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Ge X, Cui K, Qin Y, Chen D, Han H, Yu H. Screening strategies and dynamic risk prediction models for Alzheimer's disease. J Psychiatr Res 2023; 166:92-99. [PMID: 37757706 DOI: 10.1016/j.jpsychires.2023.09.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 07/16/2023] [Accepted: 09/15/2023] [Indexed: 09/29/2023]
Abstract
BACKGROUND Characterizing the progression from Mild cognitive impairment (MCI) to Alzheimer's disease (AD) is essential for early AD prevention and targeted intervention. Our goal was to construct precise screening schemes for individuals with different risk of AD and to establish prognosis models for them. METHODS We constructed a retrospective cohort by reviewing individuals with baseline diagnosis of MCI and at least one follow-up visits between November 2005 and May 2021. They were stratified into high-risk and low-risk groups with longitudinal cognitive trajectory. Then, we established a screening framework and obtained optimal screening strategies for two risk groups. Cox and random survival forest (RSF) models were developed for dynamic prognosis prediction. RESULTS In terms of screening strategies, the combination of Clinical Dementia Rating Sum of Boxes (CDRSB) and hippocampus volume was recommended for the high-risk MCI group, while the combination of Alzheimer's Disease Assessment Scale Cognitive 13 items (ADAS13) and FAQ was recommended for low-risk MCI group. The concordance index (C-index) of the Cox model for the high-risk group was 0.844 (95% CI: 0.815-0.873) and adjustments for demographic information and APOE ε4. The RSF model incorporating longitudinal ADAS13, FAQ, and demographic information and APOE ε4 performed for the low-risk group. CONCLUSION This precise screening scheme will optimize allocation of medical resources and reduce the economic burden on individuals with low risk of MCI. Moreover, dynamic prognosis models may be helpful for early identification of individuals at risk and clinical decisions, which will promote the secondary prevention of AD.
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Affiliation(s)
- Xiaoyan Ge
- Department of Health Statistics, School of Public Health, Jinzhou Medical University, 40 SongPo Road, Jinzhou, China.
| | - Kai Cui
- Department of Health Statistics, School of Public Health, Jinzhou Medical University, 40 SongPo Road, Jinzhou, China.
| | - Yao Qin
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 XinJian South Road, Taiyuan, China.
| | - Durong Chen
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 XinJian South Road, Taiyuan, China.
| | - Hongjuan Han
- Department of Mathematics, School of Basic Medical Sciences, Shanxi Medical University, Taiyuan, China.
| | - Hongmei Yu
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 XinJian South Road, Taiyuan, China; Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, 56 XinJian South Road, Taiyuan, China.
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2
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Xing D, Chen L, Zhang W, Yi Q, Huang H, Wu J, Yu W, Lü Y. Prediction of 3-Year Survival in Patients with Cognitive Impairment Based on Demographics, Neuropsychological Data, and Comorbidities: A Prospective Cohort Study. Brain Sci 2023; 13:1220. [PMID: 37626576 PMCID: PMC10452564 DOI: 10.3390/brainsci13081220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 08/15/2023] [Accepted: 08/18/2023] [Indexed: 08/27/2023] Open
Abstract
OBJECTIVES Based on readily available demographic data, neuropsychological assessment results, and comorbidity data, we aimed to develop and validate a 3-year survival prediction model for patients with cognitive impairment. METHODS In this prospective cohort study, 616 patients with cognitive impairment were included. Demographic information, data on comorbidities, and scores of the Mini-Mental State Examination (MMSE), Instrumental Activities of Daily Living (IADL) scale, and Neuropsychiatric Inventory Questionnaire were collected. Survival status was determined via telephone interviews and further verified in the official death register in the third year. A 7:3 ratio was used to divide patients into the training and validation sets. Variables with statistical significance (p < 0.05) in the single-factor analysis were incorporated into the binary logistic regression model. A nomogram was constructed according to multivariate analysis and validated. RESULTS The final cohort included 587 patients, of whom 525 (89.44%) survived and 62 (10.56%) died. Younger age, higher MMSE score, lower IADL score, absence of disinhibition, and Charlson comorbidity index score ≤ 1 were all associated with 3-year survival. These predictors yielded good discrimination with C-indices of 0.80 (0.73-0.87) and 0.85 (0.77-0.94) in the training and validation cohorts, respectively. According to the Hosmer-Lemeshow test results, neither cohort displayed any statistical significance, and calibration curves displayed a good match between predictions and results. CONCLUSIONS Our study provided further insight into the factors contributing to the survival of patients with cognitive impairment. CLINICAL IMPLICATIONS Our model showed good accuracy and discrimination ability, and it can be used at community hospitals or primary care facilities that lack sophisticated equipment.
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Affiliation(s)
- Dianxia Xing
- Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China; (D.X.)
- Department of Geriatrics, Chongqing University Three Gorges Hospital, Chongqing 404100, China
| | - Lihua Chen
- Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China; (D.X.)
| | - Wenbo Zhang
- Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China; (D.X.)
| | - Qingjie Yi
- Department of Quality Control, Chongqing University Three Gorges Hospital, Chongqing 404100, China
| | - Hong Huang
- Department of Geriatrics, Chongqing University Three Gorges Hospital, Chongqing 404100, China
| | - Jiani Wu
- Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China; (D.X.)
| | - Weihua Yu
- Institute of Neuroscience, Chongqing Medical University, Chongqing 400016, China
| | - Yang Lü
- Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China; (D.X.)
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Katabathula S, Davis PB, Xu R. Comorbidity-driven multi-modal subtype analysis in mild cognitive impairment of Alzheimer's disease. Alzheimers Dement 2023; 19:1428-1439. [PMID: 36166485 PMCID: PMC10040466 DOI: 10.1002/alz.12792] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
BACKGROUND Mild cognitive impairment (MCI) is a heterogeneous condition with high individual variabilities in clinical outcomes driven by patient demographics, genetics, brain structure features, blood biomarkers, and comorbidities. Multi-modality data-driven approaches have been used to discover MCI subtypes; however, disease comorbidities have not been included as a modality though multiple diseases including hypertension are well-known risk factors for Alzheimer's disease (AD). The aim of this study was to examine MCI heterogeneity in the context of AD-related comorbidities along with other AD-relevant features and biomarkers. METHODS A total of 325 MCI subjects with 32 AD-relevant comorbidities and features were considered. Mixed-data clustering is applied to discover and compare MCI subtypes with and without including AD-related comorbidities. Finally, the relevance of each comorbidity-driven subtype was determined by examining their MCI to AD disease prognosis, descriptive statistics, and conversion rates. RESULTS We identified four (five) MCI subtypes: poor-, average-, good-, and best-AD prognosis by including comorbidities (without including comorbidities). We demonstrated that comorbidity-driven MCI subtypes differed from those identified without comorbidity information. We further demonstrated the clinical relevance of comorbidity-driven MCI subtypes. Among the four comorbidity-driven MCI subtypes there were substantial differences in the proportions of participants who reverted to normal function, remained stable, or converted to AD. The groups showed different behaviors, having significantly different MCI to AD prognosis, significantly different means for cognitive test-related and plasma features, and by the proportion of comorbidities. CONCLUSIONS Our study indicates that AD comorbidities should be considered along with other diverse AD-relevant characteristics to better understand MCI heterogeneity.
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Affiliation(s)
- Sreevani Katabathula
- Center for Artificial Intelligence in Drug Discovery, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Pamela B Davis
- Center for Community Health Integration, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Rong Xu
- Center for Artificial Intelligence in Drug Discovery, Case Western Reserve University School of Medicine, Cleveland, OH, USA
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4
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Karaman BK, Mormino EC, Sabuncu MR. Machine learning based multi-modal prediction of future decline toward Alzheimer's disease: An empirical study. PLoS One 2022; 17:e0277322. [PMID: 36383528 PMCID: PMC9668188 DOI: 10.1371/journal.pone.0277322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 10/24/2022] [Indexed: 11/17/2022] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative condition that progresses over decades. Early detection of individuals at high risk of future progression toward AD is likely to be of critical significance for the successful treatment and/or prevention of this devastating disease. In this paper, we present an empirical study to characterize how predictable an individual subjects' future AD trajectory is, several years in advance, based on rich multi-modal data, and using modern deep learning methods. Crucially, the machine learning strategy we propose can handle different future time horizons and can be trained with heterogeneous data that exhibit missingness and non-uniform follow-up visit times. Our experiments demonstrate that our strategy yields predictions that are more accurate than a model trained on a single time horizon (e.g. 3 years), which is common practice in prior literature. We also provide a comparison between linear and nonlinear models, verifying the well-established insight that the latter can offer a boost in performance. Our results also confirm that predicting future decline for cognitively normal (CN) individuals is more challenging than for individuals with mild cognitive impairment (MCI). Intriguingly, however, we discover that prediction accuracy decreases with increasing time horizon for CN subjects, but the trend is in the opposite direction for MCI subjects. Additionally, we quantify the contribution of different data types in prediction, which yields novel insights into the utility of different biomarkers. We find that molecular biomarkers are not as helpful for CN individuals as they are for MCI individuals, whereas magnetic resonance imaging biomarkers (hippocampus volume, specifically) offer a significant boost in prediction accuracy for CN individuals. Finally, we show how our model's prediction reveals the evolution of individual-level progression risk over a five-year time horizon. Our code is available at https://github.com/batuhankmkaraman/mlbasedad.
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Affiliation(s)
- Batuhan K. Karaman
- School of Electrical and Computer Engineering, Cornell University and Cornell Tech, New York, NY, United States of America
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States of America
| | - Elizabeth C. Mormino
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, United States of America
| | - Mert R. Sabuncu
- School of Electrical and Computer Engineering, Cornell University and Cornell Tech, New York, NY, United States of America
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States of America
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Monteiro JC, Yokomichi ALY, de Carvalho Bovolato AL, Schelp AO, Ribeiro SJL, Deffune E, Moraes MLD. Alzheimer's disease diagnosis based on detection of autoantibodies against Aβ using Aβ40 peptide in liposomes. Clin Chim Acta 2022; 531:223-229. [PMID: 35447142 DOI: 10.1016/j.cca.2022.04.235] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 04/14/2022] [Accepted: 04/15/2022] [Indexed: 11/03/2022]
Abstract
BACKGROUND Alzheimer's disease (AD) is the most common form of dementia and affect more than 50 million people worldwide. Thus, there is a high demand by non-invasive methods for an early diagnosis. This work explores the AD diagnostic using the amyloid beta 1-40 (Aβ40) peptide encapsulated into dipalmitoyl phosphatidyl glycerol (DPPG) liposomes and immobilized on polyethylene imine previously deposited on screen-printed carbon electrodes to detect autoantibodies against Aβ40, a potential biomarker found in plasma samples. METHODS The immunosensor assembly was accompanied by atomic force microscopy (AFM) images that showed globular aggregates from 20 to 200 nm corresponding liposomes and by cyclic voltammetry (CV) through increase of the voltammogram area each material deposited. After building the immunosensor, when it was exposed to antibody anti-Aβ40, there was an increase in film roughness of approximately 9 nm, indicating the formation of the immunocomplex. RESULTS In the detection by CV, the presence of specific antibody, in the range of 0.1 to 10 μg/ml, resulted in an increase in the voltammograms area and current in 0.45 V reaching 3.2 µA.V and 5.7 μA, respectively, in comparison with the control system, which remained almost unchanged from 0.1 μg/ml. In patient samples, both cerebrospinal fluid (CSF) and plasma, was possible separated among positive and negative samples for AD using CV profile and area, with a difference of 0.1 μA.V from the upper error bar of healthy samples for CSF sample and 0.6 μA.V for plasma sample. CONCLUSIONS These results showed the feasibility of the method employed for the non-invasive diagnostic of Alzheimer's disease detecting natural autoantibodies that circulate in plasma through a simple and easy-to-interpret method.
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Affiliation(s)
- Júlio César Monteiro
- Universidade Federal de São Paulo, Instituto de Ciência e Tecnologia, São José dos Campos, SP, Brazil
| | - Anna Laura Yuri Yokomichi
- Universidade Federal de São Paulo, Instituto de Ciência e Tecnologia, São José dos Campos, SP, Brazil
| | | | - Arthur Oscar Schelp
- Universidade Estadual Paulista, Hemocentro de Botucatu, Botucatu, SP, Brazil
| | | | - Elenice Deffune
- Universidade Estadual Paulista, Hemocentro de Botucatu, Botucatu, SP, Brazil
| | - Marli Leite de Moraes
- Universidade Federal de São Paulo, Instituto de Ciência e Tecnologia, São José dos Campos, SP, Brazil.
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Zhudenkov K, Gavrilov S, Sofronova A, Stepanov O, Kudryashova N, Helmlinger G, Peskov K. A workflow for the joint modeling of longitudinal and event data in the development of therapeutics: Tools, statistical methods, and diagnostics. CPT Pharmacometrics Syst Pharmacol 2022; 11:425-437. [PMID: 35064957 PMCID: PMC9007602 DOI: 10.1002/psp4.12763] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 12/15/2021] [Accepted: 01/03/2022] [Indexed: 12/12/2022] Open
Abstract
Clinical trials investigate treatment endpoints that usually include measurements of pharmacodynamic and efficacy biomarkers in early‐phase studies and patient‐reported outcomes as well as event risks or rates in late‐phase studies. In recent years, a systematic trend in clinical trial data analytics and modeling has been observed, where retrospective data are integrated into a quantitative framework to prospectively support analyses of interim data and design of ongoing and future studies of novel therapeutics. Joint modeling is an advanced statistical methodology that allows for the investigation of clinical trial outcomes by quantifying the association between baseline and/or longitudinal biomarkers and event risk. Using an exemplar data set from non‐small cell lung cancer studies, we propose and test a workflow for joint modeling. It allows a modeling scientist to comprehensively explore the data, build survival models, investigate goodness‐of‐fit, and subsequently perform outcome predictions using interim biomarker data from an ongoing study. The workflow illustrates a full process, from data exploration to predictive simulations, for selected multivariate linear and nonlinear mixed‐effects models and software tools in an integrative and exhaustive manner.
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Affiliation(s)
| | - Sergey Gavrilov
- M&S Decisions LLC Moscow Russia
- The faculty of Computational Mathematics and Cybernetics Lomonosov MSU Moscow Russia
| | | | | | | | - Gabriel Helmlinger
- Clinical Pharmacology & Toxicology Obsidian Therapeutics Cambridge Massachusetts USA
| | - Kirill Peskov
- M&S Decisions LLC Moscow Russia
- Research Center of Model‐Informed Drug Development Sechenov First Moscow State Medical University Moscow Russia
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7
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Ben-Hassen C, Helmer C, Berr C, Jacqmin-Gadda H. Five-Year Dynamic Prediction of Dementia Using Repeated Measures of Cognitive Tests and a Dependency Scale. Am J Epidemiol 2022; 191:453-464. [PMID: 34753171 DOI: 10.1093/aje/kwab269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 09/23/2021] [Accepted: 11/04/2021] [Indexed: 11/13/2022] Open
Abstract
The progression of dementia prevalence over the years and the lack of efficient treatments to stop or reverse the cognitive decline make dementia a major public health challenge in the developed world. Identifying people at high risk of developing dementia could improve the treatment of these patients and help select the target population for preventive clinical trials. We used joint modeling to build a dynamic prediction tool of dementia based on the change over time of 2 neurocognitive tests (the Mini-Mental State Examination and the Isaacs Set Tests) as well as an autonomy scale (the Instrumental Activities of Daily Living). The model was estimated with data from the French cohort Personnes Agées QUID (1988-2015) and validated both by cross-validation and externally with data from the French Three City cohort (1999-2018). We evaluated its predictive abilities through area under the receiver operating characteristics curve and Brier score, accounting for right censoring and competing risk of death, and obtained an average area under the curve value of 0.95 for the risk of dementia in the next 5 or 10 years. This tool is able to discriminate a high-risk group of people from the rest of the population. This could be of help in clinical practice and research.
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8
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Tripathi S, Gupta U, Ujjwal RR, Yadav AK. Nano-lipidic formulation and therapeutic strategies for Alzheimer's disease via intranasal route. J Microencapsul 2021; 38:572-593. [PMID: 34591731 DOI: 10.1080/02652048.2021.1986585] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
AIM The inability of drug molecules to cross the 'Blood-Brain Barrier' restrict the effective treatment of Alzheimer's disease. Lipid nanocarriers have proven to be a novel paradigm in brain targeting of bioactive by facilitating suitable therapeutic concentrations to be attained in the brain. METHODS The relevant information regarding the title of this review article was collected from the peer-reviewed published articles. Also, the physicochemical properties, and their in vitro and in vivo evaluations were presented in this review article. RESULTS Administration of lipid-based nano-carriers have abilities to target the brain, improve the pharmacokinetic and pharmacodynamics properties of drugs, and mitigate the side effects of encapsulated therapeutic active agents. CONCLUSION Unlike oral and other routes, the Intranasal route promises high bioavailability, low first-pass effect, better pharmacokinetic properties, bypass of the systemic circulation, fewer incidences of unwanted side effects, and direct delivery of anti-AD drugs to the brain via circumventing 'Blood-Brain Barrier'.
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Affiliation(s)
- Shourya Tripathi
- Department of Pharmaceutics, National Institute of Pharmaceutical Education and Research- Raebareli, Lucknow, India
| | - Ujala Gupta
- Department of Pharmaceutics, National Institute of Pharmaceutical Education and Research- Raebareli, Lucknow, India
| | - Rewati Raman Ujjwal
- Department of Pharmaceutics, National Institute of Pharmaceutical Education and Research- Raebareli, Lucknow, India
| | - Awesh K Yadav
- Department of Pharmaceutics, National Institute of Pharmaceutical Education and Research- Raebareli, Lucknow, India
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9
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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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10
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Ge XY, Cui K, Liu L, Qin Y, Cui J, Han HJ, Luo YH, Yu HM. Screening and predicting progression from high-risk mild cognitive impairment to Alzheimer's disease. Sci Rep 2021; 11:17558. [PMID: 34475445 PMCID: PMC8413294 DOI: 10.1038/s41598-021-96914-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Accepted: 08/18/2021] [Indexed: 11/09/2022] Open
Abstract
Individuals with mild cognitive impairment (MCI) are clinically heterogeneous, with different risks of progression to Alzheimer's disease. Regular follow-up and examination may be time-consuming and costly, especially for MRI and PET. Therefore, it is necessary to identify a more precise MRI population. In this study, a two-stage screening frame was proposed for evaluating the predictive utility of additional MRI measurements among high-risk MCI subjects. In the first stage, the K-means cluster was performed for trajectory-template based on two clinical assessments. In the second stage, high-risk individuals were filtered out and imputed into prognosis models with varying strategies. As a result, the ADAS-13 was more sensitive for filtering out high-risk individuals among patients with MCI. The optimal model included a change rate of clinical assessments and three neuroimaging measurements and was significantly associated with a net reclassification improvement (NRI) of 0.246 (95% CI 0.021, 0.848) and integrated discrimination improvement (IDI) of 0.090 (95% CI - 0.062, 0.170). The ADAS-13 longitudinal models had the best discrimination performance (Optimism-corrected concordance index = 0.830), as validated by the bootstrap method. Considering the limited medical and financial resources, our findings recommend follow-up MRI examination 1 year after identification for high-risk individuals, while regular clinical assessments for low-risk individuals.
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Affiliation(s)
- Xiao-Yan Ge
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 XinJian South Road, Taiyuan, China
- Department of Health Statistics, School of Public Health, Jinzhou Medical University, 40 SongPo Road, Jinzhou, China
| | - Kai Cui
- Department of Health Statistics, School of Public Health, Jinzhou Medical University, 40 SongPo Road, Jinzhou, China
| | - Long Liu
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 XinJian South Road, Taiyuan, China
| | - Yao Qin
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 XinJian South Road, Taiyuan, China
| | - Jing Cui
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 XinJian South Road, Taiyuan, China
| | - Hong-Juan Han
- Department of Mathematics, School of Basic Medical Sciences, Shanxi Medical University, Taiyuan, China
| | - Yan-Hong Luo
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 XinJian South Road, Taiyuan, China
| | - Hong-Mei Yu
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 XinJian South Road, Taiyuan, China.
- Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, 56 XinJian South Road, Taiyuan, China.
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11
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Kumar S, Oh I, Schindler S, Lai AM, Payne PRO, Gupta A. Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review. JAMIA Open 2021; 4:ooab052. [PMID: 34350389 PMCID: PMC8327375 DOI: 10.1093/jamiaopen/ooab052] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 06/21/2021] [Accepted: 06/30/2021] [Indexed: 11/17/2022] Open
Abstract
OBJECTIVE Alzheimer disease (AD) is the most common cause of dementia, a syndrome characterized by cognitive impairment severe enough to interfere with activities of daily life. We aimed to conduct a systematic literature review (SLR) of studies that applied machine learning (ML) methods to clinical data derived from electronic health records in order to model risk for progression of AD dementia. MATERIALS AND METHODS We searched for articles published between January 1, 2010, and May 31, 2020, in PubMed, Scopus, ScienceDirect, IEEE Explore Digital Library, Association for Computing Machinery Digital Library, and arXiv. We used predefined criteria to select relevant articles and summarized them according to key components of ML analysis such as data characteristics, computational algorithms, and research focus. RESULTS There has been a considerable rise over the past 5 years in the number of research papers using ML-based analysis for AD dementia modeling. We reviewed 64 relevant articles in our SLR. The results suggest that majority of existing research has focused on predicting progression of AD dementia using publicly available datasets containing both neuroimaging and clinical data (neurobehavioral status exam scores, patient demographics, neuroimaging data, and laboratory test values). DISCUSSION Identifying individuals at risk for progression of AD dementia could potentially help to personalize disease management to plan future care. Clinical data consisting of both structured data tables and clinical notes can be effectively used in ML-based approaches to model risk for AD dementia progression. Data sharing and reproducibility of results can enhance the impact, adaptation, and generalizability of this research.
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Affiliation(s)
- Sayantan Kumar
- Institute for Informatics, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Inez Oh
- Institute for Informatics, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Suzanne Schindler
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Albert M Lai
- Institute for Informatics, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Philip R O Payne
- Institute for Informatics, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Aditi Gupta
- Institute for Informatics, Washington University School of Medicine, St. Louis, Missouri, USA
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12
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Zhang Y, Li Y, Ma L. Recent advances in research on Alzheimer's disease in China. J Clin Neurosci 2020; 81:43-46. [PMID: 33222956 DOI: 10.1016/j.jocn.2020.09.018] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 07/29/2020] [Accepted: 09/06/2020] [Indexed: 01/11/2023]
Abstract
China has the largest number of individuals with dementia worldwide. Alzheimer's disease (AD) is a growing global health issue that seriously threatens human health and quality of life and imposes a significant burden on families and society. To date, no treatment exists that can delay AD progression. This review describes the current understanding of AD in China, including its prevalence, cost burden, diagnosis, and treatment, and summarizes the major advances in AD in China, including government strategies and research. Such findings highlight the need for a brain health action plan to prevent and control AD and to reduce its increasing prevalence and dementia-related costs in China.
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Affiliation(s)
- Yaxin Zhang
- Department of Geriatrics, Xuanwu Hospital, Capital Medical University, China National Clinical Research Center for Geriatric Medicine, Beijing 100053, China
| | - Ying Li
- Department of Geriatrics, Xuanwu Hospital, Capital Medical University, China National Clinical Research Center for Geriatric Medicine, Beijing 100053, China
| | - Lina Ma
- Department of Geriatrics, Xuanwu Hospital, Capital Medical University, China National Clinical Research Center for Geriatric Medicine, Beijing 100053, China.
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