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Recognizing the pivotal role of circadian rhythm in the human aging process and its scalability through wearables, we introduce CosinorAge, a digital biomarker of aging developed from wearable-derived circadian rhythmicity from 80,000 midlife and older adults in the UK and US. A one-year increase in CosinorAge corresponded to 8–12% higher all-cause and cause-specific mortality risks and 3–14% increased prospective incidences of age-related diseases. CosinorAge also captured a non-linear decline in resilience and physical functioning, evidenced by an 8–33% reduction in self-rated health and a 3–23% decline in health-related quality of life score, adjusting for covariates and multiple testing. The associations were robust in sensitivity analyses and external validation using an independent cohort from a disparate geographical region using a different wearable device. Moreover, we illustrated a heterogeneous impact of circadian parameters associated with biological aging, with young (<45 years) and fast agers experiencing a substantially delayed acrophase with a 25-minute difference in peak timing compared to slow agers, diminishing to a 7-minute difference in older adults (>65 years). We demonstrated a significant enhancement in the predictive performance when integrating circadian rhythmicity in the estimation of biological aging over physical activity. Our findings underscore CosinorAge's potential as a scalable, economic, and digital solution for promoting healthy longevity, elucidating the critical and multifaceted circadian rhythmicity in aging processes. Consequently, our research contributes to advancing preventive measures in digital medicine. [ABSTRACT FROM AUTHOR]

Whether non-alcoholic fatty liver disease (NAFLD) improves risk prediction for subsequent type 2 diabetes mellitus (T2DM) in women with prior gestational diabetes mellitus (pGDM) is uncertain. We examined the combined effects of NAFLD and pGDM on risk prediction for incident T2DM. This retrospective cohort study included 97,347 Korean parous women without diabetes mellitus at baseline whose mean (SD) age was 39.0 (7.8) years. Cox proportional hazards models were used to estimate hazard ratios (HRs) for incident T2DM according to self-reported pGDM and ultrasound-diagnosed NAFLD at baseline. When combined with conventional diabetes risk factors, the incremental predictive ability of NAFLD and pGDM to identify incident T2DM was assessed. During a median follow-up of 3.9 years, 1,515 cases of incident T2DM occurred. Multivariable-adjusted HRs (95% confidence intervals [CIs]) for incident T2DM comparing pGDM alone, NAFLD alone, and both NAFLD and pGDM to the reference (neither NAFLD nor pGDM) were 2.61 (2.06–3.31), 2.26 (1.96–2.59), and 6.45 (5.19-8.00), respectively (relative excess risk of interaction = 2.58 [95% CI, 1.21–3.95]). These associations were maintained after adjusting for insulin resistance, body mass index, and other potential confounders as time-dependent covariates. The combination of NAFLD and pGDM improved risk prediction for incident T2DM (based on Harrell's C−index, also known as the concordance index, and net reclassification improvement) compared to conventional diabetes risk factors. In conclusion, NAFLD synergistically increases the risk of subsequent T2DM in women with pGDM. The combination of NAFLD and pGDM improves risk prediction for T2DM. [ABSTRACT FROM AUTHOR]

The standard treatment for platinum-sensitive relapsed ovarian cancer (PSROC) is platinum-based chemotherapy followed by olaparib monotherapy. A retrospective study was conducted to identify factors affecting the survival of patients with PSROC undergoing olaparib monotherapy in real-world clinical settings. The study enrolled 122 patients who received olaparib monotherapy between April 2018 and December 2020 at three national centers in Japan. The study used the Kaplan–Meier method and univariable and multivariable Cox proportional hazards models to evaluate the associations between factors and progression-free survival (PFS). Patients with BRCA1/2 mutations had a significantly longer median PFS than those without these mutations. Both the BRCA1/2 mutation-positive and mutation-negative groups exhibited a prolonged PFS when the platinum-free interval (PFI) was ≥ 12 months. Cancer antigen 125 (CA-125) level within reference values was significantly linked to prolonged PFS, while a high platelet-to-lymphocyte ratio (≥ 210) was significantly associated with poor PFS in the BRCA1/2 mutation-negative group. The study suggests that a PFI of ≥ 12 months may predict survival after olaparib monotherapy in patients with PSROC, regardless of their BRCA1/2 mutation status. Additionally, a CA-125 level within reference values may be associated with extended survival in patients without BRCA1/2 mutations. A larger prospective study should confirm these findings. [ABSTRACT FROM AUTHOR]

The classical approach to analyze time-to-event data, e.g. in clinical trials, is to fit Kaplan–Meier curves yielding the treatment effect as the hazard ratio between treatment groups. Afterwards, a log-rank test is commonly performed to investigate whether there is a difference in survival or, depending on additional covariates, a Cox proportional hazard model is used. However, in numerous trials these approaches fail due to the presence of non-proportional hazards, resulting in difficulties of interpreting the hazard ratio and a loss of power. When considering equivalence or non-inferiority trials, the commonly performed log-rank based tests are similarly affected by a violation of this assumption. Here we propose a parametric framework to assess equivalence or non-inferiority for survival data. We derive pointwise confidence bands for both, the hazard ratio and the difference of the survival curves. Further we propose a test procedure addressing non-inferiority and equivalence by directly comparing the survival functions at certain time points or over an entire range of time. Once the model's suitability is proven the method provides a noticeable power benefit, irrespectively of the shape of the hazard ratio. On the other hand, model selection should be carried out carefully as misspecification may cause type I error inflation in some situations. We investigate the robustness and demonstrate the advantages and disadvantages of the proposed methods by means of a simulation study. Finally, we demonstrate the validity of the methods by a clinical trial example. [ABSTRACT FROM AUTHOR]

Purpose: Little is known about the prognostic factors for survival after endoscopic submucosal dissection (ESD) in elderly patients with early gastric cancer (EGC). The aim of this study is to determine prognostic factors and a prediction model of 3-year survival after ESD for EGC in patients aged ≥ 85 years. Methods: We retrospectively evaluated the clinical outcomes of 740 patients with EGC aged ≥ 85 years, who were treated by ESD at 30 institutions in Japan. Overall survival (OS) and disease-specific survival (DSS) were calculated with the Kaplan–Meier method. Prediction models for 3-year OS after ESD were estimated using the Cox proportional hazards model based on Uno's C-statistics. Results: During the follow-up period, 309 patients died of any cause and 10 patients died of gastric cancer. OS and DSS after 3 years were 82.7% and 99.2%, respectively. No significant differences in OS were found among curability categories. The Cox proportional hazards model revealed the geriatric nutritional risk index (GNRI) and the Charlson comorbidity index (CCI) to be predictors of 3-year survival. We established a final model (EGC-2 model) expressed by GNRI − (2.2×CCI) with a cutoff value of 96. The overall survival rate was significantly lower in the model value < 96 group than in the model value ≥ 96 group (P < 0.001). Conclusions: The prediction model using GNRI and CCI will be useful to support decision-making for the treatment of EGC in elderly patients aged ≥ 85 years. [ABSTRACT FROM AUTHOR]

Background: Hyporesponsiveness to erythropoiesis-stimulating agents (ESAs) is associated with increased risks of all cause and cardiovascular mortality in patients undergoing hemodialysis (HD). However, the impact of the hematopoietic response to ESAs on the development of stroke, including brain hemorrhage and infarction, remains unclear. Methods: In total, 2886 patients undergoing maintenance HD registered in the Q-Cohort Study who were treated with ESAs were prospectively followed up for 4 years. The hematopoietic response to ESAs was evaluated by the erythropoietin resistance index (ERI), calculated by dividing the weekly dose of ESA by post-HD weight and hemoglobin (U/kg/week/g/dL). The primary outcomes were the incidences of brain hemorrhage and infarction. Patients were divided into quartiles based on their ERI at baseline (Q1, ≤ 4.1; Q2, 4.2–7.0; Q3, 7.1–11.2; and Q4, ≥ 11.3). The risks of brain hemorrhage and infarction were estimated using Cox proportional hazards models, adjusting for potential confounders. Results: During the 4 year follow-up period, 71 patients developed brain hemorrhage and 116 developed brain infarction. In the multivariable analysis, the incidence of brain hemorrhage in the highest quartile (Q4) was significantly higher than that in the lowest quartile (Q1) (hazard ratio [95% confidence interval], 2.18 [1.08–4.42]). However, the association between the ERI and the incidence of brain infarction was not significant. Conclusions: A higher ERI was associated with an increased risk of brain hemorrhage, but not brain infarction, in patients undergoing maintenance HD. A high ERI is thus an important risk factor for brain hemorrhage in these patients. [ABSTRACT FROM AUTHOR]

Our study investigates the effects of heterogeneity in image parameters on the reproducibility of prognostic performance of models built using radiomic biomarkers. We compare the prognostic performance of models derived from the heterogeneity-mitigated features with that of models obtained from raw features, to assess whether reproducibility of prognostic scores improves upon application of our methods. We used two datasets: The Breast I-SPY1 dataset—Baseline DCE-MRI scans of 156 women with locally advanced breast cancer, treated with neoadjuvant chemotherapy, publicly available via The Cancer Imaging Archive (TCIA); The NSCLC IO dataset—Baseline CT scans of 107 patients with stage 4 non-small cell lung cancer (NSCLC), treated with pembrolizumab immunotherapy at our institution. Radiomic features (n = 102) are extracted from the tumor ROIs. We use a variety of resampling and harmonization scenarios to mitigate the heterogeneity in image parameters. The patients were divided into groups based on batch variables. For each group, the radiomic phenotypes are combined with the clinical covariates into a prognostic model. The performance of the groups is assessed using the c-statistic, derived from a Cox proportional hazards model fitted on all patients within a group. The heterogeneity-mitigation scenario (radiomic features, derived from images that have been resampled to minimum voxel spacing, are harmonized using the image acquisition parameters as batch variables) gave models with highest prognostic scores (for e.g., IO dataset; batch variable: high kernel resolution—c-score: 0.66). The prognostic performance of patient groups is not comparable in case of models built using non-heterogeneity mitigated features (for e.g., I-SPY1 dataset; batch variable: small pixel spacing—c-score: 0.54, large pixel spacing—c-score: 0.65). The prognostic performance of patient groups is closer in case of heterogeneity-mitigated scenarios (for e.g., scenario—harmonize by voxel spacing parameters: IO dataset; thin slice—c-score: 0.62, thick slice—c-score: 0.60). Our results indicate that accounting for heterogeneity in image parameters is important to obtain more reproducible prognostic scores, irrespective of image site or modality. For non-heterogeneity mitigated models, the prognostic scores are not comparable across patient groups divided based on batch variables. This study can be a step in the direction of constructing reproducible radiomic biomarkers, thus increasing their application in clinical decision making. [ABSTRACT FROM AUTHOR]

Individual participant data (IPD) from oncology clinical trials is invaluable for identifying factors that influence trial success and failure, improving trial design and interpretation, and comparing pre-clinical studies to clinical outcomes. However, the IPD used to generate published survival curves are not generally publicly available. We impute survival IPD from ~500 arms of Phase 3 oncology trials (representing ~220,000 events) and find that they are well fit by a two-parameter Weibull distribution. Use of Weibull functions with overall survival significantly increases the precision of small arms typical of early phase trials: analysis of a 50-patient trial arm using parametric forms is as precise as traditional, non-parametric analysis of a 90-patient arm. We also show that frequent deviations from the Cox proportional hazards assumption, particularly in trials of immune checkpoint inhibitors, arise from time-dependent therapeutic effects. Trial duration therefore has an underappreciated impact on the likelihood of success. Analysis of more than 150 Phase 3 oncology clinical trials supports parametric statistical analysis, significantly increasing the precision of small early-phase trials and relating deviations from the Cox proportional hazards model to trial duration. [ABSTRACT FROM AUTHOR]

There are several different topics that can be addressed with multivariate failure time regression data. Data analysis methods are needed that are suited to each such topic. Specifically, marginal hazard rate models are well suited to the analysis of exposures or treatments in relation to individual failure time outcomes, when failure time dependencies are themselves of little or no interest. On the other hand semiparametric copula models are well suited to analyses where interest focuses primarily on the magnitude of dependencies between failure times. These models overlap with frailty models, that seem best suited to exploring the details of failure time clustering. Recently proposed multivariate marginal hazard methods, on the other hand, are well suited to the exploration of exposures or treatments in relation to single, pairwise, and higher dimensional hazard rates. Here these methods will be briefly described, and the final method will be illustrated using the Women's Health Initiative hormone therapy trial data. [ABSTRACT FROM AUTHOR]

We aim to determine whether ischemic stroke(IS)-related PRSs are also associated with and further predict 3-year all-cause mortality. 1756 IS patients with European ancestry were randomly split into training (n = 1226) and testing (n = 530) groups with 3-year post-event observations. Univariate Cox proportional hazards regression model (CoxPH) was used for primary screening of individual prognostic PRSs. Only the significantly associated PRSs and clinical risk factors with the same direction for a causal relationship with IS were used to construct a multivariate CoxPH. Feature selection was conducted by the LASSO method. After feature selection, a prediction model with 11 disease-associated pathway-specific PRSs outperformed the base model, as demonstrated by a higher concordance index (0.751, 95%CI [0.693–0.809] versus 0.729, 95%CI [0.676–0.782]) in the testing sample. A PRS derived from endothelial cell apoptosis showed independent predictability in the multivariate CoxPH (Hazard Ratio = 1.193 [1.027–1.385], p = 0.021). These PRSs fine-tuned the model by better stratifying high, intermediate, and low-risk groups. Several pathway-specific PRSs were associated with clinical risk factors in an age-dependent manner and further confirmed some known etiologies of IS and all-cause mortality. In conclusion, Pathway-specific PRSs for IS are associated with all-cause mortality, and the integrated multivariate risk model provides prognostic value in this context. [ABSTRACT FROM AUTHOR]

Given the growing number of prediction algorithms developed to predict COVID-19 mortality, we evaluated the transportability of a mortality prediction algorithm using a multi-national network of healthcare systems. We predicted COVID-19 mortality using baseline commonly measured laboratory values and standard demographic and clinical covariates across healthcare systems, countries, and continents. Specifically, we trained a Cox regression model with nine measured laboratory test values, standard demographics at admission, and comorbidity burden pre-admission. These models were compared at site, country, and continent level. Of the 39,969 hospitalized patients with COVID-19 (68.6% male), 5717 (14.3%) died. In the Cox model, age, albumin, AST, creatine, CRP, and white blood cell count are most predictive of mortality. The baseline covariates are more predictive of mortality during the early days of COVID-19 hospitalization. Models trained at healthcare systems with larger cohort size largely retain good transportability performance when porting to different sites. The combination of routine laboratory test values at admission along with basic demographic features can predict mortality in patients hospitalized with COVID-19. Importantly, this potentially deployable model differs from prior work by demonstrating not only consistent performance but also reliable transportability across healthcare systems in the US and Europe, highlighting the generalizability of this model and the overall approach. [ABSTRACT FROM AUTHOR]

Accurate risk prediction has been the central goal in many studies of survival outcomes. In the presence of multiple risk factors, a censored regression model can be employed to estimate a risk prediction rule. Before the prediction tool can be popularized for practical use, it is crucial to rigorously assess its prediction performance. In our motivating example, researchers are interested in developing and validating a risk prediction tool to identify future lung cancer cases by integrating demographic information, disease characteristics and smoking-related data. Considering the long latency period of cancer, it is desirable for a prediction tool to achieve discriminative performance that does not weaken over time. We propose estimation and inferential procedures to comprehensively assess both the overall predictive discrimination and the temporal pattern of an estimated prediction rule. The proposed methods readily accommodate commonly used censored regression models, including the Cox proportional hazards model and the accelerated failure time model. The estimators are consistent and asymptotically normal, and reliable variance estimators are also developed. The proposed methods offer an informative tool for inferring time-dependent predictive discrimination, as well as for comparing the discrimination performance between candidate models. Applications of the proposed methods demonstrate enduring performance of the risk prediction tool in the PLCO study and detected decaying performance in a study of liver disease. [ABSTRACT FROM AUTHOR]

Individual participant data (IPD) from oncology clinical trials is invaluable for identifying factors that influence trial success and failure, improving trial design and interpretation, and comparing pre-clinical studies to clinical outcomes. However, the IPD used to generate published survival curves are not generally publicly available. We impute survival IPD from ~500 arms of Phase 3 oncology trials (representing ~220,000 events) and find that they are well fit by a two-parameter Weibull distribution. Use of Weibull functions with overall survival significantly increases the precision of small arms typical of early phase trials: analysis of a 50-patient trial arm using parametric forms is as precise as traditional, non-parametric analysis of a 90-patient arm. We also show that frequent deviations from the Cox proportional hazards assumption, particularly in trials of immune checkpoint inhibitors, arise from time-dependent therapeutic effects. Trial duration therefore has an underappreciated impact on the likelihood of success. Analysis of more than 150 Phase 3 oncology clinical trials supports parametric statistical analysis, significantly increasing the precision of small early-phase trials and relating deviations from the Cox proportional hazards model to trial duration. [ABSTRACT FROM AUTHOR]

Appropriate management of hypertensive patients relies on the accurate identification of clinically relevant features. However, traditional statistical methods may ignore important information in datasets or overlook possible interactions among features. Machine learning may improve the prediction accuracy and interpretability of regression models by identifying the most relevant features in hypertensive patients. We sought the most relevant features for prediction of cardiovascular (CV) events in a hypertensive population. We used the penalized regression models least absolute shrinkage and selection operator (LASSO) and elastic net (EN) to obtain the most parsimonious and accurate models. The clinical parameters and laboratory biomarkers were collected from the clinical records of 1,471 patients receiving care at Mostoles University Hospital. The outcome was the development of major adverse CV events. Cox proportional hazards regression was performed alone and with penalized regression analyses (LASSO and EN), producing three models. The modeling was performed using 10-fold cross-validation to fit the penalized models. The three predictive models were compared and statistically analyzed to assess their classification accuracy, sensitivity, specificity, discriminative power, and calibration accuracy. The standard Cox model identified five relevant features, while LASSO and EN identified only three (age, LDL cholesterol, and kidney function). The accuracies of the models (prediction vs. observation) were 0.767 (Cox model), 0.754 (LASSO), and 0.764 (EN), and the areas under the curve were 0.694, 0.670, and 0.673, respectively. However, pairwise comparison of performance yielded no statistically significant differences. All three calibration curves showed close agreement between the predicted and observed probabilities of the development of a CV event. Although the performance was similar for all three models, both penalized regression analyses produced models with good fit and fewer features than the Cox regression predictive model but with the same accuracy. This case study of predictive models using penalized regression analyses shows that penalized regularization techniques can provide predictive models for CV risk assessment that are parsimonious, highly interpretable, and generalizable and that have good fit. For clinicians, a parsimonious model can be useful where available data are limited, as such a model can offer a simple but efficient way to model the impact of the different features on the prediction of CV events. Management of these features may lower the risk for a CV event. Graphical Abstract In a clinical setting, with numerous biological and laboratory features and incomplete datasets, traditional statistical methods may ignore important information and overlook possible interactions among features. Our aim was to identify the most relevant features to predict cardiovascular events in a hypertensive population, using three different regression approaches for feature selection, to improve the prediction accuracy and interpretability of regression models by identifying the relevant features in these patients. [ABSTRACT FROM AUTHOR]

Motivated by high-dimensional genomic studies, we develop an improved procedure for adaptive Lasso in high-dimensional survival analysis. The proposed procedure effectively reduces the false discoveries while successfully maintaining the false negative proportions, which improves the existing adaptive Lasso procedures. The implementation of the proposed procedure is straightforward and it is sufficiently flexible to accommodate large-scale problems where traditional procedures are impractical. To quantify the uncertainty of variable selection and control the family-wise error rate, a multiple sample-splitting based testing algorithm is developed. The practical utility of the proposed procedure are examined through simulation studies. The methods developed are then applied to a multiple myeloma data set. [ABSTRACT FROM AUTHOR]

We present a new estimator of the restricted mean survival time in randomized trials where there is right censoring that may depend on treatment and baseline variables. The proposed estimator leverages prognostic baseline variables to obtain equal or better asymptotic precision compared to traditional estimators. Under regularity conditions and random censoring within strata of treatment and baseline variables, the proposed estimator has the following features: (i) it is interpretable under violations of the proportional hazards assumption; (ii) it is consistent and at least as precise as the Kaplan-Meier and inverse probability weighted estimators, under identifiability conditions; (iii) it remains consistent under violations of independent censoring (unlike the Kaplan-Meier estimator) when either the censoring or survival distributions, conditional on covariates, are estimated consistently; and (iv) it achieves the nonparametric efficiency bound when both of these distributions are consistently estimated. We illustrate the performance of our method using simulations based on resampling data from a completed, phase 3 randomized clinical trial of a new surgical treatment for stroke; the proposed estimator achieves a 12% gain in relative efficiency compared to the Kaplan-Meier estimator. The proposed estimator has potential advantages over existing approaches for randomized trials with time-to-event outcomes, since existing methods either rely on model assumptions that are untenable in many applications, or lack some of the efficiency and consistency properties (i)-(iv). We focus on estimation of the restricted mean survival time, but our methods may be adapted to estimate any treatment effect measure defined as a smooth contrast between the survival curves for each study arm. We provide R code to implement the estimator. [ABSTRACT FROM AUTHOR]

Although machine learning models are increasingly being developed for clinical decision support for patients with type 2 diabetes, the adoption of these models into clinical practice remains limited. Currently, machine learning (ML) models are being constructed on local healthcare systems and are validated internally with no expectation that they would validate externally and thus, are rarely transferrable to a different healthcare system. In this work, we aim to demonstrate that (1) even a complex ML model built on a national cohort can be transferred to two local healthcare systems, (2) while a model constructed on a local healthcare system's cohort is difficult to transfer; (3) we examine the impact of training cohort size on the transferability; and (4) we discuss criteria for external validity. We built a model using our previously published Multi-Task Learning-based methodology on a national cohort extracted from OptumLabs® Data Warehouse and transferred the model to two local healthcare systems (i.e., University of Minnesota Medical Center and Mayo Clinic) for external evaluation. The model remained valid when applied to the local patient populations and performed as well as locally constructed models (concordance:.73–.92), demonstrating transferability. The performance of the locally constructed models reduced substantially when applied to each other's healthcare system (concordance:.62–.90). We believe that our modeling approach, in which a model is learned from a national cohort and is externally validated, produces a transferable model, allowing patients at smaller healthcare systems to benefit from precision medicine. [ABSTRACT FROM AUTHOR]

Identifying important biomarkers that are predictive for cancer patients' prognosis is key in gaining better insights into the biological influences on the disease and has become a critical component of precision medicine. The emergence of large-scale biomedical survival studies, which typically involve excessive number of biomarkers, has brought high demand in designing efficient screening tools for selecting predictive biomarkers. The vast amount of biomarkers defies any existing variable selection methods via regularization. The recently developed variable screening methods, though powerful in many practical setting, fail to incorporate prior information on the importance of each biomarker and are less powerful in detecting marginally weak while jointly important signals. We propose a new conditional screening method for survival outcome data by computing the marginal contribution of each biomarker given priorily known biological information. This is based on the premise that some biomarkers are known to be associated with disease outcomes a priori. Our method possesses sure screening properties and a vanishing false selection rate. The utility of the proposal is further confirmed with extensive simulation studies and analysis of a diffuse large B-cell lymphoma dataset. We are pleased to dedicate this work to Jack Kalbfleisch, who has made instrumental contributions to the development of modern methods of analyzing survival data. [ABSTRACT FROM AUTHOR]

Restricted mean survival time (RMST) is often of great clinical interest in practice. Several existing methods involve explicitly projecting out patient-specific survival curves using parameters estimated through Cox regression. However, it would often be preferable to directly model the restricted mean for convenience and to yield more directly interpretable covariate effects. We propose generalized estimating equation methods to model RMST as a function of baseline covariates. The proposed methods avoid potentially problematic distributional assumptions pertaining to restricted survival time. Unlike existing methods, we allow censoring to depend on both baseline and time-dependent factors. Large sample properties of the proposed estimators are derived and simulation studies are conducted to assess their finite sample performance. We apply the proposed methods to model RMST in the absence of liver transplantation among end-stage liver disease patients. This analysis requires accommodation for dependent censoring since pre-transplant mortality is dependently censored by the receipt of a liver transplant. [ABSTRACT FROM AUTHOR]

Introduction: Among patients with acute myeloid leukemia (AML), the DACO-016 randomized study showed reduction in mortality for decitabine [Dacogen(®) (DAC), Eisai Inc., Woodcliff Lake, NJ, USA] compared with treatment choice (TC): at primary analysis the hazard ratio (HR) was 0.85 (95% confidence interval 0.69-1.04; stratified log-rank P = 0.108). With two interim analyses, two-sided alpha was adjusted to 0.0462. With 1-year additional follow-up the HR reached 0.82 (nominal P = 0.0373). These data resulted in approval of DAC in the European Union, though not in the United States. Though pre-specified, the log-rank test could be considered not optimal to assess the observed survival difference because of the non-proportional hazard nature of the survival curves.Methods: We applied the Wilcoxon test as a sensitivity analysis. Patients were randomized to DAC (N = 242) or TC (N = 243). One-hundred and eight (44.4%) patients in the TC arm and 91 (37.6%) patients in the DAC arm selectively crossed over to subsequent disease modifying therapies at progression, which might impact the survival beyond the median with resultant converging curves (and disproportional hazards).Results: The stratified Wilcoxon test showed a significant improvement in median (CI 95%) overall survival with DAC [7.7 (6.2; 9.2) months] versus TC [5.0 (4.3; 6.3) months; P = 0.0458].Conclusion: Wilcoxon test indicated significant increase in survival for DAC versus TC compared to log-rank test.Funding: Janssen-Cilag GmbH. [ABSTRACT FROM AUTHOR]

Background: Clinical practice guidelines for management of anemia in children with end-stage kidney disease (ESKD) remain largely opinion-based. In this study, we evaluated the risk of mortality and hospitalization by hemoglobin (Hb) level in a large prevalent population of U.S. children on peritoneal dialysis (PD). Methods: Hemoglobin levels in prevalent PD patients from the 2005 End Stage Renal Disease Clinical Performance Measures Project were linked with 5-year mortality and 4-year hospitalization records from the United States Renal Data System. Results: Of the 468 patients included in the study, the mean age was 11 years, 55 % were male, 67 % were white, 254 (54 %) were hospitalized, and 23 (5 %) died. Median (interquartile range) Hb levels were 11.7 (10.7-12.6) g/dl, and 30 % had Hb levels of <11 g/dl. In adjusted survival analysis, Hb thresholds of 10, 11, or 12 g/dl were not associated with a significant difference in risk of death. The incidence rate ratio (IRR) of hospitalization for patients with a mean Hb of ≥11 g/dl was 0.56 (95 % CI 0.43-0.73). Compared to a reference range of Hb of 11 to <12, Hb of ≥12 g/dl was not associated with a significant difference in hospitalization risk (IRR 0.88; 95 % CI 0.61-1.25). Using age- and sex specific cut-offs for anemia, children who were not anemic had a 27 % decreased risk of hospitalization compared to those with anemia (IRR 0.73; 95 % CI 0.55-0.97). Compared to the first erythropoiesis stimulating agent (ESA) dosing quartile, higher ESA doses were associated with an increased risk of both hospitalization and mortality. Conclusions: U.S. children on PD with Hb levels of ≥11 g/dl were less likely to be hospitalized but had no observed difference in mortality. Children who were not anemic were also less likely to be hospitalized. Further study is necessary to elucidate whether a single optimal Hb level or a range applies to the pediatric ESKD population. [ABSTRACT FROM AUTHOR]

Management of renal anemia in the large and at-risk population of non-dialysis chronic kidney disease (CKD) patients is a critical issue. In particular, definition of the optimal hemoglobin (Hb) target for therapy is controversial but highly warranted by physicians and patients worldwide. Recently, international clinical practice guidelines have recommended delayed initiation of erythropoiesis-stimulating agents (ESA) and lower Hb target levels during maintenance therapy. However, geographical differences in terms of ESA dose needed to achieve a given Hb value can be evidenced, with US patients showing higher prevalence of ESA resistance. On the other hand, non-US patients are often maintained in a higher Hb range by means of low ESA doses. This critical point has never been addressed. Nevertheless, outside of the US, translating the restrictive recommendations of new guidelines, which are essentially based on trials in US patients, can lead to negative effects, such as an increased need for a blood transfusion, and worsening of quality of life. In this article we provide a reappraisal of current recommendations on anemia management in non-dialysis CKD in light of the geographical differences in individual responsiveness to ESA. [ABSTRACT FROM AUTHOR]