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Wang Y, Li M, Haughton D, Kazis LE. Transition of mild cognitive impairment to Alzheimer's disease: Medications as modifiable risk factors. PLoS One 2024; 19:e0306270. [PMID: 39141609 DOI: 10.1371/journal.pone.0306270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 06/13/2024] [Indexed: 08/16/2024] Open
Abstract
BACKGROUND Mild cognitive impairment (MCI) is a pre-clinical stage of Alzheimer's disease (AD). Understanding the transition probabilities across the disease continuum of AD, ranging from MCI to AD to Mortality is crucial for the economic modeling of AD and effective planning of future interventions and healthcare resource allocation decisions. This study uses the Multi-state Markov model to quantify the transition probabilities along the disease progression and specifically investigates medications as modifiable risk factors of AD associated with accelerated or decelerated transition times from MCI to AD, MCI to mortality, and AD to mortality. METHODS Individuals with MCI were identified from the National Alzheimer's Coordinating Center between September 2005 and May 2021. A three-state Markov model was postulated to model the disease progression among three states: MCI, AD, and mortality with adjustment for demographics, genetic characteristics, comorbidities and medications. Transition probabilities, the total length of stay in each state, and the hazard ratios of the use of medications for diabetes, hypertension, and hypercholesterolemia (the known modifiable risk factors of AD) were evaluated for these transitions. RESULTS 3,324 individuals with MCI were identified. The probability of developing AD after one year since the initial diagnosis of MCI is 14.9%. After approximately 6 years from the initial diagnosis of MCI, the probability of transitioning to AD increases to nearly 41.7% before experiencing a subsequent decline. The expected total lengths of stay were 5.38 (95% CI: 0.002-6.03) years at MCI state and 7.61 (95%CI: 0.002-8.88) years at AD state. Patients with active use of lipid-lowering agents were associated with significantly lower hazards of transitioning from MCI to AD (HR: 0.83, 95%CI:0.71-0.96), MCI to mortality (HR: 0.51, 95%CI:0.34-0.77), and AD to mortality (HR: 0.81, 95%CI:0.66-0.99). CONCLUSIONS Results suggest that lipid-lowering agents may confer a protective effect, delaying the onset of AD. Additionally, lipid-lowering agents indicate a favorable association with a longer survival time.
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Affiliation(s)
- Ying Wang
- Department of Mathematical Sciences, Bentley University, Waltham, Massachusetts, United States of America
- School of Computing and Data Science, Wentworth Institute of Technology, Boston, Massachusetts, United States of America
- Geriatric Research Education and Clinical Center, Bedford VA Healthcare System, Bedford, Massachusetts, United States of America
| | - Mingfei Li
- Department of Mathematical Sciences, Bentley University, Waltham, Massachusetts, United States of America
- Center for Healthcare Organization and Implementation Research, Bedford VA Healthcare System, Bedford, Massachusetts, United States of America
| | - Dominique Haughton
- Department of Mathematical Sciences, Bentley University, Waltham, Massachusetts, United States of America
- Affiliated Researcher, Université Paris 1 (SAMM), Paris, France
- Affiliated Researcher, Université Toulouse 1 (TSE-R), Toulouse, France
| | - Lewis E Kazis
- Department of Health Law, Policy and Management, Boston University School of Public Health, Boston, Massachusetts, United States of America
- Rehabilitation Outcomes Center (ROC), Spaulding Rehabilitation Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
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Tahami Monfared AA, Fu S, Hummel N, Qi L, Chandak A, Zhang R, Zhang Q. Estimating Transition Probabilities Across the Alzheimer's Disease Continuum Using a Nationally Representative Real-World Database in the United States. Neurol Ther 2023; 12:1235-1255. [PMID: 37256433 PMCID: PMC10310620 DOI: 10.1007/s40120-023-00498-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 05/12/2023] [Indexed: 06/01/2023] Open
Abstract
INTRODUCTION Clinical Alzheimer's disease (AD) begins with mild cognitive impairment (MCI) and progresses to mild, moderate, or severe dementia, constituting a disease continuum that eventually leads to death. This study aimed to estimate the probabilities of transitions across those disease states. METHODS We developed a mixed-effects multi-state Markov model to estimate the transition probabilities, adjusted for 5 baseline covariates, using the Health and Retirement Study (HRS) database. HRS surveys older adults in the United States bi-annually. Alzheimer states were defined using the modified Telephone Interview of Cognitive Status (TICS-m). RESULTS A total of 11,292 AD patients were analyzed. Patients were 70.8 ± 9.0 years old, 54.9% female, and with 12.0 ± 3.3 years of education. Within 1 year from the initial state, the model estimated a higher probability of transition to the next AD state in earlier disease: 12.8% from MCI to mild AD and 5.0% from mild to moderate AD, but < 1% from moderate to severe AD. After 10 years, the probability of transition to the next state was markedly higher for all states, but still higher in earlier disease: 29.8% from MCI to mild AD, 23.5% from mild to moderate AD, and 5.7% from moderate to severe AD. Across all AD states, the probability of transition to death was < 5% after 1 year and > 15% after 10 years. Older age, fewer years of education, unemployment, and nursing home stay were associated with a higher risk of disease progression (p < 0.01). CONCLUSIONS This analysis shows that the risk of progression is greater in earlier AD states, increases over time, and is higher in patients who are older, with fewer years of education, unemployed, or in a nursing home at baseline. The estimated transition probabilities can provide guidance for future disease management and clinical trial design optimization, and can be used to refine existing cost-effectiveness frameworks.
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Affiliation(s)
- Amir Abbas Tahami Monfared
- Eisai Inc., 200 Metro Blvd, Nutley, NJ, 07110, USA.
- Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada.
| | - Shuai Fu
- Certara, Integrated Drug Development, Office 610, South Tower, HongKong Plaza, No. 283 Huaihai Road Middle, Huangpu District, Shanghai, China
| | - Noemi Hummel
- Certara GmbH, Chesterplatz 1, 79539, Lörrach, Germany
| | - Luyuan Qi
- Certara Sarl, 54 Rue de Londres, 75008, Paris, France
| | - Aastha Chandak
- Certara Inc., 100 Overlook Center, Suite 101, Princeton, NJ, 08540, USA
| | | | - Quanwu Zhang
- Eisai Inc., 200 Metro Blvd, Nutley, NJ, 07110, USA
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Herrero-Zazo M, Fitzgerald T, Taylor V, Street H, Chaudhry AN, Bradley JR, Birney E, Keevil VL. Using machine learning to model older adult inpatient trajectories from electronic health records data. iScience 2022; 26:105876. [PMID: 36691609 PMCID: PMC9860485 DOI: 10.1016/j.isci.2022.105876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 10/25/2022] [Accepted: 12/20/2022] [Indexed: 12/26/2022] Open
Abstract
Electronic Health Records (EHR) data can provide novel insights into inpatient trajectories. Blood tests and vital signs from de-identified patients' hospital admission episodes (AE) were represented as multivariate time-series (MVTS) to train unsupervised Hidden Markov Models (HMM) and represent each AE day as one of 17 states. All HMM states were clinically interpreted based on their patterns of MVTS variables and relationships with clinical information. Visualization differentiated patients progressing toward stable 'discharge-like' states versus those remaining at risk of inpatient mortality (IM). Chi-square tests confirmed these relationships (two states associated with IM; 12 states with ≥1 diagnosis). Logistic Regression and Random Forest (RF) models trained with MVTS data rather than states had higher prediction performances of IM, but results were comparable (best RF model AUC-ROC: MVTS data = 0.85; HMM states = 0.79). ML models extracted clinically interpretable signals from hospital data. The potential of ML to develop decision-support tools for EHR systems warrants investigation.
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Affiliation(s)
- Maria Herrero-Zazo
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
- Department of Medicine for the Elderly, Addenbrooke’s Hospital, Cambridge University Hospitals NHS Foundation Trust, Hills Road, Cambridge CB2 0QQ, UK
| | - Tomas Fitzgerald
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Vince Taylor
- Cambridge Clinical Informatics, Addenbrooke’s Hospital, Cambridge University Hospitals NHS Foundation Trust, Hills Road, Cambridge CB2 0QQ, UK
| | - Helen Street
- Research and Development, Cambridge University Hospitals NHS Foundation Trust, Hills Road, Cambridge CB2 0QQ, UK
| | - Afzal N. Chaudhry
- Department of Medicine, University of Cambridge, Addenbrooke’s Hospital, Hills Road, Cambridge CB2 0QQ, UK
- NIHR Cambridge Biomedical Research Centre, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK
| | - John R. Bradley
- Department of Medicine, University of Cambridge, Addenbrooke’s Hospital, Hills Road, Cambridge CB2 0QQ, UK
- NIHR Cambridge Biomedical Research Centre, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK
| | - Ewan Birney
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
- Corresponding author
| | - Victoria L. Keevil
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
- Department of Medicine for the Elderly, Addenbrooke’s Hospital, Cambridge University Hospitals NHS Foundation Trust, Hills Road, Cambridge CB2 0QQ, UK
- Department of Medicine, University of Cambridge, Addenbrooke’s Hospital, Hills Road, Cambridge CB2 0QQ, UK
- Corresponding author
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4
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Comparative Analysis of Performance Metrics for Machine Learning Classifiers with a Focus on Alzheimer's Disease Data. ACTA INFORMATICA PRAGENSIA 2022. [DOI: 10.18267/j.aip.198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
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Yang J, Wang S. A Novel Coupling Model of Physiological Degradation and Emotional State for Prediction of Alzheimer's Disease Progression. Brain Sci 2022; 12:1132. [PMID: 36138868 PMCID: PMC9496856 DOI: 10.3390/brainsci12091132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 08/19/2022] [Accepted: 08/22/2022] [Indexed: 11/16/2022] Open
Abstract
The prediction of Alzheimer's disease (AD) progression plays a very important role in the early intervention of patients and the improvement of life quality. Cognitive scales are commonly used to assess the patient's status. However, due to the complicated pathogenesis of AD and the individual differences in AD, the prediction of AD progression is challenging. This paper proposes a novel coupling model (P-E model) that takes into account the processes of physiological degradation and emotional state transition of AD patients. We conduct experiments on synthetic data to validate the effectiveness of the proposed P-E model. Next, we conduct experiments on 134 subjects with more than 10 follow-ups from the Alzheimer's Disease Neuroimaging Initiative. The prediction performance of the P-E model is significantly better than other state-of-the-art methods, which achieves the mean squared error of 7.137 ± 0.035. The experimental results show that the P-E model can well characterize the non-monotonic properties of AD cognitive data and can also have a good predictive ability for time series data with individual differences.
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Affiliation(s)
- Jiawei Yang
- School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
| | - Shaoping Wang
- School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing 100191, China
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Birkenbihl C, Salimi Y, Fröhlich H. Unraveling the heterogeneity in Alzheimer's disease progression across multiple cohorts and the implications for data-driven disease modeling. Alzheimers Dement 2022; 18:251-261. [PMID: 34109729 DOI: 10.1002/alz.12387] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 04/19/2021] [Accepted: 04/25/2021] [Indexed: 12/18/2022]
Abstract
INTRODUCTION Given study-specific inclusion and exclusion criteria, Alzheimer's disease (AD) cohort studies effectively sample from different statistical distributions. This heterogeneity can propagate into cohort-specific signals and subsequently bias data-driven investigations of disease progression patterns. METHODS We built multi-state models for six independent AD cohort datasets to statistically compare disease progression patterns across them. Additionally, we propose a novel method for clustering cohorts with regard to their progression signals. RESULTS We identified significant differences in progression patterns across cohorts. Models trained on cohort data learned cohort-specific effects that bias their estimations. We demonstrated how six cohorts relate to each other regarding their disease progression. DISCUSSION Heterogeneity in cohort datasets impedes the reproducibility of data-driven results and validation of progression models generated on single cohorts. To ensure robust scientific insights, it is advisable to externally validate results in independent cohort datasets. The proposed clustering assesses the comparability of cohorts in an unbiased, data-driven manner.
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Affiliation(s)
- Colin Birkenbihl
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Yasamin Salimi
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Holger Fröhlich
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
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Howlett J, Hill SM, Ritchie CW, Tom BDM. Disease Modelling of Cognitive Outcomes and Biomarkers in the European Prevention of Alzheimer's Dementia Longitudinal Cohort. Front Big Data 2021; 4:676168. [PMID: 34490422 PMCID: PMC8417903 DOI: 10.3389/fdata.2021.676168] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 07/30/2021] [Indexed: 12/04/2022] Open
Abstract
A key challenge for the secondary prevention of Alzheimer’s dementia is the need to identify individuals early on in the disease process through sensitive cognitive tests and biomarkers. The European Prevention of Alzheimer’s Dementia (EPAD) consortium recruited participants into a longitudinal cohort study with the aim of building a readiness cohort for a proof-of-concept clinical trial and also to generate a rich longitudinal data-set for disease modelling. Data have been collected on a wide range of measurements including cognitive outcomes, neuroimaging, cerebrospinal fluid biomarkers, genetics and other clinical and environmental risk factors, and are available for 1,828 eligible participants at baseline, 1,567 at 6 months, 1,188 at one-year follow-up, 383 at 2 years, and 89 participants at three-year follow-up visit. We novelly apply state-of-the-art longitudinal modelling and risk stratification approaches to these data in order to characterise disease progression and biological heterogeneity within the cohort. Specifically, we use longitudinal class-specific mixed effects models to characterise the different clinical disease trajectories and a semi-supervised Bayesian clustering approach to explore whether participants can be stratified into homogeneous subgroups that have different patterns of cognitive functioning evolution, while also having subgroup-specific profiles in terms of baseline biomarkers and longitudinal rate of change in biomarkers.
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Affiliation(s)
- James Howlett
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
| | - Steven M Hill
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
| | - Craig W Ritchie
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Brian D M Tom
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
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8
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Ficiarà E, Crespi V, Gadewar SP, Thomopoulos SI, Boyd J, Thompson PM, Jahanshad N, Pizzagalli F. Predicting Progression from Mild Cognitive Impairment to Alzheimer's Disease using MRI-based Cortical Features and a Two-State Markov Model. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2021; 2021:1145-1149. [PMID: 35321154 PMCID: PMC8935949 DOI: 10.1109/isbi48211.2021.9434143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Magnetic resonance imaging (MRI) has a potential for early diagnosis of individuals at risk for developing Alzheimer's disease (AD). Cognitive performance in healthy elderly people and in those with mild cognitive impairment (MCI) has been associated with measures of cortical gyrification [1] and thickness (CT) [2], yet the extent to which sulcal measures can help to predict AD conversion above and beyond CT measures is not known. Here, we analyzed 721 participants with MCI from phases 1 and 2 of the Alzheimer's Disease Neuroimaging Initiative, applying a two-state Markov model to study the conversion from MCI to AD condition. Our preliminary results suggest that MRI-based cortical features, including sulcal morphometry, may help to predict conversion from MCI to AD.
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Affiliation(s)
| | - Valentino Crespi
- Information Sciences Institute (ISI), AI Division, University of Southern California, USA
| | - Shruti Prashant Gadewar
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
| | - Joshua Boyd
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
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Gustavsson A, Pemberton-Ross P, Gomez Montero M, Hashim M, Thompson R. Challenges in demonstrating the value of disease-modifying therapies for Alzheimer’s disease. Expert Rev Pharmacoecon Outcomes Res 2020; 20:563-570. [DOI: 10.1080/14737167.2020.1822738] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Affiliation(s)
- Anders Gustavsson
- Quantify Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institute, Stockholm, Sweden
| | | | | | | | - Robin Thompson
- Value & Access, Biogen International GmbH, Baar, Switzerland
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10
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Zhou X, Kang K, Song X. Two-part hidden Markov models for semicontinuous longitudinal data with nonignorable missing covariates. Stat Med 2020; 39:1801-1816. [PMID: 32101332 DOI: 10.1002/sim.8513] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Revised: 01/09/2020] [Accepted: 01/31/2020] [Indexed: 11/10/2022]
Abstract
This study develops a two-part hidden Markov model (HMM) for analyzing semicontinuous longitudinal data in the presence of missing covariates. The proposed model manages a semicontinuous variable by splitting it into two random variables: a binary indicator for determining the occurrence of excess zeros at all occasions and a continuous random variable for examining its actual level. For the continuous longitudinal response, an HMM is proposed to describe the relationship between the observation and unobservable finite-state transition processes. The HMM consists of two major components. The first component is a transition model for investigating how potential covariates influence the probabilities of transitioning from one hidden state to another. The second component is a conditional regression model for examining the state-specific effects of covariates on the response. A shared random effect is introduced to each part of the model to accommodate possible unobservable heterogeneity among observation processes and the nonignorability of missing covariates. A Bayesian adaptive least absolute shrinkage and selection operator (lasso) procedure is developed to conduct simultaneous variable selection and estimation. The proposed methodology is applied to a study on the Alzheimer's Disease Neuroimaging Initiative dataset. New insights into the pathology of Alzheimer's disease and its potential risk factors are obtained.
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Affiliation(s)
- Xiaoxiao Zhou
- Department of Statistics, Chinese University of Hong Kong, Hong Kong
| | - Kai Kang
- Department of Statistics, Chinese University of Hong Kong, Hong Kong
| | - Xinyuan Song
- Department of Statistics, Chinese University of Hong Kong, Hong Kong
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11
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Li Y, Zhang L, Maiti T. High dimensional classification for spatially dependent data with application to neuroimaging. Electron J Stat 2020. [DOI: 10.1214/20-ejs1743] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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12
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Li Y, Zhang L, Bozoki A, Zhu DC, Choi J, Maiti T. Early prediction of Alzheimer’s disease using longitudinal volumetric MRI data from ADNI. HEALTH SERVICES AND OUTCOMES RESEARCH METHODOLOGY 2019. [DOI: 10.1007/s10742-019-00206-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
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13
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Ventimiglia E, Van Hemelrijck M, Lindhagen L, Stattin P, Garmo H. How to measure temporal changes in care pathways for chronic diseases using health care registry data. BMC Med Inform Decis Mak 2019; 19:103. [PMID: 31146754 PMCID: PMC6543619 DOI: 10.1186/s12911-019-0823-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2018] [Accepted: 05/20/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Disease trajectories for chronic diseases can span over several decades, with several time-dependent factors affecting treatment decisions. Thus, there is a need for long-term predictions of disease trajectories to inform patients and healthcare professionals on the long-term outcomes and provide information on the need of future health care. Here, we propose a state transition model to describe and predict disease trajectories up to 25 years after diagnosis in men with prostate cancer (PCa), as a proof of principle. METHODS States, state transitions, and transition probabilities were identified and estimated in Prostate Cancer data Base of Sweden (PCBaSeTraject), using nationwide population-based data from 118,743 men diagnosed with PCa. A state transition model in discrete time steps (i.e., 4 weeks) was developed and applied to capture all possible transitions (PCBaSeSim). Transition probabilities were estimated for changes in both treatment and comorbidity. These models combined yielded parameter estimates to run an individual-level simulation based on the state-transition model to obtain prediction estimates. Predicted estimates were then compared to real world data in PCBaSeTraject. RESULTS PCBaSeSim estimates for the cumulative incidence of first and second transitions, death from PCa and death from other causes were compared to observed transitions in PCBaSeTraject. A good agreement was found between simulated and observed estimates. CONCLUSIONS We developed a reliable and accurate simulation tool, PCBaSeSim that provides information on disease trajectories for subjects with a chronic disease on an individual and population-based level.
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Affiliation(s)
- Eugenio Ventimiglia
- Division of Experimental Oncology/Unit of Urology, IRCCS Ospedale San Raffaele, Milan, Italy.,Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Mieke Van Hemelrijck
- King's College London, School of Cancer and Pharmaceutical Sciences, Translational Oncology & Urology Research (Tour), 3rd Floor, Bermondsey Wing, Guy's Hospital, London, SE1 9RT, UK
| | | | - Pär Stattin
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Hans Garmo
- King's College London, School of Cancer and Pharmaceutical Sciences, Translational Oncology & Urology Research (Tour), 3rd Floor, Bermondsey Wing, Guy's Hospital, London, SE1 9RT, UK. .,Regional Cancer Centre, Uppsala Örebro, Uppsala, Sweden.
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Yan T, He B, Xu M, Wu B, Xiao F, Bi K, Jia Y. Kaempferide prevents cognitive decline via attenuation of oxidative stress and enhancement of brain‐derived neurotrophic factor/tropomyosin receptor kinase B/cAMP response element‐binding signaling pathway. Phytother Res 2019; 33:1065-1073. [DOI: 10.1002/ptr.6300] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2018] [Revised: 12/17/2018] [Accepted: 01/11/2019] [Indexed: 01/03/2023]
Affiliation(s)
- Tingxu Yan
- School of Functional Food and WineShenyang Pharmaceutical University Shenyang China
| | - Bosai He
- School of Functional Food and WineShenyang Pharmaceutical University Shenyang China
| | - Mengjie Xu
- School of Traditional Chinese Materia MedicaShenyang Pharmaceutical University Shenyang China
| | - Bo Wu
- School of Functional Food and WineShenyang Pharmaceutical University Shenyang China
| | - Feng Xiao
- School of Functional Food and WineShenyang Pharmaceutical University Shenyang China
| | - Kaishun Bi
- School of PharmacyShenyang Pharmaceutical University Shenyang China
| | - Ying Jia
- School of Functional Food and WineShenyang Pharmaceutical University Shenyang China
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