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Neuro-oncology Dec 2022
Topics: Humans; Quality of Life; Progression-Free Survival; Disease Progression; Glioma
PubMed: 36112492
DOI: 10.1093/neuonc/noac218 -
PLoS Genetics Feb 2023Genetic studies of disease progression can be used to identify factors that may influence survival or prognosis, which may differ from factors that influence on disease... (Review)
Review
Genetic studies of disease progression can be used to identify factors that may influence survival or prognosis, which may differ from factors that influence on disease susceptibility. Studies of disease progression feed directly into therapeutics for disease, whereas studies of incidence inform prevention strategies. However, studies of disease progression are known to be affected by collider (also known as "index event") bias since the disease progression phenotype can only be observed for individuals who have the disease. This applies equally to observational and genetic studies, including genome-wide association studies and Mendelian randomisation (MR) analyses. In this paper, our aim is to review several statistical methods that can be used to detect and adjust for index event bias in studies of disease progression, and how they apply to genetic and MR studies using both individual- and summary-level data. Methods to detect the presence of index event bias include the use of negative controls, a comparison of associations between risk factors for incidence in individuals with and without the disease, and an inspection of Miami plots. Methods to adjust for the bias include inverse probability weighting (with individual-level data), or Slope-Hunter and Dudbridge et al.'s index event bias adjustment (when only summary-level data are available). We also outline two approaches for sensitivity analysis. We then illustrate how three methods to minimise bias can be used in practice with two applied examples. Our first example investigates the effects of blood lipid traits on mortality from coronary heart disease, while our second example investigates genetic associations with breast cancer mortality.
Topics: Humans; Genome-Wide Association Study; Bias; Risk Factors; Phenotype; Mendelian Randomization Analysis; Disease Progression
PubMed: 36821633
DOI: 10.1371/journal.pgen.1010596 -
International Journal of Molecular... Apr 2023Diseases affecting the glomerulus, the filtration unit of the kidney, are a major cause of chronic kidney disease. Glomerular disease is characterised by injury of... (Review)
Review
Diseases affecting the glomerulus, the filtration unit of the kidney, are a major cause of chronic kidney disease. Glomerular disease is characterised by injury of glomerular cells and is often accompanied by an inflammatory response that drives disease progression. New strategies are needed to slow the progression to end-stage kidney disease, which requires dialysis or transplantation. Thymosin β4 (Tβ4), an endogenous peptide that sequesters G-actin, has shown potent anti-inflammatory function in experimental models of heart, kidney, liver, lung, and eye injury. In this review, we discuss the role of endogenous and exogenous Tβ4 in glomerular disease progression and the current understanding of the underlying mechanisms.
Topics: Humans; Disease Progression; Kidney Glomerulus; Renal Dialysis; Renal Insufficiency, Chronic; Thymosin
PubMed: 37175390
DOI: 10.3390/ijms24097684 -
Emerging Infectious Diseases Jul 2021We investigated outcomes for patients born after 1983 and hospitalized with initial acute rheumatic fever (ARF) in New Zealand during 1989-2012. We linked ARF...
We investigated outcomes for patients born after 1983 and hospitalized with initial acute rheumatic fever (ARF) in New Zealand during 1989-2012. We linked ARF progression outcome data (recurrent hospitalization for ARF, hospitalization for rheumatic heart disease [RHD], and death from circulatory causes) for 1989-2015. Retrospective analysis identified initial RHD patients <40 years of age who were hospitalized during 2010-2015 and previously hospitalized for ARF. Most (86.4%) of the 2,182 initial ARF patients did not experience disease progression by the end of 2015. Progression probability after 26.8 years of theoretical follow-up was 24.0%; probability of death, 1.0%. Progression was more rapid and ≈2 times more likely for indigenous Māori or Pacific Islander patients. Of 435 initial RHD patients, 82.2% had not been previously hospitalized for ARF. This young cohort demonstrated low mortality rates but considerable illness, especially among underserved populations. A national patient register could help monitor, prevent, and reduce ARF progression.
Topics: Disease Progression; Humans; New Zealand; Retrospective Studies; Rheumatic Fever; Rheumatic Heart Disease
PubMed: 34153221
DOI: 10.3201/eid2707.203045 -
Nature Reviews. Drug Discovery Aug 2022Multiple sclerosis (MS) is an immune-mediated disease of the central nervous system that causes demyelination, axonal degeneration and astrogliosis, resulting in... (Review)
Review
Multiple sclerosis (MS) is an immune-mediated disease of the central nervous system that causes demyelination, axonal degeneration and astrogliosis, resulting in progressive neurological disability. Fuelled by an evolving understanding of MS immunopathogenesis, the range of available immunotherapies for clinical use has expanded over the past two decades. However, MS remains an incurable disease and even targeted immunotherapies often fail to control insidious disease progression, indicating the need for new and exceptional therapeutic options beyond the established immunological landscape. In this Review, we highlight such non-canonical targets in preclinical MS research with a focus on five highly promising areas: oligodendrocytes; the blood-brain barrier; metabolites and cellular metabolism; the coagulation system; and tolerance induction. Recent findings in these areas may guide the field towards novel targets for future therapeutic approaches in MS.
Topics: Disease Progression; Humans; Multiple Sclerosis
PubMed: 35668103
DOI: 10.1038/s41573-022-00477-5 -
International Journal of Environmental... Jan 2022The highly rapid spread of the current pandemic has quickly overwhelmed hospitals all over the world and motivated extensive research to address a wide range of emerging...
The highly rapid spread of the current pandemic has quickly overwhelmed hospitals all over the world and motivated extensive research to address a wide range of emerging problems. The unforeseen influx of COVID-19 patients to hospitals has made it inevitable to deploy a rapid and accurate triage system, monitor progression, and predict patients at higher risk of deterioration in order to make informed decisions regarding hospital resource management. Disease detection in radiographic scans, severity estimation, and progression and prognosis prediction have been extensively studied with the help of end-to-end methods based on deep learning. The majority of recent works have utilized a single scan to determine severity or predict progression of the disease. In this paper, we present a method based on deep sequence learning to predict improvement or deterioration in successive chest X-ray scans and build a mathematical model to determine individual patient disease progression profile using successive scans. A deep convolutional neural network pretrained on a diverse lung disease dataset was used as a feature extractor to generate the sequences. We devised three strategies for sequence modeling in order to obtain both fine-grained and coarse-grained features and construct sequences of different lengths. We also devised a strategy to quantify positive or negative change in successive scans, which was then combined with age-related risk factors to construct disease progression profile for COVID-19 patients. The age-related risk factors allowed us to model rapid deterioration and slower recovery in older patients. Experiments conducted on two large datasets showed that the proposed method could accurately predict disease progression. With the best feature extractor, the proposed method was able to achieve AUC of 0.98 with the features obtained from radiographs. Furthermore, the proposed patient profiling method accurately estimated the health profile of patients.
Topics: Aged; COVID-19; Deep Learning; Disease Progression; Humans; Neural Networks, Computer; SARS-CoV-2
PubMed: 35010740
DOI: 10.3390/ijerph19010480 -
Clinical Cancer Research : An Official... Nov 2016Changes in tumor metabolism may accompany disease progression and can occur following treatment, often before there are changes in tumor size. We focus here on imaging... (Review)
Review
Changes in tumor metabolism may accompany disease progression and can occur following treatment, often before there are changes in tumor size. We focus here on imaging methods that can be used to image various aspects of tumor metabolism, with an emphasis on methods that can be used for tumor grading, assessing disease progression, and monitoring treatment response. Clin Cancer Res; 22(21); 5196-203. ©2016 AACR.
Topics: Animals; Diagnostic Imaging; Disease Progression; Humans; Neoplasm Grading; Neoplasms
PubMed: 27609841
DOI: 10.1158/1078-0432.CCR-16-0159 -
British Journal of Clinical Pharmacology Jan 2015Clinical pharmacology is concerned with understanding how to use medicines to treat disease. Pharmacokinetics and pharmacodynamics have provided powerful methodologies... (Review)
Review
Clinical pharmacology is concerned with understanding how to use medicines to treat disease. Pharmacokinetics and pharmacodynamics have provided powerful methodologies for describing the time course of concentration and effect in individuals and in populations. This population approach may also be applied to describing the progression of disease and the action of drugs to change disease progress. Quantitative models for symptomatic and disease-modifying effects of drugs are valuable not only for describing drugs and diseases but also for identifying criteria to distinguish between types of drug actions, with implications for regulatory decisions and long-term patient care.
Topics: Disease Progression; Drug Therapy; Humans; Models, Biological; Pharmacology, Clinical
PubMed: 23713816
DOI: 10.1111/bcp.12170 -
Frontiers in Public Health 2022Depression is a common mental health condition that affects millions of people worldwide. Care pathways for depression are complex and the demand across different parts...
INTRODUCTION
Depression is a common mental health condition that affects millions of people worldwide. Care pathways for depression are complex and the demand across different parts of the healthcare system is often uncertain and not entirely understood. Clinical progression with depression can be equally complex and relates to whether or not a patient is seeking care, the care pathway they are on, and the ability for timely access to healthcare services. Considering both pathways and progression for depression are however rarely studied together in the literature.
METHODS
This paper presents a hybrid simulation modeling framework that is uniquely able to capture both disease progression, using Agent Based Modeling, and related care pathways, using a System Dynamics. The two simulation paradigms within the framework are connected to run synchronously to investigate the impact of depression progression on healthcare services and, conversely, how any limitations in access to services may impact clinical progression. The use of the developed framework is illustrated by parametrising it with published clinical data and local service level data from Wales, UK.
RESULTS AND DISCUSSION
The framework is able to quantify demand, service capacities and costs across all care pathways for a range of different scenarios. These include those for varying service coverage and provision, such as the cost-effectiveness of treating patients more quickly in community settings to reduce patient progression to more severe states of depression, and thus reducing the costs and utilization of more expensive specialist settings.
Topics: Humans; Depression; Mental Disorders; Delivery of Health Care; Systems Analysis; Disease Progression
PubMed: 36817182
DOI: 10.3389/fpubh.2022.1011104 -
Rheumatology (Oxford, England) Mar 2021
Topics: Disease Progression; Female; Humans; Male; Middle Aged; Scleroderma, Systemic; Time Factors
PubMed: 33404661
DOI: 10.1093/rheumatology/keaa911