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He X, Wei R, Huang Y, Chen Z, Lyu T, Bost S, Tong J, Li L, Zhou Y, Li Z, Guo J, Tang H, Wang F, DeKosky S, Xu H, Chen Y, Zhang R, Xu J, Guo Y, Wu Y, Bian J. Develop and validate a computable phenotype for the identification of Alzheimer's disease patients using electronic health record data. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2024; 16:e12613. [PMID: 38966622 PMCID: PMC11220631 DOI: 10.1002/dad2.12613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 05/13/2024] [Accepted: 05/18/2024] [Indexed: 07/06/2024]
Abstract
INTRODUCTION Alzheimer's disease (AD) is often misclassified in electronic health records (EHRs) when relying solely on diagnosis codes. This study aimed to develop a more accurate, computable phenotype (CP) for identifying AD patients using structured and unstructured EHR data. METHODS We used EHRs from the University of Florida Health (UFHealth) system and created rule-based CPs iteratively through manual chart reviews. The CPs were then validated using data from the University of Texas Health Science Center at Houston (UTHealth) and the University of Minnesota (UMN). RESULTS Our best-performing CP was "patient has at least 2 AD diagnoses and AD-related keywords in AD encounters," with an F1-score of 0.817 at UF, 0.961 at UTHealth, and 0.623 at UMN, respectively. DISCUSSION We developed and validated rule-based CPs for AD identification with good performance, which will be crucial for studies that aim to use real-world data like EHRs. Highlights Developed a computable phenotype (CP) to identify Alzheimer's disease (AD) patients using EHR data.Utilized both structured and unstructured EHR data to enhance CP accuracy.Achieved a high F1-score of 0.817 at UFHealth, and 0.961 and 0.623 at UTHealth and UMN.Validated the CP across different demographics, ensuring robustness and fairness.
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Affiliation(s)
- Xing He
- Department of Health Outcomes & Biomedical InformaticsCollege of MedicineUniversity of FloridaGainesvilleFloridaUSA
| | - Ruoqi Wei
- Department of Health Outcomes & Biomedical InformaticsCollege of MedicineUniversity of FloridaGainesvilleFloridaUSA
| | - Yu Huang
- Department of Health Outcomes & Biomedical InformaticsCollege of MedicineUniversity of FloridaGainesvilleFloridaUSA
| | - Zhaoyi Chen
- Center for Biomedical Informatics & Information TechnologyNational Cancer InstituteRockvilleMarylandUSA
| | - Tianchen Lyu
- Department of Health Outcomes & Biomedical InformaticsCollege of MedicineUniversity of FloridaGainesvilleFloridaUSA
| | - Sarah Bost
- Department of Health Outcomes & Biomedical InformaticsCollege of MedicineUniversity of FloridaGainesvilleFloridaUSA
| | - Jiayi Tong
- Department of Biostatistics, Epidemiology and InformaticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Lu Li
- Department of Biostatistics, Epidemiology and InformaticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Yujia Zhou
- Biomedical Informatics and Data ScienceSchool of Medicine, YaleNew HavenConnecticutUSA
| | - Zhao Li
- School of Biomedical InformaticsUniversity of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Jingchuan Guo
- Department of Pharmaceutical Outcomes and PolicyCollege of PharmacyUniversity of FloridaGainesvilleFloridaUSA
| | - Huilin Tang
- Department of Pharmaceutical Outcomes and PolicyCollege of PharmacyUniversity of FloridaGainesvilleFloridaUSA
| | - Fei Wang
- Department of Population Health SciencesWeill Cornell MedicineNew YorkNew YorkUSA
| | - Steven DeKosky
- Department of NeurologyCollege of MedicineUniversity of FloridaGainesvilleFloridaUSA
| | - Hua Xu
- Biomedical Informatics and Data ScienceSchool of Medicine, YaleNew HavenConnecticutUSA
| | - Yong Chen
- Department of Biostatistics, Epidemiology and InformaticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Rui Zhang
- Division of Computational Health SciencesDepartment of SurgeryUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Jie Xu
- Department of Health Outcomes & Biomedical InformaticsCollege of MedicineUniversity of FloridaGainesvilleFloridaUSA
| | - Yi Guo
- Department of Health Outcomes & Biomedical InformaticsCollege of MedicineUniversity of FloridaGainesvilleFloridaUSA
| | - Yonghui Wu
- Department of Health Outcomes & Biomedical InformaticsCollege of MedicineUniversity of FloridaGainesvilleFloridaUSA
| | - Jiang Bian
- Department of Health Outcomes & Biomedical InformaticsCollege of MedicineUniversity of FloridaGainesvilleFloridaUSA
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Ye M, Chan LN, Douglas I, Margolis DJ, Langan SM, Abuabara K. Antihypertensive Medications and Eczematous Dermatitis in Older Adults. JAMA Dermatol 2024; 160:710-716. [PMID: 38776099 PMCID: PMC11112493 DOI: 10.1001/jamadermatol.2024.1230] [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: 10/11/2023] [Accepted: 03/23/2024] [Indexed: 05/25/2024]
Abstract
Importance Rates of physician-diagnosed eczema have been increasing among older adults, but little is known regarding the pathophysiologic processes and best treatments in this subgroup. Preliminary data suggest that medications-antihypertensive medications in particular-may contribute to eczematous dermatitis; however, there are limited population-based data on the proportion of eczematous dermatitis diagnoses among older adults that may be attributed to antihypertensive drugs. Objectives To determine whether antihypertensive drug use is associated with eczematous dermatitis in older adults. Design, Settings, and Participants This was a longitudinal cohort study of a population-based sample of individuals 60 years and older without a diagnosis of eczematous dermatitis at baseline. It was conducted at primary care practices participating in The Health Improvement Network in the United Kingdom from January 1, 1994, to January 1, 2015. Data analyses were performed from January 6, 2020, to February 6, 2024. Exposure Exposure date by first prescription for an antihypertensive drug within each drug class. Main outcome measures Newly active eczematous dermatitis was based on the first date for 1 of the 5 most common eczema codes used in a previously validated algorithm. Results Among the total study sample of 1 561 358 older adults (mean [SD] age, 67 [9] years; 54% female), the overall prevalence of eczematous dermatitis was 6.7% during a median (IQR) follow-up duration of 6 (3-11) years. Eczematous dermatitis incidence was higher among participants receiving antihypertensive drugs than those who did not (12 vs 9 of 1000 person-years of follow-up). Adjusted Cox proportional hazard models found that participants who received any antihypertensive drugs had a 29% increased hazard rate of any eczematous dermatitis (hazard ratio [HR], 1.29; 95% CI, 1.26-1.31). When assessing each antihypertensive drug class individually, the largest effect size was observed for diuretic drugs (HR, 1.21; 95% CI, 1.19-1.24) and calcium channel blockers (HR, 1.16; 95% CI, 1.14-1.18), and the smallest effect sizes were for angiotensin-converting enzyme inhibitors (HR, 1.02; 95% CI, 1.00-1.04) and β-blockers (HR, 1.04; 95% CI, 1.02-1.06). Conclusions and Relevance This cohort study found that antihypertensive drugs were associated with a small increased rate of eczematous dermatitis, with effect sizes largest for calcium channel blockers and diuretic drugs, and smallest for angiotensin-converting enzyme inhibitors and β-blockers. Although additional research is needed to understand the mechanisms underlying the association, these data could be helpful to clinicians to guide management when a patient presents with eczematous dermatitis in older age.
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Affiliation(s)
- Morgan Ye
- Department of Dermatology, University of California, San Francisco
| | - Leslie N. Chan
- Department of Dermatology, University of California, San Francisco
| | - Ian Douglas
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | | | - Sinéad M. Langan
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Katrina Abuabara
- Department of Dermatology, University of California, San Francisco
- Division of Epidemiology and Biostatistics, University of California, Berkeley
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He X, Wei R, Huang Y, Chen Z, Lyu T, Bost S, Tong J, Li L, Zhou Y, Guo J, Tang H, Wang F, DeKosky S, Xu H, Chen Y, Zhang R, Xu J, Guo Y, Wu Y, Bian J. Develop and Validate a Computable Phenotype for the Identification of Alzheimer's Disease Patients Using Electronic Health Record Data. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.06.24302389. [PMID: 38370766 PMCID: PMC10871460 DOI: 10.1101/2024.02.06.24302389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
INTRODUCTION Alzheimer's Disease (AD) are often misclassified in electronic health records (EHRs) when relying solely on diagnostic codes. This study aims to develop a more accurate, computable phenotype (CP) for identifying AD patients by using both structured and unstructured EHR data. METHODS We used EHRs from the University of Florida Health (UF Health) system and created rule-based CPs iteratively through manual chart reviews. The CPs were then validated using data from the University of Texas Health Science Center at Houston (UT Health) and the University of Minnesota (UMN). RESULTS Our best-performing CP is " patient has at least 2 AD diagnoses and AD-related keywords " with an F1-score of 0.817 at UF, and 0.961 and 0.623 at UT Health and UMN, respectively. DISCUSSION We developed and validated rule-based CPs for AD identification with good performance, crucial for studies that aim to use real-world data like EHRs.
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Zheng B, Su B, Ahmadi-Abhari S, Kapogiannis D, Tzoulaki I, Riboli E, Middleton L. Dementia risk in patients with type 2 diabetes: Comparing metformin with no pharmacological treatment. Alzheimers Dement 2023; 19:5681-5689. [PMID: 37395154 DOI: 10.1002/alz.13349] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 04/26/2023] [Accepted: 05/19/2023] [Indexed: 07/04/2023]
Abstract
INTRODUCTION Metformin has been suggested as a therapeutic agent for dementia, but the relevant evidence has been partial and inconsistent. METHODS We established a national cohort of 210,237 type 2 diabetes patients in the UK Clinical Practice Research Datalink. Risks of incident dementia were compared between metformin initiators and those who were not prescribed any anti-diabetes medication during follow-up. RESULTS Compared with metformin initiators (n = 114,628), patients who received no anti-diabetes medication (n = 95,609) had lower HbA1c and better cardiovascular health at baseline. Both Cox regression and propensity score weighting analysis showed metformin initiators had lower risk of dementia compared to those non-users (adjusted hazard ratio = 0.88 [95% confidence interval: 0.84-0.92] and 0.90 [0.84-0.96]). Patients on long-term metformin treatment had an even lower risk of dementia. DISCUSSION Metformin may act beyond its glycemic effect and reduce dementia risk to an even lower level than that of patients with milder diabetes and better health profiles. HIGHLIGHTS Metformin initiators had a significantly lower risk of dementia compared with patients not receiving anti-diabetes medication. Compared with metformin initiators, diabetes patients not receiving pharmacological treatment had better glycemic profiles at baseline and during follow-up. Patients on long-term metformin treatment had an even lower risk of subsequent dementia incidence. Metformin may act beyond its effect on hyperglycemia and has the potential of being repurposed for dementia prevention.
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Affiliation(s)
- Bang Zheng
- Ageing Epidemiology Research Unit, School of Public Health, Imperial College London, London, UK
- Department of Non-communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Bowen Su
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Sara Ahmadi-Abhari
- Ageing Epidemiology Research Unit, School of Public Health, Imperial College London, London, UK
| | - Dimitrios Kapogiannis
- Laboratory of Clinical Investigation, Intramural Research Program, National Institute on Aging, Baltimore, USA
| | - Ioanna Tzoulaki
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Elio Riboli
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Public Health Directorate, Imperial College NHS Healthcare Trust, London, UK
| | - Lefkos Middleton
- Ageing Epidemiology Research Unit, School of Public Health, Imperial College London, London, UK
- Public Health Directorate, Imperial College NHS Healthcare Trust, London, UK
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Lee W, Kang S, Kim S, Lee S, Myung W, Jheon K, Yoon C, Suh J, Youn T, Chae I. Impact of dementia and drug compliance on patients with acute myocardial infarction. Clin Cardiol 2023; 46:1253-1259. [PMID: 37488767 PMCID: PMC10577568 DOI: 10.1002/clc.24091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 06/21/2023] [Accepted: 07/03/2023] [Indexed: 07/26/2023] Open
Abstract
BACKGROUND In South Korea, the number of people with dementia is rising at a worrisome rate, and many of them also have acute myocardial infarction (AMI), a disease with a high mortality rate. HYPOTHESIS We speculated that dementia and drug compliance have significant impact on the mortality of patients with AMI. METHODS The study derived data from the National Health Insurance Service-Senior for a retrospective cohort study. The total number of patients diagnosed with AMI for the first time between 2007 and 2013 was 16 835, among whom 2021 had dementia. Medication possession ratio (MPR) was used to assess medication adherence. RESULTS AMI patients with dementia had unfavorable baseline characteristics; they had significantly higher risk of all-cause mortality (hazard ratio [HR]: 2.49; 95% confidence interval [CI]: 2.34-2.66; p < .001) and lower MPR (aspirin: 21.9% vs. 42.8%; p < .001). AMI patients were stratified by presence of dementia and medication adherence, and the survival rate was the highest among those with no dementia and good adherence, followed by those with no dementia and poor adherence, those with dementia and good adherence, and those with dementia and poor adherence. The multivariable analysis revealed that dementia (HR: 1.64; 95% CI: 1.53-1.75; p < .001) and poor adherence to medication (HR: 1.60; 95% CI: 1.49-1.71; p < .001) had a significant association with all-cause mortality in AMI patients. CONCLUSIONS AMI patients with dementia have a higher mortality rate. Their prognosis is negatively affected by their poorer medication adherence than patients without dementia.
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Affiliation(s)
- Wonjae Lee
- Department of Internal Medicine, Division of CardiologyCardiovascular Center, Seoul National University Bundang HospitalSeongnam‐siGyeonggi‐doKorea
| | - Si‐Hyuck Kang
- Department of Internal Medicine, Division of CardiologyCardiovascular Center, Seoul National University Bundang HospitalSeongnam‐siGyeonggi‐doKorea
| | - Sun‐Hwa Kim
- Department of Internal Medicine, Division of CardiologyCardiovascular Center, Seoul National University Bundang HospitalSeongnam‐siGyeonggi‐doKorea
| | - Seung‐Yeon Lee
- International Healthcare CenterSeoul National University Bundang HospitalSeongnam‐siGyeonggi‐doKorea
| | - Woojae Myung
- Department of PsychiatrySeoul National University Bundang HospitalSeongnam‐siGyeonggi‐doKorea
| | - Ki‐Hyun Jheon
- Department of Internal Medicine, Division of CardiologyCardiovascular Center, Seoul National University Bundang HospitalSeongnam‐siGyeonggi‐doKorea
| | - Chang‐Hwan Yoon
- Department of Internal Medicine, Division of CardiologyCardiovascular Center, Seoul National University Bundang HospitalSeongnam‐siGyeonggi‐doKorea
| | - Jung‐Won Suh
- Department of Internal Medicine, Division of CardiologyCardiovascular Center, Seoul National University Bundang HospitalSeongnam‐siGyeonggi‐doKorea
| | - Tae‐Jin Youn
- Department of Internal Medicine, Division of CardiologyCardiovascular Center, Seoul National University Bundang HospitalSeongnam‐siGyeonggi‐doKorea
| | - In‐Ho Chae
- Department of Internal Medicine, Division of CardiologyCardiovascular Center, Seoul National University Bundang HospitalSeongnam‐siGyeonggi‐doKorea
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Li Q, Yang X, Xu J, Guo Y, He X, Hu H, Lyu T, Marra D, Miller A, Smith G, DeKosky S, Boyce RD, Schliep K, Shenkman E, Maraganore D, Wu Y, Bian J. Early prediction of Alzheimer's disease and related dementias using real-world electronic health records. Alzheimers Dement 2023; 19:3506-3518. [PMID: 36815661 PMCID: PMC10976442 DOI: 10.1002/alz.12967] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 12/31/2022] [Accepted: 01/05/2023] [Indexed: 02/24/2023]
Abstract
INTRODUCTION This study aims to explore machine learning (ML) methods for early prediction of Alzheimer's disease (AD) and related dementias (ADRD) using the real-world electronic health records (EHRs). METHODS A total of 23,835 ADRD and 1,038,643 control patients were identified from the OneFlorida+ Research Consortium. Two ML methods were used to develop the prediction models. Both knowledge-driven and data-driven approaches were explored. Four computable phenotyping algorithms were tested. RESULTS The gradient boosting tree (GBT) models trained with the data-driven approach achieved the best area under the curve (AUC) scores of 0.939, 0.906, 0.884, and 0.854 for early prediction of ADRD 0, 1, 3, or 5 years before diagnosis, respectively. A number of important clinical and sociodemographic factors were identified. DISCUSSION We tested various settings and showed the predictive ability of using ML approaches for early prediction of ADRD with EHRs. The models can help identify high-risk individuals for early informed preventive or prognostic clinical decisions.
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Affiliation(s)
- Qian Li
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Xi Yang
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Jie Xu
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Yi Guo
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Xing He
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Hui Hu
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Tianchen Lyu
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - David Marra
- Department of Psychology, VA Boston Healthcare System, Boston, Massachusetts, USA
| | - Amber Miller
- Department of Neurology, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Glenn Smith
- Department of Clinical and Health Psychology, University of Florida, Gainesville, Florida, USA
| | - Steven DeKosky
- Department of Neurology, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Richard D. Boyce
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Karen Schliep
- Department of Family and Preventive Medicine, University of Utah, Salt Lake City, Utah, USA
| | - Elizabeth Shenkman
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Demetrius Maraganore
- Department of Neurology, School of Medicine, Tulane University, New Orleans, Louisiana, USA
| | - Yonghui Wu
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA
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Amiri P, Pirnejad H, Bahaadinbeigy K, Baghini MS, Khazaee PR, Niazkhani Z. A qualitative study of factors influencing ePHR adoption by caregivers and care providers of Alzheimer's patients: An extension of the unified theory of acceptance and use of technology model. Health Sci Rep 2023; 6:e1394. [PMID: 37425233 PMCID: PMC10323167 DOI: 10.1002/hsr2.1394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 05/06/2023] [Accepted: 06/21/2023] [Indexed: 07/11/2023] Open
Abstract
Background and Aims As the nowadays provision of many healthcare services relies on technology, a better understanding of the factors contributing to the acceptance and use of technology in health care is essential. For Alzheimer's patients, an electronic personal health record (ePHR) is one such technology. Stakeholders should understand the factors affecting the adoption of this technology for its smooth implementation, adoption, and sustainable use. So far, these factors have not fully been understood for Alzheimer's disease (AD)-specific ePHR. Therefore, the present study aimed to understand these factors in ePHR adoption based on the perceptions and views of care providers and caregivers involved in AD care. Methods This qualitative study was conducted from February 2020 to August 2021 in Kerman, Iran. Seven neurologists and 13 caregivers involved in AD care were interviewed using semi-structured and in-depth interviews. All interviews were conducted through phone contacts amid Covid-19 imposed restrictions, recorded, and transcribed verbatim. The transcripts were coded using thematic analysis based on the unified theory of acceptance and use of technology (UTAUT) model. ATLAS.ti8 was used for data analysis. Results The factors affecting ePHR adoption in our study comprised subthemes under the five main themes of performance expectancy, effort expectancy, social influence, facilitating conditions of the UTAUT model, and the participants' sociodemographic factors. From the 37 facilitating factors and 13 barriers identified for ePHR adoption, in general, the participants had positive attitudes toward the ease of use of this system. The stated obstacles were dependent on the participants' sociodemographic factors (such as age and level of education) and social influence (including concern about confidentiality and privacy). In general, the participants considered ePHRs efficient and useful in increasing neurologists' information about their patients and managing their symptoms in order to provide better and timely treatment. Conclusion The present study gives a comprehensive insight into the acceptance of ePHR for AD in a developing setting. The results of this study can be utilized for similar healthcare settings with regard to technical, legal, or cultural characteristics. To develop a useful and user-friendly system, ePHR developers should involve users in the design process to take into account the functions and features that match their skills, requirements, and preferences.
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Affiliation(s)
- Parastoo Amiri
- Student Research CommitteeKerman University of Medical SciencesKermanIran
| | - Habibollah Pirnejad
- Patient Safety Research Center, Clinical Research InstituteUrmia University of Medical SciencesUrmiaIran
- Erasmus School of Health Policy and ManagementErasmus University RotterdamRotterdamThe Netherlands
| | - Kambiz Bahaadinbeigy
- Medical Informatics Research Center, Institute of Futures Studies in HealthKerman University of Medical SciencesKermanIran
| | - Mahdie Shojaei Baghini
- Medical Informatics Research Center, Institute of Futures Studies in HealthKerman University of Medical SciencesKermanIran
| | | | - Zahra Niazkhani
- Nephrology and Kidney Transplant Research Center, Clinical Research InstituteUrmia University of Medical SciencesUrmiaIran
- Health Care Governance, Erasmus School of Health Policy and ManagementErasmus University RotterdamRotterdamThe Netherlands
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Zieschang T, Schütze S. [General aspects of dementia disorders]. INNERE MEDIZIN (HEIDELBERG, GERMANY) 2023; 64:127-130. [PMID: 36692517 DOI: 10.1007/s00108-022-01462-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 12/19/2022] [Indexed: 01/25/2023]
Abstract
The prevalence of dementia increases with age. In rare cases, people younger than 65 years old are also affected, with substantial consequences for the professional life. The symptoms depend on the form of dementia and can vary individually. Impairment of short-term memory is not always in the foreground and other neurocognitive domains, such as the disturbance of executive functions can have a significant impact on the ability to cope with everyday life. Pathophysiologically, neurodegenerative dementias with the major forms of Alzheimer's dementia, Lewy body dementia, and frontotemporal dementia are distinguished from vascular dementias. Mixed forms are common. There is no curative treatment, but progression can be slowed by nonpharmacological measures and, especially in Alzheimer's dementia, by pharmacological treatment. Appropriate measures can promote independence and autonomy for as long as possible; however, in the course of the disease restrictions in the extended activities of independent living will initially occur, such as banking transactions, use of means of transport and, in the further course, also in the basic activities of daily living. Legal capacity and the ability to consent to health interventions are restricted sooner or later; however, this must always be evaluated for the specific situation and is not generally the case with the diagnosis of dementia. Instruments such as living wills, identification of a health care proxy, and advanced care planning should be used at an early stage. To decrease family caregiver burden with the increased risk of developing depression, supportive, accompanying measures and education are of great importance.
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Affiliation(s)
- Tania Zieschang
- Abteilung für Geriatrie, Department für Versorgungsforschung, Fakultät VI Medizin und Gesundheitswissenschaften, Carl von Ossietzky Universität Oldenburg, Oldenburg, Deutschland. .,Geriatrisches Zentrum Oldenburg (GZO), Universitätsklinik für Geriatrie, Klinikum Oldenburg, Rahel-Straus-Str. 10, 26133, Oldenburg, Deutschland.
| | - Sandra Schütze
- Sektion Neurogeriatrie, Medizinisch-Geriatrische Klinik, AGAPLESION Frankfurter Diakonie Kliniken, Wilhelm-Epstein-Str. 4, 60431, Frankfurt am Main, Deutschland.
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Chung S, Providencia R, Sofat R, Pujades‐Rodriguez M, Torralbo A, Fatemifar G, Fitzpatrick NK, Taylor J, Li K, Dale C, Rossor M, Acosta‐Mena D, Whittaker J, Denaxas S. Incidence, morbidity, mortality and disparities in dementia: A population linked electronic health records study of 4.3 million individuals. Alzheimers Dement 2023; 19:123-135. [PMID: 35290719 PMCID: PMC10078672 DOI: 10.1002/alz.12635] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 11/08/2021] [Accepted: 01/30/2022] [Indexed: 01/18/2023]
Abstract
INTRODUCTION We report dementia incidence, comorbidities, reasons for health-care visits, mortality, causes of death, and examined dementia patterns by relative deprivation in the UK. METHOD A longitudinal cohort analysis of linked electronic health records from 4.3 million people in the UK was conducted to investigate dementia incidence and mortality. Reasons for hospitalization and causes of death were compared in individuals with and without dementia. RESULTS From 1998 to 2016 we observed 145,319 (3.1%) individuals with incident dementia. Repeated hospitalizations among senior adults for infection, unknown morbidity, and multiple primary care visits for chronic pain were observed prior to dementia diagnosis. Multiple long-term conditions are present in half of the individuals at the time of diagnosis. Individuals living in high deprivation areas had higher dementia incidence and high fatality. DISCUSSION There is a considerable disparity of dementia that informs priorities of prevention and provision of patient care.
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Affiliation(s)
- Sheng‐Chia Chung
- Department of Health InformaticsHealth Data Research UKLondonUK
- Department of Health InformaticsUniversity College LondonLondonUK
| | | | - Reecha Sofat
- Department of Health InformaticsHealth Data Research UKLondonUK
| | | | - Ana Torralbo
- Department of Health InformaticsHealth Data Research UKLondonUK
- Department of Health InformaticsUniversity College LondonLondonUK
| | - Ghazaleh Fatemifar
- Department of Health InformaticsHealth Data Research UKLondonUK
- Department of Health InformaticsUniversity College LondonLondonUK
| | - Natalie K. Fitzpatrick
- Department of Health InformaticsHealth Data Research UKLondonUK
- Department of Health InformaticsUniversity College LondonLondonUK
| | - Julie Taylor
- Department of Health InformaticsHealth Data Research UKLondonUK
| | - Ken Li
- Department of Health InformaticsHealth Data Research UKLondonUK
| | - Caroline Dale
- Department of Health InformaticsHealth Data Research UKLondonUK
| | - Martin Rossor
- Department of NeurodegenerationUCL Institute of NeurologyLondonUK
| | | | | | - Spiros Denaxas
- Department of Health InformaticsHealth Data Research UKLondonUK
- Department of Health InformaticsUniversity College LondonLondonUK
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Charpignon ML, Vakulenko-Lagun B, Zheng B, Magdamo C, Su B, Evans K, Rodriguez S, Sokolov A, Boswell S, Sheu YH, Somai M, Middleton L, Hyman BT, Betensky RA, Finkelstein SN, Welsch RE, Tzoulaki I, Blacker D, Das S, Albers MW. Causal inference in medical records and complementary systems pharmacology for metformin drug repurposing towards dementia. Nat Commun 2022; 13:7652. [PMID: 36496454 PMCID: PMC9741618 DOI: 10.1038/s41467-022-35157-w] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Accepted: 11/21/2022] [Indexed: 12/13/2022] Open
Abstract
Metformin, a diabetes drug with anti-aging cellular responses, has complex actions that may alter dementia onset. Mixed results are emerging from prior observational studies. To address this complexity, we deploy a causal inference approach accounting for the competing risk of death in emulated clinical trials using two distinct electronic health record systems. In intention-to-treat analyses, metformin use associates with lower hazard of all-cause mortality and lower cause-specific hazard of dementia onset, after accounting for prolonged survival, relative to sulfonylureas. In parallel systems pharmacology studies, the expression of two AD-related proteins, APOE and SPP1, was suppressed by pharmacologic concentrations of metformin in differentiated human neural cells, relative to a sulfonylurea. Together, our findings suggest that metformin might reduce the risk of dementia in diabetes patients through mechanisms beyond glycemic control, and that SPP1 is a candidate biomarker for metformin's action in the brain.
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Affiliation(s)
- Marie-Laure Charpignon
- Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | - Bang Zheng
- Ageing Epidemiology Research Unit, School of Public Health, Imperial College London, London, UK
| | - Colin Magdamo
- Department of Neurology, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA
| | - Bowen Su
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Kyle Evans
- Department of Neurology, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA
| | - Steve Rodriguez
- Department of Neurology, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA
| | - Artem Sokolov
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA
| | - Sarah Boswell
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA
| | - Yi-Han Sheu
- Department of Psychiatry, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA
| | - Melek Somai
- Inception Labs, Collaborative for Health Delivery Sciences, Medical College of Wisconsin, Wauwatosa, WI, USA
| | - Lefkos Middleton
- Ageing Epidemiology Research Unit, School of Public Health, Imperial College London, London, UK
- Public Health Directorate, Imperial College London NHS Healthcare Trust, London, UK
| | - Bradley T Hyman
- Department of Neurology, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA
| | - Rebecca A Betensky
- Department of Biostatistics, School of Global Public Health, New York University, New York, NY, USA
| | - Stan N Finkelstein
- Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA, USA
- Division of Clinical Informatics, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Roy E Welsch
- Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA, USA
- Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ioanna Tzoulaki
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.
- Dementia Research Institute, Imperial College London, London, UK.
- Department of Hygiene and Epidemiology, University of Ioannina, Ioannina, Greece.
| | - Deborah Blacker
- Department of Psychiatry, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Sudeshna Das
- Department of Neurology, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA.
| | - Mark W Albers
- Department of Neurology, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA.
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA.
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11
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Magyari A, Ye M, Margolis DJ, McCulloch CE, Cummings SR, Yaffe K, Langan SM, Abuabara K. Adult atopic eczema and the risk of dementia: A population-based cohort study. J Am Acad Dermatol 2022; 87:314-322. [PMID: 35367295 DOI: 10.1016/j.jaad.2022.03.049] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 03/03/2022] [Accepted: 03/09/2022] [Indexed: 12/15/2022]
Abstract
BACKGROUND Chronic inflammatory conditions have been linked to dementia, but little is known about the role of atopic eczema, an inflammatory condition recently recognized to be common among older adults. OBJECTIVE To determine whether active atopic eczema is associated with incident dementia. METHODS A longitudinal cohort study of 1,767,667 individuals aged 60 to 99 years registered with The Health Improvement Network, a primary care cohort in the United Kingdom. The diagnoses of atopic eczema and dementia were identified using medical record codes. RESULTS The incidence of dementia was 57 per 10,000 person-years among those with atopic eczema during follow-up (12.1% of the population) compared with 44 per 10,000 person-years in the control group. This translated to a 27% increased risk of dementia (hazard ratio, 1.27; 95% CI, 1.23-1.30) in adjusted Cox proportional hazard models. Similar associations were observed in subgroup analyses of vascular dementia and Alzheimer's disease. The association persisted after additionally adjusting for the use of systemic corticosteroids (hazard ratio, 1.29; 95% CI, 1.26-1.33) and potential mediators (hazard ratio, 1.19; 95% CI, 1.16-1.22). More severe eczema was associated with a higher risk of dementia. LIMITATIONS Lack of detailed data on severity. CONCLUSION Atopic eczema was associated with a small but increased risk of incident dementia. The association increased with the severity of atopic eczema.
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Affiliation(s)
- Alexa Magyari
- UC Berkeley School of Public Health, Berkeley, California
| | - Morgan Ye
- Department of Dermatology, University of California, San Francisco School of Medicine (UCSF), San Francisco, California
| | - David J Margolis
- Department of Dermatology and Center for Epidemiology and Biostatistics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Charles E McCulloch
- Division of Biostatistics, University of California, San Francisco School of Medicine (UCSF), San Francisco, California
| | - Steven R Cummings
- San Francisco Coordinating Center, California Pacific Medical Center Research Institute, San Francisco, California; Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California
| | - Kristine Yaffe
- Center for Population Brain Health, University of California, San Francisco School of Medicine (UCSF), San Francisco, California
| | - Sinéad M Langan
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, Keppel Street, London, United Kingdom
| | - Katrina Abuabara
- UC Berkeley School of Public Health, Berkeley, California; Department of Dermatology, University of California, San Francisco School of Medicine (UCSF), San Francisco, California.
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12
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Hsu C, Yeh M, Liu YE. Three‐month Chan‐Chuang qigong program improves physical performance and quality of life of patients with cognitive impairment: A randomized controlled trial. Res Nurs Health 2022; 45:327-336. [DOI: 10.1002/nur.22219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 01/22/2022] [Accepted: 01/28/2022] [Indexed: 11/10/2022]
Affiliation(s)
- Chin‐Yun Hsu
- Department of Nursing Tri‐Service General Hospital, National Taipei University of Nursing and Health Sciences Taipei Taiwan
| | - Mei‐Ling Yeh
- Department of Nursing National Taipei University of Nursing and Health Sciences Taipei Taiwan
- Cochrane Taiwan, Taipei Medical University Taipei Taiwan
| | - Yu‐Chi E. Liu
- Department of Nursing National Taipei University of Nursing and Health Sciences Taipei Taiwan
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13
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Alexander N, Alexander DC, Barkhof F, Denaxas S. Identifying and evaluating clinical subtypes of Alzheimer's disease in care electronic health records using unsupervised machine learning. BMC Med Inform Decis Mak 2021; 21:343. [PMID: 34879829 PMCID: PMC8653614 DOI: 10.1186/s12911-021-01693-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 11/15/2021] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND Alzheimer's disease (AD) is a highly heterogeneous disease with diverse trajectories and outcomes observed in clinical populations. Understanding this heterogeneity can enable better treatment, prognosis and disease management. Studies to date have mainly used imaging or cognition data and have been limited in terms of data breadth and sample size. Here we examine the clinical heterogeneity of Alzheimer's disease patients using electronic health records (EHR) to identify and characterise disease subgroups using multiple clustering methods, identifying clusters which are clinically actionable. METHODS We identified AD patients in primary care EHR from the Clinical Practice Research Datalink (CPRD) using a previously validated rule-based phenotyping algorithm. We extracted and included a range of comorbidities, symptoms and demographic features as patient features. We evaluated four different clustering methods (k-means, kernel k-means, affinity propagation and latent class analysis) to cluster Alzheimer's disease patients. We compared clusters on clinically relevant outcomes and evaluated each method using measures of cluster structure, stability, efficiency of outcome prediction and replicability in external data sets. RESULTS We identified 7,913 AD patients, with a mean age of 82 and 66.2% female. We included 21 features in our analysis. We observed 5, 2, 5 and 6 clusters in k-means, kernel k-means, affinity propagation and latent class analysis respectively. K-means was found to produce the most consistent results based on four evaluative measures. We discovered a consistent cluster found in three of the four methods composed of predominantly female, younger disease onset (43% between ages 42-73) diagnosed with depression and anxiety, with a quicker rate of progression compared to the average across other clusters. CONCLUSION Each clustering approach produced substantially different clusters and K-Means performed the best out of the four methods based on the four evaluative criteria. However, the consistent appearance of one particular cluster across three of the four methods potentially suggests the presence of a distinct disease subtype that merits further exploration. Our study underlines the variability of the results obtained from different clustering approaches and the importance of systematically evaluating different approaches for identifying disease subtypes in complex EHR.
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Affiliation(s)
- Nonie Alexander
- Institute of Health Informatics, University College London, London, UK. .,Health Data Research UK, London, UK.
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Frederik Barkhof
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK.,UCL Institute of Neurology, University College London, London, UK.,Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK.,Health Data Research UK, London, UK.,Alan Turing Institute, London, UK
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14
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Davis KAS, Mueller C, Ashworth M, Broadbent M, Jewel A, Molokhia M, Perera G, Stewart RJ. What gets recorded, counts: dementia recording in primary care compared with a specialist database. Age Ageing 2021; 50:2206-2213. [PMID: 34417796 PMCID: PMC8581382 DOI: 10.1093/ageing/afab164] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 06/21/2021] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND databases of electronic health records are powerful tools for dementia research, but data can be influenced by incomplete recording. We examined whether people with dementia recorded in a specialist database (from a mental health and dementia care service) differ from those recorded in primary care. METHODS a retrospective cohort study of the population covered by Lambeth DataNet (primary care electronic records) between 2007 and 2019. Documentation of dementia diagnosis in primary care coded data and linked records in a specialist database (Clinical Records Interactive Search) were compared. RESULTS 3,859 people had dementia documented in primary care codes and 4,266 in the specialist database, with 2,886/5,239 (55%) documented in both sources. Overall, 55% were labelled as having Alzheimer's dementia and 29% were prescribed dementia medication, but these proportions were significantly higher in those documented in both sources. The cohort identified from the specialist database were less likely to live in a care home (prevalence ratio 0.73, 95% confidence interval 0.63-0.85), have multimorbidity (0.87, 0.77-0.98) or consult frequently (0.91, 0.88-0.95) than those identified through primary care codes, although mortality did not differ (0.98, 0.91-1.06). DISCUSSION there is under-recording of dementia diagnoses in both primary care and specialist databases. This has implications for clinical care and for generalizability of research. Our results suggest that using a mental health database may under-represent those patients who have more frailty, reflecting differential referral to mental health services, and demonstrating how the patient pathways are an important consideration when undertaking database studies.
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Affiliation(s)
- Katrina A S Davis
- King's College London Institute of Psychiatry Psychology and Neuroscience, London, UK
- South London and Maudsley NHS Foundation Trust, London, UK
| | - Christoph Mueller
- King's College London Institute of Psychiatry Psychology and Neuroscience, London, UK
- South London and Maudsley NHS Foundation Trust, London, UK
| | - Mark Ashworth
- King's College London Population Health Sciences, London, UK
| | | | - Amelia Jewel
- South London and Maudsley NHS Foundation Trust, London, UK
| | - Mariam Molokhia
- King's College London Population Health Sciences, London, UK
| | - Gayan Perera
- King's College London Institute of Psychiatry Psychology and Neuroscience, London, UK
- South London and Maudsley NHS Foundation Trust, London, UK
| | - Robert J Stewart
- King's College London Institute of Psychiatry Psychology and Neuroscience, London, UK
- South London and Maudsley NHS Foundation Trust, London, UK
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15
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Campbell P, Rathod-Mistry T, Marshall M, Bailey J, Chew-Graham CA, Croft P, Frisher M, Hayward R, Negi R, Singh S, Tantalo-Baker S, Tarafdar S, Babatunde OO, Robinson L, Sumathipala A, Thein N, Walters K, Weich S, Jordan KP. Markers of dementia-related health in primary care electronic health records. Aging Ment Health 2021; 25:1452-1462. [PMID: 32578454 DOI: 10.1080/13607863.2020.1783511] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
OBJECTIVES Identifying routinely recorded markers of poor health in patients with dementia may help treatment decisions and evaluation of earlier outcomes in research. Our objective was to determine whether a set of credible markers of dementia-related health could be identified from primary care electronic health records (EHR). METHODS The study consisted of (i) rapid review of potential measures of dementia-related health used in EHR studies; (ii) consensus exercise to assess feasibility of identifying these markers in UK primary care EHR; (iii) development of UK EHR code lists for markers; (iv) analysis of a regional primary care EHR database to determine further potential markers; (v) consensus exercise to finalise markers and pool into higher domains; (vi) determination of 12-month prevalence of domains in EHR of 2328 patients with dementia compared to matched patients without dementia. RESULTS Sixty-three markers were identified and mapped to 13 domains: Care; Home Pressures; Severe Neuropsychiatric; Neuropsychiatric; Cognitive Function; Daily Functioning; Safety; Comorbidity; Symptoms; Diet/Nutrition; Imaging; Increased Multimorbidity; Change in Dementia Drug. Comorbidity was the most prevalent recorded domain in dementia (69%). Home Pressures were the least prevalent domain (1%). Ten domains had a statistically significant higher prevalence in dementia patients, one (Comorbidity) was higher in non-dementia patients, and two (Home Pressures, Diet/Nutrition) showed no association with dementia. CONCLUSIONS EHR captures important markers of dementia-related health. Further research should assess if they indicate dementia progression. These markers could provide the basis for identifying individuals at risk of faster progression and outcome measures for use in research.
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Affiliation(s)
- Paul Campbell
- School of Primary, Community and Social Care, Keele University, Keele, Staffordshire, UK.,Midlands Partnership NHS Foundation Trust, St. George's Hospital, Stafford, UK
| | - Trishna Rathod-Mistry
- School of Primary, Community and Social Care, Keele University, Keele, Staffordshire, UK
| | - Michelle Marshall
- School of Primary, Community and Social Care, Keele University, Keele, Staffordshire, UK
| | - James Bailey
- School of Primary, Community and Social Care, Keele University, Keele, Staffordshire, UK
| | - Carolyn A Chew-Graham
- School of Primary, Community and Social Care, Keele University, Keele, Staffordshire, UK.,Midlands Partnership NHS Foundation Trust, St. George's Hospital, Stafford, UK
| | - Peter Croft
- School of Primary, Community and Social Care, Keele University, Keele, Staffordshire, UK
| | - Martin Frisher
- School of Pharmacy and Bioengineering, Keele University, Keele, Staffordshire, UK
| | - Richard Hayward
- School of Primary, Community and Social Care, Keele University, Keele, Staffordshire, UK
| | - Rashi Negi
- Midlands Partnership NHS Foundation Trust, St. George's Hospital, Stafford, UK
| | - Swaran Singh
- Division of Mental Health and Wellbeing, Warwick Medical School, University of Warwick, Coventry, UK
| | - Shula Tantalo-Baker
- School of Primary, Community and Social Care, Keele University, Keele, Staffordshire, UK
| | - Suhail Tarafdar
- School of Primary, Community and Social Care, Keele University, Keele, Staffordshire, UK
| | - Opeyemi O Babatunde
- School of Primary, Community and Social Care, Keele University, Keele, Staffordshire, UK.,Centre for Prognosis Research, Keele University, Keele, Staffordshire, UK
| | - Louise Robinson
- Institute of Health and Society and Newcastle University Institute for Ageing, Newcastle upon Tyne, UK
| | - Athula Sumathipala
- School of Primary, Community and Social Care, Keele University, Keele, Staffordshire, UK.,Midlands Partnership NHS Foundation Trust, St. George's Hospital, Stafford, UK
| | - Nwe Thein
- Midlands Partnership NHS Foundation Trust, St. George's Hospital, Stafford, UK
| | - Kate Walters
- Research Department of Primary Care & Population Health, University College London, Royal Free Campus, London, UK
| | - Scott Weich
- Mental Health Research Unit, School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - Kelvin P Jordan
- School of Primary, Community and Social Care, Keele University, Keele, Staffordshire, UK.,Centre for Prognosis Research, Keele University, Keele, Staffordshire, UK
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16
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Klijs B, Mitratza M, Harteloh PP, Moll van Charante EP, Richard E, Nielen MM, Kunst AE. Estimating the lifetime risk of dementia using nationwide individually linked cause-of-death and health register data. Int J Epidemiol 2021; 50:809-816. [PMID: 33354723 DOI: 10.1093/ije/dyaa219] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/08/2020] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Previous estimates of the lifetime risk of dementia are restricted to older age groups and may suffer from selection bias. In this study, we estimated the lifetime risk of dementia starting at birth using nationwide integral linked health register data. METHODS We studied all deaths in The Netherlands in 2017 (n = 147 866). Dementia was assessed using the cause-of-death registration, individually linked with registers covering long-term care, specialized mental care, dispensed medicines, hospital discharges and claims, and primary care. The proportion of deaths with dementia was calculated for the total population and according to age at death and sex. RESULTS According to all data sources combined, 24.0% of the population dies in the presence of dementia. This proportion is higher for females (29.4%) than for males (18.3%). Using multiple causes of death only, the proportion with dementia is 17.9%. Sequential addition of long-term care and hospital discharge data increased the estimate by 4.0 and 1.5%-points, respectively. Further addition of dispensed medicines, hospital claims and specialized mental care data added another 0.6%-points. Among persons who die at age ≤65-70 years, the proportion with dementia is ≤6.2%. After age 70, the proportion rises sharply, with a peak of 43.9% for females and 33.1% for males at age 90-95 years. CONCLUSIONS Around one-fourth of the Dutch population is diagnosed with dementia at some point in life and dies in the presence of dementia. It is a major challenge to arrange optimal care for this group.
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Affiliation(s)
- Bart Klijs
- Department of Health and Care, Statistics Netherlands, The Hague, The Netherlands.,Department of Public Health, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Marianna Mitratza
- Department of Public Health, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Peter Pm Harteloh
- Department of Health and Care, Statistics Netherlands, The Hague, The Netherlands
| | - Eric P Moll van Charante
- Department of Public Health, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.,Department of General Practice, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Edo Richard
- Department of Neurology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Markus Mj Nielen
- Netherlands Institute for Health Services Research (NIVEL), Utrecht, The Netherlands
| | - Anton E Kunst
- Department of Public Health, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
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17
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Subota A, Jetté N, Josephson CB, McMillan J, Keezer MR, Gonzalez-Izquierdo A, Holroyd-Leduc J. Risk factors for dementia development, frailty, and mortality in older adults with epilepsy - A population-based analysis. Epilepsy Behav 2021; 120:108006. [PMID: 33964541 DOI: 10.1016/j.yebeh.2021.108006] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Revised: 03/25/2021] [Accepted: 04/12/2021] [Indexed: 12/18/2022]
Abstract
OBJECTIVE Although the prevalence of comorbid epilepsy and dementia is expected to increase, the impact is not well understood. Our objectives were to examine risk factors associated with incident dementia and the impact of frailty and dementia on mortality in older adults with epilepsy. METHODS The CALIBER scientific platform was used. People with incident epilepsy at or after age 65 were identified using Read codes and matched by age, sex, and general practitioner to a cohort without epilepsy (10:1). Baseline cohort characteristics were compared using conditional logistic regression models. Multivariate Cox proportional hazard regression models were used to examine the impact of frailty and dementia on mortality, and to assess risk factors for dementia development. RESULTS One thousand forty eight older adults with incident epilepsy were identified. The odds of having dementia at baseline were 7.39 [95% CI 5.21-10.50] times higher in older adults with epilepsy (n = 62, 5.92%) compared to older adults without epilepsy (n = 88, 0.86%). In the final multivariate Cox model (n = 326), age [HR: 1.20, 95% CI 1.09-1.32], Charlson comorbidity index score [HR: 1.26, 95% CI 1.10-1.44], and sleep disturbances [HR: 2.41, 95% CI 1.07-5.43] at baseline epilepsy diagnosis were significantly associated with an increased hazard of dementia development over the follow-up period. In a multivariate Cox model (n = 1047), age [HR: 1.07, 95% CI 1.03-1.11], baseline dementia [HR: 2.66, 95% CI 1.65-4.27] and baseline e-frailty index score [HR: 11.55, 95% CI 2.09-63.84] were significantly associated with a higher hazard of death among those with epilepsy. Female sex [HR: 0.77, 95% CI 0.59-0.99] was associated with a lower hazard of death. SIGNIFICANCE The odds of having dementia were higher in older adults with incident epilepsy. A higher comorbidity burden acts as a risk factor for dementia, while prevalent dementia and increasing frailty were associated with mortality.
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Affiliation(s)
- Ann Subota
- Department of Medicine, University of Calgary, North Tower, 1403-29 St NW, Calgary, AB T2N 2T9, Canada; Department of Community Health Sciences, University of Calgary, 3D10 - 3280 Hospital Drive NW, Calgary, AB T2N 4Z6, Canada
| | - Nathalie Jetté
- Department of Community Health Sciences, University of Calgary, 3D10 - 3280 Hospital Drive NW, Calgary, AB T2N 4Z6, Canada; Hotchkiss Brain Institute, University of Calgary, 1A10 - 3330 Hospital Drive NW, Calgary, AB T2N 4N1, Canada; Department of Neurology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1137, New York, NY 10029, USA
| | - Colin B Josephson
- Department of Community Health Sciences, University of Calgary, 3D10 - 3280 Hospital Drive NW, Calgary, AB T2N 4Z6, Canada; Department of Clinical Neurosciences, University of Calgary, 1195 1403-29 Street NW, Calgary, AB T2N 2T9, Canada; Hotchkiss Brain Institute, University of Calgary, 1A10 - 3330 Hospital Drive NW, Calgary, AB T2N 4N1, Canada; Alberta Health Services, Foothills Medical Centre, 1403-29 St. NW, Calgary, Alberta T2N 2T9, Canada
| | - Jaqueline McMillan
- Department of Medicine, University of Calgary, North Tower, 1403-29 St NW, Calgary, AB T2N 2T9, Canada; Department of Community Health Sciences, University of Calgary, 3D10 - 3280 Hospital Drive NW, Calgary, AB T2N 4Z6, Canada; Alberta Health Services, Foothills Medical Centre, 1403-29 St. NW, Calgary, Alberta T2N 2T9, Canada; O'Brien Institute for Public Health, University of Calgary, 3rd Floor TRW Building, 3280 Hospital Drive NW, Calgary, AB T2N 4Z6, Canada
| | - Mark R Keezer
- Research Center of the Centre Hospitalier de l'Université de Montréal, 1051 Rue Sanguinet, Montréal, QC H2X 3E4, Canada
| | - Arturo Gonzalez-Izquierdo
- Institute of Health Informatics, University College London, 222 Euston Rd, London NW1 2DA, United Kingdom
| | - Jayna Holroyd-Leduc
- Department of Medicine, University of Calgary, North Tower, 1403-29 St NW, Calgary, AB T2N 2T9, Canada; Department of Community Health Sciences, University of Calgary, 3D10 - 3280 Hospital Drive NW, Calgary, AB T2N 4Z6, Canada; Hotchkiss Brain Institute, University of Calgary, 1A10 - 3330 Hospital Drive NW, Calgary, AB T2N 4N1, Canada; Alberta Health Services, Foothills Medical Centre, 1403-29 St. NW, Calgary, Alberta T2N 2T9, Canada; O'Brien Institute for Public Health, University of Calgary, 3rd Floor TRW Building, 3280 Hospital Drive NW, Calgary, AB T2N 4Z6, Canada.
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18
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Zheng B, Su B, Price G, Tzoulaki I, Ahmadi-Abhari S, Middleton L. Glycemic Control, Diabetic Complications, and Risk of Dementia in Patients With Diabetes: Results From a Large U.K. Cohort Study. Diabetes Care 2021; 44:1556-1563. [PMID: 34035076 DOI: 10.2337/dc20-2850] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2020] [Accepted: 04/23/2021] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Type 2 diabetes is an established risk factor for dementia. However, the roles of glycemic control and diabetic complications in the development of dementia have been less well substantiated. This large-scale cohort study aims to examine associations of longitudinal HbA1c levels and diabetic complications with the risk of dementia incidence among patients with type 2 diabetes. RESEARCH DESIGN AND METHODS Data of eligible patients with diabetes, aged ≥50 years in the U.K. Clinical Practice Research Datalink from 1987 to 2018, were analyzed. Time-varying Cox regressions were used to estimate adjusted hazard ratios (HRs) and 95% CIs for dementia risk. RESULTS Among 457,902 patients with diabetes, 28,627 (6.3%) incident dementia cases were observed during a median of 6 years' follow-up. Patients with recorded hypoglycemic events or microvascular complications were at higher risk of dementia incidence compared with those without such complications (HR 1.30 [95% CI 1.22-1.39] and 1.10 [1.06-1.14], respectively). The HbA1c level, modeled as a time-varying exposure, was associated with increased dementia risk (HR 1.08 [95% CI 1.07-1.09] per 1% HbA1c increment) among 372,287 patients with diabetes with postdiagnosis HbA1c records. Similarly, a higher coefficient of variation of HbA1c during the initial 3 years of follow-up was associated with higher subsequent dementia risk (HR 1.03 [95% CI 1.01-1.04] per 1-SD increment). CONCLUSIONS Higher or unstable HbA1c levels and the presence of diabetic complications in patients with type 2 diabetes are associated with increased dementia risk. Effective management of glycemia might have a significant role in maintaining cognitive health among older adults with diabetes.
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Affiliation(s)
- Bang Zheng
- Ageing Epidemiology Research Unit, School of Public Health, Imperial College London, London, U.K
| | - Bowen Su
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, U.K
| | - Geraint Price
- Ageing Epidemiology Research Unit, School of Public Health, Imperial College London, London, U.K
| | - Ioanna Tzoulaki
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, U.K
| | - Sara Ahmadi-Abhari
- Ageing Epidemiology Research Unit, School of Public Health, Imperial College London, London, U.K
| | - Lefkos Middleton
- Ageing Epidemiology Research Unit, School of Public Health, Imperial College London, London, U.K. .,Public Health Directorate, Imperial College Healthcare NHS Trust, London, U.K
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19
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Ahmad S, Carey IM, Harris T, Cook DG, DeWilde S, Strachan DP. The rising tide of dementia deaths: triangulation of data from three routine data sources using the Clinical Practice Research Datalink. BMC Geriatr 2021; 21:375. [PMID: 34154546 PMCID: PMC8218386 DOI: 10.1186/s12877-021-02306-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 05/27/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Dementia is currently the leading certified underlying cause of death in England. We assess how dementia recording on Office for National Statistics death certificates (ONS) corresponded to recording in general practice records (GP) and Hospital Episode Statistics (HES). METHODS Retrospective study of deaths (2001-15) in 153 English General Practices contributing to the Clinical Practice Research Datalink, with linked ONS and HES records. RESULTS Of 207,068 total deaths from any cause, 19,627 mentioned dementia on the death certificate with 10,253 as underlying cause; steady increases occurred from 2001 to 2015 (any mention 5.3 to 15.4 %, underlying cause 2.7 to 10 %). Including all data sources, recording of any dementia increased from 13.2 to 28.6 %. In 2015, only 53.8 % of people dying with dementia had dementia recorded on their death certificates. Among deaths mentioning dementia on the death certificate, the recording of a prior diagnosis of dementia in GP and HES rose markedly over the same period. In 2001, only 76.3 % had a prior diagnosis in GP and/or HES records; by 2015 this had risen to 95.7 %. However, over the same period the percentage of all deaths with dementia recorded in GP or HES but not mentioned on the death certificate rose from 7.9 to 13.3 %. CONCLUSIONS Dementia recording in all data sources increased between 2001 and 2015. By 2015 the vast majority of deaths mentioning dementia had supporting evidence in primary and/or secondary care. However, death certificates were still providing an inadequate picture of the number of people dying with dementia.
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Affiliation(s)
- Shaleen Ahmad
- Population Health Research Institute, St George's University of London, Cranmer Terrace, SW17 0RE, London, United Kingdom
| | - Iain M Carey
- Population Health Research Institute, St George's University of London, Cranmer Terrace, SW17 0RE, London, United Kingdom.
| | - Tess Harris
- Population Health Research Institute, St George's University of London, Cranmer Terrace, SW17 0RE, London, United Kingdom
| | - Derek G Cook
- Population Health Research Institute, St George's University of London, Cranmer Terrace, SW17 0RE, London, United Kingdom
| | - Stephen DeWilde
- Population Health Research Institute, St George's University of London, Cranmer Terrace, SW17 0RE, London, United Kingdom
| | - David P Strachan
- Population Health Research Institute, St George's University of London, Cranmer Terrace, SW17 0RE, London, United Kingdom
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20
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Giebel C, Hollinghurst J, Akbari A, Schnier C, Wilkinson T, North L, Gabbay M, Rodgers S. Socio-economic predictors of time to care home admission in people living with dementia in Wales: A routine data linkage study. Int J Geriatr Psychiatry 2021; 36:511-520. [PMID: 33045103 PMCID: PMC7984448 DOI: 10.1002/gps.5446] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Accepted: 10/02/2020] [Indexed: 11/17/2022]
Abstract
OBJECTIVES Limited research has shown that people with dementia (PwD) from lower socio-economic backgrounds can face difficulties in accessing the right care at the right time. This study examined whether socio-economic status (SES) and rural versus urban living location are associated with the time between diagnosis and care home admission in PwD living in Wales, UK. METHODS/DESIGN This study linked routine health data and an e-cohort of PwD who have been admitted into a care home between 2000 and 2018 living in Wales. Survival analysis explored the effects of SES, living location, living situation, and frailty on the time between diagnosis and care home admission. RESULTS In 34,514 PwD, the average time between diagnosis and care home admission was 1.5 (±1.4) years. Cox regression analysis showed that increased age, living alone, frailty, and living in less disadvantaged neighbourhoods were associated with faster rate to care home admission. Living in rural regions predicted a slower rate until care home admission. CONCLUSIONS This is one of the first studies to show a link between socio-economic factors on time to care home admission in dementia. Future research needs to address variations in care needs between PwD from different socio-economic and geographical backgrounds.
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Affiliation(s)
- Clarissa Giebel
- Institute of Population Health SciencesUniversity of LiverpoolLiverpoolUK,NIHR ARC NWCLiverpoolUK
| | - Joe Hollinghurst
- Health Data Research UK (HDR‐UK)Data Science BuildingSwansea UniversitySwanseaUK
| | - Ashley Akbari
- Health Data Research UK (HDR‐UK)Data Science BuildingSwansea UniversitySwanseaUK,Administrative Data Research WalesSwansea UniversitySwanseaUK,Dementia PlatformLondonUK
| | - Christian Schnier
- Dementia PlatformLondonUK,Usher InstituteUniversity of EdinburghEdinburghUK
| | - Tim Wilkinson
- Dementia PlatformLondonUK,Usher InstituteUniversity of EdinburghEdinburghUK,Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUK
| | - Laura North
- Health Data Research UK (HDR‐UK)Data Science BuildingSwansea UniversitySwanseaUK,Dementia PlatformLondonUK
| | - Mark Gabbay
- Institute of Population Health SciencesUniversity of LiverpoolLiverpoolUK,NIHR ARC NWCLiverpoolUK
| | - Sarah Rodgers
- Institute of Population Health SciencesUniversity of LiverpoolLiverpoolUK,NIHR ARC NWCLiverpoolUK
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21
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Lee S, Doktorchik C, Martin EA, D'Souza AG, Eastwood C, Shaheen AA, Naugler C, Lee J, Quan H. Electronic Medical Record-Based Case Phenotyping for the Charlson Conditions: Scoping Review. JMIR Med Inform 2021; 9:e23934. [PMID: 33522976 PMCID: PMC7884219 DOI: 10.2196/23934] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 11/20/2020] [Accepted: 12/05/2020] [Indexed: 12/16/2022] Open
Abstract
Background Electronic medical records (EMRs) contain large amounts of rich clinical information. Developing EMR-based case definitions, also known as EMR phenotyping, is an active area of research that has implications for epidemiology, clinical care, and health services research. Objective This review aims to describe and assess the present landscape of EMR-based case phenotyping for the Charlson conditions. Methods A scoping review of EMR-based algorithms for defining the Charlson comorbidity index conditions was completed. This study covered articles published between January 2000 and April 2020, both inclusive. Embase (Excerpta Medica database) and MEDLINE (Medical Literature Analysis and Retrieval System Online) were searched using keywords developed in the following 3 domains: terms related to EMR, terms related to case finding, and disease-specific terms. The manuscript follows the Preferred Reporting Items for Systematic reviews and Meta-analyses extension for Scoping Reviews (PRISMA) guidelines. Results A total of 274 articles representing 299 algorithms were assessed and summarized. Most studies were undertaken in the United States (181/299, 60.5%), followed by the United Kingdom (42/299, 14.0%) and Canada (15/299, 5.0%). These algorithms were mostly developed either in primary care (103/299, 34.4%) or inpatient (168/299, 56.2%) settings. Diabetes, congestive heart failure, myocardial infarction, and rheumatology had the highest number of developed algorithms. Data-driven and clinical rule–based approaches have been identified. EMR-based phenotype and algorithm development reflect the data access allowed by respective health systems, and algorithms vary in their performance. Conclusions Recognizing similarities and differences in health systems, data collection strategies, extraction, data release protocols, and existing clinical pathways is critical to algorithm development strategies. Several strategies to assist with phenotype-based case definitions have been proposed.
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Affiliation(s)
- Seungwon Lee
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Alberta Health Services, Calgary, AB, Canada.,Data Intelligence for Health Lab, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Chelsea Doktorchik
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Elliot Asher Martin
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Alberta Health Services, Calgary, AB, Canada
| | - Adam Giles D'Souza
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Alberta Health Services, Calgary, AB, Canada
| | - Cathy Eastwood
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Abdel Aziz Shaheen
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Christopher Naugler
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Pathology and Laboratory Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Joon Lee
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Data Intelligence for Health Lab, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Cardiac Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Hude Quan
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
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22
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Rathod-Mistry T, Marshall M, Campbell P, Bailey J, Chew-Graham CA, Croft P, Frisher M, Hayward R, Negi R, Robinson L, Singh S, Sumathipala A, Thein N, Walters K, Weich S, Jordan KP. Indicators of dementia disease progression in primary care: An electronic health record cohort study. Eur J Neurol 2021; 28:1499-1510. [PMID: 33378599 DOI: 10.1111/ene.14710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 12/17/2020] [Accepted: 12/20/2020] [Indexed: 11/27/2022]
Abstract
BACKGROUND AND PURPOSE The objectives were to assess the feasibility and validity of using markers of dementia-related health as indicators of dementia progression in primary care, by assessing the frequency with which they are recorded and by testing the hypothesis that they are associated with recognised outcomes of dementia. The markers, in 13 domains, were derived previously through literature review, expert consensus, and analysis of regional primary care records. METHODS The study population consisted of patients with a recorded dementia diagnosis in the Clinical Practice Research Datalink, a UK primary care database linked to secondary care records. Incidence of recorded domains in the 36 months after diagnosis was determined. Associations of recording of domains with future hospital admission, palliative care, and mortality were derived. RESULTS There were 30,463 people with diagnosed dementia. Incidence of domains ranged from 469/1000 person-years (Increased Multimorbidity) to 11/1000 (Home Pressures). An increasing number of domains in which a new marker was recorded in the first year after diagnosis was associated with hospital admission (hazard ratio for ≥4 domains vs. no domains = 1.24; 95% confidence interval = 1.15-1.33), palliative care (1.87; 1.62-2.15), and mortality (1.57; 1.47-1.67). Individual domains were associated with outcomes with varying strengths of association. CONCLUSIONS Feasibility and validity of potential indicators of progression of dementia derived from primary care records are supported by their frequency of recording and associations with recognised outcomes. Further research should assess whether these markers can help identify patients with poorer prognosis to improve outcomes through stratified care and targeted support.
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Affiliation(s)
| | | | - Paul Campbell
- School of Medicine, Keele University, Keele, UK.,Midlands Partnership NHS Foundation Trust, Stafford, UK
| | | | - Carolyn A Chew-Graham
- School of Medicine, Keele University, Keele, UK.,Midlands Partnership NHS Foundation Trust, Stafford, UK
| | - Peter Croft
- School of Medicine, Keele University, Keele, UK
| | - Martin Frisher
- School of Pharmacy and Bioengineering, Keele University, Keele, UK
| | | | - Rashi Negi
- Midlands Partnership NHS Foundation Trust, Stafford, UK
| | - Louise Robinson
- Institute of Health and Society and Newcastle University Institute for Ageing, Newcastle Upon Tyne, UK
| | - Swaran Singh
- Division of Mental Health and Wellbeing, Warwick Medical School, University of Warwick, Coventry, UK
| | - Athula Sumathipala
- School of Medicine, Keele University, Keele, UK.,Midlands Partnership NHS Foundation Trust, Stafford, UK
| | - Nwe Thein
- Midlands Partnership NHS Foundation Trust, Stafford, UK
| | - Kate Walters
- Research Department of Primary Care & Population Health, University College London, London, UK
| | - Scott Weich
- Mental Health Research Unit, School of Health and Related Research, University of Sheffield, Sheffield, UK
| | - Kelvin P Jordan
- School of Medicine, Keele University, Keele, UK.,Centre for Prognosis Research, Keele University, Keele, UK
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23
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Xu J, Wang F, Xu Z, Adekkanattu P, Brandt P, Jiang G, Kiefer RC, Luo Y, Mao C, Pacheco JA, Rasmussen LV, Zhang Y, Isaacson R, Pathak J. Data-driven discovery of probable Alzheimer's disease and related dementia subphenotypes using electronic health records. Learn Health Syst 2020; 4:e10246. [PMID: 33083543 PMCID: PMC7556420 DOI: 10.1002/lrh2.10246] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 07/19/2020] [Accepted: 08/06/2020] [Indexed: 12/04/2022] Open
Abstract
Introduction We sought to assess longitudinal electronic health records (EHRs) using machine learning (ML) methods to computationally derive probable Alzheimer's Disease (AD) and related dementia subphenotypes. Methods A retrospective analysis of EHR data from a cohort of 7587 patients seen at a large, multi‐specialty urban academic medical center in New York was conducted. Subphenotypes were derived using hierarchical clustering from 792 probable AD patients (cases) who had received at least one diagnosis of AD using their clinical data. The other 6795 patients, labeled as controls, were matched on age and gender with the cases and randomly selected in the ratio of 9:1. Prediction models with multiple ML algorithms were trained on this cohort using 5‐fold cross‐validation. XGBoost was used to rank the variable importance. Results Four subphenotypes were computationally derived. Subphenotype A (n = 273; 28.2%) had more patients with cardiovascular diseases; subphenotype B (n = 221; 27.9%) had more patients with mental health illnesses, such as depression and anxiety; patients in subphenotype C (n = 183; 23.1%) were overall older (mean (SD) age, 79.5 (5.4) years) and had the most comorbidities including diabetes, cardiovascular diseases, and mental health disorders; and subphenotype D (n = 115; 14.5%) included patients who took anti‐dementia drugs and had sensory problems, such as deafness and hearing impairment. The 0‐year prediction model for AD risk achieved an area under the receiver operating curve (AUC) of 0.764 (SD: 0.02); the 6‐month model, 0.751 (SD: 0.02); the 1‐year model, 0.752 (SD: 0.02); the 2‐year model, 0.749 (SD: 0.03); and the 3‐year model, 0.735 (SD: 0.03), respectively. Based on variable importance, the top‐ranked comorbidities included depression, stroke/transient ischemic attack, hypertension, anxiety, mobility impairments, and atrial fibrillation. The top‐ranked medications included anti‐dementia drugs, antipsychotics, antiepileptics, and antidepressants. Conclusions Four subphenotypes were computationally derived that correlated with cardiovascular diseases and mental health illnesses. ML algorithms based on patient demographics, diagnosis, and treatment demonstrated promising results in predicting the risk of developing AD at different time points across an individual's lifespan.
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Affiliation(s)
- Jie Xu
- Department of Population Health Sciences Information Technologies and Services, Weill Cornell Medicine New York New York USA
| | - Fei Wang
- Department of Population Health Sciences Information Technologies and Services, Weill Cornell Medicine New York New York USA
| | - Zhenxing Xu
- Department of Population Health Sciences Information Technologies and Services, Weill Cornell Medicine New York New York USA
| | - Prakash Adekkanattu
- Information Technologies and Services, Weill Cornell Medicine New York New York USA
| | - Pascal Brandt
- Biomedical Informatics and Medical Education University of Washington Seattle Washington USA
| | - Guoqian Jiang
- Department of Health Sciences Research Mayo Clinic Rochester Minnesota USA
| | - Richard C Kiefer
- Department of Health Sciences Research Mayo Clinic Rochester Minnesota USA
| | - Yuan Luo
- Feinberg School of Medicine Northwestern University Chicago Illinois USA
| | - Chengsheng Mao
- Feinberg School of Medicine Northwestern University Chicago Illinois USA
| | - Jennifer A Pacheco
- Feinberg School of Medicine Northwestern University Chicago Illinois USA
| | - Luke V Rasmussen
- Feinberg School of Medicine Northwestern University Chicago Illinois USA
| | - Yiye Zhang
- Department of Population Health Sciences Information Technologies and Services, Weill Cornell Medicine New York New York USA
| | - Richard Isaacson
- Department of Population Health Sciences Information Technologies and Services, Weill Cornell Medicine New York New York USA
| | - Jyotishman Pathak
- Department of Population Health Sciences Information Technologies and Services, Weill Cornell Medicine New York New York USA
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24
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McWilliams L. An Overview of Treating People with Comorbid Dementia: Implications for Cancer Care. Clin Oncol (R Coll Radiol) 2020; 32:562-568. [PMID: 32718761 DOI: 10.1016/j.clon.2020.06.014] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Revised: 06/02/2020] [Accepted: 06/18/2020] [Indexed: 01/26/2023]
Abstract
With increasing prevalence of both cancer and dementia in the UK, due to an ageing population, oncology healthcare professionals will experience higher numbers of people with both conditions. As dementia is highly heterogeneous and symptoms vary from individual to individual, it presents specific challenges for healthcare professionals, people with dementia and caregivers alike. This overview will describe current theories that explain the association between cancer and dementia, report prevalence rates and highlight the evidence on the impact of having a diagnosis of dementia on outcomes along the cancer pathway from cancer symptom detection to cancer treatment outcomes. It suggests that although prevalence rates of cancer and dementia are typically lower than other comorbidities, people with cancer and dementia have poorer cancer-related outcomes. This includes later stage cancer diagnoses, fewer cancer treatment options and an increased risk of death compared with people who have cancer alone or other comorbid conditions. Considerations for cancer treatment decision making and management are proposed to improve patient experience for this group.
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Affiliation(s)
- L McWilliams
- Division of Psychology & Mental Health, School of Health Sciences, Faculty of Biology, Medicine & Health, University of Manchester, Manchester, UK.
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25
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Walesby KE, Exeter DJ, Gibb S, Wood PC, Starr JM, Russ TC. Prevalence and geographical variation of dementia in New Zealand from 2012 to 2015: Brief report utilising routinely collected data within the Integrated Data Infrastructure. Australas J Ageing 2020; 39:297-304. [PMID: 32394527 DOI: 10.1111/ajag.12790] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Revised: 02/17/2020] [Accepted: 02/18/2020] [Indexed: 11/28/2022]
Abstract
OBJECTIVES There are no national dementia epidemiological studies using New Zealand (NZ) data. NZ routinely collects health-care data within the Integrated Data Infrastructure (IDI). The study objectives were to 1) investigate late-onset dementia estimates using the IDI between 2012-2015 and compare these with 2) published estimates, and 3) variations between North and South Islands and ethnicity. METHODS A population-based, retrospective cohort design was applied to routinely collected de-identified health/administrative IDI data. Dementia was defined by ICD-10-AM dementia codes or anti-dementia drugs. RESULTS Approximately 2% of those aged ≥60 years had dementia, lower than published estimates. Dementia was higher in North Island; in 80- to 89-year-olds; among the Māori population when age-standardised, and 9% of all dementia cases had >1 dementia sub-type. CONCLUSIONS To our knowledge, this is the first study ascertaining dementia estimates using NZ's whole-of-population IDI data. Estimates were lower than existing NZ estimates, for several reasons. Further work is required, including expanding IDI data sets, to develop future estimates that better reflect NZ's diverse population.
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Affiliation(s)
- Katherine Elizabeth Walesby
- Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, UK.,Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | - Daniel John Exeter
- Epidemiology and Biostatistics, School of Population Health, University of Auckland, Auckland, New Zealand
| | - Sheree Gibb
- Department of Public Health, University of Otago, Wellington, New Zealand
| | - Philip Clive Wood
- North Shore Hospital, Auckland, New Zealand.,Auckland Dementia Prevention Research Clinic, Auckland, New Zealand.,Healthy Ageing, Ministry of Health New Zealand, Wellington, New Zealand
| | - John Michael Starr
- Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, UK.,Western General Hospital, NHS Lothian, Edinburgh, UK
| | - Tom Charles Russ
- Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, UK.,Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK.,Centre for Dementia Prevention, University of Edinburgh, Edinburgh, UK.,Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.,NHS Lothian, Edinburgh, UK
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26
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Chen YC, Oyang YJ, Lin TY, Sun WZ. Risk assessment of dementia after hysterectomy: Analysis of 14-year data from the National Health Insurance Research Database in Taiwan. J Chin Med Assoc 2020; 83:394-399. [PMID: 32149891 DOI: 10.1097/jcma.0000000000000286] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/24/2022] Open
Abstract
BACKGROUND Anesthesia and surgery may increase the risk of dementia in the elderly, but the higher prevalence of dementia in women and other evidence suggest that dementia risk increases in younger women undergoing hysterectomy. In this study, we assessed the risk of dementia after hysterectomy. METHODS Hysterectomies registered in the National Health Insurance Research Database from 2000 to 2013 were evaluated using a retrospective generational research method. Multivariate Cox regression analysis was used to assess the effect of age at surgery, anesthesia method, and surgery type on the hazard ratio (HR) for the development of dementia. RESULTS Among 280 308 patients who underwent hysterectomy, 4753 (1.7%) developed dementia. Age at surgery and anesthesia method were associated with the occurrence of dementia, independent of surgery type. Among patients 30-49 years of age, general anesthesia (GA) was associated with a higher risk of dementia than spinal anesthesia (SA). The HR for GA was 2.678 (95% confidence interval [CI] = 1.269-5.650) and the risk of dementia increased by 7.4% for every 1-year increase in age (HR = 1.074; 95% CI = 1.048-1.101). In patients >50 years of age, the HR for GA was 1.206 (95% CI = 1.057-1.376), and the risk of dementia increased by 13.0% for every 1-year increase in age (HR = 1.130; 95% CI = 1.126-1.134). CONCLUSION The risk of dementia in women who underwent hysterectomy was significantly affected by older age at surgery, and the risk might not increase linearly with age, but show instead an S-curve with exponential increase at about 50 years of age. Although less significant, GA was associated with higher risk than SA, and the effect of the anesthesia method was greater in patients <50 years of age. In contrast, the surgical procedure used was not associated to the risk of dementia.
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Affiliation(s)
- Yi-Chun Chen
- Department of Anesthesiology, Far Eastern Memorial Hospital, New Taipei City, Taiwan, ROC
| | - Yen-Jen Oyang
- Graduate Institute of Biomedical Electronics and Bioinformatics, College of Electrical Engineering & Computer Science, National Taiwan University, Taipei, Taiwan, ROC
| | - Tzu-Yun Lin
- Department of Anesthesiology, Far Eastern Memorial Hospital, New Taipei City, Taiwan, ROC
| | - Wei-Zen Sun
- Graduate Institute of Biomedical Electronics and Bioinformatics, College of Electrical Engineering & Computer Science, National Taiwan University, Taipei, Taiwan, ROC
- Department of Anesthesiology, National Taiwan University Hospital, Taipei, Taiwan, ROC
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27
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Schnier C, Wilkinson T, Akbari A, Orton C, Sleegers K, Gallacher J, Lyons RA, Sudlow C. The Secure Anonymised Information Linkage databank Dementia e-cohort (SAIL-DeC). Int J Popul Data Sci 2020; 5:1121. [PMID: 32935048 PMCID: PMC7473277 DOI: 10.23889/ijpds.v5i1.1121] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Introduction The rising burden of dementia is a global concern, and there is a need to study its causes, natural history and outcomes. The Secure Anonymised Information Linkage (SAIL) Databank contains anonymised, routinely-collected healthcare data for the population of Wales, UK. It has potential to be a valuable resource for dementia research owing to its size, long follow-up time and prospective collection of data during clinical care. Objectives We aimed to apply reproducible methods to create the SAIL dementia e-cohort (SAIL-DeC). We created SAIL-DeC with a view to maximising its utility for a broad range of research questions whilst minimising duplication of effort for researchers. Methods SAIL contains individual-level, linked primary care, hospital admission, mortality and demographic data. Data are currently available until 2018 and future updates will extend participant follow-up time. We included participants who were born between 1st January 1900 and 1st January 1958 and for whom primary care data were available. We applied algorithms consisting of International Classification of Diseases (versions 9 and 10) and Read (version 2) codes to identify participants with and without all-cause dementia and dementia subtypes. We also created derived variables for comorbidities and risk factors. Results From 4.4 million unique participants in SAIL, 1.2 million met the cohort inclusion criteria, resulting in 18.8 million person-years of follow-up. Of these, 129,650 (10%) developed all-cause dementia, with 77,978 (60%) having dementia subtype codes. Alzheimer's disease was the most common subtype diagnosis (62%). Among the dementia cases, the median duration of observation time was 14 years. Conclusion We have created a generalisable, national dementia e-cohort, aimed at facilitating epidemiological dementia research.
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Affiliation(s)
- C Schnier
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - T Wilkinson
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK.,Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - A Akbari
- Health Data Research UK Wales and Northern Ireland, Swansea University, Swansea, UK.,Administrative Data Research Partnership Wales, Swansea University, Swansea, UK
| | - C Orton
- Health Data Research UK Wales and Northern Ireland, Swansea University, Swansea, UK
| | - K Sleegers
- Center for Molecular Neurology, University of Antwerp, Antwerp, Belgium
| | - J Gallacher
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - R A Lyons
- Health Data Research UK Wales and Northern Ireland, Swansea University, Swansea, UK.,National Centre for Population Health and Wellbeing Research, Swansea University, Swansea, UK
| | - Clm Sudlow
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK.,Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.,Health Data Research UK Scotland, University of Edinburgh, Edinburgh, UK
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28
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Ponjoan A, Garre-Olmo J, Blanch J, Fages E, Alves-Cabratosa L, Martí-Lluch R, Comas-Cufí M, Parramon D, García-Gil M, Ramos R. How well can electronic health records from primary care identify Alzheimer's disease cases? Clin Epidemiol 2019; 11:509-518. [PMID: 31456649 PMCID: PMC6620769 DOI: 10.2147/clep.s206770] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Accepted: 04/24/2019] [Indexed: 12/03/2022] Open
Abstract
Background Electronic health records (EHR) from primary care are emerging in Alzheimer’s disease (AD) research, but their accuracy is a concern. We aimed to validate AD diagnoses from primary care using additional information provided by general practitioners (GPs), and a register of dementias. Patients and methods This retrospective observational study obtained data from the System for the Development of Research in Primary Care (SIDIAP). Three algorithms combined International Statistical Classification of Diseases (ICD-10) and Anatomical Therapeutic Chemical codes to identify AD cases in SIDIAP. GPs evaluated dementia diagnoses by means of an online survey. We linked data from the Register of Dementias of Girona and from SIDIAP. We estimated the positive predictive value (PPV) and sensitivity and provided results stratified by age, sex and severity. Results Using survey data from the GPs, PPV of AD diagnosis was 89.8% (95% CI: 84.7–94.9). Using the dataset linkage, PPV was 74.8 (95% CI: 73.1–76.4) for algorithm A1 (AD diagnoses), and 72.3 (95% CI: 70.7–73.9) for algorithm A3 (diagnosed or treated patients without previous conditions); sensitivity was 71.4 (95% CI: 69.6–73.0) and 83.3 (95% CI: 81.8–84.6) for algorithms A1 (AD diagnoses) and A3, respectively. Stratified results did not differ by age, but PPV and sensitivity estimates decreased amongst men and severe patients, respectively. Conclusions PPV estimates differed depending on the gold standard. The development of algorithms integrating diagnoses and treatment of dementia improved the AD case ascertainment. PPV and sensitivity estimates were high and indicated that AD codes recorded in a large primary care database were sufficiently accurate for research purposes.
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Affiliation(s)
- Anna Ponjoan
- Vascular Health Research Group (ISV-Girona), Jordi Gol Institute for Primary Care Research (IDIAPJGol), Barcelona, Catalonia, Spain.,Universitat Autònoma de Barcelona , Bellaterra, Catalonia, Spain.,Girona Biomedical Research Institute (IDIBGI) , Girona, Catalonia, Spain
| | - Josep Garre-Olmo
- Girona Biomedical Research Institute (IDIBGI) , Girona, Catalonia, Spain
| | - Jordi Blanch
- Vascular Health Research Group (ISV-Girona), Jordi Gol Institute for Primary Care Research (IDIAPJGol), Barcelona, Catalonia, Spain
| | - Ester Fages
- Vascular Health Research Group (ISV-Girona), Jordi Gol Institute for Primary Care Research (IDIAPJGol), Barcelona, Catalonia, Spain.,Primary Care Services, Catalan Health Institute (ICS), Girona, Catalonia, Spain
| | - Lia Alves-Cabratosa
- Vascular Health Research Group (ISV-Girona), Jordi Gol Institute for Primary Care Research (IDIAPJGol), Barcelona, Catalonia, Spain
| | - Ruth Martí-Lluch
- Vascular Health Research Group (ISV-Girona), Jordi Gol Institute for Primary Care Research (IDIAPJGol), Barcelona, Catalonia, Spain.,Universitat Autònoma de Barcelona , Bellaterra, Catalonia, Spain.,Girona Biomedical Research Institute (IDIBGI) , Girona, Catalonia, Spain
| | - Marc Comas-Cufí
- Vascular Health Research Group (ISV-Girona), Jordi Gol Institute for Primary Care Research (IDIAPJGol), Barcelona, Catalonia, Spain
| | - Dídac Parramon
- Vascular Health Research Group (ISV-Girona), Jordi Gol Institute for Primary Care Research (IDIAPJGol), Barcelona, Catalonia, Spain.,Primary Care Services, Catalan Health Institute (ICS), Girona, Catalonia, Spain
| | - María García-Gil
- Vascular Health Research Group (ISV-Girona), Jordi Gol Institute for Primary Care Research (IDIAPJGol), Barcelona, Catalonia, Spain
| | - Rafel Ramos
- Vascular Health Research Group (ISV-Girona), Jordi Gol Institute for Primary Care Research (IDIAPJGol), Barcelona, Catalonia, Spain.,Department of Medical Sciences, School of Medicine, Campus Salut, University of Girona, Girona, Catalonia, Spain
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Henry A, Katsoulis M, Masi S, Fatemifar G, Denaxas S, Acosta D, Garfield V, Dale CE. The relationship between sleep duration, cognition and dementia: a Mendelian randomization study. Int J Epidemiol 2019; 48:849-860. [PMID: 31062029 PMCID: PMC6659373 DOI: 10.1093/ije/dyz071] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/26/2019] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND Short and long sleep duration have been linked with poorer cognitive outcomes, but it remains unclear whether these associations are causal. METHODS We conducted the first Mendelian randomization (MR) study with 77 single-nucleotide polymorphisms (SNPs) for sleep duration using individual-participant data from the UK Biobank cohort (N = 395 803) and summary statistics from the International Genomics of Alzheimer's Project (N cases/controls = 17 008/37 154) to investigate the potential impact of sleep duration on cognitive outcomes. RESULTS Linear MR suggested that each additional hour/day of sleep was associated with 1% [95% confidence interval (CI) = 0-2%; P = 0.008] slower reaction time and 3% more errors in visual-memory test (95% CI = 0-6%; P = 0.05). There was little evidence to support associations of increased sleep duration with decline in visual memory [odds ratio (OR) per additional hour/day of sleep = 1.10 (95% CI = 0.76-1.57); P = 0.62], decline in reaction time [OR = 1.28 (95% CI = 0.49-3.35); P = 0.61], all-cause dementia [OR = 1.19 (95% CI = 0.65-2.19); P = 0.57] or Alzheimer's disease risk [OR = 0.89 (95% CI = 0.67-1.18); P = 0.41]. Non-linear MR suggested that both short and long sleep duration were associated with poorer visual memory (P for non-linearity = 3.44e-9) and reaction time (P for non-linearity = 6.66e-16). CONCLUSIONS Linear increase in sleep duration has a small negative effect on reaction time and visual memory, but the true association might be non-linear, with evidence of associations for both short and long sleep duration. These findings suggest that sleep duration may represent a potential causal pathway for cognition.
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Affiliation(s)
- Albert Henry
- Institute of Health Informatics, University College London, London, UK
- Department of Population Science and Experimental Medicine, Institute of Cardiovascular Science, University College London, London, UK
| | - Michail Katsoulis
- Institute of Health Informatics, University College London, London, UK
| | - Stefano Masi
- Department of Population Science and Experimental Medicine, Institute of Cardiovascular Science, University College London, London, UK
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | | | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK
| | - Dionisio Acosta
- Institute of Health Informatics, University College London, London, UK
| | - Victoria Garfield
- Department of Population Science and Experimental Medicine, Institute of Cardiovascular Science, University College London, London, UK
| | - Caroline E Dale
- Institute of Health Informatics, University College London, London, UK
- Department of Population Science and Experimental Medicine, Institute of Cardiovascular Science, University College London, London, UK
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30
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Pelegrini LNC, Mota GMP, Ramos CF, Jesus E, Vale FAC. Diagnosing dementia and cognitive dysfunction in the elderly in primary health care: A systematic review. Dement Neuropsychol 2019; 13:144-153. [PMID: 31285788 PMCID: PMC6601305 DOI: 10.1590/1980-57642018dn13-020002] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2019] [Accepted: 03/20/2019] [Indexed: 11/21/2022] Open
Abstract
Dementia is a public health issue making the screening and diagnosing of dementia and its prodromal phases in all health settings imperative. OBJECTIVE using PRISMA, this systematic review aimed to identify how low-, middle-, and high-income countries establish dementia and cognitive dysfunction diagnoses in primary health care. METHODS studies from the past five years in English, Spanish, and Portuguese were retrieved from Scopus, PubMed, Embase, Lilacs, Scielo, and Web of Science. Of 1987 articles, 33 were selected for analysis. RESULTS only three articles were from middle-income countries and there were no studies from low-income countries. The most used instrument was the Mini-Mental State Examination (MMSE). Mild Cognitive Impairment (MCI) and dementia criteria were based on experts' recommendation as well as on the Diagnostic and Statistical Manual of Mental Disorders (DSM) and International Classification of Diseases (ICD-10), respectively. CONCLUSION differences between these criteria among high- and middle-income countries were observed.
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Affiliation(s)
- Lucas N C Pelegrini
- PhD student on the Graduate Program in Fundamental Nursing - Nursing School of Ribeirão Preto/ University of São Paulo (EERP/USP), Ribeirão Preto, SP, Brazil
| | - Gabriela M P Mota
- Master's student on the Graduate Program in Nursing - Federal University of São Carlos (UFSCar), São Carlos, SP, Brazil
| | - Caio F Ramos
- Master's student on the Graduate Program in Nursing - Federal University of São Carlos (UFSCar), São Carlos, SP, Brazil
| | | | - Francisco A C Vale
- Professor on the Graduate Program in Nursing - Federal University of São Carlos (UFSCar), São Carlos, SP, Brazil
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31
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Pujades-Rodriguez M, Assi V, Gonzalez-Izquierdo A, Wilkinson T, Schnier C, Sudlow C, Hemingway H, Whiteley WN. Correction: The diagnosis, burden and prognosis of dementia: A record-linkage cohort study in England. PLoS One 2018; 13:e0201213. [PMID: 30024957 PMCID: PMC6053215 DOI: 10.1371/journal.pone.0201213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Abstract
[This corrects the article DOI: 10.1371/journal.pone.0199026.].
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