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In randomized controlled trials (RCTs) with time-to-event outcomes, the difference in restricted mean survival times (RMSTD) offers an absolute measure of the treatment effect on the time scale. Computation of the RMSTD relies on the choice of a time horizon, $\tau$. In a meta-analysis, varying follow-up durations may lead to the exclusion of RCTs with follow-up shorter than $\tau$. We introduce an individual patient data multivariate meta-analysis model for RMSTD estimated at multiple time horizons. We derived the within-trial covariance for the RMSTD enabling the synthesis of all data by borrowing strength from multiple time points. In a simulation study covering 60 scenarios, we compared the statistical performance of the proposed method to that of two univariate meta-analysis models, based on available data at each time point and based on predictions from flexible parametric models. Our multivariate model yields smaller mean squared error over univariate methods at all time points. We illustrate the method with a meta-analysis of five RCTs comparing transcatheter aortic valve replacement (TAVR) with surgical replacement in patients with aortic stenosis. Over 12, 24, and 36 months of follow-up, those treated by TAVR live 0.28 [95% confidence interval (CI) 0.01 to 0.56], 0.46 (95% CI $-$0.08 to 1.01), and 0.79 (95% CI $-$0.43 to 2.02) months longer on average compared to those treated by surgery, respectively. [ABSTRACT FROM AUTHOR]

Predicting the survival time of a cancer patient based on his/her genome-wide gene expression remains a challenging problem. For certain types of cancer, the effects of gene expression on survival are both weak and abundant, so identifying non-zero effects with reasonable accuracy is difficult. As an alternative to methods that use variable selection, we propose a Gaussian process accelerated failure time model to predict survival time using genome-wide or pathway-wide gene expression data. Using a Monte Carlo expectation-maximization algorithm, we jointly impute censored log-survival time and estimate model parameters. We demonstrate the performance of our method and its advantage over existing methods in both simulations and real data analysis. The real data that we analyze were collected from 513 patients with kidney renal clear cell carcinoma and include survival time, demographic/clinical variables, and expression of more than 20 000 genes. In addition to the right-censored survival time, our method can also accommodate left-censored or interval-censored outcomes; and it provides a natural way to combine multiple types of high-dimensional -omics data. An R package implementing our method is available in the Supplementary material available at Biostatistics online. [ABSTRACT FROM AUTHOR]

We created a database of reconstructed patient-level data from published clinical trials that includes multiple time-to-event outcomes such as overall survival and progression-free survival. Outcomes were extracted from Kaplan–Meier (KM) curves reported in 153 oncology Phase III clinical trial publications identified through a PubMed search of clinical trials in breast, lung, prostate and colorectal cancer, published between 2014 and 2016. For each trial that met our search criteria, we curated study-level information and digitized all reported KM curves with the software Digitizelt. We then used the digitized KM survival curves to estimate (possibly censored) patient-level time-to-event outcomes. Collections of time-to-event datasets from completed trials can be used to support the choice of appropriate trial designs for future clinical studies. Patient-level data allow investigators to tailor clinical trial designs to diseases and classes of treatments. Patient-level data also allow investigators to estimate the operating characteristics (e.g. power and type I error rate) of candidate statistical designs and methods. Database URL : https://10.6084/m9.figshare.14642247.v1 [ABSTRACT FROM AUTHOR]

Summary Restricted mean survival time (RMST) is a useful summary measurement of the time-to-event data, and it has attracted great attention for its straightforward clinical interpretation. In this article, I propose a deep neural network model that directly relates the RMST to its baseline covariates for simultaneous prediction of RSMT at multiple times. Each subject's survival time is transformed into a series of jackknife pseudo observations and then used as quantitative response variables in a deep neural network model. By using the pseudo values, a complex survival analysis is reduced to a standard regression problem, which greatly simplifies the neural network construction. By jointly modeling RMST at multiple times, the neural network model gains prediction accuracy by information sharing across times. The proposed network model was evaluated by extensive simulation studies and was further illustrated on three real datasets. In real data analyses, I also used methods to open the blackbox by identifying subject-specific predictors and their importance in contributing to the risk prediction. Availability and implementation The source code is freely available at http://github.com/lilizhaoUM/DnnRMST. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Background Results of several randomized clinical trials (RCTs) testing the combination of an epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (TKI) plus an anti-angiogenic drug in advanced EGFR-mutated non–small cell lung cancer were reported. Methods We first report a systematic review and meta-analysis of all RCTs to estimate effectiveness and toxicity of this new therapeutic approach compared with first-generation EGFR-TKI monotherapy. Subsequently, we present a network meta-analysis comparing the combination of an EGFR-TKI plus an anti-angiogenic drug with 2 new treatment options: combination of an EGFR-TKI plus chemotherapy or new EGFR-TKIs of second or third generation as monotherapy. Results Five RCTs were included in the first meta-analysis. The progression-free survival (PFS) was statistically significantly larger in patients treated with an EGFR-TKI plus an anti-angiogenic drug compared with EGFR-TKI monotherapy: the pooled PFS–hazard ratio (HR) was 0.59 (95% confidence interval [CI] = 0.51 to 0.69). The pooled median-PFS was 17.8 months (95% CI = 16.5 to 19.3 months) for the combination vs 11.7 months (95% CI = 11.1 to 12.7 months) for EGFR-TKI as monotherapy. No statistically significant differences between the 2 treatment arms were observed in overall survival or objective response rate. The rate of grade equal or higher than 3 adverse events was statistically significantly higher in patients treated with EGFR-TKI plus an anti-angiogenic drug: the pooled-relative risk was 1.72 (95% CI = 1.43 to 2.06). Ten RCTs were included in the network meta-analysis. All 3 experimental treatments were associated with a statistically significant improvement in PFS compared with first-generation EGFR-TKIs. When compared to each other, none of the 3 experimental treatments were statistically significantly associated with larger PFS or lower rate of grade 3 or higher adverse events. Conclusion Patients with EGFR-mutated non small-cell lung cancer derived clinically meaningful larger PFS benefit from the addition of an anti-angiogenic drug to a first-generation EGFR-TKI at the cost of an increase of toxicities. [ABSTRACT FROM AUTHOR]

The t‐year mean survival or restricted mean survival time (RMST) has been used as an appealing summary of the survival distribution within a time window [0, t]. RMST is the patient's life expectancy until time t and can be estimated nonparametrically by the area under the Kaplan‐Meier curve up to t. In a comparative study, the difference or ratio of two RMSTs has been utilized to quantify the between‐group‐difference as a clinically interpretable alternative summary to the hazard ratio. The choice of the time window [0, t] may be prespecified at the design stage of the study based on clinical considerations. On the other hand, after the survival data have been collected, the choice of time point t could be data‐dependent. The standard inferential procedures for the corresponding RMST, which is also data‐dependent, ignore this subtle yet important issue. In this paper, we clarify how to make inference about a random "parameter." Moreover, we demonstrate that under a rather mild condition on the censoring distribution, one can make inference about the RMST up to t, where t is less than or even equal to the largest follow‐up time (either observed or censored) in the study. This finding reduces the subjectivity of the choice of t empirically. The proposal is illustrated with the survival data from a primary biliary cirrhosis study, and its finite sample properties are investigated via an extensive simulation study. [ABSTRACT FROM AUTHOR]

Tree‐based methods are popular nonparametric tools in studying time‐to‐event outcomes. In this article, we introduce a novel framework for survival trees and ensembles, where the trees partition the dynamic survivor population and can handle time‐dependent covariates. Using the idea of randomized tests, we develop generalized time‐dependent receiver operating characteristic (ROC) curves for evaluating the performance of survival trees. The tree‐building algorithm is guided by decision‐theoretic criteria based on ROC, targeting specifically for prediction accuracy. To address the instability issue of a single tree, we propose a novel ensemble procedure based on averaging martingale estimating equations, which is different from existing methods that average the predicted survival or cumulative hazard functions from individual trees. Extensive simulation studies are conducted to examine the performance of the proposed methods. We apply the methods to a study on AIDS for illustration. [ABSTRACT FROM AUTHOR]

Background: Albuminuria is highly prevalent among older adults, especially those with diabetes. It is associated with several chronic diseases, but its overall impact on the health of older adults, as measured by hospitalization, has not been quantified.Method: We followed up 3,110 adults, mean age 78 years, for a median 9.75 years, of whom 654 (21%) had albuminuria (≥30 mg albumin/gram creatinine) at baseline. Poisson regression models, adjusted for cardiovascular, renal, and demographic factors, were used to evaluate the association of albuminuria with all-cause and cause-specific hospitalizations, as defined by ICD, version 9, categories.Results: The rates of hospitalization per 100 patient-years were 65.85 for participants with albuminuria and 37.55 for participants without albuminuria. After adjustment for covariates, participants with albuminuria were more likely to be hospitalized for any cause than participants without albuminuria (incident rate ratio, 1.39 [95% confidence intervals, 1.27. 1.53]) and to experience more days in hospital (incident rate ratio 1.56 [1.37, 1.76]). The association of albuminuria with hospitalization was similar among participants with and without diabetes (adjusted incident rate ratio for albuminuria versus no albuminuria: diabetes 1.37 [1.11, 1.70], no diabetes 1.40 [1.26, 1.55]; p interaction nonsignificant). Albuminuria was significantly associated with hospitalization for circulatory, endocrine, genitourinary, respiratory, and injury categories.Conclusions: Albuminuria in older adults is associated with an increased risk of hospitalization for a broad range of illnesses. Albuminuria in the presence or absence of diabetes appears to mark a generalized vulnerability to diseases of aging among older adults. [ABSTRACT FROM AUTHOR]

Summary: Background: Drug reaction with eosinophilia and systemic symptoms (DRESS) is a condition caused by a drug‐induced immune response. Previous reports have found that CXCL10, also known as interferon‐γ‐induced protein (IP)‐10, may participate in the pathogenesis of cutaneous adverse drug reactions. However, the exact role of IP‐10 in DRESS and Stevens–Johnson syndrome (SJS)/toxic epidermal necrolysis (TEN) has remained unknown. Objectives: This comparative prospective cohort study aimed to ascertain the roles of the IP‐10/CXCR3 axis in DRESS and SJS/TEN. Methods: Plasma IP‐10 levels were analysed, and univariate analyses were conducted to assess the relationship between IP‐10, human herpesvirus (HHV)‐6 reactivation and the development of long‐term sequelae. We also performed immunohistochemical staining using skin specimens and flow cytometry to determine the expression of CXCR3 in peripheral blood mononuclear cells (PBMCs). Results: Significantly higher plasma IP‐10 levels were observed in patients with DRESS with long‐term sequelae (effect size 0·81) and also in those with HHV‐6 reactivation (effect size 0·83). By immunohistochemistry, more abundant IP‐10+ and CXCR3+ cells were demonstrated in the skin lesions of patients with DRESS with HHV‐6 reactivation. The percentages of CLA+ CXCR3+ CD4+ cells and CLA+ CXCR3+ CD8+ cells were also higher in the PBMCs of HHV‐6‐reactivated patients with DRESS than in those of patients with SJS/TEN. Conclusions: Higher plasma IP‐10 levels are associated with the development of long‐term sequelae in DRESS. Higher IP‐10/CXCR3 expression in skin and more abundant CLA+ CXCR3+ CD4+ cells and CLA+ CXCR3+ CD8+ cells were observed in patients with DRESS with HHV‐6 reactivation. The IP‐10/CXCR3 axis is associated with HHV‐6 reactivation and development of long‐term sequelae in DRESS. What is already known about this topic? Elevated levels of interferon‐γ‐induced protein‐10 (IP‐10) have been observed in patients with drug reaction with eosinophilia and systemic symptoms (DRESS) and Stevens–Johnson syndrome (SJS)/toxic epidermal necrolysis (TEN).Patients with DRESS tend to develop long‐term autoimmune sequelae, including type 1 diabetes and autoimmune thyroiditis. IP‐10 has been associated with these autoimmune diseases in previous studies. What does this study add? The patients with DRESS with HHV‐6 reactivation exhibited higher levels of IP‐10 in the plasma and skin than the patients with DRESS without HHV‐6 reactivation and the patients with SJS/TEN.Patients with DRESS with higher plasma IP‐10 levels tended to develop sequelae during long‐term follow‐up. What is the translational message? IP‐10 is a useful biomarker to predict the development of long‐term sequelae in patients with DRESS. Linked Comment:Belloón and Kardaun. Br J Dermatol 2020; 183:804–805. What is already known about this topic? Elevated levels of interferon‐γ‐induced protein‐10 (IP‐10) have been observed in patients with drug reaction with eosinophilia and systemic symptoms (DRESS) and Stevens–Johnson syndrome (SJS)/toxic epidermal necrolysis (TEN).Patients with DRESS tend to develop long‐term autoimmune sequelae, including type 1 diabetes and autoimmune thyroiditis. IP‐10 has been associated with these autoimmune diseases in previous studies. What does this study add? The patients with DRESS with HHV‐6 reactivation exhibited higher levels of IP‐10 in the plasma and skin than the patients with DRESS without HHV‐6 reactivation and the patients with SJS/TEN.Patients with DRESS with higher plasma IP‐10 levels tended to develop sequelae during long‐term follow‐up. What is the translational message? IP‐10 is a useful biomarker to predict the development of long‐term sequelae in patients with DRESS. Linked Comment:Belloón and Kardaun. Br J Dermatol 2020; 183:804–805. Plain language summary available online [ABSTRACT FROM AUTHOR]

Aims Left ventricular (LV) mechanical dispersion (MD) may result from heterogeneous electrical conduction and is associated with adverse events. The present study investigated (i) the association between LV MD and the extent of LV scar as assessed with contrast-enhanced cardiac magnetic resonance (CMR) and (ii) the prognostic implications of LV MD in patients after ST-segment elevation myocardial infarction. Methods and results LV MD was calculated by echocardiography and myocardial scar was analysed on CMR data retrospectively. Infarct core and border zone were defined as ≥50% and 35–50% of maximal signal intensity, respectively. Patients were followed for the occurrence of the combined endpoint (all-cause mortality and appropriate implantable cardioverter-defibrillator therapy). In total, 96 patients (87% male, 57 ± 10 years) were included. Median LV MD was 53.5 ms [interquartile range (IQR) 43.4–62.8]. On CMR, total scar burden was 11.4% (IQR 3.8–17.1%), infarct core tissue 6.2% (IQR 2.0–12.7%), and border zone was 3.5% (IQR 1.5–5.7%). Correlations were observed between LV MD and infarct core (r  = 0.517, P  < 0.001), total scar burden (r  = 0.497, P  < 0.001), and border zone (r  = 0.298, P  = 0.003). In total, 14 patients (15%) reached the combined endpoint. Patients with LV MD >53.5 ms showed higher event rates as compared to their counterparts. Finally, LV MD showed the highest area under the curve for the prediction of the combined endpoint. Conclusion LV MD is correlated with LV scar burden. In addition, patients with prolonged LV MD showed higher event rates. Finally, LV MD provided the highest predictive value for the combined endpoint when compared with other parameters. [ABSTRACT FROM AUTHOR]

Background Studies estimating excess length of stay (LOS) attributable to nosocomial infections have failed to address time-varying confounding, likely leading to overestimation of their impact. We present a methodology based on inverse probability–weighted survival curves to address this limitation. Methods A case study focusing on intensive care unit–acquired bacteremia using data from 2 general intensive care units (ICUs) from 2 London teaching hospitals were used to illustrate the methodology. The area under the curve of a conventional Kaplan-Meier curve applied to the observed data was compared with that of an inverse probability–weighted Kaplan-Meier curve applied after treating bacteremia as censoring events. Weights were based on the daily probability of acquiring bacteremia. The difference between the observed average LOS and the average LOS that would be observed if all bacteremia cases could be prevented was multiplied by the number of admitted patients to obtain the total excess LOS. Results The estimated total number of extra ICU days caused by 666 bacteremia cases was estimated at 2453 (95% confidence interval [CI], 1803–3103) days. The excess number of days was overestimated when ignoring time-varying confounding (2845 [95% CI, 2276–3415]) or when completely ignoring confounding (2838 [95% CI, 2101–3575]). Conclusions ICU-acquired bacteremia was associated with a substantial excess LOS. Wider adoption of inverse probability–weighted survival curves or alternative techniques that address time-varying confounding could lead to better informed decision making around nosocomial infections and other time-dependent exposures. [ABSTRACT FROM AUTHOR]

Background: Exact numbers of breast cancer recurrences are currently unknown at the population level, because they are challenging to actively collect. Previously, real-world data such as administrative claims have been used within expert- or data-driven (machine learning) algorithms for estimating cancer recurrence. We present the first systematic review and meta-analysis, to our knowledge, of publications estimating breast cancer recurrence at the population level using algorithms based on administrative data.Methods: The systematic literature search followed Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. We evaluated and compared sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy of algorithms. A random-effects meta-analysis was performed using a generalized linear mixed model to obtain a pooled estimate of accuracy.Results: Seventeen articles met the inclusion criteria. Most articles used information from medical files as the gold standard, defined as any recurrence. Two studies included bone metastases only in the definition of recurrence. Fewer studies used a model-based approach (decision trees or logistic regression) (41.2%) compared with studies using detection rules without specified model (58.8%). The generalized linear mixed model for all recurrence types reported an accuracy of 92.2% (95% confidence interval = 88.4% to 94.8%).Conclusions: Publications reporting algorithms for detecting breast cancer recurrence are limited in number and heterogeneous. A thorough analysis of the existing algorithms demonstrated the need for more standardization and validation. The meta-analysis reported a high accuracy overall, which indicates algorithms as promising tools to identify breast cancer recurrence at the population level. The rule-based approach combined with emerging machine learning algorithms could be interesting to explore in the future. [ABSTRACT FROM AUTHOR]

Background The estimated glomerular filtration rate (eGFR) measured at 1 year is the usual benchmark applied in kidney transplantation (KT). However, acting on earlier eGFR values could help in managing KT during the first post-operative year. We aimed to assess the prognostic value for long-term graft survival of the early (3 months) quantification of eGFR and proteinuria following KT. Methods The 3-, 6- and 12-month eGFR using the Modified Diet in Renal Disease equation (eGFRMDRD) was determined and proteinuria was measured in 754 patients who underwent their first KT between 2000 and 2010 (with a mean follow-up of 8.3 years) in our centre. Adjusted associations with graft survival were estimated using a multivariable Cox model. The predictive accuracy was estimated using the C-index and net reclassification index. These same analyses were measured in a multicentre validation cohort of 1936 patients. Results Both 3-month eGFRMDRD and proteinuria were independent predictors of return to dialysis (all P < 0.05) and there was a strong correlation between eGFR at 3 and 12 months (Spearman's ρ = 0.76). The predictive accuracy of the 3-month eGFR was within a similar range and did not differ significantly from the 12-month eGFR in either the derivation cohort [C-index 62.6 (range 57.2–68.1) versus 66.0 (range 60.1–71.9), P = 0.41] or the validation cohort [C-index 69.3 (range 66.4–72.1) versus 71.7 (range 68.7–74.6), P = 0.25]. Conclusion The 3-month eGFR was a valuable predictor of the long-term return to dialysis whose predictive accuracy was not significantly less than that of the 12-month eGFR in multicentre cohorts totalling >2500 patients. Three-month outcomes may be useful in randomized controlled trials targeting early therapeutic interventions. [ABSTRACT FROM AUTHOR]

Acquiring real-world evidence is crucial to support health policy, but observational studies are prone to serious biases. An approach was recently proposed to overcome confounding and immortal-time biases within the emulated trial framework. This tutorial provides a step-by-step description of the design and analysis of emulated trials, as well as R and Stata code, to facilitate its use in practice. The steps consist in: (i) specifying the target trial and inclusion criteria; (ii) cloning patients; (iii) defining censoring and survival times; (iv) estimating the weights to account for informative censoring introduced by design; and (v) analysing these data. These steps are illustrated with observational data to assess the benefit of surgery among 70-89-year-old patients diagnosed with early-stage lung cancer. Because of the severe unbalance of the patient characteristics between treatment arms (surgery yes/no), a naïve Kaplan-Meier survival analysis of the initial cohort severely overestimated the benefit of surgery on 1-year survival (22% difference), as did a survival analysis of the cloned dataset when informative censoring was ignored (17% difference). By contrast, the estimated weights adequately removed the covariate imbalance. The weighted analysis still showed evidence of a benefit, though smaller (11% difference), of surgery among older lung cancer patients on 1-year survival. Complementing the CERBOT tool, this tutorial explains how to proceed to conduct emulated trials using observational data in the presence of immortal-time bias. The strength of this approach is its transparency and its principles that are easily understandable by non-specialists. [ABSTRACT FROM AUTHOR]

A typical biosimilar study in oncology uses the overall response evaluated at a specific time point as the primary endpoint, which is generally acceptable regulatorily, to assess clinical equivalence between a biosimilar and its reference product. The standard primary endpoint for evaluating an anticancer therapy, progression-free or overall survival would be a secondary endpoint in a biosimilar trial. With a conventional analytic procedure via, for example, hazard ratio to quantify the group difference, it is difficult and challenging to assess clinical equivalence with respect to progression-free or overall survival because the study generally has a limited number of clinical events observed in the study. In this article, we show that an alternative procedure based on the restricted mean survival time, which has been discussed extensively for design and analysis of a general equivalence study, is readily applicable to a biosimilar trial. Unlike the hazard ratio, this procedure provides a clinically interpretable estimate for assessing equivalence. Using the restricted mean survival time as a summary measure of the survival curve will enhance better treatment decision making in adopting a biosimilar product over the reference product. [ABSTRACT FROM AUTHOR]

Rejoinder: Improving precision and power in randomized trials for COVID-19 treatments using covariate adjustment, for binary, ordinal, and time-to-event outcomes An alternative estimator to consider for time-to-event outcomes is the augmented, inverse probability weighted estimator from Díaz I et al i . M. Zhang and B. Zhang describe a general approach for constructing covariate adjusted estimators that applies to many estimands. [Extracted from the article]

Asymptotic distributions under alternative hypotheses and their corresponding sample size and power equations are derived for nonparametric test statistics commonly used to compare two survival curves. Test statistics include the weighted log‐rank test and the Wald test for difference in (or ratio of) Kaplan‐Meier survival probability, percentile survival, and restricted mean survival time. Accrual, survival, and loss to follow‐up are allowed to follow any arbitrary continuous distribution. We show that Schoenfeld's equation—often used by practitioners to calculate the required number of events for the unweighted log‐rank test—can be inaccurate even when the proportional hazards (PH) assumption holds. In fact, it can mislead one to believe that 1:1 is the optimal randomization ratio (RR), when actually power can be gained by assigning more patients to the active arm. Meaningful improvements to Schoenfeld's equation are made. The present theory should be useful in designing clinical trials, particularly in immuno‐oncology where nonproportional hazards are frequently encountered. We illustrate the application of our theory with an example exploring optimal RR under PH and a second example examining the impact of delayed treatment effect. A companion R package npsurvSS is available for download on CRAN. [ABSTRACT FROM AUTHOR]

G‐estimation of structural nested models (SNMs) plays an important role in estimating the effects of time‐varying treatments with appropriate adjustment for time‐dependent confounding. As SNMs for a failure time outcome, structural nested accelerated failure time models (SNAFTMs) and structural nested cumulative failure time models have been developed. The latter models are included in the class of structural nested mean models (SNMMs) and are not involved in artificial censoring, which induces several difficulties in g‐estimation of SNAFTMs. Recently, restricted mean time lost (RMTL), which corresponds to the area under a distribution function up to a restriction time, is attracting attention in clinical trial communities as an appropriate summary measure of a failure time outcome. In this study, we propose another SNMM for a failure time outcome, which is called structural nested RMTL model (SNRMTLM) and describe randomized and observational g‐estimation procedures that use different assumptions for the treatment mechanism in a randomized trial setting. We also provide methods to estimate marginal RMTLs under static treatment regimes using estimated SNRMTLMs. A simulation study evaluates finite‐sample performances of the proposed methods compared with the conventional intention‐to‐treat and per‐protocol analyses. We illustrate the proposed methods using data from a randomized controlled trial for cardiovascular disease with treatment changes. G‐estimation of SNRMTLMs is a useful tool to estimate the effects of time‐varying treatments on a failure time outcome. [ABSTRACT FROM AUTHOR]

Background: Recent efforts of gene expression profiling analyses recognized at least four different triple-negative breast cancer (TNBC) molecular subtypes. However, little is known regarding their tumor microenvironment (TME) heterogeneity.Methods: Here, we investigated TME heterogeneity within each TNBC molecular subtype, including immune infiltrate localization and composition together with expression of targetable immune pathways, using publicly available transcriptomic and genomic datasets from a large TNBC series totaling 1512 samples. Associations between molecular subtypes and specific features were assessed using logistic regression models. All statistical tests were two-sided.Results: We demonstrated that each TNBC molecular subtype exhibits distinct TME profiles associated with specific immune, vascularization, stroma, and metabolism biological processes together with specific immune composition and localization. The immunomodulatory subtype was associated with the highest expression of adaptive immune-related gene signatures and a fully inflamed spatial pattern appearing to be the optimal candidate for immune check point inhibitors. In contrast, most mesenchymal stem-like and luminal androgen receptor tumors showed an immunosuppressive phenotype as witnessed by high expression levels of stromal signatures. Basal-like, luminal androgen receptor, and mesenchymal subtypes exhibited an immune cold phenotype associated with stromal and metabolism TME signatures and enriched in margin-restricted spatial pattern. Tumors with high chromosomal instability and copy number loss in the chromosome 5q and 15q regions, including genomic loss of major histocompatibility complex related genes, showed reduced cytotoxic activity as a plausible immune escape mechanism.Conclusions: Our results demonstrate that each TNBC subtype is associated with specific TME profiles, setting the ground for a rationale tailoring of immunotherapy in TNBC patients. [ABSTRACT FROM AUTHOR]

Cross-study validation (CSV) of prediction models is an alternative to traditional cross-validation (CV) in domains where multiple comparable datasets are available. Although many studies have noted potential sources of heterogeneity in genomic studies, to our knowledge none have systematically investigated their intertwined impacts on prediction accuracy across studies. We employ a hybrid parametric/non-parametric bootstrap method to realistically simulate publicly available compendia of microarray, RNA-seq, and whole metagenome shotgun microbiome studies of health outcomes. Three types of heterogeneity between studies are manipulated and studied: (i) imbalances in the prevalence of clinical and pathological covariates, (ii) differences in gene covariance that could be caused by batch, platform, or tumor purity effects, and (iii) differences in the "true" model that associates gene expression and clinical factors to outcome. We assess model accuracy, while altering these factors. Lower accuracy is seen in CSV than in CV. Surprisingly, heterogeneity in known clinical covariates and differences in gene covariance structure have very limited contributions in the loss of accuracy when validating in new studies. However, forcing identical generative models greatly reduces the within/across study difference. These results, observed consistently for multiple disease outcomes and omics platforms, suggest that the most easily identifiable sources of study heterogeneity are not necessarily the primary ones that undermine the ability to accurately replicate the accuracy of omics prediction models in new studies. Unidentified heterogeneity, such as could arise from unmeasured confounding, may be more important. [ABSTRACT FROM AUTHOR]

Iron (Fe) is vital for plant growth. Plants balance the beneficial and toxic effects of this micronutrient, and tightly control Fe uptake and allocation. Here, we review the role of the basic helix–loop–helix (bHLH) transcription factor FIT (FER-LIKE FE DEFICIENCY-INDUCED TRANSCRIPTION FACTOR) in Fe acquisition. FIT is not only essential, it is also a central regulatory hub in root cells to steer and adjust the rate of Fe uptake by the root in a changing environment. FIT regulates a subset of root Fe deficiency (–Fe) response genes. Based on a combination of co-expression network and FIT-dependent transcriptome analyses, we defined a set of FIT-dependent and FIT-independent gene expression signatures and co-expression clusters that encode specific functions in Fe regulation and Fe homeostasis. These gene signatures serve as markers to integrate novel regulatory factors and signals into the –Fe response cascade. FIT forms a complex with bHLH subgroup Ib transcription factors. Furthermore, it interacts with key regulators from different signaling pathways that either activate or inhibit FIT function to adjust Fe acquisition to growth and environmental constraints. Co-expression clusters and FIT protein interactions suggest a connection of –Fe with ABA responses and root cell elongation processes that can be explored in future studies. [ABSTRACT FROM AUTHOR]

For the etiology, progression, and treatment of complex diseases, gene‐environment (G‐E) interactions have important implications beyond the main G and E effects. G‐E interaction analysis can be more challenging with higher dimensionality and need for accommodating the "main effects, interactions" hierarchy. In recent literature, an array of novel methods, many of which are based on the penalization technique, have been developed. In most of these studies, however, the structures of G measurements, for example, the adjacency structure of single nucleotide polymorphisms (SNPs; attributable to their physical adjacency on the chromosomes) and the network structure of gene expressions (attributable to their coordinated biological functions and correlated measurements) have not been well accommodated. In this study, we develop structured G‐E interaction analysis, where such structures are accommodated using penalization for both the main G effects and interactions. Penalization is also applied for regularized estimation and selection. The proposed structured interaction analysis can be effectively realized. It is shown to have consistency properties under high‐dimensional settings. Simulations and analysis of GENEVA diabetes data with SNP measurements and TCGA melanoma data with gene expression measurements demonstrate its competitive practical performance. [ABSTRACT FROM AUTHOR]

Background: The International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) criteria are the gold standard for risk‐stratifying patients with metastatic renal cell cancer (mRCC). We developed a novel risk scoring system for patients with mRCC treated with immune checkpoint inhibitors (ICIs). Methods: We performed a retrospective analysis of 100 ICI‐treated patients with mRCC at Winship Cancer Institute from 2015 to 2018. Several baseline variables were collected, including markers of inflammation, body mass index (BMI), and sites of metastatic disease, and all were considered for inclusion in our risk scoring system. Upon variable selection in multivariable model, monocyte‐to‐lymphocyte ratio (MLR), BMI, and number and sites of metastases at baseline were used for risk score calculation. Patients were categorized using four‐level risk groups as good (risk score = 0), intermediate (risk score = 1), poor (risk score = 2), or very poor (risk score = 3–4). Cox's proportional hazard model and the Kaplan‐Meier method were implemented for survival outcomes. Results: Most patients were male (66%) with clear cell renal cell carcinoma (72%). The majority (71%) received anti–programmed cell death protein‐1 monotherapy. Our risk scoring criteria had higher Uno's concordance statistics than IMDC in predicting overall survival (OS; 0.71 vs. 0.57) and progression‐free survival (0.61 vs. 0.58). Setting good risk (MLR <0.93, BMI ≥24, and D_Met = 0) as the reference, the OS hazard ratios were 29.5 (95% confidence interval [CI], 3.64–238.9), 6.58 (95% CI, 0.84–51.68), and 3.75 (95% CI, 0.49–28.57) for very poor, poor, and intermediate risk groups, respectively. Conclusion: Risk scoring using MLR, BMI, and number and sites of metastases may be an effective way to predict survival in patients with mRCC receiving ICI. These results should be validated in a larger, prospective study. Implications for Practice: A risk scoring system was created for patients with metastatic renal cell carcinoma treated with immune checkpoint inhibitors. The results of this study have significant implications for practicing oncologists in the community and academic setting. Importantly, these results identify readily available risk factors that can be used clinically to risk‐stratify patients with metastatic renal cell carcinoma who are treated with immune checkpoint inhibitors. Considering the increasing number of treatment options for patients with metastatic renal cell carcinoma, it is becoming more important to identify factors that increase the likelihood of responses to systemic therapy. This article describes a novel risk scoring system for patients with metastatic renal cell carcinoma treated with ICI, focusing on risk from systemic inflammation, number and sites of metastatic disease, and body composition. [ABSTRACT FROM AUTHOR]

Aims Glucagon-like peptide 1 (GLP-1) is a gut incretin hormone inducing post-prandial insulin secretion. Glucagon-like peptide 1 levels were recently found to be increased in patients with acute myocardial infarction. Glucagon-like peptide 1 receptor agonists improve cardiovascular outcomes in patients with diabetes. The aim of this study was to assess the predictive capacity of GLP-1 serum levels for cardiovascular outcome in patients with myocardial infarction. Methods and results In 918 patients presenting with myocardial infarction [321 ST-segment elevation myocardial infarction and 597 non-ST-segment elevation myocardial infarction (NSTEMI)] total GLP-1, N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels and the Global Registry of Acute Coronary Events (GRACE) score were assessed at time of hospital admission. The primary composite outcome of the study was the first occurrence of cardiovascular death, non-fatal myocardial infarction, or non-fatal stroke. Kaplan–Meier survival plots and univariable Cox regression analyses found GLP-1 to be associated with adverse outcome [hazard ratio (HR) of logarithmized GLP-1 values: 6.29, 95% confidence interval (CI): 2.67–14.81; P < 0.0001]. After further adjustment for age, sex, family history of cardiovascular disease, smoking, diabetes, hypertension, hypercholesterinaemia, glomerular filtration rate (GFR) CKD-EPI, hs-CRP, hs-Troponin T, and NT-proBNP levels the HR remained significant at 10.98 (95% CI: 2.63–45.90; P  = 0.0010). Time-dependent receiver operating characteristic curve analyses illustrated that GLP-1 levels are a strong indicator for early events. For events up to 30 days after admission, GLP-1 proved to be superior to other biomarkers including hs-Troponin T, GFR CKD-EPI, hs-CRP, and NT-proBNP. Adjustment of the GRACE risk estimate by addition of GLP-1 increased the area under the receiver operating characteristic curve over time in NSTEMI patients. Conclusion In patients hospitalized for myocardial infarction, GLP-1 levels are associated with cardiovascular events. [ABSTRACT FROM AUTHOR]

Summary: Randomization is a basis for the statistical inference of treatment effects without strong assumptions on the outcome‐generating process. Appropriately using covariates further yields more precise estimators in randomized experiments. R. A. Fisher suggested blocking on discrete covariates in the design stage or conducting analysis of covariance in the analysis stage. We can embed blocking in a wider class of experimental design called rerandomization, and extend the classical analysis of covariance to more general regression adjustment. Rerandomization trumps complete randomization in the design stage, and regression adjustment trumps the simple difference‐in‐means estimator in the analysis stage. It is then intuitive to use both rerandomization and regression adjustment. Under the randomization inference framework, we establish a unified theory allowing the designer and analyser to have access to different sets of covariates. We find that asymptotically, for any given estimator with or without regression adjustment, rerandomization never hurts either the sampling precision or the estimated precision, and, for any given design with or without rerandomization, our regression‐adjusted estimator never hurts the estimated precision. Therefore, combining rerandomization and regression adjustment yields better coverage properties and thus improves statistical inference. To quantify these statements theoretically, we discuss optimal regression‐adjusted estimators in terms of the sampling precision and the estimated precision, and then measure the additional gains of the designer and the analyser. We finally suggest the use of rerandomization in the design and regression adjustment in the analysis followed by the Huber–White robust standard error. [ABSTRACT FROM AUTHOR]

Background The prognostic relevance of health-related quality of life (HRQoL) has been scarcely studied in the dialysis population and the prognostic power for mortality of the HRQoL domains is unknown. Methods We tested the prognostic value for mortality of the HRQoL domains included in the 36-item Short Form Health Survey (SF-36) by Cox's regression analysis and by state-of-the-art prognostic techniques {explained variation in mortality [ R 2], calibration, discrimination [Harrell's C], risk reclassification [Net Reclassification Index (NRI)], Integrated Discrimination Index [IDI]} in a cohort of 951 patients on chronic haemodialysis. Results In multivariable Cox models, all but two domains (role limitation due to physical health and due to emotional problems) were independently related with mortality. Physical functioning was the domain adding the highest explanatory power (R 2+5.3%) to a basic model including established risk factors for mortality in the dialysis population. The same domain improved risk calibration and showed the highest Harrell's C (+1.7%) and the highest reclassification power (categorical NRI + 8.7%, continuous NRI +46%, P ≤ 0.006) and the highest IDI (+3.4%, P < 0.001). These results were fully confirmed in analyses testing the additional prognostic power of SF-36 domains when combined to a well-validated risk score in dialysis patients. Conclusions Physical functioning holds the highest prognostic power for mortality among the domains of SF-36. The gain in prognostic ability by this domain is relevant for clinical practice. Physical functioning has the potential for refining the prognosis and for informing exercise programmes in the dialysis population. [ABSTRACT FROM AUTHOR]

Background In non-dialysis chronic kidney disease (CKD), absolute proteinuria (Uprot) depends on the extent of kidney damage and residual glomerular filtration rate (GFR). We therefore evaluated, as compared with Uprot, the strength of association of proteinuria indexed to estimated GFR (eGFR) with end-stage renal disease (ESRD) risk. Methods In a multi-cohort prospective study in 3957 CKD patients of Stages G3–G5 referred to nephrology clinics, we tested two multivariable Cox models for ESRD risk, with either Uprot (g/24 h) or filtration-adjusted proteinuria (F-Uprot) calculated as Uprot/eGFR ×100. Results Mean ± SD age was 67 ± 14 years, males 60%, diabetics 29%, cardiovascular disease (CVD) 34%, eGFR 32 ± 13 mL/min/1.73 m2, median (interquartile range) Uprot 0.41 (0.12–1.29) g/24 h and F-Uprot 1.41 (0.36–4.93) g/24 h per 100 mL/min/1.73 m2 eGFR. Over a median follow-up of 44 months, 862 patients reached ESRD. At competing risk analysis, ESRD risk progressively increased when F-Uprot was 1.0–4.9 and ≥5.0 versus <1.0 g/24 h per 100 mL/min/1.73 m2 eGFR in Stages G3a–G4 (P < 0.001) and Stage G5 (P = 0.002). Multivariable Cox analysis showed that Uprot predicts ESRD in Stages G3a–G4 while in G5 the effect was not significant; conversely, F-Uprot significantly predicted ESRD at all stages. The F-Uprot model allowed a significantly better prediction versus the Uprot model according to Akaike information criterion. Net reclassification improvement was 12.2% (95% confidence interval 4.2–21.1), with higher reclassification in elderly, diabetes and CVD, as well as in diabetic nephropathy and glomerulonephritis, and in CKD Stages G4 and G5. Conclusions In patients referred to nephrology clinics, F-Uprot predicts ESRD at all stages of overt CKD and improves, as compared with Uprot, reclassification of patients for renal risk, especially in more advanced and complicated disease. [ABSTRACT FROM AUTHOR]

Summary: Hospital readmission is a key marker of quality of healthcare and an important policy measure, used by the Centers for Medicare and Medicaid Services to determine, in part, reimbursement rates. Currently, analyses of readmissions are based on a logistic–normal generalized linear mixed model that permits estimation of hospital‐specific measures while adjusting for case mix differences. Recent moves to identify and address healthcare disparities call for expanding case mix adjustment to include measures of socio‐economic status while minimizing additional burden to hospitals associated with collecting data on such measures. Towards resolving this dilemma, we propose that detailed socio‐economic data be collected on a subsample of patients via an outcome‐dependent sampling scheme, specifically the cluster‐stratified case–control design. Estimation and inference, for both the fixed and the random‐effects components, are performed via pseudo‐maximum‐likelihood wherein inverse probability weights are incorporated in the usual integrated likelihood to account for the design. In comprehensive simulations, cluster‐stratified case–control sampling proves to be an efficient design whenever interest lies in fixed or random effects of a generalized linear mixed model and covariates are unobserved or expensive to collect. The methods are motivated by and illustrated with an analysis of N = 889661 Medicare beneficiaries hospitalized between 2011 and 2013 with congestive heart failure at one of K = 3116 hospitals. Results highlight that the framework proposed provides a means of mitigating disparities in terms of which hospitals are indicated as being poor performers, relative to a naive analysis that fails to adjust for missing case mix variables. [ABSTRACT FROM AUTHOR]

A substantial amount of research focuses on the error-related negativity (ERN)—a negative deflection in the event-related potential waveform that occurs when individuals commit errors on lab-based tasks. The ERN has been link to concurrent and prospective risk for psychopathology and is thought to index sensitivity or reactivity to errors. The ERN can be potentiated in the lab with punishment and has been shown to be increased among offspring of harsh or controlling parents. A separate line of work has demonstrated that the ERN is increased among individuals high in perfectionism. In the current study, we integrate these separate lines of work by examining parenting styles, perfectionism and the ERN in a sample of young adults. Results suggest that the ERN is increased among offspring of controlling parents (both maternal and paternal). Additionally, the ERN is increased among individuals who report being high in perfectionism—specifically, the concerns over mistake and the personal standard perfectionism subscales of the Frost Multidimensional Perfectionism Scale. Moreover, results supported a mediation model wherein the indirect pathway from controlling parenting style to perfectionism (personal standard subscale) was mediated by the ERN—for paternal parenting. [ABSTRACT FROM AUTHOR]

Aims To investigate the risk of stroke/thromboembolism (TE) and major bleeding associated with anaemia among patients with atrial fibrillation (AF). Also, to assess the effects of oral anticoagulation (OAC) and time in therapeutic range (TTR) with vitamin K antagonists according to level of haemoglobin (Hb). Methods and results Through administrative registry databases, we identified all Danish patients diagnosed with AF from 1997 to 2012. We included 18 734 AF patients with recent available data on Hb. Multiple Cox regression analyses were used to estimate hazard ratios and to compute standardized absolute 1-year risks of stroke/TE and major bleeding. Among included patients, 3796 (20%) had mild anaemia (Hb 6.83–7.45 mmol/L for women and Hb 6.83–8.03 mmol/L for men) and 2562 (14%) had moderate/severe anaemia (Hb <6.83 mmol/L). Moderate/severe anaemia was associated with increased risk of major bleeding and 9.1% lower median TTR compared with no anaemia. Use of OAC was associated with reduced risk of stroke/TE among patients without anaemia [standardized absolute 1-year difference −2.5%, 95% confidence interval (CI) −3.8 to −1.7%] or with mild anaemia (−2.3%, 95% CI −2.8 to −1.8%), but not with moderate/severe anaemia, (0.03%, −1.8 to +2.8%, interaction P = 0.01). Oral anticoagulation was associated with a 5.3% (95% CI 2.1–8.7%) increased standardized absolute risk of major bleeding among AF patients with moderate/severe anaemia. Conclusion Anaemia was common in patients with AF and associated with major bleeding and lower TTR. Oral anticoagulation was associated with more major bleeding, but no reduction in risk of stroke/TE among AF patients with moderate/severe anaemia. Open in new tab Download slide Open in new tab Download slide [ABSTRACT FROM AUTHOR]

Background: A 'mortality risk score' (MS) based on ten prominent mortality-related cytosine-phosphate-guanine (CpG) sites was previously associated with all-cause mortality, but has not been verified externally. We aimed to validate the association of MS with mortality and to compare MS with three aging biomarkers: telomere length (TL), DNA methylation age (DNAmAge) and phenotypic age (DNAmPhenoAge) to explore whether MS can serve as a reliable measure of biological aging and mortality.Methods: Among 534 males aged 55-85 years from the US Normative Aging Study, the MS, DNAmAge and DNAmPhenoAge were derived from blood DNA methylation profiles from the Illumina HumanMethylation450 BeadChip, and TL was measured by quantitative real-time polymerase chain reaction (qRT-PCR).Results: A total of 147 participants died during a median follow-up of 9.4 years. The MS showed strong associations with all-cause, cardiovascular disease (CVD) and cancer mortality. After controlling for all potential covariates, participants with high MS (>5 CpG sites with aberrant methylation) had almost 4-fold all-cause mortality (hazard ratio: 3.84, 95% confidence interval: 1.92-7.67) compared with participants with a low MS (0-1 CpG site with aberrant methylation). Similar patterns were observed with respect to CVD and cancer mortality. MS was associated with TL and DNAmPhenoAge acceleration but not with DNAmAge acceleration. Although the MS and DNAmPhenoAge acceleration were independently associated with all-cause mortality, the former exhibited a higher predictive accuracy of mortality than the latter.Conclusions: MS has the potential to be a prominent predictor of mortality that could enhance survival prediction in clinical settings. [ABSTRACT FROM AUTHOR]

The conventional nonparametric tests in survival analysis, such as the log‐rank test, assess the null hypothesis that the hazards are equal at all times. However, hazards are hard to interpret causally, and other null hypotheses are more relevant in many scenarios with survival outcomes. To allow for a wider range of null hypotheses, we present a generic approach to define test statistics. This approach utilizes the fact that a wide range of common parameters in survival analysis can be expressed as solutions of differential equations. Thereby, we can test hypotheses based on survival parameters that solve differential equations driven by cumulative hazards, and it is easy to implement the tests on a computer. We present simulations, suggesting that our tests perform well for several hypotheses in a range of scenarios. As an illustration, we apply our tests to evaluate the effect of adjuvant chemotherapies in patients with colon cancer, using data from a randomized controlled trial. [ABSTRACT FROM AUTHOR]

"Covariate adjustment" in the randomized trial context refers to an estimator of the average treatment effect that adjusts for chance imbalances between study arms in baseline variables (called "covariates"). The baseline variables could include, for example, age, sex, disease severity, and biomarkers. According to two surveys of clinical trial reports, there is confusion about the statistical properties of covariate adjustment. We focus on the analysis of covariance (ANCOVA) estimator, which involves fitting a linear model for the outcome given the treatment arm and baseline variables, and trials that use simple randomization with equal probability of assignment to treatment and control. We prove the following new (to the best of our knowledge) robustness property of ANCOVA to arbitrary model misspecification: Not only is the ANCOVA point estimate consistent (as proved by Yang and Tsiatis, 2001) but so is its standard error. This implies that confidence intervals and hypothesis tests conducted as if the linear model were correct are still asymptotically valid even when the linear model is arbitrarily misspecified, for example, when the baseline variables are nonlinearly related to the outcome or there is treatment effect heterogeneity. We also give a simple, robust formula for the variance reduction (equivalently, sample size reduction) from using ANCOVA. By reanalyzing completed randomized trials for mild cognitive impairment, schizophrenia, and depression, we demonstrate how ANCOVA can achieve variance reductions of 4 to 32%. [ABSTRACT FROM AUTHOR]

Aims: There are limited data regarding factors that identify implantable cardioverter-defibrillator (ICD) patients who will experience either ventricular tachyarrhythmic (VTA) or non-arrhythmic (NA) mortality, and the commonly used clinical classification of sudden cardiac death (SCD) vs. non-sudden cardiac death (NSCD) may not be accurate enough. We aimed to correlate clinical adjudication of mortality events to device interrogation data and to identify risk factors for VTA mortality in Multicenter Automatic Defibrillator Implantation Trial II (MADIT-II).Methods and Results: Of the 746 patients who received an ICD in MADIT-II, 44 died from cardiac causes and had available interrogation data at the time of death. Sudden cardiac death vs. NSCD was defined by an adjudication committee. Ventricular tachyarrhythmic and NA arrhythmic deaths were categorized by the presence or absence of ventricular tachycardia or fibrillation (VT/VF) during the terminal event. Mode of death was found to be inaccurate when validated by device interrogation for VTA events: 50% patients adjudicated as SCD did not have a VTA event at the time of death; and 25% of adjudicated NSCD were found to have VT/VF during the mortality event. Multivariate analysis showed that factors independently associated with VTA mortality included: VT/VF >72 h prior to the mortality event [hazard ratio (HR) 8.0; P < 0.001], hospitalization for heart failure (HR 6.7; P = 0.001), and a history of hypertension (HR 4; P = 0.04).Conclusion: Current classification of SCD vs. NSCD fails to identify VTA events at the time of death in a significant proportion of patients, and simple clinical parameters can be used to identify ICD recipients with increased risk for VTA mortality. [ABSTRACT FROM AUTHOR]

BACKGROUND To compare in individuals with type 1 diabetes the prediction of incident coronary artery disease (CAD) by components of resting blood pressure—systolic, diastolic, pulse pressure, and mean arterial pressure. METHODS In 605 participants without known CAD at baseline and followed sequentially for 25 years, we used Cox modeling built for each blood pressure component associated with incident CAD, overall and stratified by age (<35 and ≥35 years) or hemoglobin A1c (HbA1c) (<9% and ≥9%). RESULTS Baseline mean age and diabetes duration were 27 and 19 years, respectively. We observed an early asymptote and then fall in diastolic blood pressure in their late 30s and early 40s in this group of type 1 diabetes individuals, followed by an early rise of pulse pressure. Adjusted hazard ratios (HR) (95% con) for CAD associated with 1 SD pressure increase were 1.35 (1.17, 1.56) for systolic pressure; 1.30 (1.12, 1.51) for diastolic pressure; 1.20 (1.03, 1.39) for pulse pressure; and 1.35 (1.17, 1.56) for mean arterial pressure. Pulse pressure emerged as a strong predictor of CAD at age ≥ 35 years (HR: 1.49 [1.15, 1.94]) and for HbA1c ≥ 9% (HR: 1.32 [1.01, 1.72]). CONCLUSIONS Individuals with type 1 diabetes may manifest early vascular aging by an early decline in diastolic blood pressure and rise in pulse pressure, the latter parameter becoming a comparable to systolic blood pressure in predictor incident CAD in those aged over 35 years and those with poor glycemic control. [ABSTRACT FROM AUTHOR]

Open in new tab Download slide Open in new tab Download slide OBJECTIVES The International Association for the Study of Lung Cancer (IASLC) proposed further subdivisions of pathological N1 (pN1) and pN2 by including the location and the number of involved lymph node (LN) stations. We adopted the subdivided N descriptors and reclassified them according to the involved LN zones or LN stations, and compared the discrimination abilities of the 2 classifications. METHODS A retrospective analysis was carried out on patients who underwent complete resection with systematic LN dissection for non-small cell lung cancer diagnosed as pathological stages I–III between 2006 and 2015. N classification was grouped into 6 categories: no LN involvement, single-station N1, multiple-station N1, single-station N2 without N1, single-station N2 with N1 and multiple-station N2. LN zones were defined by grouping the LN stations: peripheral or hilar for N1 nodes, and upper mediastinal, lower mediastinal, aortopulmonary and subcarinal for N2 nodes. RESULTS A total of 3971 patients (2451 men, median age: 63 years) were analysed. Median follow-up was 59 months. A multivariable analysis showed that the subdivided N descriptors based on LN station and zone were both independent prognostic factors in terms of both overall survival and freedom from recurrence. Whether multiple LN involvements were confined within a single LN zone was a significant prognostic factor in the multiple-station N2 group. A zone-based classification showed similar discrimination ability to the station-based classification. CONCLUSIONS Both LN station- and zone-based classifications showed favourable prognostic discrimination abilities. The new N classifications could be considered for future revisions of tumour, node and metastasis (TNM) staging system for lung cancer. [ABSTRACT FROM AUTHOR]

Introduction: Understanding the efficacy of treatments is crucial for patients, physicians, and policymakers. Median survival, the most common measure used in the outcome reporting of oncology clinical trials, is easy to understand; however, it describes only a single time point. The interpretation of the hazard ratio is difficult, and its underlying statistical assumptions are not always met. The objective of this study was to evaluate alternative measures based on the mean benefit of novel oncology treatments. Materials and Methods: We reviewed all U.S. Food and Drug Administration (FDA) approvals for oncology agents between 2013 and 2017. We digitized survival curves as reported in the clinical trials used for the FDA approvals and implemented statistical transformations to calculate for each trial the restricted mean survival time (RMST), as well as the mean survival using Weibull distribution. We compared the mean survival with the median survival benefit in each clinical trial. Results: The FDA approved 83 solid tumor indications for oncology agents between 2013 and 2017, of which 27 approvals based on response rates, whereas 49 approvals were based on survival endpoints (progression‐free survival and overall survival). The average improvement in median overall survival or progression‐free survival was 4.6 months versus 3.6 months improvement in the average RMST and 6.1 months improvement in mean survival using Weibull distribution. Conclusion: Mean survival may supply valuable information for different stakeholders. Its inclusion should be considered in the reporting of prospective clinical trials. Implications for Practice: Mean survival may supply valuable information for different stakeholders. Its inclusion should be considered in the reporting of clinical trials. Understanding the efficacy of cancer treatments is essential for patients, physicians, and policymakers. This article explores the value of using mean survival as an endpoint for outcome reporting of oncology clinical trials. [ABSTRACT FROM AUTHOR]

Aims Cardiac resynchronization therapy (CRT) can reduce left ventricular end-systolic volume (LVESV), and a decrease of ≥15% is defined as a response. CRT can also improve LV global longitudinal strain (GLS). Changes in LVESV and LV GLS are individually associated with outcome post-CRT. We investigated LVESV and LV GLS changes and prognostic implications of improvement in LVESV and/or LV GLS, compared with no improvement in either parameter. Methods and results Baseline and 6-month echocardiograms were analysed from CRT recipients with heart failure. LV reverse remodelling was defined as a ≥15% reduction in LVESV at 6 months post-CRT. A ≥5% absolute improvement in LV GLS was defined as a change in LV GLS. A total of 1185 patients were included (mean age 65 ± 10 years, 73% male), and those with an improvement in LVESV and LV GLS (n  = 131, 11.1%) had significantly lower mortality compared with other groups. On multivariable analysis, an improvement in both LVESV and LV GLS [hazard ratio (HR): 0.47; 95% confidence interval (CI): 0.31–0.71; P  < 0.001] or an improvement in either LVESV or LV GLS (HR: 0.57; 95% CI: 0.47–0.71; P  < 0.001) were independently associated with better prognosis, compared with no improvement in either parameter. Conclusion Either a reduction in LVESV and/or an improvement in LV GLS at 6 months post-CRT are independently associated with improved long-term prognosis, compared with no change in both LVESV and LV GLS. This supports the use of LV GLS as a meaningful parameter in defining CRT response. [ABSTRACT FROM AUTHOR]

Aims The Toll-like receptor 7 (TLR7) is an intracellular innate immune receptor activated by nucleic acids shed from dying cells leading to activation of the innate immune system. Since innate immune system activation is involved in the response to myocardial infarction (MI), this study aims to identify if TLR7 is involved in post-MI ischaemic injury and adverse remodelling after MI. Methods and results TLR7 involvement in MI was investigated in human tissue from patients with ischaemic heart failure, as well as in a mouse model of permanent left anterior descending artery occlusion in C57BL/6J wild type and TLR7 deficient (TLR7−/−) mice. TLR7 expression was up-regulated in human and mouse ischaemic myocardium after MI. Compared to wild type mice, TLR7−/− mice had less acute cardiac rupture associated with blunted activation of matrix metalloproteinase 2, increased expression of tissue inhibitor of metalloproteinase 1, recruitment of more myofibroblasts, and the formation of a myocardial scar with higher collagen fibre density. Furthermore, inflammatory cell influx and inflammatory cytokine expression post-MI were reduced in the TLR7−/− heart. During a 28-day follow-up after MI, TLR7 deficiency resulted in less chronic adverse left ventricular remodelling and better cardiac function. Bone marrow (BM) transplantation experiments showed that TLR7 deficiency in BM-derived cells preserved cardiac function after MI. Conclusions In acute MI, TLR7 mediates the response to acute cardiac injury and chronic remodelling probably via modulation of post-MI scar formation and BM-derived inflammatory infiltration of the myocardium. [ABSTRACT FROM AUTHOR]

Aims: Global spread of carbapenem‐resistant Pseudomonas aeruginosa (CRPsA) and Acinetobacter baumannii (CRAB) is an emerging clinical problem. Since, quercetin is a well‐known antibacterial agent; it would be relevant to demonstrate synergy between quercetin and meropenem and elucidate molecular basis of effective bactericidal activity of quercetin‐meropenem against CRPsA and CRAB. Methods and Results: Hence, minimum inhibitory concentration (MIC), checkerboard, time‐kill and Baclight assays were performed to determine the antibacterial and synergistic activity of quercetin with meropenem against CRPsA and CRAB. To corroborate synergism, fractional inhibitory concentration index, combination index calculation and isobologram analysis were performed on selected isolates. Finally, to elucidate molecular basis of effective bactericidal activity of quercetin‐meropenem against CRPsA and CRAB, effect of meropenem, quercetin and their combinations on cellular morphology and levels of blaNDM, mexB and adeB expressions were evaluated on selected isolates by scanning electron microscope and qRT‐PCR. Quercetin exhibited synergy with meropenem. It inhibited blaNDM and AdeB expression. Half‐MIC combination of quercetin‐meropenem exerted bactericidal activity by disrupting cell wall/membrane integrity and altering cellular morphology. Conclusion: Quercetin could potentiate meropenem activity against CRPsA and CRAB. Significance and Impact of the Study: This study had demonstrated bactericidal and synergistic activity of quercetin with meropenem against CRPsA and CRAB. [ABSTRACT FROM AUTHOR]

Background Dipeptidyl peptidase-4 (DPP-4) inhibitors have pleotropic anti-inflammatory and immune regulatory effects in addition to glucoregulation. We evaluated inflammation and immune markers in suppressed human immunodeficiency virus (HIV) infection during treatment with the DPP-4 inhibitor sitagliptin. Methods Virologically suppressed adults with HIV without diabetes on stable antiretroviral therapy (ART) with ≥100/μL CD4 cells were randomized to 16 weeks of sitagliptin 100 mg/day vs placebo in a multicenter trial. The primary endpoint was the change in plasma soluble CD14 (sCD14) from baseline to week 15–16. Results Ninety participants were randomized, and 42 from each arm were included in per-protocol analyses. Participants were 45% non-Hispanic white, 38% non-Hispanic black, and 15% Hispanic, with a median age of 51 years; 83% were male; and the median CD4 count was 602 cells/μL. At week 15–16, there was no difference in sCD14 change between the 2 arms (P =.69). Relative to placebo, the sitagliptin arm had 47% greater decline in CXCL10 (95% confidence interval, –57% to –35%) at week 15 (P <.001). There were no significant between-arm differences in other soluble biomarkers, total CD4 and CD8 counts, or markers of lymphocyte or monocyte activation. Sitagliptin was well tolerated. Conclusions Sixteen weeks of sitagliptin had no effect on sCD14 levels in virologically suppressed participants with HIV. CXCL10, a chemokine involved in atherogenesis that predicts non-AIDS events during ART, declined markedly with sitagliptin. This suggests that DPP-4 inhibition has the potential to reduce cardiovascular morbidity in treated HIV infection. Clinical Trials Registration NCT01426438. [ABSTRACT FROM AUTHOR]

Cancer treatment exposures and outcomes can often be identified in administrative claims and electronic health record data ([[6], [8]]). Limited availability of data to inform the generation of robust treatment-specific risk estimates for perinatal and maternal outcomes during cancer therapy has been a long-standing barrier to providing accurate risk counseling to inform decision making. [Extracted from the article]