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Current Opinion in Ophthalmology Mar 2023The purpose of this review is to examine contemporary techniques for detecting the progression of glaucoma. We provide a general overview of detection principles and... (Review)
Review
PURPOSE OF REVIEW
The purpose of this review is to examine contemporary techniques for detecting the progression of glaucoma. We provide a general overview of detection principles and review evidence-based diagnostic strategies and specific considerations for detecting glaucomatous progression in patients with mild, moderate and severe disease.
RECENT FINDINGS
Diagnostic techniques and technologies for glaucoma have dramatically evolved in recent years, affording clinicians an expansive toolkit with which to detect glaucoma progression. Each stage of glaucoma, however, presents unique diagnostic challenges. In mild disease, either structural or functional changes can develop first in disease progression. In moderate disease, structural or functional changes can occur either in tandem or in isolation. In severe disease, standard techniques may fail to detect further disease progression, but such detection can still be measured using other modalities.
SUMMARY
Detecting disease progression is central to the management of glaucoma. Glaucomatous progression has both structural and functional elements, both of which must be carefully monitored at all disease stages to determine when interventions are warranted.
Topics: Humans; Disease Progression; Glaucoma
PubMed: 36730773
DOI: 10.1097/ICU.0000000000000925 -
International Journal of Medical... Jul 2023Early recognition and prevention are crucial for reducing the risk of disease progression. This study aimed to develop a novel technique based on a temporal disease... (Review)
Review
OBJECTIVE
Early recognition and prevention are crucial for reducing the risk of disease progression. This study aimed to develop a novel technique based on a temporal disease occurrence network to analyze and predict disease progression.
METHODS
This study used a total of 3.9 million patient records. Patient health records were transformed into temporal disease occurrence networks, and a supervised depth first search was used to find frequent disease sequences to predict the onset of disease progression. The diseases represented nodes in the network and paths between nodes represented edges that co-occurred in a patient cohort with temporal order. The node and edge level attributes contained meta-information about patients' gender, age group, and identity as labels where the disease occurred. The node and edge level attributes guided the depth first search to identify frequent disease occurrences in specific genders and age groups. The patient history was used to match the most frequent disease occurrences and then the obtained sequences were merged together to generate a ranked list of diseases with their conditional probability and relative risk.
RESULTS
The study found that the proposed method had improved performance compared to other methods. Specifically, when predicting a single disease, the method achieved an area under the receiver operating characteristic curve (AUC) of 0.65 and an F1-score of 0.11. When predicting a set of diseases relative to ground truth, the method achieved an AUC of 0.68 and an F1-score of 0.13.
CONCLUSION
The ranked list generated by the proposed method, which includes the probability of occurrence and relative risk score, can provide physicians with valuable information about the sequential development of diseases in patients. This information can help physicians to take preventive measures in a timely manner, based on the best available information.
Topics: Humans; Male; Female; Disease Progression; Risk Factors
PubMed: 37104895
DOI: 10.1016/j.ijmedinf.2023.105068 -
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 -
Pharmaceutical Research Aug 2022The use of Disease progression models (DPMs) in Drug Development has been widely adopted across therapeutic areas as a method for integrating previously obtained disease... (Review)
Review
The use of Disease progression models (DPMs) in Drug Development has been widely adopted across therapeutic areas as a method for integrating previously obtained disease knowledge to elucidate the impact of novel therapeutics or vaccines on disease course, thus quantifying the potential clinical benefit at different stages of drug development programs. This paper provides a brief overview of DPMs and the evolution in data types, analytic methods, and applications that have occurred in their use by Quantitive Clinical Pharmacologists. It also provides examples of how these models have informed decisions and clinical trial design across several therapeutic areas and at various stages of development. It briefly describes potential new applications of DPMs utilizing emerging data sources, and utilizing new analytic techniques, and discuss new challenges faced such as requiring description of multiple endpoints, rapid model development, application of machine learning-based analytics, and use of high dimensional and real-world data. Considerations for the continued evolution future of DPMs to serve as community-maintained expert systems are also provided.
Topics: Clinical Trials as Topic; Disease Progression; Drug Development; Humans; Research Design
PubMed: 35411507
DOI: 10.1007/s11095-022-03257-3 -
Statistics in Medicine Aug 2023Disease modeling is an essential tool to describe disease progression and its heterogeneity across patients. Usual approaches use continuous data such as biomarkers to...
Disease modeling is an essential tool to describe disease progression and its heterogeneity across patients. Usual approaches use continuous data such as biomarkers to assess progression. Nevertheless, categorical or ordinal data such as item responses in questionnaires also provide insightful information about disease progression. In this work, we propose a disease progression model for ordinal and categorical data. We built it on the principles of disease course mapping, a technique that uniquely describes the variability in both the dynamics of progression and disease heterogeneity from multivariate longitudinal data. This extension can also be seen as an attempt to bridge the gap between longitudinal multivariate models and the field of item response theory. Application to the Parkinson's progression markers initiative cohort illustrates the benefits of our approach: a fine-grained description of disease progression at the item level, as compared to the aggregated total score, together with improved predictions of the patient's future visits. The analysis of the heterogeneity across individual trajectories highlights known disease trends such as tremor dominant or postural instability and gait difficulties subtypes of Parkinson's disease.
Topics: Humans; Disease Progression; Tremor; Parkinson Disease; Biomarkers
PubMed: 37231622
DOI: 10.1002/sim.9770 -
Journal of the American Dental... Sep 2022
Topics: Data Collection; Dental Care; Disease Progression; Humans; Periodontal Diseases; Periodontics
PubMed: 35525683
DOI: 10.1016/j.adaj.2022.03.005 -
Frontiers in Immunology 2023
Topics: Humans; Tumor Microenvironment; Disease Progression
PubMed: 36761722
DOI: 10.3389/fimmu.2023.1141084 -
Movement Disorders : Official Journal... Aug 2022Multiple system atrophy (MSA) is a rare and aggressive neurodegenerative disease that typically leads to death 6 to 10 years after symptom onset. The rapid evolution...
BACKGROUND
Multiple system atrophy (MSA) is a rare and aggressive neurodegenerative disease that typically leads to death 6 to 10 years after symptom onset. The rapid evolution renders it crucial to understand the general disease progression and factors affecting the disease course.
OBJECTIVES
The aims of this study were to develop a novel disease-progression model to estimate a population-level MSA progression trajectory and predict patient-specific continuous disease stages describing the degree of progress into the disease.
METHODS
The disease-progression model estimated a population-level progression trajectory of subscales of the Unified MSA Rating Scale and the Unified Parkinson's Disease Rating Scale using patients in the European MSA natural history study. The predicted disease continuum was validated via multiple analyses based on reported anchor points, and the effect of MSA subtype on the rate of disease progression was evaluated.
RESULTS
The predicted disease continuum spanned approximately 6 years, with an estimated average duration of 51 months for a patient with global disability score 0 to reach the highest level of 4. The predicted continuous disease stages were shown to be correlated with time of symptom onset and predictive of survival time. MSA motor subtype was found to significantly affect disease progression, with MSA-parkinsonian (MSA-P) type patients having an accelerated rate of progression.
CONCLUSIONS
The proposed modeling framework introduces a new method of analyzing and interpreting the progression of MSA. It can provide new insights and opportunities for investigating covariate effects on the rate of progression and provide well-founded predictions of patient-level future progressions. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
Topics: Disease Progression; Humans; Multiple System Atrophy
PubMed: 35668573
DOI: 10.1002/mds.29077 -
Nature Reviews. Clinical Oncology Sep 2023
Topics: Humans; Disease Progression; Pyrimidines; Pyrroles
PubMed: 37311901
DOI: 10.1038/s41571-023-00790-x -
Pharmacology & Therapeutics Jul 2024The recent development of the first disease-modifying drug for Alzheimer's disease represents a major advancement in dementia treatment. Behind this breakthrough is a... (Review)
Review
The recent development of the first disease-modifying drug for Alzheimer's disease represents a major advancement in dementia treatment. Behind this breakthrough is a quarter century of research efforts to understand the disease not by a particular symptom at a given moment, but by long-term sequential changes in multiple biomarkers. Disease progression modeling with temporal realignment (DPM-TR) is an emerging computational approach proposed with this biomarker-based disease concept. By integrating short-term clinical observations of multiple disease biomarkers in a data-driven manner, DPM-TR provides a way to understand the progression of chronic diseases over decades and predict individual disease stages more accurately. DPM-TR has been developed primarily in the area of neurodegenerative diseases but has recently been extended to non-neurodegenerative diseases, including chronic obstructive pulmonary, autoimmune, and ophthalmologic diseases. This review focuses on opportunities for DPM-TR in clinical practice and drug development and discusses its current status and challenges.
Topics: Humans; Disease Progression; Chronic Disease; Biomarkers; Drug Development; Animals; Models, Biological
PubMed: 38710372
DOI: 10.1016/j.pharmthera.2024.108655