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Background In kidney transplant recipients (KTR), the end-stage kidney disease (ESKD) risk dependent on the risk factors acting in native chronic kidney disease (CKD) remains undefined. Methods We compared risk and determinants of ESKD between 757 adult KTR and 1940 patients with native CKD before and after propensity-score (PS) analysis matched for unmodifiable risk factors [(age, sex, diabetes, cardiovascular disease and estimated glomerular filtration rate (eGFR)]. Results In unmatched cohorts, eGFR was lower in CKD versus KTR (45.9 ± 11.3 versus 59.2 ± 13.4 mL/min/1.73 m2, P < 0.001). During a median follow-up of 5.4 years, the unadjusted cumulative incidence of ESKD was consistently lower in unmatched KTR versus CKD. Conversely, in PS-matched analysis, the risk of ESKD in KTR was 78% lower versus CKD at 1 year of follow-up while progressively increased over time resulting similar to that of native CKD patients after 5 years and 2.3-fold higher than that observed in CKD at 10 years. R2 analysis in unmatched patients showed that the proportion of the outcome variance explained by traditional ESKD determinants was smaller in KTR versus native CKD (31% versus 70%). After PS matching, the risk of ESKD [hazard ratio (HR), 95% confidence interval (95% CI)] was significantly associated with systolic blood pressure (1.02, 1.01–1.02), phosphorus (1.31, 1.05–1.64), 24-h proteinuria (1.11, 1.05–1.17) and haemoglobin (0.85, 0.78–0.93) irrespective of KTR status. Similar data were obtained after matching also for modifiable risk factors. Conclusions In KTR, when compared with matched native CKD patients, the risk of ESKD is lower in the first 5 years and higher later on. Traditional determinants of ESKD account for one-third of the variability of time-to-graft failure. [ABSTRACT FROM AUTHOR]

Objective: Accurate and rapid phenotyping is a prerequisite to leveraging electronic health records for biomedical research. While early phenotyping relied on rule-based algorithms curated by experts, machine learning (ML) approaches have emerged as an alternative to improve scalability across phenotypes and healthcare settings. This study evaluates ML-based phenotyping with respect to (1) the data sources used, (2) the phenotypes considered, (3) the methods applied, and (4) the reporting and evaluation methods used.Materials and Methods: We searched PubMed and Web of Science for articles published between 2018 and 2022. After screening 850 articles, we recorded 37 variables on 100 studies.Results: Most studies utilized data from a single institution and included information in clinical notes. Although chronic conditions were most commonly considered, ML also enabled the characterization of nuanced phenotypes such as social determinants of health. Supervised deep learning was the most popular ML paradigm, while semi-supervised and weakly supervised learning were applied to expedite algorithm development and unsupervised learning to facilitate phenotype discovery. ML approaches did not uniformly outperform rule-based algorithms, but deep learning offered a marginal improvement over traditional ML for many conditions.Discussion: Despite the progress in ML-based phenotyping, most articles focused on binary phenotypes and few articles evaluated external validity or used multi-institution data. Study settings were infrequently reported and analytic code was rarely released.Conclusion: Continued research in ML-based phenotyping is warranted, with emphasis on characterizing nuanced phenotypes, establishing reporting and evaluation standards, and developing methods to accommodate misclassified phenotypes due to algorithm errors in downstream applications. [ABSTRACT FROM AUTHOR]

Background Timely diagnosis of heart failure (HF) is essential to optimize treatment opportunities that improve symptoms, quality of life, and survival. While most patients consult their general practitioner (GP) prior to HF, the early stages of HF may be difficult to identify. An integrated clinical support tool may aid in identifying patients at high risk of HF. We therefore constructed a prediction model using routine health care data. Methods Our study involved a dynamic cohort of patients (≥35 years) who consulted their GP with either dyspnoea and/or peripheral oedema within the Amsterdam metropolitan area from 2011 to 2020. The outcome of interest was incident HF, verified by an expert panel. We developed a regularized, cause-specific multivariable proportional hazards model (TARGET-HF). The model was evaluated with bootstrapping on an isolated validation set and compared to an existing model developed with hospital insurance data as well as patient age as a sole predictor. Results Data from 31,905 patients were included (40% male, median age 60 years) of whom 1,301 (4.1%) were diagnosed with HF over 124,676 person-years of follow-up. Data were allocated to a development (n  = 25,524) and validation (n  = 6,381) set. TARGET-HF attained a C-statistic of 0.853 (95% CI, 0.834 to 0.872) on the validation set, which proved to provide a better discrimination than C = 0.822 for age alone (95% CI, 0.801 to 0.842, P  < 0.001) and C = 0.824 for the hospital-based model (95% CI, 0.802 to 0.843, P  < 0.001). Conclusion The TARGET-HF model illustrates that routine consultation codes can be used to build a performant model to identify patients at risk for HF at the time of GP consultation. [ABSTRACT FROM AUTHOR]

Cardiometabolic diseases contribute more to global morbidity and mortality than any other group of disorders. Polygenic risk scores (PRSs), the weighted summation of individually small-effect genetic variants, represent an advance in our ability to predict the development and complications of cardiometabolic diseases. This article reviews the evidence supporting the use of PRS in seven common cardiometabolic diseases: coronary artery disease (CAD), stroke, hypertension, heart failure and cardiomyopathies, obesity, atrial fibrillation (AF), and type 2 diabetes mellitus (T2DM). Data suggest that PRS for CAD, AF, and T2DM consistently improves prediction when incorporated into existing clinical risk tools. In other areas such as ischaemic stroke and hypertension, clinical application appears premature but emerging evidence suggests that the study of larger and more diverse populations coupled with more granular phenotyping will propel the translation of PRS into practical clinical prediction tools. [ABSTRACT FROM AUTHOR]

Slowly reproducing and long-lived terrestrial mammals are often more at risk from challenges that influence fitness and survival. It is, therefore, important to understand how animals cope with such challenges and how coping mechanisms translate over generations and affect phenotypic plasticity. Rapidly escalating anthropogenic challenges may further diminish an animal's ability to reinstate homeostasis. Research to advance insights on elephant stress physiology has predominantly focused on relative or comparative analyses of a major stress response marker, glucocorticoids (GCs), across different ecological, anthropogenic, and reproductive contexts. This paper presents an extensive review of published findings on Asian and African elephants from 1980 to 2023 (May) and reveals that stress responses, as measured by alterations in GCs in different sample matrices, often are highly dynamic and vary within and across individuals exposed to similar stimuli, and not always in a predictable fashion. Such dynamicity in physiological reactivity may be mediated by individual differences in personality traits or coping styles, ecological conditions, and technical factors that often are not considered in study designs. We describe probable causations under the 'Physiological Dynamicity Model', which considers context–experience–individuality effects. Highly variable adrenal responses may affect physiological plasticity with potential fitness and survival consequences. This review also addresses the significance of cautious interpretations of GCs data in the context of normal adaptive stress versus distress. We emphasize the need for long-term assessments of GCs that incorporate multiple markers of 'stress' and 'well-being' to decipher the probable fitness consequences of highly dynamic physiological adrenal responses in elephants. Ultimately, we propose that assessing GC responses to current and future challenges is one of the most valuable and informative conservation tools we have for guiding conservation strategies. [ABSTRACT FROM AUTHOR]

One of the first studies to investigate this important question prospectively followed LS carriers getting colonoscopies every 3 years compared with those getting no screening and found that the 3-year screening interval decreased CRC risk and improved overall survival, establishing routine colonoscopy screening as the standard of care ([6]). At present, however, colonoscopy screening remains critical to LS surveillance, CRC risk reduction, and improved outcomes for all LS carriers. Lynch syndrome (LS) is one of the most common hereditary colorectal cancer (CRC) predisposition syndromes and is present in 1 out of 279 individuals in the general population ([1]) and in approximately 3% of unselected cases of CRC ([2]). [Extracted from the article]

Background The comparative effectiveness of trimodality therapy vs definitive chemoradiation for treating locally advanced esophageal cancer in older adults is uncertain. Existing trials lack generalizability to older adults, a population with heightened frailty. We sought to emulate a hypothetical trial comparing these treatments using real-world data. Methods A cohort of adults aged 66-79 years diagnosed with locally advanced esophageal cancer between 2004 and 2017 was identified in the Surveillance Epidemiology and End Results–Medicare database. The clone-censor-weight method was leveraged to eliminate time-related biases when comparing outcomes between treatments. Outcomes included overall mortality, esophageal cancer–specific mortality, functional adverse events, and healthy days at home. Results A total of 1240 individuals with adenocarcinomas and 661 with squamous cell carcinomas were identified. For adenocarcinomas, the standardized 5-year risk of mortality was 73.4% for trimodality therapy and 83.8% for definitive chemoradiation (relative risk [RR] = 0.88, 95% confidence interval [CI] = 0.82 to 0.95). Trimodality therapy was associated with mortality risk reduction for squamous cell carcinomas (RR = 0.87, 95% CI = 0.70 to 1.01). The 1-year incidence of functional adverse events was higher in the trimodality group (adenocarcinomas RR = 1.40, 95% CI = 1.22 to 1.65; squamous cell carcinomas RR = 1.21, 95% CI = 1.00 to 1.49). Over 5 years, trimodality therapy was associated with 160 (95% CI = 67 to 229) and 177 (95% CI = 51 to 313) additional home days in individuals with adenocarcinomas and squamous cell carcinomas, respectively. Conclusions Compared with definitive chemoradiation, trimodality therapy was associated with reduced mortality but increased risk of function-related adverse events. Discussing these tradeoffs may help optimize care plans. [ABSTRACT FROM AUTHOR]

Multiple types of molecular (genetic, genomic, epigenetic, etc.) measurements, environmental risk factors, and their interactions have been found to contribute to the outcomes and phenotypes of complex diseases. In each of the previous studies, only the interactions between one type of molecular measurement and environmental risk factors have been analyzed. In recent biomedical studies, multidimensional profiling, in which data from multiple types of molecular measurements are collected from the same subjects, is becoming popular. A myriad of recent studies have shown that collectively analyzing multiple types of molecular measurements is not only biologically sensible but also leads to improved estimation and prediction. In this study, we conduct an M–E interaction analysis, with M standing for multidimensional molecular measurements and E standing for environmental risk factors. This can accommodate multiple types of molecular measurements and sufficiently account for their overlapping as well as independent information. Extensive simulation shows that it outperforms several closely related alternatives. In the analysis of TCGA (The Cancer Genome Atlas) data on lung adenocarcinoma and cutaneous melanoma, we make some stable biological findings and achieve stable prediction. [ABSTRACT FROM AUTHOR]

Aims Patients with heart failure with reduced ejection fraction (HFrEF) are at significant risk of stroke. Anticoagulation reduces this risk in patients with and without atrial fibrillation (AF), but the risk-to-benefit balance in the latter group, overall, is not favourable. Identification of patients with HFrEF, without AF, at the highest risk of stroke may allow targeted and safer use of prophylactic anticoagulant therapy. Methods and results In a pooled patient-level cohort of the PARADIGM-HF, ATMOSPHERE, and DAPA-HF trials, a previously derived simple risk model for stroke, consisting of three variables (history of prior stroke, insulin-treated diabetes, and plasma N -terminal pro-B-type natriuretic peptide level), was validated. Of the 20 159 patients included, 12 751 patients did not have AF at baseline. Among patients without AF, 346 (2.7%) experienced a stroke over a median follow up of 2.0 years (rate 11.7 per 1000 patient-years). The risk for stroke increased with increasing risk score: fourth quintile hazard ratio (HR) 2.35 [95% confidence interval (CI) 1.60–3.45]; fifth quintile HR 3.73 (95% CI 2.58–5.38), with the first quintile as reference. For patients in the top quintile, the rate of stroke was 21.2 per 1000 patient-years, similar to participants with AF not receiving anticoagulation (20.1 per 1000 patient-years). Model discrimination was good with a C-index of 0.84 (0.75–0.91). Conclusion It is possible to identify a subset of HFrEF patients without AF with a stroke-risk equivalent to that of patients with AF who are not anticoagulated. In these patients, the risk-to-benefit balance might justify the use of prophylactic anticoagulation, but this hypothesis needs to be tested prospectively. [ABSTRACT FROM AUTHOR]

Background Anaemia is common in chronic kidney disease (CKD) and assessment of the risks and benefits of new therapies is important. Methods The Anaemia Study in CKD: Erythropoiesis via a Novel prolyl hydroxylase inhibitor Daprodustat-Non-Dialysis (ASCEND-ND) trial includes adult patients with CKD Stages 3–5, not using erythropoiesis-stimulating agents (ESAs) with screening haemoglobin (Hb) 8–10 g/dL or receiving ESAs with screening Hb of 8–12 g/dL. Participants were randomized to daprodustat or darbepoetin alfa (1:1) in an open-label trial (steering committee- and sponsor-blinded), with blinded endpoint assessment. The co-primary endpoints are mean change in Hb between baseline and evaluation period (average over Weeks 28–52) and time to first adjudicated major adverse cardiovascular (CV) event. Baseline characteristics were compared with those of participants in similar anaemia trials. Results Overall, 3872 patients were randomized from 39 countries (median age 67 years, 56% female, 56% White, 27% Asian and 10% Black). The median baseline Hb was 9.9 g/dL, blood pressure was 135/74 mmHg and estimated glomerular filtration rate was 18 mL/min/1.73 m2. Among randomized patients, 53% were ESA non-users, 57% had diabetes and 37% had a history of CV disease. At baseline, 61% of participants were using renin–angiotensin system blockers, 55% were taking statins and 49% were taking oral iron. Baseline demographics were similar to those in other large non-dialysis anaemia trials. Conclusion ASCEND-ND will define the efficacy and safety of daprodustat compared with darbepoetin alfa in the treatment of patients with anaemia associated with CKD not on dialysis. [ABSTRACT FROM AUTHOR]

This paper develops a nonparametric inference framework that is applicable to occupation time curves derived from wearable device data. These curves consider all activity levels within the range of device readings, which is preferable to the practice of classifying activity into discrete categories. Motivated by certain features of these curves, we introduce a powerful likelihood ratio approach to construct confidence bands and compare functional means. Notably, our approach allows discontinuities in the functional covariances while accommodating discretization of the observed trajectories. A simulation study shows that the proposed procedures outperform competing functional data procedures. We illustrate the proposed methods using wearable device data from an NHANES study. [ABSTRACT FROM AUTHOR]

Background: Klemera-Doubal's method (KDM) is an advanced and widely applied algorithm for estimating biological age (BA), but it has no uniform paradigm for biomarker processing. This article proposed all subsets of biomarkers for estimating BAs and assessed their association with mortality to determine the most predictive subset and BA.Methods: Clinical biomarkers, including those from physical examinations and blood assays, were assessed in the China Health and Nutrition Survey (CHNS) 2009 wave. Those correlated with chronological age (CA) were combined to produce complete subsets, and BA was estimated by KDM from each subset of biomarkers. A Cox proportional hazards regression model was used to examine and compare each BA's effect size and predictive capacity for all-cause mortality. Validation analysis was performed in the Chinese Longitudinal Healthy Longevity Survey (CLHLS) and National Health and Nutrition Examination Survey (NHANES). KD-BA and Levine's BA were compared in all cohorts.Results: A total of 130 918 panels of BAs were estimated from complete subsets comprising 3-17 biomarkers, whose Pearson coefficients with CA varied from 0.39 to 1. The most predictive subset consisted of 5 biomarkers, whose estimated KD-BA had the most predictive accuracy for all-cause mortality. Compared with Levine's BA, the accuracy of the best-fitting KD-BA in predicting death varied among specific populations.Conclusion: All-subset analysis could effectively reduce the number of redundant biomarkers and significantly improve the accuracy of KD-BA in predicting all-cause mortality. [ABSTRACT FROM AUTHOR]

Aims: End-stage renal disease (ESRD) treated by chronic hemodialysis (HD) is associated with poor cardiovascular (CV) outcomes, with no available evidence-based therapeutics. A multiplexed proteomic approach may identify new pathophysiological pathways associated with CV outcomes, potentially actionable for precision medicine. Methods and results: The AURORA trial was an international, multicentre, randomized, double-blind trial involving 2776 patients undergoing maintenance HD. Rosuvastatin vs. placebo had no significant effect on the composite primary endpoint of death from CV causes, nonfatal myocardial infarction or nonfatal stroke. We first compared CV risk-matched cases and controls (n = 410) to identify novel biomarkers using a multiplex proximity extension immunoassay (276 proteomic biomarkers assessed with OlinkTM). We replicated our findings in 200 unmatched cases and 200 controls. External validation was conducted from a multicentre real-life Danish cohort [Aarhus-Aalborg (AA), n = 331 patients] in which 92 OlinkTM biomarkers were assessed. In AURORA, only N-terminal pro-brain natriuretic peptide (NT-proBNP, positive association) and stem cell factor (SCF) (negative association) were found consistently associated with the trial's primary outcome across exploration and replication phases, independently from the baseline characteristics. Stem cell factor displayed a lower added predictive ability compared with NT-ProBNP. In the AA cohort, in multivariable analyses, BNP was found significantly associated with major CV events, while higher SCF was associated with less frequent CV deaths. Conclusions: Our findings suggest that NT-proBNP and SCF may help identify ESRD patients with respectively high and low CV risk, beyond classical clinical predictors and also point at novel pathways for prevention and treatment. Graphical abstract [ABSTRACT FROM AUTHOR]

Aims Positron emission tomography (PET) myocardial perfusion imaging (MPI) is often combined with coronary artery calcium (CAC) scanning, allowing for a combined anatomic and functional assessment. We evaluated the independent prognostic value of quantitative assessment of myocardial perfusion and CAC scores in patients undergoing PET. Methods and results Consecutive patients who underwent Rb-82 PET with CAC scoring between 2010 and 2018, with follow-up for major adverse cardiovascular events (MACE), were identified. Perfusion was quantified automatically with total perfusion deficit (TPD). Our primary outcome was MACE including all-cause mortality, myocardial infarction (MI), admission for unstable angina, and late revascularization. Associations with MACE were assessed using multivariable Cox models adjusted for age, sex, medical history, and MPI findings including myocardial flow reserve. In total, 2507 patients were included with median age 70. During median follow-up of 3.9 years (interquartile range 2.1–6.1), 594 patients experienced at least one MACE. Increasing CAC and ischaemic TPD were associated with increased MACE, with the highest risk associated with CAC > 1000 [adjusted hazard ratio (HR) 1.67, 95% CI 1.24–2.26] and ischaemic TPD > 10% (adjusted HR 1.80, 95% CI 1.40–2.32). Ischaemic TPD and CAC improved overall patient classification, but ischaemic TPD improved classification of patients who experienced MACE while CAC mostly improved classification of low-risk patients. Conclusions Ischaemic TPD and CAC were independently associated with MACE. Combining extent of atherosclerosis and functional measures improves the prediction of MACE risk, with CAC 0 identifying low-risk patients and regional ischaemia identifying high-risk patients in those with CAC > 0. [ABSTRACT FROM AUTHOR]

In medical studies, some therapeutic decisions could lead to dependent censoring for the survival outcome of interest. This is exemplified by a study of paediatric acute liver failure, where death was subject to dependent censoring due to liver transplantation. Existing methods for assessing the predictive performance of biomarkers often pose the independent censoring assumption and are thus not applicable. In this work, we propose to tackle the dependence between the failure event and dependent censoring event using auxiliary information in multiple longitudinal risk factors. We propose estimators of sensitivity, specificity and area under curve, to discern the predictive power of biomarkers for the failure event by removing the disturbance of dependent censoring. Point estimation and inferential procedures were developed by adopting the joint modelling framework. The proposed methods performed satisfactorily in extensive simulation studies. We applied them to examine the predictive value of various biomarkers and risk scores for mortality in the motivating example. [ABSTRACT FROM AUTHOR]

Formation and elimination of synapses reflect structural plasticity of neuronal connectivity. Here we performed high-resolution two-photon imaging of dendritic spines in the prefrontal cortex of four macaque monkeys and found that spines were in general highly stable, with low percentages undergoing synaptic turnover. By observing the same spines at weekly intervals, we found that newly formed spines were more susceptible to elimination, with only 40% persisting over a period of months. Analyses of spatial distribution of large numbers of spines revealed that spine distribution was neither uniform nor random, favoring inter-spine distances of 2–4 μm. Furthermore, spine formation and elimination occurred more often in low- and high-density dendritic segments, respectively, and preferentially within a hot zone of ∼4 μm from existing spines. Our results demonstrate long-term stability and spatially regulated spine dynamics in the macaque cortex and provide a structural basis for understanding neural circuit plasticity in the primate brain. [ABSTRACT FROM AUTHOR]

Cross‐validation is the de facto standard for predictive model evaluation and selection. In proper use, it provides an unbiased estimate of a model's predictive performance. However, data sets often undergo various forms of data‐dependent preprocessing, such as mean‐centring, rescaling, dimensionality reduction and outlier removal. It is often believed that such preprocessing stages, if done in an unsupervised manner (that does not incorporate the class labels or response values) are generally safe to do prior to cross‐validation. In this paper, we study three commonly practised preprocessing procedures prior to a regression analysis: (i) variance‐based feature selection; (ii) grouping of rare categorical features; and (iii) feature rescaling. We demonstrate that unsupervised preprocessing can, in fact, introduce a substantial bias into cross‐validation estimates and potentially hurt model selection. This bias may be either positive or negative and its exact magnitude depends on all the parameters of the problem in an intricate manner. Further research is needed to understand the real‐world impact of this bias across different application domains, particularly when dealing with small sample sizes and high‐dimensional data. [ABSTRACT FROM AUTHOR]

In disease settings where study participants are at risk for death and a serious nonfatal event, composite endpoints defined as the time until the earliest of death or the nonfatal event are often used as the primary endpoint in clinical trials. In practice, if the nonfatal event can only be detected at clinic visits and the death time is known exactly, the resulting composite endpoint exhibits "component‐wise censoring." The standard method used to estimate event‐free survival in this setting fails to account for component‐wise censoring. We apply a kernel smoothing method previously proposed for a marker process in a novel way to produce a nonparametric estimator for event‐free survival that accounts for component‐wise censoring. The key insight that allows us to apply this kernel method is thinking of nonfatal event status as an intermittently observed binary time‐dependent variable rather than thinking of time to the nonfatal event as interval‐censored. We also propose estimators for the probability in state and restricted mean time in state for reversible or irreversible illness‐death models, under component‐wise censoring, and derive their large‐sample properties. We perform a simulation study to compare our method to existing multistate survival methods and apply the methods on data from a large randomized trial studying a multifactor intervention for reducing morbidity and mortality among men at above average risk of coronary heart disease. [ABSTRACT FROM AUTHOR]

Motivation In this article, we consider how to evaluate survival distribution predictions with measures of discrimination. This is non-trivial as discrimination measures are the most commonly used in survival analysis and yet there is no clear method to derive a risk prediction from a distribution prediction. We survey methods proposed in literature and software and consider their respective advantages and disadvantages. Results Whilst distributions are frequently evaluated by discrimination measures, we find that the method for doing so is rarely described in the literature and often leads to unfair comparisons or 'C-hacking'. We demonstrate by example how simple it can be to manipulate results and use this to argue for better reporting guidelines and transparency in the literature. We recommend that machine learning survival analysis software implements clear transformations between distribution and risk predictions in order to allow more transparent and accessible model evaluation. Availability and implementation The code used in the final experiment is available at https://github.com/RaphaelS1/distribution_discrimination. [ABSTRACT FROM AUTHOR]

The objective of this study was to compare the outcomes of nonfatal road traffic accidents by the victims' age group and sex. We used the Portuguese medico-legal rules for personal injury assessment, in the scope of the Civil Law in that country, which includes a three-dimensional methodology. This was a retrospective study including 667 victims of road traffic accidents aged 3–94 years old. Their final medico-legal reports all used the Portuguese methodology for personal injury assessment. Outcomes were analysed by the victims' age group (children, working-age adults, and older people) and sex. Road traffic accidents were generally serious (ISS mean 9.5), with higher severity in children and older people. The most frequent body sequelae were musculoskeletal (64.8%), which were associated with functional and situational outcomes. Temporary damage resulted in an average length of impairment of daily life of 199.6 days, 171.7 days to return to work, and an average degree of quantum doloris (noneconomic damage related to physical and psychological harm) of 3.7/7. The average permanent damage was 7.3/100 points for Permanent Functional Deficit, 0.43/3 for Permanent Professional Repercussion, 2/7 for Permanent Aesthetic Damage, 3.9/7 for Permanent Repercussion on Sexual Activity and 3.2/7 for Permanent Repercussion on Sport and Leisure Activities. Overall, 19% of people became permanently dependent (10.6% needed third-party assistance). The medico-legal methodology used, considering victims' real-life situation, allows a comprehensive assessment. There were several significant differences among the three age groups but none between sexes. These differences and the impact of the more severe cases justify further detailed medico-legal studies in these specific situations on children, older people, and severely injured victims. This was a retrospective study of accident mechanisms and injury outcomes in Portugal, and considered the outcomes in the victims' real-life situation. Lesions from road traffic accidents were generally serious, with higher severity among children and older people. The most frequent sequels were musculoskeletal, and associated with functional and situational outcomes. Both temporary and permanent outcomes had repercussions for the victims. There were significant differences between children, working-age adults and older people, but none between sexes. [ABSTRACT FROM AUTHOR]

Aims To study the impact of genotype on the performance of the 2019 risk model for arrhythmogenic right ventricular cardiomyopathy (ARVC). Methods and results The study cohort comprised 554 patients with a definite diagnosis of ARVC and no history of sustained ventricular arrhythmia (VA). During a median follow-up of 6.0 (3.1,12.5) years, 100 patients (18%) experienced the primary VA outcome (sustained ventricular tachycardia, appropriate implantable cardioverter defibrillator intervention, aborted sudden cardiac arrest, or sudden cardiac death) corresponding to an annual event rate of 2.6% [95% confidence interval (CI) 1.9–3.3]. Risk estimates for VA using the 2019 ARVC risk model showed reasonable discriminative ability but with overestimation of risk. The ARVC risk model was compared in four gene groups: PKP2 (n  = 118, 21%); desmoplakin (DSP) (n  = 79, 14%); other desmosomal (n  = 59, 11%); and gene elusive (n  = 160, 29%). Discrimination and calibration were highest for PKP2 and lowest for the gene-elusive group. Univariable analyses revealed the variable performance of individual clinical risk markers in the different gene groups, e.g. right ventricular dimensions and systolic function are significant risk markers in PKP2 but not in DSP patients and the opposite is true for left ventricular systolic function. Conclusion The 2019 ARVC risk model performs reasonably well in gene-positive ARVC (particularly for PKP2) but is more limited in gene-elusive patients. Genotype should be included in future risk models for ARVC. [ABSTRACT FROM AUTHOR]

Aims: The aim of this study is to evaluate whether the MADIT-ICD benefit score can predict who benefits most from the addition of implantable cardioverter-defibrillator (ICD) to cardiac resynchronization therapy (CRT) in real-world patients with heart failure with reduced ejection fraction (HFrEF) and to compare this with selection according to a multidisciplinary expert centre approach.Methods and Results: Consecutive HFrEF patients who received a CRT for a guideline indication at a tertiary care hospital (Ziekenhuis Oost-Limburg, Genk, Belgium) between October 2008 and September 2016, were retrospectively evaluated. The MADIT-ICD benefit groups (low, intermediate, and high) were compared with the current multidisciplinary expert centre approach. Endpoints were (i) sustained ventricular tachycardia (VT) or ventricular fibrillation (VF) and (ii) non-arrhythmic mortality. Of the 475 included patients, 165 (34.7%) were in the lowest, 220 (46.3%) in the intermediate, and 90 (19.0%) in the highest benefit group. After a median follow-up of 34 months, VT/VF occurred in 3 (1.8%) patients in the lowest, 9 (4.1%) in the intermediate, and 13 (14.4%) in the highest benefit group (P < 0.001). Vice versa, non-arrhythmic death occurred in 32 (19.4%) in the lowest, 32 (14.6%) in the intermediate, and 3 (3.3%) in the highest benefit group (P = 0.002). The predictive power for ICD benefit was comparable between expert multidisciplinary judgement and the MADIT-ICD benefit score: Uno's C-statistic 0.69 vs. 0.69 (P = 0.936) for VT/VF and 0.62 vs. 0.60 (P = 0.790) for non-arrhythmic mortality.Conclusion: The MADIT-ICD benefit score can identify who benefits most from CRT-D and is comparable with multidisciplinary judgement in a CRT expert centre. [ABSTRACT FROM AUTHOR]

Aims Previously, guidelines recommended initiating therapy in patients with heart failure and reduced ejection fraction (HFrEF) in a sequence that follows the chronological order in which trials were conducted, with cautious up-titration of each treatment. It remains unclear whether this historical approach is optimal and alternative approaches may improve patient outcomes. Methods and results The potential reductions in events that might result from (i) more rapid up-titration of therapies used in the conventional order (based on the chronology of the trials), and (ii) accelerated up-titration and using treatments in different orders than is conventional were modelled using data from six pivotal trials in HFrEF. Over the first 12 months from starting therapy, using a rapid up-titration schedule led to 23 fewer patients per 1000 patients experiencing the composite of heart failure hospitalization or cardiovascular death and seven fewer deaths from any cause. In addition to accelerating up-titration of treatments, optimized alternative ordering of the drugs used resulted in a further reduction of 24 patients experiencing the composite outcome and six fewer deaths at 12 months. The optimal alternative sequences included sodium–glucose cotransporter 2 inhibition and a mineralocorticoid receptor antagonist as the first two therapies. Conclusion Modelling of accelerated up-titration schedule and optimized ordering of treatment suggested that at least 14 deaths and 47 patients experiencing the composite outcome per 1000 treated might be prevented over the first 12 months after starting therapy. Standard treatment guidance may not lead to the best patient outcomes in HFrEF, though these findings should be tested in clinical trials. [ABSTRACT FROM AUTHOR]

Background to assess the efficacy of comprehensive geriatric assessment (CGA) for preventing treatment-related toxicity in older people undergoing non-surgical cancer therapies. Methods MEDLINE, EMBASE and Cochrane library databases were searched from inception till January 2022 to identify randomised controlled trials (RCTs) on the incidence of toxicity measured by the Common Terminology Criteria for Adverse Events (primary outcome) and that of therapeutic modifications, early treatment discontinuation, progression-free survival, overall survival and hospitalisation (secondary outcomes). Results analysis of six RCTs published from 2016 to 2021 recruiting 2,126 participants (median age: 71–77) who received chemotherapy as the major therapeutic approach revealed 51.7% and 64.7% of Grade 3+ toxicity in the CGA and control (i.e. standard care) groups, respectively (RR = 0.81, 95% CI: 0.7–0.94, P  = 0.005, I 2 = 65%, certainty of evidence [COE]: moderate). There were no significant differences in the incidence of early treatment discontinuation (RR = 0.88, P  = 0.47; I 2 = 63%,1,408 participants, COE: low), initial reduction in treatment intensity (RR = 0.99, P  = 0.94; I 2 = 83%, 2055 participants, COE: low), treatment delay (RR = 1.06, P  = 0.77, I 2 = 0%, 309 participants, COE: moderate), hospitalisation (RR = 0.86, P  = 0.39, I 2 = 41%, 914 participants, COE: moderate), progression-free and overall survival with or without CGA. However, there was an association between CGA and a lower incidence of dose reduction during treatment (RR = 0.73, P  < 0.00001, 956 participants, COE: moderate). Conclusions our results demonstrated that comprehensive geriatric assessment may be associated with a lower incidence of treatment-related toxicity and dose reduction compared to standard care in older people receiving non-surgical cancer treatments. Further large-scale studies are warranted to support our findings. [ABSTRACT FROM AUTHOR]

Background: Negotiating goals of care (GoC) with patients is an essential skill for all health-care professionals (HCPs) in hospitals. End-of-Life Essentials (EOLE) is a Commonwealth-funded project that delivers free, peer-reviewed, evidence-based, online education and practice change resources. To date, around 26 000 doctors, nurses and allied health professionals have registered to access the education. 'Planning End-of-Life Care-Goals of Care' features in the suite of EOLE modules and includes education around negotiating GoC with patients and families.Objective: The aim of the study was to explore the views of module learners (HCPs) on challenges they have faced when negotiating GoC at the EOL with patients and families.Methods: Participants were learners (HCPs) who registered to the EOLE website and engaged with the GoC module. Learners' responses to the question posed at the end of the module 'What are the hardest or most challenging things about negotiating GoC with patients and families?' were extracted for a 12-month period. Qualitative data were analysed thematically in NVivo V.12, guided by the theoretical framework of pragmatism. An open, inductive approach was used to code the data, with axial coding used to refine and organize themes and subthemes.Results: A total of 451 learner statements were analysed. Five themes emerged from the data: (i) differing views and opinions; (ii) challenges to understanding; (iii) managing emotions; (iv) initiating the EOL conversation and (v) lack of professional knowledge or capacity. Five subthemes were also organized under the theme 'differing views and opinions'.Conclusion: Planning EOL care demands high-level, compassionately skilful and sensitive care and services, which are in line with the patient's and family's wishes. In practice, however, there are many challenges to this, such as ensuring that patients, families, and HCPs are aware of different expectations regarding future health-care possibilities, and that HCPs are prepared for negotiating GoC to achieve quality and safe EOL care in hospitals. [ABSTRACT FROM AUTHOR]

Motivation Multiomics cancer profiles provide essential signals for predicting cancer survival. It is challenging to reveal the complex patterns from multiple types of data and link them to survival outcomes. We aim to develop a new deep learning-based algorithm to integrate three types of high-dimensional omics data measured on the same individuals to improve cancer survival outcome prediction. Results We built a three-dimension tensor to integrate multi-omics cancer data and factorized it into two-dimension matrices of latent factors, which were fed into neural networks-based survival networks. The new algorithm and other multi-omics-based algorithms, as well as individual genomic-based survival analysis algorithms, were applied to the breast cancer data colon and rectal cancer data from The Cancer Genome Atlas (TCGA) program. We evaluated the goodness-of-fit using the concordance index (C-index) and Integrated Brier Score (IBS). We demonstrated that the proposed tight integration framework has better survival prediction performance than the models using individual genomic data and other conventional data integration methods. Availability and implementation https://github.com/jasperzyzhang/DeepTensorSurvival Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Background Anemia is a common complication of chronic kidney disease (CKD), but its incidence in nephrology settings is poorly investigated. Similarly, the risks of adverse outcomes associated with new-onset anemia are not known. Methods We performed a pooled analysis of three observational cohort studies including 1031 non-anemic CKD patients with eGFR <60 mL/min/1.73 m2 regularly followed in renal clinics. We estimated the incidence of mild anemia (hemoglobin 11–12 g/dL in women and 11–13 g/dL in men) and severe anemia (hemoglobin <11 g/dL or use of erythropoiesis-stimulating agents) during a 3-year follow-up period. Thereafter we estimated the risk of end-stage kidney disease (ESKD) and all-cause death associated with new-onset mild and severe anemia. Results The mean age was 63 ± 14 years, 60% were men and 20% had diabetes. The mean estimated glomerular filtration rate (eGFR) was 37 ± 13 mL/min/1.73 m2 and the median proteinuria was 0.4 g/day [interquartile range (IQR) 0.1–1.1]. The incidence of mild and severe anemia was 13.7/100 patients-year and 6.2/100 patients-year, respectively. Basal predictors of either mild or severe anemia were diabetes, lower hemoglobin, higher serum phosphate, eGFR <30 mL/min/1.73 m2 and proteinuria >0.50 g/day. Male sex, moderate CKD (eGFR 30–44 mL/min/1.73 m2) and moderate proteinuria (0.15–0.50 g/day) predicted only mild anemia. The incidence of anemia increased progressively with CKD stages (from 8.77 to 76.59/100 patients-year) and the proteinuria category (from 13.99 to 25.02/100 patients-year). During a median follow-up of 3.1 years, 232 patients reached ESKD and 135 died. Compared with non-anemic patients, mild anemia was associated with a higher adjusted risk of ESKD {hazard ratio [HR] 1.42 [95% confidence interval (CI) 1.02–1.98]} and all-cause death [HR 1.55 (95% CI 1.04–2.32)]. Severe anemia was associated with an even higher risk of ESKD [HR 1.73 (95% CI 1.20–2.51)] and death [HR 1.83 (95% CI 1.05–3.19)]. Conclusions New-onset anemia is frequent, particularly in patients with more severe renal damage and in those with diabetes mellitus. The occurrence of anemia, even of a mild degree, is associated with mortality risk and faster progression towards ESKD. [ABSTRACT FROM AUTHOR]

Aims Left atrial (LA) function is a marker of prognosis in patients with heart failure. The prognostic implications of an improvement in LA function in addition to an improvement in left ventricular (LV) function after cardiac resynchronization therapy (CRT) implantation are unknown. This study aimed to evaluate the prognostic value of a significant change in LA reservoir strain (RS) and/or LV global longitudinal strain (GLS) after initiation of CRT. Methods and results LARS and LVGLS were measured with speckle-tracking echocardiography. Significant improvement in LARS and LVGLS was defined as a percentage change of +5% and +20% at 6 months after CRT implantation, respectively. Patients were divided into three groups: no significant reverse remodelling (no improvement in LARS and LVGLS), incomplete reverse remodelling (improvement in LARS or LVGLS), and complete reverse remodelling (improvement in LARS and LVGLS). The primary endpoint was all-cause mortality. A total of 923 patients (mean age 65 ± 10 years, 77% male) were included, of which 221 (24%) had complete reverse remodelling, 414 (45%) incomplete reverse remodelling, and 288 (31%) no significant reverse remodelling. Five-years' mortality was 24%, 29%, and 36% for patients with complete, incomplete, and no significant reverse remodelling, respectively (P < 0.001). On multivariable analysis, complete reverse remodelling (hazard ratio 0.477; 95% confidence interval: 0.362–0.628; P < 0.001) was associated with the lowest risk of mortality. Conclusions Patients with complete reverse remodelling have a lower mortality risk than those showing incomplete or no significant reverse remodelling. The use of integrated LA and LV deformation imaging may improve risk-stratification of CRT recipients. [ABSTRACT FROM AUTHOR]

Background The A nemia S tudies in chronic kidney disease (C KD): E rythropoiesis via a N ovel prolyl hydroxylase inhibitor D aprodustat- D ialysis (ASCEND-D) trial will test the hypothesis that daprodustat is noninferior to comparator epoetin alfa or darbepoetin alfa for two co-primary endpoints: hemoglobin (Hb) efficacy and cardiovascular (CV) safety. Methods We report the trial design, key demographic, clinical and laboratory findings, and baseline therapies of 2964 patients randomized in the open-label (sponsor-blinded) active-controlled, parallel-group, randomized ASCEND-D clinical trial. We also compare baseline characteristics of ASCEND-D patients with patients who are on dialysis (CKD G5D) enrolled in other large CV outcome trials (CVOTs) and in the most relevant registries. Results The median age of patients was 58 years, 43% were female; 67% were White and 16% were Black. The median Hb at baseline was 10.4 g/dL. Among randomized patients, 89% were receiving hemodialysis and 11% peritoneal dialysis. Among key comorbidities, 42% reported a history of diabetes mellitus and 45% a history of CV disease. Median blood pressure was 134/74 mmHg. The median weekly dose of epoetin was 5751 units. Intravenous and oral iron uses were noted in 64 and 11% of patients, respectively. Baseline demographics were similar to patients with CKD G5D enrolled in other CVOTs and renal patient registries. Conclusions ASCEND-D will evaluate the efficacy and safety of daprodustat compared with epoetin alfa or darbepoetin alfa in the treatment of patients with anemia with CKD G5D. This trial is registered with ClinicalTrials.gov: NCT02879305. EudraCT Number: 2016-000541-31; Sponsor Protocol Number: 200807. [ABSTRACT FROM AUTHOR]

Cellular senescence (CS), a state of permanent growth arrest, is intertwined with tumorigenesis. Due to the absence of specific markers, characterizing senescence levels and senescence-related phenotypes across cancer types remain unexplored. Here, we defined computational metrics of senescence levels as CS scores to delineate CS landscape across 33 cancer types and 29 normal tissues and explored CS-associated phenotypes by integrating multiplatform data from ~20 000 patients and ~212 000 single-cell profiles. CS scores showed cancer type-specific associations with genomic and immune characteristics and significantly predicted immunotherapy responses and patient prognosis in multiple cancers. Single-cell CS quantification revealed intra-tumor heterogeneity and activated immune microenvironment in senescent prostate cancer. Using machine learning algorithms, we identified three CS genes as potential prognostic predictors in prostate cancer and verified them by immunohistochemical assays in 72 patients. Our study provides a comprehensive framework for evaluating senescence levels and clinical relevance, gaining insights into CS roles in cancer- and senescence-related biomarker discovery. [ABSTRACT FROM AUTHOR]

Aims Elevated left ventricular filling pressure (LVFP) is a powerful indicator of worsening clinical outcomes in heart failure with preserved ejection fraction (HFpEF); however, detection of elevated LVFP is often challenging. This study aimed to determine the association between the newly proposed echocardiographic LVFP parameter, visually assessed time difference between the mitral valve and tricuspid valve opening (VMT) score, and clinical outcomes of HFpEF. Methods and results We retrospectively investigated 310 well-differentiated HFpEF patients in stable conditions. VMT was scored from 0 to 3 using two-dimensional echocardiographic images, and VMT ≥2 was regarded as a sign of elevated LVFP. The primary endpoint was a composite of cardiac death or heart failure hospitalization during the 2 years after the echocardiographic examination. In all patients, Kaplan–Meier curves showed that VMT ≥2 (n  = 54) was associated with worse outcomes than the VMT ≤1 group (n  = 256) (P  < 0.001). Furthermore, VMT ≥2 was associated with worse outcomes when tested in 100 HFpEF patients with atrial fibrillation (AF) (P  = 0.026). In the adjusted model, VMT ≥2 was independently associated with the primary outcome (hazard ratio 2.60, 95% confidence interval 1.46–4.61; P  = 0.001). Additionally, VMT scoring provided an incremental prognostic value over clinically relevant variables and diastolic function grading (χ 2 10.8–16.3, P  = 0.035). Conclusions In patients with HFpEF, the VMT score was independently and incrementally associated with adverse clinical outcomes. Moreover, it could also predict clinical outcomes in HFpEF patients with AF. [ABSTRACT FROM AUTHOR]

Background The COVID-19 pandemic has led to delays in patients seeking care for life-threatening conditions; however, its impact on treatment patterns for patients with metastatic cancer is unknown. We assessed the COVID-19 pandemic's impact on time to treatment initiation (TTI) and treatment selection for patients newly diagnosed with metastatic solid cancer. Methods We used an electronic health record–derived longitudinal database curated via technology-enabled abstraction to identify 14 136 US patients newly diagnosed with de novo or recurrent metastatic solid cancer between January 1 and July 31 in 2019 or 2020. Patients received care at approximately 280 predominantly community-based oncology practices. Controlled interrupted time series analyses assessed the impact of the COVID-19 pandemic period (April-July 2020) on TTI, defined as the number of days from metastatic diagnosis to receipt of first-line systemic therapy, and use of myelosuppressive therapy. Results The adjusted probability of treatment within 30 days of diagnosis was similar across periods (January-March 2019 = 41.7%, 95% confidence interval [CI] = 32.2% to 51.1%; April-July 2019 = 42.6%, 95% CI = 32.4% to 52.7%; January-March 2020 = 44.5%, 95% CI = 30.4% to 58.6%; April-July 2020 = 46.8%, 95% CI= 34.6% to 59.0%; adjusted percentage-point difference-in-differences = 1.4%, 95% CI = −2.7% to 5.5%). Among 5962 patients who received first-line systemic therapy, there was no association between the pandemic period and use of myelosuppressive therapy (adjusted percentage-point difference-in-differences = 1.6%, 95% CI = −2.6% to 5.8%). There was no meaningful effect modification by cancer type, race, or age. Conclusions Despite known pandemic-related delays in surveillance and diagnosis, the COVID-19 pandemic did not affect TTI or treatment selection for patients with metastatic solid cancers. [ABSTRACT FROM AUTHOR]

The net reclassification improvement (NRI) and the integrated discrimination improvement (IDI) were originally proposed to characterize accuracy improvement in predicting a binary outcome, when new biomarkers are added to regression models. These two indices have been extended from binary outcomes to multi-categorical and survival outcomes. Working on an AIDS study where the onset of cognitive impairment is competing risk censored by death, we extend the NRI and the IDI to competing risk outcomes, by using cumulative incidence functions to quantify cumulative risks of competing events, and adopting the definitions of the two indices for multi-category outcomes. The "missing" category due to independent censoring is handled through inverse probability weighting. Various competing risk models are considered, such as the Fine and Gray, multistate, and multinomial logistic models. Estimation methods for the NRI and the IDI from competing risk data are presented. The inference for the NRI is constructed based on asymptotic normality of its estimator, and the bias-corrected and accelerated bootstrap procedure is used for the IDI. Simulations demonstrate that the proposed inferential procedures perform very well. The Multicenter AIDS Cohort Study is used to illustrate the practical utility of the extended NRI and IDI for competing risk outcomes. [ABSTRACT FROM AUTHOR]

Divide-and-conquer (DAC) is a commonly used strategy to overcome the challenges of extraordinarily large data, by first breaking the dataset into series of data blocks, then combining results from individual data blocks to obtain a final estimation. Various DAC algorithms have been proposed to fit a sparse predictive regression model in the $L_1$ regularization setting. However, many existing DAC algorithms remain computationally intensive when sample size and number of candidate predictors are both large. In addition, no existing DAC procedures provide inference for quantifying the accuracy of risk prediction models. In this article, we propose a screening and one-step linearization infused DAC (SOLID) algorithm to fit sparse logistic regression to massive datasets, by integrating the DAC strategy with a screening step and sequences of linearization. This enables us to maximize the likelihood with only selected covariates and perform penalized estimation via a fast approximation to the likelihood. To assess the accuracy of a predictive regression model, we develop a modified cross-validation (MCV) that utilizes the side products of the SOLID, substantially reducing the computational burden. Compared with existing DAC methods, the MCV procedure is the first to make inference on accuracy. Extensive simulation studies suggest that the proposed SOLID and MCV procedures substantially outperform the existing methods with respect to computational speed and achieve similar statistical efficiency as the full sample-based estimator. We also demonstrate that the proposed inference procedure provides valid interval estimators. We apply the proposed SOLID procedure to develop and validate a classification model for disease diagnosis using narrative clinical notes based on electronic medical record data from Partners HealthCare. [ABSTRACT FROM AUTHOR]

Background: External validation of prognostic models is necessary to assess the accuracy and generalizability of the model to new patients. If models are validated in a setting in which competing events occur, these competing risks should be accounted for when comparing predicted risks to observed outcomes.Methods: We discuss existing measures of calibration and discrimination that incorporate competing events for time-to-event models. These methods are illustrated using a clinical-data example concerning the prediction of kidney failure in a population with advanced chronic kidney disease (CKD), using the guideline-recommended Kidney Failure Risk Equation (KFRE). The KFRE was developed using Cox regression in a diverse population of CKD patients and has been proposed for use in patients with advanced CKD in whom death is a frequent competing event.Results: When validating the 5-year KFRE with methods that account for competing events, it becomes apparent that the 5-year KFRE considerably overestimates the real-world risk of kidney failure. The absolute overestimation was 10%age points on average and 29%age points in older high-risk patients.Conclusions: It is crucial that competing events are accounted for during external validation to provide a more reliable assessment the performance of a model in clinical settings in which competing risks occur. [ABSTRACT FROM AUTHOR]

Background The role of postmastectomy radiotherapy (PMRT) in patients with node-positive hormone receptor-positive (HoR) and HER2-positive breast cancer (BC) regarding AJCC pathological prognostic staging (PPS) has not been fully determined. This study aimed to validate PPS in patients with node-positive HoR+/HER2+ BC after mastectomy and to investigate the role of PPS on PMRT decision-making in this patient subset. Methods Patients diagnosed with BC from the Surveillance, Epidemiology, and End Results database were included. Patients were classified based on the anatomical staging (AS) and PPS. Breast cancer-specific survival (BCSS) was calculated. Results In total, 6862 patients were included: 4306 (62.8 per cent) patients received PMRT and 2556 (37.2 per cent) patients had not. Compared to AS, PPS downstaged 5260 patients (76.7 per cent) and no patients were upstaged. The C-index was similar between PPS and AS (0.690 versus 0.682; P  = 0.346). Regarding AS, patients who received PMRT had significantly better BCSS than those who had not in stage IIIA (P  = 0.017) and stage IIIC (P  < 0.001) disease, but not in stage IB (P  = 0.675), IIA (P  = 0.677), IIB (P  = 0.100), and IIIB (P  = 0.747) disease. Regarding PPS, patients who received PMRT had significantly better BCSS than those who had not in stage IIIA (P  = 0.038) and stage IIIB (P  = 0.017) disease, but not in stage IA (P  = 0.336), IB (P  = 0.893), IIA (P  = 0.815), and IIB (P  = 0.120) disease. PPS might allow approximately 1390 stage III patients (45.0 per cent) in the AS criterion to avoid PMRT. Conclusion PPS does not provide better risk discriminatory ability in predicting prognosis than AS in patients with node-positive HoR+/HER2+ BC after mastectomy. However, PPS is valuable in providing prognostic counselling to patients and may also guide PMRT decision-making. [ABSTRACT FROM AUTHOR]

Background: Financial toxicity is a growing problem in oncology, but no prior studies have prospectively measured the financial impact of cancer treatment in a diverse national cohort of newly diagnosed cancer patients. S1417CD was the first cooperative group-led multicenter prospective cohort study to evaluate financial hardship in metastatic colorectal cancer (mCRC) patients.Methods: Patients aged 18 years or older within 120 days of mCRC diagnosis completed quarterly questionnaires for 12 months. We estimated the cumulative incidence of major financial hardship (MFH), defined as 1 or more of increased debt, new loans from family and/or friends, selling or refinancing home, or 20% or more income decline. We evaluated the association between patient characteristics and MFH using multivariate cox regression and the association between MFH and quality of life using linear regression.Results: A total of 380 patients (median age = 59.9 years) were enrolled; 77.7% were White, 98.0% insured, and 56.5% had annual income of $50 000 or less. Cumulative incidence of MFH at 12 months was 71.3% (95% confidence interval = 65.7% to 76.1%). Age, race, marital status, and income (split at $50 000 per year) were not statistically significantly associated with MFH. However, income less than $100 000 and total assets less than $100 000 were both associated with greater MFH. MFH at 3 months was associated with decreased social functioning and quality of life at 6 months.Conclusions: Nearly 3 out of 4 mCRC patients experienced MFH despite access to health insurance. These findings underscore the need for clinic and policy solutions that protect cancer patients from financial harm. [ABSTRACT FROM AUTHOR]

Background: Tumor-based classification of human glioma portends patient prognosis, but considerable unexplained survival variability remains. Host factors (eg, age) also strongly influence survival times, partly reflecting a compromised immune system. How blood epigenetic measures of immune characteristics and age augment molecular classifications in glioma survival has not been investigated. We assess the prognostic impact of immune cell fractions and epigenetic age in archived blood across glioma molecular subtypes for the first time.Methods: We evaluated immune cell fractions and epigenetic age in archived blood from the University of California San Francisco Adult Glioma Study, which included a training set of 197 patients with IDH-wild type, 1p19q intact, TERT wild type (IDH/1p19q/TERT-WT) glioma, an evaluation set of 350 patients with other subtypes of glioma, and 454 patients without glioma.Results: IDH/1p19q/TERT-WT patients had lower lymphocyte fractions (CD4+ T, CD8+ T, natural killer, and B cells) and higher neutrophil fractions than people without glioma. Recursive partitioning analysis delineated 4 statistically significantly different survival groups for patients with IDH/1p19q/TERT-WT based on an interaction between chronological age and 2 blood immune factors, CD4+ T cells, and neutrophils. Median overall survival ranged from 0.76 years (95% confidence interval = 0.55-0.99) for the worst survival group (n = 28) to 9.72 years (95% confidence interval = 6.18 to not available) for the best (n = 33). The recursive partitioning analysis also statistically significantly delineated 4 risk groups in patients with other glioma subtypes.Conclusions: The delineation of different survival groups in the training and evaluation sets based on an interaction between chronological age and blood immune characteristics suggests that common host immune factors among different glioma types may affect survival. The ability of DNA methylation-based markers of immune status to capture diverse, clinically relevant information may facilitate noninvasive, personalized patient evaluation in the neuro-oncology clinic. [ABSTRACT FROM AUTHOR]

Background Dialysis confers the highest risk of coronavirus disease 2019 (COVID-19) death among comorbidities predisposing to severe COVID-19. However, reports of COVID-19-associated mortality frequently refer to mortality during the initial hospitalization or first month after diagnosis. Methods In a prospective, observational study, we analysed the long-term (1-year follow-up) serological and clinical outcomes of 56 haemodialysis (HD) patients who were infected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during the first pandemic wave. COVID-19 was diagnosed by a positive polymerase chain reaction (PCR) test (n  = 37) or by the development of anti-SARS-CoV-2 antibodies (n  = 19). Results After >1 year of follow-up, 35.7% of HD patients infected by SARS-CoV-2 during the first pandemic wave had died, 6 (11%) during the initial admission and 14 (25%) in the following months, mainly within the first 3 months after diagnosis. Overall, 30% of patients died from vascular causes and 40% from respiratory causes. In adjusted analysis, a positive SARS-CoV-2 PCR test for diagnosis {hazard ratio [HR] 5.18 [interquartile range (IQR) 1.30–20.65], P = 0.020}, higher baseline C-reactive protein levels [HR 1.10 (IQR 1.03–1.16), P = 0.002] and lower haemoglobin levels [HR 0.62 (IQR 0.45–0.86), P = 0.005] were associated with higher 1-year mortality. Mortality in the 144 patients who did not have COVID-19 was 21 (14.6%) over 12 months [HR of death for COVID-19 patients 3.00 (IQR 1.62–5.53), log-rank P = 0.00023]. Over the first year, the percentage of patients having anti-SARS-CoV-2 immunoglobulin G (IgG) decreased from 36/49 (73.4%) initially to 27/44 (61.3%) at 6 months and 14/36 (38.8%) at 12 months. Conclusions The high mortality of HD patients with COVID-19 is not limited to the initial hospitalization. Defining COVID-19 deaths as those occurring within 3 months of a COVID-19 diagnosis may better represent the burden of COVID-19. In HD patients, the anti-SARS-CoV-2 IgG response was suboptimal and short-lived. [ABSTRACT FROM AUTHOR]