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Practical Neurology Oct 2022
Topics: Africa; Creutzfeldt-Jakob Syndrome; Disease Progression; Humans
PubMed: 35314494
DOI: 10.1136/practneurol-2022-003385 -
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 -
Clinical Gastroenterology and... Feb 2023Globally, 25% of people have nonalcoholic fatty liver disease (NAFLD), and, currently, there are no approved pharmacologic treatments for NAFLD. With a slow disease... (Review)
Review
BACKGROUND & AIMS
Globally, 25% of people have nonalcoholic fatty liver disease (NAFLD), and, currently, there are no approved pharmacologic treatments for NAFLD. With a slow disease progression, long-term impact of pharmacologic treatments can be assessed only by complementing emerging clinical trial evidence with data from other sources in disease progression modeling. Although this modeling is crucial for economic evaluation studies assessing the clinical and economic consequences of new treatments, the approach to modeling the natural history of NAFLD differs in contemporary research. This systematic literature review investigated modeling of the natural history of NAFLD.
METHODS
A systematic literature review was conducted searching PubMed, Scopus, Cochrane, and the National Health Service Economic Evaluation Database to identify articles focusing on modeling of the natural history of NAFLD. Model structure and transition probabilities were extracted from included studies.
RESULTS
Of the 28 articles identified, differences were seen in model structure and data input. Clear definitions of nonalcoholic steatohepatitis and NAFLD often were lacking; differences in the granularity of modeling fibrosis progression, the approach to disease regression, and modeling of advanced liver disease varied across studies. Observed transition probabilities for F0 to F1, F1 to F2, F2 to F3, and F3 to compensated cirrhosis varied between 0.059 to 0.095, 0.023 to 0.140, 0.018 to 0.070, and 0.040 to 0.118, respectively.
CONCLUSIONS
The difference in disease progression modeling for seemingly similar models warrants further inquiry regarding how to model the natural course of NAFLD. Such differences may have a large impact when assessing the value of emerging pharmacologic treatments.
Topics: Humans; Non-alcoholic Fatty Liver Disease; Cost-Benefit Analysis; State Medicine; Liver Cirrhosis; Disease Progression
PubMed: 34757199
DOI: 10.1016/j.cgh.2021.10.040 -
Muscle & Nerve Jan 2023Rate of disease progression (ΔFS), measured as change in the revised Amyotrophic Lateral Sclerosis Functional Rating Scale (ALSFRS-R) and body mass index (BMI), are...
INTRODUCTION/AIMS
Rate of disease progression (ΔFS), measured as change in the revised Amyotrophic Lateral Sclerosis Functional Rating Scale (ALSFRS-R) and body mass index (BMI), are predictors of survival in amyotrophic lateral sclerosis (ALS). Our aim in this study was to assess the utility of these clinical biomarkers along with neurophysiological measures, such as the split hand index (SI), in monitoring disease progression.
METHODS
Clinical trial data were collected from 107 patients recruited into the Tecfidera in ALS trial. The prognostic utility of clinical and neurophysiological measures, including ΔFS, BMI, SI, and neurophysiological index (NPI), were assessed cross-sectionally and longitudinally (40 weeks). The outcome measures of disease severity and progression included: (i) ALSFRS-R score; (ii) Medical Research Council (MRC) score; and (iii) forced vital capacity and sniff nasal inspiratory pressure.
RESULTS
Fast-progressor ALS patients (ΔFS ≥1.1) exhibited significantly lower ALSFRS-R and total MRC scores at baseline. A baseline ΔFS score ≥1.1 was associated with a greater reduction in ALSFRS-R (P = .002) and MRC (P = .002) scores over 40 weeks. Baseline BMI <25 was also associated with faster reduction of ALSFRS-R and MRC scores. SI and NPI were associated with disease severity at baseline, but not with subsequent rate of disease progression.
DISCUSSION
Implementation of the assessed clinical and neurophysiological biomarkers may assist in patient management and stratification into clinical trials.
Topics: Humans; Amyotrophic Lateral Sclerosis; Disease Progression; Prognosis; Biomarkers; Body Mass Index
PubMed: 36214183
DOI: 10.1002/mus.27736 -
Movement Disorders : Official Journal... Mar 2022
Topics: Big Data; Disease Progression; Humans; Huntington Disease; Movement Disorders
PubMed: 35315555
DOI: 10.1002/mds.28943 -
Journal of Biomedical Informatics Mar 2021As Electronic Health Records (EHR) data accumulated explosively in recent years, the tremendous amount of patient clinical data provided opportunities to discover real...
OBJECTIVE
As Electronic Health Records (EHR) data accumulated explosively in recent years, the tremendous amount of patient clinical data provided opportunities to discover real world evidence. In this study, a graphical disease network, named progressive cardiovascular disease network (progCDN), was built to delineate the progression profiles of cardiovascular diseases (CVD).
MATERIALS AND METHODS
The EHR data of 14.3 million patients with CVD diagnoses were collected for building disease network and further analysis. We applied a new designed method, progression rates (PR), to calculate the progression relationship among different diagnoses. Based on the disease network outcome, 23 disease progression pair were selected to screen for salient features.
RESULTS
The network depicted the dominant diseases in CVD development, such as the heart failure and coronary arteriosclerosis. Novel progression relationships were also discovered, such as the progression path from long QT syndrome to major depression. In addition, three age-group progCDNs identified a series of age-associated disease progression paths and important successor diseases with age bias. Furthermore, a list of important features with sufficient abundance and high correlation was extracted for building disease risk models.
DISCUSSION
The PR method designed for identifying the progression relationship could be widely applied in any EHR database due to its flexibility and robust functionality. Meanwhile, researchers could use the progCDN network to validate or explore novel disease relationships in real world data.
CONCLUSION
The first-time interrogation of such a huge CVD patients cohort enabled us to explore the general and age-specific disease progression patterns in CVD development.
Topics: Cardiovascular Diseases; Cohort Studies; Databases, Factual; Disease Progression; Electronic Health Records; Humans
PubMed: 33493631
DOI: 10.1016/j.jbi.2021.103686 -
Clinical Gastroenterology and... Feb 2020
Topics: Crohn Disease; Disease Progression; Feces; Humans; Leukocyte L1 Antigen Complex
PubMed: 31351132
DOI: 10.1016/j.cgh.2019.07.031 -
Nature Cancer Dec 2023
Topics: Humans; Disease Progression; Immunotherapy
PubMed: 38102345
DOI: 10.1038/s43018-023-00666-0 -
Clinical Pharmacology and Therapeutics Aug 2023Disease progression modeling (DPM) represents an important model-informed drug development framework. The scientific communities support the use of DPM to accelerate and... (Review)
Review
Disease progression modeling (DPM) represents an important model-informed drug development framework. The scientific communities support the use of DPM to accelerate and increase efficiency in drug development. This article summarizes International Consortium for Innovation & Quality (IQ) in Pharmaceutical Development mediated survey conducted across multiple biopharmaceutical companies on challenges and opportunities for DPM. Additionally, this summary highlights the viewpoints of IQ from the 2021 workshop hosted by the US Food and Drug Administration (FDA). Sixteen pharmaceutical companies participated in the IQ survey with 36 main questions. The types of questions included single/multiple choice, dichotomous, rank questions, and open-ended or free text. The key results show that DPM has different representation, it encompasses natural disease history, placebo response, standard of care as background therapy, and can even be interpreted as pharmacokinetic/pharmacodynamic modeling. The most common reasons for not implementing DPM as frequently seem to be difficulties in internal cross-functional alignment, lack of knowledge of disease/data, and time constraints. If successfully implemented, DPM can have an impact on dose selection, reduction of sample size, trial read-out support, patient selection/stratification, and supportive evidence for regulatory interactions. The key success factors and key challenges of disease progression models were highlighted in the survey and about 24 case studies across different therapeutic areas were submitted from various survey sponsors. Although DPM is still evolving, its current impact is limited but promising. The success of such models in the future will depend on collaboration, advanced analytics, availability of and access to relevant and adequate-quality data, collaborative regulatory guidance, and published examples of impact.
Topics: Humans; Drug Development; Pharmaceutical Preparations; Forecasting; Disease Progression
PubMed: 36802040
DOI: 10.1002/cpt.2873 -
Mayo Clinic Proceedings Feb 2023
Topics: Humans; Amyotrophic Lateral Sclerosis; Disease Progression
PubMed: 36737118
DOI: 10.1016/j.mayocp.2022.10.004