Mclaughlindueholm5457

Most statistical tests for treatment effects used in randomized clinical trials with survival outcomes are based on the proportional hazards assumption, which often fails in practice. Data from early exploratory studies may provide evidence of nonproportional hazards, which can guide the choice of alternative tests in the design of practice-changing confirmatory trials. We developed a test to detect treatment effects in a late-stage trial, which accounts for the deviations from proportional hazards suggested by early-stage data. Conditional on early-stage data, among all tests that control the frequentist Type I error rate at a fixed α level, our testing procedure maximizes the Bayesian predictive probability that the study will demonstrate the efficacy of the experimental treatment. Hence, the proposed test provides a useful benchmark for other tests commonly used in the presence of nonproportional hazards, for example, weighted log-rank tests. We illustrate this approach in simulations based on data from a published cancer immunotherapy phase III trial.We review the evolution, achievements, and limitations of the current paradigm shift in medicine, from the "one-size-fits-all" model to "Precision Medicine." Precision, or personalized, medicine - tailoring the medical treatment to the personal characteristics of each patient - engages advanced statistical methods to evaluate the relationships between static patient profiling, e.g., genomic and proteomic, and a simple clinically-motivated output, e.g., yes/no responder. Today, precision medicine technologies that have facilitated groundbreaking advances in oncology, notably in cancer immunotherapy, are approaching the limits of their potential. A different approach to treatment personalization involves methodologies focusing on the dynamic interactions in the patient-disease-drug system, as portrayed in mathematical modeling. Achievements of this scientific approach, in the form of algorithms for predicting personal disease dynamics and in individual patients under immunotherapeutic drugs, are reviewed as well. The contribution of the dynamic approaches to precision medicine is limited, at present, due to insufficient applicability and validation. Yet, the time is ripe for amalgamating together these two approaches, for maximizing their joint potential to personalize and improve cancer immunotherapy. We suggest the roadmaps towards achieving this goal, technologically, and urge clinicians, pharmacologists and computational biologists to join forces along the pharmaco-clinical track of this development.Background During COVID-19 outbreak, oncological care has been reorganized. Patients with cancer have been reported to experience a more severe COVID-19 syndrome; moreover, there are concerns of a potential interference between immune checkpoint inhibitors (ICIs) and SARS-CoV-2 pathogenesis. Materials and methods Between 6 and 16 May 2020, a 22-item survey was sent to Italian physicians involved in administering ICIs. It aimed at exploring the perception about SARS-CoV-2-related risks in cancer patients receiving ICIs, and the attitudes towards their management. Results The 104 respondents had a median age of 35.5 years, 58.7% were females and 71.2% worked in Northern Italy. 47.1% of respondents argued a synergism between ICIs and SARS-CoV-2 pathogenesis leading to worse outcomes, but 97.1% would not deny an ICI only for the risk of infection. During COVID-19 outbreak, to reduce hospital visits, 55.8% and 30.8% opted for the highest labelled dose of each ICI and/or, among different ICIs for the same indication, for the one with the longer interval between cycles, respectively. 53.8% of respondents suggested testing for SARS-CoV-2 every cancer patient candidate to ICIs. 71.2% declared to manage patients with onset of dyspnoea and cough as infected by SARS-CoV-2 until otherwise proven; however, 96.2% did not reduce the use of steroids to manage immune-related toxicities. The administration of ICIs in specific situations for different cancer types has not been drastically conditioned. Conclusions These results highlight the uncertainties around the perception of a potential interference between ICIs and COVID-19, supporting the need of focused studies on this topic.Ecological literature offers a myriad of methods for quantifying β-diversity. One such methods is determining BDtotal (BD), which, unlike other methods, can be decomposed into meaningful components that indicate how unique a sampling unit is regarding its composition (local contribution) and how unique a species is regarding its occurrence in the community (species contribution). find more Despite this advantage, the original formulation of the BD metric only assesses taxonomic variation and neglects other important dimensions of biodiversity. We expanded the original formulation of BD to capture variation in the functional and phylogenetic dimensions of community data by computing two new metrics - BDFun and BDPhy - as well as their respective components that represent the local and species contribution. We tested the statistical performance of these new metrics for capturing variation in functional and phylogenetic composition through simulated communities and illustrated the potential use of these new metrics by analyzing β-diversity of stream fish communities. Our results demonstrated that BDPhy and BDFun have acceptable type I error and great power to detect the effect of deep evolutionary relationships and attributes mediating patterns of β-diversity. The empirical example illustrated how BDPhy and BDFun reveal complementary aspects of β-diversity relative to the original BD metric. These new metrics can be used to identify local communities that are of conservation importance because they represent unique functional, phylogenetic and taxonomic compositions. We conclude that BDPhy and BDFun are important tools for providing complementary information in the investigation of the structure of biological communities.Background We hypothesized that a multiparametric approach incorporating medical co-morbidity information, electrocardiographic P-wave indices, echocardiographic assessment, neutrophil-to-lymphocyte ratio (NLR) and prognostic nutritional index (PNI) calculated from laboratory data, can improve risk stratification in mitral regurgitation (MR). Methods Patients diagnosed with mitral regurgitation between 1st March 2005 and 30th October 2018 from a single center were retrospectively analyzed. Outcomes analyzed were incident atrial fibrillation (AF), transient ischemic attack (TIA)/stroke and mortality. Results This study cohort included 706 patients, of whom 171 had normal inter-atrial conduction, 257 had inter-atrial block (IAB) and 266 had AF at baseline. Logistic regression analysis showed that age, hypertension and mean P-wave duration (PWD) were significant predictors of new onset AF. Low left ventricular ejection fraction (LVEF), abnormal P wave terminal force in V1 (PTFV1) predicted TIA/stroke. Age, smoking, hypertension, diabetes mellitus, hypercholesterolaemia, ischemic heart disease, secondary mitral regurgitation, urea, creatinine, NLR, PNI, left atrial diameter (LAD), left ventricular end-diastolic dimension, LVEF, pulmonary arterial systolic pressure, IAB, baseline AF and heart failure predicted all-cause mortality. A multi-task Gaussian process learning model demonstrated significant improvement in risk stratification compared to logistic regression and a decision tree method. Conclusions A multi-parametric approach incorporating multi-modality clinical data improves risk stratification in mitral regurgitation. Multi-task machine learning can significantly improve overall risk stratification performance.Background There is growing evidence that oxidative stress (OS) is a critical factor linking obesity with its associated comorbidities, such as cardiovascular diseases. Aim To evaluate the degree of OS in people with morbid obesity and its relationship with glycoproteins, determined using 1H-NMR spectroscopy, before and after bariatric surgery (BS). Methods In this observational cohort study, plasma from 24 patients with BMI ≥ 40 kg/m2 (age 21-65 years) was used to measure metabolites implicated in OS. We measured glycoprotein (GlycA, GlycB and GlycF) areas and shape factors (H/W = height/width). Results One year after BS, oxidized low-density lipoprotein had decreased by 49% (p less then 0.0001), malondialdehyde by 32% (p = 0.0019) and lipoprotein (a) by 21% (p = 0.0039). The antioxidant enzymes paraoxonase-1 and catalase increased after BS (43%, p less then 0.0001 and 54%, p = 0.0002, respectively). Superoxide dismutase-2 had fallen one year after BS (32%, p = 0.0052). After BS, both the glycoprotein areas and shape factors decreased by 20%-26%. These glycoproteins were significantly correlated with OS parameters. The plasma atherogenic index was 63% higher in obese individuals than one year after BS and correlated positively with glycoproteins. For the first time, we here demonstrate the relationship between OS parameters and glycoproteins in people with morbid obesity. So glycoproteins could therefore be a good indicator, together with the oxidative state to assess patient prognosis after BS.Personalized Medicine, or the tailoring of health interventions to an individual's nuanced and often unique genetic, biochemical, physiological, behavioral and/or exposure profile, is seen by many as a biological necessity given the great heterogeneity of pathogenic processes underlying most diseases. However, testing, and ultimately proving the benefit of, strategies or algorithms connecting the mechanisms of action of specific interventions to patient pathophysiological profiles (referred to here as 'intervention matching schemes' (IMS)) is complex for many reasons. We argue that IMS are likely to be pervasive, if not ubiquitous, in future health care, but raise important questions about their broad deployment and the contexts within which their utility can be proven. For example, one could question the need to, the efficiency associated with, and the reliability of, strategies for comparing competing or perhaps complementary IMS. We briefly summarize some of the more salient issues surrounding the vetting of IMS in cancer contexts and argue that IMS are at the foundation of many modern clinical trials and intervention strategies, as in basket, umbrella and adaptive trials. In addition, IMS are at the heart of proposed 'rapid learning systems' in hospitals, and implicit in cell replacement strategies such as cytotoxic T-cell therapies targeting patient-specific neo-antigen profiles. We also consider the need for sensitivity to issues surrounding the deployment of IMS and comment on directions for future research.Introduction Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected 189 000 people in Italy, with more than 25 000 deaths. Several predictive factors of mortality have been identified; however, none has been validated in patients presenting with mild disease. Methods Patients with a diagnosis of interstitial pneumonia caused by SARS-CoV-2, presenting with mild symptoms, and requiring hospitalization in a non-intensive care unit with known discharge status were prospectively collected and retrospectively analysed. Demographical, clinical and biochemical parameters were recorded, as need for non-invasive mechanical ventilation and admission in intensive care unit. Univariate and multivariate logistic regression analyses were used to identify independent predictors of death. Results Between 28 February and 10 April 2020, 229 consecutive patients were included in the study cohort; the majority were males with a mean age of 60 years. 54% of patients had at least one comorbidity, with hypertension being the most commonly represented, followed by diabetes mellitus.