally, we employed the power of a probability sample survey design to infer headwater stream conditions across a hydrologic region of approximately 47,000 km². Our final MMI for Brazilian savanna streams included six metrics: % common species; % characiform individuals; % loricariid individuals; % trichomycterid individuals; % invertivore species, and % Poecilia reticulata individuals. MMI₁ (unadjusted metrics) performed better than MMI₂ (natural variation-adjusted metrics) in discriminating least- and most-disturbed sites, but MMI₂ distinguished intermediate from most-disturbed sites better than MMI₁. Both indices were negatively correlated with the CDI scores; however, only MMI₂ was negatively correlated with the IDI scores. We inferred that 709 km (9.35%) of streams in the studied hydrologic region were in good condition, 8115 km (82.73%) were intermediate, and 641 km (7.91%) were in poor condition. We conclude that the MMIs proposed in this study have great potential for widespread application because they integrate data from two of the most important Brazilian river basins included in a biome that represents more than 20% of the country. Furthermore, the metrics retained in the indexes are easy to access with a rapid low-cost field method. However, their feasibility in areas influenced by mining, as well as in different biomes, should be tested.