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Buawangpong N, Phinyo P, Angkurawaranon C, Soontornpun A, Jiraporncharoen W, Sirikul W, Pinyopornpanish K. External Validation of the Charlson Comorbidity Index-based Model for Survival Prediction in Thai Patients Diagnosed with Dementia. BMC Geriatr 2024; 24:675. [PMID: 39134981 PMCID: PMC11318235 DOI: 10.1186/s12877-024-05238-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 07/22/2024] [Indexed: 08/16/2024] Open
Abstract
BACKGROUND The Charlson Comorbidity Index (CCI) is commonly employed for predicting mortality. Nonetheless, its performance has rarely been evaluated in patients with dementia. This study aimed to examine the predictive capability of the CCI-based model for survival prediction in Thai patients diagnosed with dementia. METHODS An external validation study was conducted using retrospective data from adults with dementia who had visited the outpatient departments at Maharaj Nakorn Chiang Mai Hospital between 2006 and 2012. The data obtained from electronic medical records included age, gender, date of dementia diagnosis and death, types of dementia, and comorbidities at the time of dementia diagnosis. The discriminative ability and calibration of the CCI-based model were estimated using Harrell's C Discrimination Index and visualized with calibration plot. As the initial performance did not meet satisfaction, model updating and recalibration were performed. RESULTS Of 702 patients, 56.9% were female. The mean age at dementia diagnosis was 75.22 (SD 9.75) year-old. During external validation, Harrell's C-statistic of the CCI-based model was 0.58 (95% CI, 0.54-0.61). The model showed poor external calibration. Model updating was subsequently performed. All updated models demonstrated a modest increase in Harrell's C-statistic. Temporal recalibration did not significantly improve the calibration of any of the updated models. CONCLUSION The CCI-based model exhibited fair discriminative ability and poor calibration for predicting survival in Thai patients diagnosed with dementia. Despite attempts at model updating, significant improvements were not achieved. Therefore, it is important to consider the incorporation of other influential prognostic factors.
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Affiliation(s)
- Nida Buawangpong
- Department of Family Medicine, Faculty of Medicine, Chiang Mai University, 110 Inthawarorot Rd., Chiang Mai, 50200, Thailand
- Global Health and Chronic Conditions Research Group, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Phichayut Phinyo
- Department of Family Medicine, Faculty of Medicine, Chiang Mai University, 110 Inthawarorot Rd., Chiang Mai, 50200, Thailand
- Center for Clinical Epidemiology and Clinical Statistics, Faculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand
- Musculoskeletal Science and Translational Research (MSTR), Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Chaisiri Angkurawaranon
- Department of Family Medicine, Faculty of Medicine, Chiang Mai University, 110 Inthawarorot Rd., Chiang Mai, 50200, Thailand
- Global Health and Chronic Conditions Research Group, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Atiwat Soontornpun
- Division of Neurology, Department of Internal Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Wichuda Jiraporncharoen
- Department of Family Medicine, Faculty of Medicine, Chiang Mai University, 110 Inthawarorot Rd., Chiang Mai, 50200, Thailand
- Global Health and Chronic Conditions Research Group, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Wachiranun Sirikul
- Department of Community Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Kanokporn Pinyopornpanish
- Department of Family Medicine, Faculty of Medicine, Chiang Mai University, 110 Inthawarorot Rd., Chiang Mai, 50200, Thailand.
- Global Health and Chronic Conditions Research Group, Chiang Mai University, Chiang Mai, 50200, Thailand.
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Pan ZM, Zeng J, Li T, Hu F, Cai XY, Wang XJ, Liu GZ, Hu XH, Yang X, Lu YH, Liu MY, Gong YP, Liu M, Li N, Li CL. Age-adjusted Charlson comorbidity index is associated with the risk of osteoporosis in older fall-prone men: a retrospective cohort study. BMC Geriatr 2024; 24:413. [PMID: 38730354 PMCID: PMC11084079 DOI: 10.1186/s12877-024-05015-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 04/26/2024] [Indexed: 05/12/2024] Open
Abstract
BACKGROUND There is growing evidence linking the age-adjusted Charlson comorbidity index (aCCI), an assessment tool for multimorbidity, to fragility fracture and fracture-related postoperative complications. However, the role of multimorbidity in osteoporosis has not yet been thoroughly evaluated. We aimed to investigate the association between aCCI and the risk of osteoporosis in older adults at moderate to high risk of falling. METHODS A total of 947 men were included from January 2015 to August 2022 in a hospital in Beijing, China. The aCCI was calculated by counting age and each comorbidity according to their weighted scores, and the participants were stratified into two groups by aCCI: low (aCCI < 5), and high (aCCI ≥5). The Kaplan Meier method was used to assess the cumulative incidence of osteoporosis by different levels of aCCI. The Cox proportional hazards regression model was used to estimate the association of aCCI with the risk of osteoporosis. Receiver operating characteristic (ROC) curve was adapted to assess the performance for aCCI in osteoporosis screening. RESULTS At baseline, the mean age of all patients was 75.7 years, the mean BMI was 24.8 kg/m2, and 531 (56.1%) patients had high aCCI while 416 (43.9%) were having low aCCI. During a median follow-up of 6.6 years, 296 participants developed osteoporosis. Kaplan-Meier survival curves showed that participants with high aCCI had significantly higher cumulative incidence of osteoporosis compared with those had low aCCI (log-rank test: P < 0.001). When aCCI was examined as a continuous variable, the multivariable-adjusted model showed that the osteoporosis risk increased by 12.1% (HR = 1.121, 95% CI 1.041-1.206, P = 0.002) as aCCI increased by one unit. When aCCI was changed to a categorical variable, the multivariable-adjusted hazard ratios associated with different levels of aCCI [low (reference group) and high] were 1.00 and 1.557 (95% CI 1.223-1.983) for osteoporosis (P < 0.001), respectively. The aCCI (cutoff ≥5) revealed an area under ROC curve (AUC) of 0.566 (95%CI 0.527-0.605, P = 0.001) in identifying osteoporosis in older fall-prone men, with sensitivity of 64.9% and specificity of 47.9%. CONCLUSIONS The current study indicated an association of higher aCCI with an increased risk of osteoporosis among older fall-prone men, supporting the possibility of aCCI as a marker of long-term skeletal-related adverse clinical outcomes.
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Affiliation(s)
- Zi-Mo Pan
- Department of Endocrinology, The Second Medical Center & National Clinical Research Center for Geriatric Disease, Chinese PLA General Hospital, 28 Fuxing Road, Beijing, 100853, China
- Graduate School of Chinese PLA General Hospital, 28 Fuxing Road, Beijing, 100853, China
| | - Jing Zeng
- Department of Endocrinology, The Second Medical Center & National Clinical Research Center for Geriatric Disease, Chinese PLA General Hospital, 28 Fuxing Road, Beijing, 100853, China
| | - Ting Li
- Department of Endocrinology, The Second Medical Center & National Clinical Research Center for Geriatric Disease, Chinese PLA General Hospital, 28 Fuxing Road, Beijing, 100853, China
| | - Fan Hu
- Department of Endocrinology, The Second Medical Center & National Clinical Research Center for Geriatric Disease, Chinese PLA General Hospital, 28 Fuxing Road, Beijing, 100853, China
| | - Xiao-Yan Cai
- Department of Nephrology, The Second Medical Center & National Clinical Research Center for Geriatric Disease, Chinese PLA General Hospital, 28 Fuxing Road, Beijing, 100853, China
| | - Xin-Jiang Wang
- Department of Radiology, The Second Medical Center & National Clinical Research Center for Geriatric Disease, Chinese PLA General Hospital, 28 Fuxing Road, Beijing, 100853, China
| | - Guan-Zhong Liu
- Department of Radiology, The Second Medical Center & National Clinical Research Center for Geriatric Disease, Chinese PLA General Hospital, 28 Fuxing Road, Beijing, 100853, China
| | - Xing-He Hu
- Department of Radiology, The Second Medical Center & National Clinical Research Center for Geriatric Disease, Chinese PLA General Hospital, 28 Fuxing Road, Beijing, 100853, China
| | - Xue Yang
- Outpatient Department, The Second Medical Center & National Clinical Research Center for Geriatric Disease, Chinese PLA General Hospital, 28 Fuxing Road, Beijing, 100853, China
| | - Yan-Hui Lu
- Department of Endocrinology, The Second Medical Center & National Clinical Research Center for Geriatric Disease, Chinese PLA General Hospital, 28 Fuxing Road, Beijing, 100853, China
| | - Min-Yan Liu
- Department of Endocrinology, The Second Medical Center & National Clinical Research Center for Geriatric Disease, Chinese PLA General Hospital, 28 Fuxing Road, Beijing, 100853, China
| | - Yan-Ping Gong
- Department of Endocrinology, The Second Medical Center & National Clinical Research Center for Geriatric Disease, Chinese PLA General Hospital, 28 Fuxing Road, Beijing, 100853, China
| | - Miao Liu
- Department of anti-NBC medicine, Graduate School of Chinese PLA General Hospital, 28 Fuxing Road, Beijing, 100853, China.
| | - Nan Li
- Department of Endocrinology, The Second Medical Center & National Clinical Research Center for Geriatric Disease, Chinese PLA General Hospital, 28 Fuxing Road, Beijing, 100853, China.
| | - Chun-Lin Li
- Department of Endocrinology, The Second Medical Center & National Clinical Research Center for Geriatric Disease, Chinese PLA General Hospital, 28 Fuxing Road, Beijing, 100853, China.
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Sugimoto T, Sakurai T, Noguchi T, Komatsu A, Nakagawa T, Ueda I, Osawa A, Lee S, Shimada H, Kuroda Y, Fujita K, Matsumoto N, Uchida K, Kishino Y, Ono R, Arai H, Saito T. Developing a predictive model for mortality in patients with cognitive impairment. Int J Geriatr Psychiatry 2023; 38:e6020. [PMID: 37909125 DOI: 10.1002/gps.6020] [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: 06/01/2023] [Accepted: 10/17/2023] [Indexed: 11/02/2023]
Abstract
OBJECTIVES We developed a predictive model for all-cause mortality and examined the risk factors for cause-specific mortality among people with cognitive impairment in a Japanese memory clinic-based cohort (2010-2018). METHODS This retrospective cohort study included people aged ≥65 years with mild cognitive impairment or dementia. The survival status was assessed based on the response of participants or their close relatives via a postal survey. Potential predictors including demographic and lifestyle-related factors, functional status, and behavioral and psychological status were assessed at the first visit at the memory clinic. A backward stepwise Cox regression model was used to select predictors, and a predictive model was developed using a regression coefficient-based scoring approach. The discrimination and calibration were assessed via Harrell's C-statistic and a calibration plot, respectively. RESULTS A total of 2610 patients aged ≥65 years (men, 38.3%) were analyzed. Over a mean follow-up of 4.1 years, 544 patients (20.8%) died. Nine predictors were selected from the sociodemographic and clinical variables: age, sex, body mass index, gait performance, physical activity, and ability for instrumental activities of daily living, cognitive function, and self-reported comorbidities (pulmonary disease and diabetes). The model showed good discrimination and calibration for 1-5-year mortality (Harrell's C-statistic, 0.739-0.779). Some predictors were specifically associated with cause-specific mortality. CONCLUSIONS This predictive model has good discriminative ability for 1- to 5-year mortality and can be easily implemented for people with mild cognitive impairment and all stages of dementia referred to a memory clinic.
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Affiliation(s)
- Taiki Sugimoto
- Department of Prevention and Care Science, Research Institute, National Center for Geriatrics and Gerontology, Obu, Japan
- Center for Comprehensive Care and Research on Memory Disorders, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Takashi Sakurai
- Department of Prevention and Care Science, Research Institute, National Center for Geriatrics and Gerontology, Obu, Japan
- Center for Comprehensive Care and Research on Memory Disorders, National Center for Geriatrics and Gerontology, Obu, Japan
- Research Institute, National Center for Geriatrics and Gerontology, Obu, Japan
- Department of Cognition and Behavior Science, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Taiji Noguchi
- Center for Gerontology and Social Science, Research Institute, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Ayane Komatsu
- Center for Gerontology and Social Science, Research Institute, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Takeshi Nakagawa
- Center for Gerontology and Social Science, Research Institute, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Ikue Ueda
- Department of Rehabilitation Medicine, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Aiko Osawa
- Department of Rehabilitation Medicine, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Sangyoon Lee
- Center for Gerontology and Social Science, Research Institute, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Hiroyuki Shimada
- Center for Gerontology and Social Science, Research Institute, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Yujiro Kuroda
- Department of Prevention and Care Science, Research Institute, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Kosuke Fujita
- Department of Prevention and Care Science, Research Institute, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Nanae Matsumoto
- Department of Prevention and Care Science, Research Institute, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Kazuaki Uchida
- Department of Prevention and Care Science, Research Institute, National Center for Geriatrics and Gerontology, Obu, Japan
- Department of Rehabilitation Science, Graduate School of Health Sciences, Kobe University, Kobe, Japan
| | - Yoshinobu Kishino
- Department of Prevention and Care Science, Research Institute, National Center for Geriatrics and Gerontology, Obu, Japan
- Department of Cognition and Behavior Science, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Rei Ono
- Department of Physical Activity Research, National Institutes of Biomedical Innovation, Health and Nutrition, Tokyo, Japan
- Department of Public Health, Graduate School of Health Sciences, Kobe University, Kobe, Japan
| | - Hidenori Arai
- National Center for Geriatrics and Gerontology, Obu, Japan
| | - Tami Saito
- Center for Gerontology and Social Science, Research Institute, National Center for Geriatrics and Gerontology, Obu, Japan
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Cho KH, Paek JM, Ko KM. Long-Term Survival Prediction Model for Elderly Community Members Using a Deep Learning Method. Geriatrics (Basel) 2023; 8:105. [PMID: 37887978 PMCID: PMC10606576 DOI: 10.3390/geriatrics8050105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 10/09/2023] [Accepted: 10/19/2023] [Indexed: 10/28/2023] Open
Abstract
In an aging society, maintaining healthy aging, preventing death, and enabling a continuation of economic activities are crucial. This study sought to develop a model for predicting survival times among community-dwelling older individuals using a deep learning method, and to identify the level of influence of various risk factors on the survival period, so that older individuals can manage their own health. This study used the Korean National Health Insurance Service claims data. We observed community-dwelling older people, aged 66 years, for 11 years and developed a survival time prediction model. Of the 189,697 individuals enrolled at baseline, 180,235 (95.0%) survived from 2009 to 2019, while 9462 (5.0%) died. Using deep-learning-based models (C statistics = 0.7011), we identified various factors impacting survival: Charlson's comorbidity index; the frailty index; long-term care benefit grade; disability grade; income level; a combination of diabetes mellitus, hypertension, and dyslipidemia; sex; smoking status; and alcohol consumption habits. In particular, Charlson's comorbidity index (SHAP value: 0.0445) and frailty index (SHAP value: 0.0443) were strong predictors of survival time. Prediction models may help researchers to identify potentially modifiable risk factors that may affect survival.
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Affiliation(s)
- Kyoung Hee Cho
- Department of Health Policy and Management, SangJi University, Wonju-si 26339, Republic of Korea;
| | - Jong-Min Paek
- Department of Computer Engineering, SangJi University, Kwang-Man Ko. 83 Sangjidae-gil, Wonju-si 26339, Republic of Korea;
| | - Kwang-Man Ko
- Department of Computer Engineering, SangJi University, Kwang-Man Ko. 83 Sangjidae-gil, Wonju-si 26339, Republic of Korea;
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Deardorff WJ, Barnes DE, Jeon SY, Boscardin WJ, Langa KM, Covinsky KE, Mitchell SL, Whitlock EL, Smith AK, Lee SJ. Development and External Validation of a Mortality Prediction Model for Community-Dwelling Older Adults With Dementia. JAMA Intern Med 2022; 182:1161-1170. [PMID: 36156062 PMCID: PMC9513707 DOI: 10.1001/jamainternmed.2022.4326] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 08/06/2022] [Indexed: 12/14/2022]
Abstract
Importance Estimating mortality risk in older adults with dementia is important for guiding decisions such as cancer screening, treatment of new and chronic medical conditions, and advance care planning. Objective To develop and externally validate a mortality prediction model in community-dwelling older adults with dementia. Design, Setting, and Participants This cohort study included community-dwelling participants (aged ≥65 years) in the Health and Retirement Study (HRS) from 1998 to 2016 (derivation cohort) and National Health and Aging Trends Study (NHATS) from 2011 to 2019 (validation cohort). Exposures Candidate predictors included demographics, behavioral/health factors, functional measures (eg, activities of daily living [ADL] and instrumental activities of daily living [IADL]), and chronic conditions. Main Outcomes and Measures The primary outcome was time to all-cause death. We used Cox proportional hazards regression with backward selection and multiple imputation for model development. Model performance was assessed by discrimination (integrated area under the receiver operating characteristic curve [iAUC]) and calibration (plots of predicted and observed mortality). Results Of 4267 participants with probable dementia in HRS, the mean (SD) age was 82.2 (7.6) years, 2930 (survey-weighted 69.4%) were female, and 785 (survey-weighted 12.1%) identified as Black. Median (IQR) follow-up time was 3.9 (2.0-6.8) years, and 3466 (81.2%) participants died by end of follow-up. The final model included age, sex, body mass index, smoking status, ADL dependency count, IADL difficulty count, difficulty walking several blocks, participation in vigorous physical activity, and chronic conditions (cancer, heart disease, diabetes, lung disease). The optimism-corrected iAUC after bootstrap internal validation was 0.76 (95% CI, 0.75-0.76) with time-specific AUC of 0.73 (95% CI, 0.70-0.75) at 1 year, 0.75 (95% CI, 0.73-0.77) at 5 years, and 0.84 (95% CI, 0.82-0.85) at 10 years. On external validation in NHATS (n = 2404), AUC was 0.73 (95% CI, 0.70-0.76) at 1 year and 0.74 (95% CI, 0.71-0.76) at 5 years. Calibration plots suggested good calibration across the range of predicted risk from 1 to 10 years. Conclusions and Relevance We developed and externally validated a mortality prediction model in community-dwelling older adults with dementia that showed good discrimination and calibration. The mortality risk estimates may help guide discussions regarding treatment decisions and advance care planning.
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Affiliation(s)
- W James Deardorff
- Division of Geriatrics, University of California, San Francisco
- Geriatrics, Palliative and Extended Care Service Line, San Francisco Veterans Affairs Health Care System, San Francisco
| | - Deborah E Barnes
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco
- Department of Epidemiology and Biostatistics, University of California, San Francisco
| | - Sun Y Jeon
- Division of Geriatrics, University of California, San Francisco
- Geriatrics, Palliative and Extended Care Service Line, San Francisco Veterans Affairs Health Care System, San Francisco
| | - W John Boscardin
- Division of Geriatrics, University of California, San Francisco
- Geriatrics, Palliative and Extended Care Service Line, San Francisco Veterans Affairs Health Care System, San Francisco
- Department of Epidemiology and Biostatistics, University of California, San Francisco
| | - Kenneth M Langa
- Department of Internal Medicine, School of Medicine, University of Michigan, Ann Arbor
- Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor
- Veterans Affairs Ann Arbor Center for Clinical Management Research, Ann Arbor, Michigan
- Institute for Social Research, University of Michigan, Ann Arbor
| | - Kenneth E Covinsky
- Division of Geriatrics, University of California, San Francisco
- Associate Editor, JAMA Internal Medicine
| | - Susan L Mitchell
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, Massachusetts
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Elizabeth L Whitlock
- Department of Anesthesia and Perioperative Care, University of California, San Francisco
| | - Alexander K Smith
- Division of Geriatrics, University of California, San Francisco
- Geriatrics, Palliative and Extended Care Service Line, San Francisco Veterans Affairs Health Care System, San Francisco
| | - Sei J Lee
- Division of Geriatrics, University of California, San Francisco
- Geriatrics, Palliative and Extended Care Service Line, San Francisco Veterans Affairs Health Care System, San Francisco
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You J, Zhang YR, Wang HF, Yang M, Feng JF, Yu JT, Cheng W. Development of a novel dementia risk prediction model in the general population: A large, longitudinal, population-based machine-learning study. EClinicalMedicine 2022; 53:101665. [PMID: 36187723 PMCID: PMC9519470 DOI: 10.1016/j.eclinm.2022.101665] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 08/29/2022] [Accepted: 08/31/2022] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND The existing dementia risk models are limited to known risk factors and traditional statistical methods. We aimed to employ machine learning (ML) to develop a novel dementia prediction model by leveraging a rich-phenotypic variable space of 366 features covering multiple domains of health-related data. METHODS In this longitudinal population-based cohort of the UK Biobank (UKB), 425,159 non-demented participants were enrolled from 22 recruitment centres across the UK between March 1, 2006 and October 31, 2010. We implemented a data-driven strategy to identify predictors from 366 candidate variables covering a comprehensive range of genetic and environmental factors and developed the ML model to predict incident dementia and Alzheimer's Disease (AD) within five, ten, and much longer years (median 11.9 [Interquartile range 11.2-12.5] years). FINDINGS During a follow-up of 5,023,337 person-years, 5287 and 2416 participants developed dementia and AD, respectively. A novel UKB dementia risk prediction (UKB-DRP) model comprising ten predictors including age, ApoE ε4, pairs matching time, leg fat percentage, number of medications taken, reaction time, peak expiratory flow, mother's age at death, long-standing illness, and mean corpuscular volume was established. Our prediction model was internally evaluated based on five-fold cross-validation on discrimination and calibration, and it was further compared with existing prediction scales. The UKB-DRP model can achieve high discriminative accuracy in dementia (AUC 0.848 ± 0.007) and even better in AD (AUC 0.862 ± 0.015). The model was well-calibrated (Hosmer-Lemeshow goodness-of-fit p-value = 0.92), and the predictive power was solid in different incidence time groups. More importantly, our model presented an apparent superiority over existing models like Cardiovascular Risk Factors, Aging, and Incidence of Dementia Risk Score (AUC 0.705 ± 0.008), the Dementia Risk Score (AUC 0.752 ± 0.007), and the Australian National University Alzheimer's Disease Risk Index (AUC 0.584 ± 0.017). The model was internally validated in the general population of European ancestry and White ethnicity; thus, further validation with independent datasets is necessary to confirm these findings. INTERPRETATION Our ML-based UKB-DRP model incorporated ten easily accessible predictors with solid predictive power for incident dementia and AD within five, ten, and much longer years, which can be used to identify individuals at high risk of dementia and AD in the general population. FUNDING This study was funded by grants from the Science and Technology Innovation 2030 Major Projects (2022ZD0211600), National Key R&D Program of China (2018YFC1312904, 2019YFA070950), National Natural Science Foundation of China (282071201, 81971032, 82071997), Shanghai Municipal Science and Technology Major Project (2018SHZDZX01), Research Start-up Fund of Huashan Hospital (2022QD002), Excellence 2025 Talent Cultivation Program at Fudan University (3030277001), Shanghai Rising-Star Program (21QA1408700), Medical Engineering Fund of Fudan University (yg2021-013), and the 111 Project (No. B18015).
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Affiliation(s)
- Jia You
- Department of Neurology, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Ya-Ru Zhang
- Department of Neurology, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Hui-Fu Wang
- Department of Neurology, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Ming Yang
- Department of Neurology, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Jian-Feng Feng
- Department of Neurology, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China
- Zhangjiang Fudan International Innovation Center, Shanghai, China
- Fudan ISTBI—ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Zhejiang, China
- Corresponding authors at: Room 2316, Guanghua Building, East Main Wing, Fudan University, No. 220 Handan Road, Shanghai, 200433, China.
| | - Jin-Tai Yu
- Department of Neurology, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Corresponding author at: Huashan Hospital, No. 12 Wulumuqi Zhong Road, Shanghai, 200040, China.
| | - Wei Cheng
- Department of Neurology, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Fudan ISTBI—ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Zhejiang, China
- Corresponding authors at: Room 2316, Guanghua Building, East Main Wing, Fudan University, No. 220 Handan Road, Shanghai, 200433, China.
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Mank A, van Maurik IS, Rijnhart JJM, bakker ED, Bouteloup V, Le Scouarnec L, Teunissen CE, Barkhof F, Scheltens P, Berkhof J, van der Flier WM. Development of multivariable prediction models for institutionalization and mortality in the full spectrum of Alzheimer’s disease. Alzheimers Res Ther 2022; 14:110. [PMID: 35932034 PMCID: PMC9354423 DOI: 10.1186/s13195-022-01053-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 07/27/2022] [Indexed: 11/15/2022]
Abstract
Background Patients and caregivers express a desire for accurate prognostic information about time to institutionalization and mortality. Previous studies predicting institutionalization and mortality focused on the dementia stage. However, Alzheimer’s disease (AD) is characterized by a long pre-dementia stage. Therefore, we developed prediction models to predict institutionalization and mortality along the AD continuum of cognitively normal to dementia. Methods This study included SCD/MCI patients (subjective cognitive decline (SCD) or mild cognitive impairment (MCI)) and patients with AD dementia from the Amsterdam Dementia Cohort. We developed internally and externally validated prediction models with biomarkers and without biomarkers, stratified by dementia status. Determinants were selected using backward selection (p<0.10). All models included age and sex. Discriminative performance of the models was assessed with Harrell’s C statistics. Results We included n=1418 SCD/MCI patients (n=123 died, n=74 were institutionalized) and n=1179 patients with AD dementia (n=413 died, n=453 were institutionalized). For both SCD/MCI and dementia stages, the models for institutionalization and mortality included after backward selection clinical characteristics, imaging, and cerebrospinal fluid (CSF) biomarkers. In SCD/MCI, the Harrell’s C-statistics of the models were 0.81 (model without biomarkers: 0.76) for institutionalization and 0.79 (model without biomarker: 0.76) for mortality. In AD-dementia, the Harrell’s C-statistics of the models were 0.68 (model without biomarkers: 0.67) for institutionalization and 0.65 (model without biomarker: 0.65) for mortality. Models based on data from amyloid-positive patients only had similar discrimination. Conclusions We constructed prediction models to predict institutionalization and mortality with good accuracy for SCD/MCI patients and moderate accuracy for patients with AD dementia. The developed prediction models can be used to provide patients and their caregivers with prognostic information on time to institutionalization and mortality along the cognitive continuum of AD. Supplementary Information The online version contains supplementary material available at 10.1186/s13195-022-01053-0.
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Charlson ME, Carrozzino D, Guidi J, Patierno C. Charlson Comorbidity Index: A Critical Review of Clinimetric Properties. PSYCHOTHERAPY AND PSYCHOSOMATICS 2022; 91:8-35. [PMID: 34991091 DOI: 10.1159/000521288] [Citation(s) in RCA: 452] [Impact Index Per Article: 226.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 12/01/2021] [Indexed: 11/19/2022]
Abstract
The present critical review was conducted to evaluate the clinimetric properties of the Charlson Comorbidity Index (CCI), an assessment tool designed specifically to predict long-term mortality, with regard to its reliability, concurrent validity, sensitivity, incremental and predictive validity. The original version of the CCI has been adapted for use with different sources of data, ICD-9 and ICD-10 codes. The inter-rater reliability of the CCI was found to be excellent, with extremely high agreement between self-report and medical charts. The CCI has also been shown either to have concurrent validity with a number of other prognostic scales or to result in concordant predictions. Importantly, the clinimetric sensitivity of the CCI has been demonstrated in a variety of medical conditions, with stepwise increases in the CCI associated with stepwise increases in mortality. The CCI is also characterized by the clinimetric property of incremental validity, whereby adding the CCI to other measures increases the overall predictive accuracy. It has been shown to predict long-term mortality in different clinical populations, including medical, surgical, intensive care unit (ICU), trauma, and cancer patients. It may also predict in-hospital mortality, although in some instances, such as ICU or trauma patients, the CCI did not perform as well as other instruments designed specifically for that purpose. The CCI thus appears to be clinically useful not only to provide a valid assessment of the patient's unique clinical situation, but also to demarcate major diagnostic and prognostic differences among subgroups of patients sharing the same medical diagnosis.
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Affiliation(s)
- Mary E Charlson
- Division of Clinical Epidemiology and Evaluative Sciences Research, Department of Medicine, Weill Cornell Medicine, New York, New York, USA
| | - Danilo Carrozzino
- Department of Psychology "Renzo Canestrari," University of Bologna, Bologna, Italy
| | - Jenny Guidi
- Department of Psychology "Renzo Canestrari," University of Bologna, Bologna, Italy
| | - Chiara Patierno
- Department of Psychology "Renzo Canestrari," University of Bologna, Bologna, Italy
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Abstract
BACKGROUND Persons with dementia have higher mortality than the general population. Objective, standardized predictions of mortality risk in persons with dementia could help with planning resources for care close to the end of life. OBJECTIVE To systematically review prediction models for risk of death in persons with dementia. METHODS The Medline and PsycInfo databases were searched on November 29, 2020, for prediction models estimating the risk of death in persons with dementia. Study quality was assessed using the Prediction model Risk Of Bias ASsessment Tool. RESULTS The literature search identified 2,828 studies, of which 18 were included. These studies described 16 different prediction models with c statistics mostly ranging from 0.67 to 0.79. Five models were externally validated, of which four were applicable. There were two models that were both applicable and had reasonably low risk of bias. One model predicted risk of death at six months in persons with advanced dementia residing in a nursing home. The other predicted risk of death at three years in persons seen in primary care practice or a dementia specialty clinic, derived from a nationwide registry in Sweden but not externally validated. CONCLUSION Valid, applicable models with low risk of bias were found in two settings: advanced dementia in a nursing home and outpatient practices. The outpatient model requires external validation. Better models are needed for persons with mild to moderate dementia in nursing homes, a common demographic. These models may be useful for educating persons living with dementia and care partners and directing resources for end of life care.Registration:The study protocol is registered on PROSPERO as RD4202018076.
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Affiliation(s)
- Eric E Smith
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Zahinoor Ismail
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
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Haaksma ML, Eriksdotter M, Rizzuto D, Leoutsakos JMS, Olde Rikkert MGM, Melis RJF, Garcia-Ptacek S. Survival time tool to guide care planning in people with dementia. Neurology 2020; 94:e538-e548. [PMID: 31843808 PMCID: PMC7080282 DOI: 10.1212/wnl.0000000000008745] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2019] [Accepted: 07/25/2019] [Indexed: 12/20/2022] Open
Abstract
OBJECTIVE To develop survival prediction tables to inform physicians and patients about survival probabilities after the diagnosis of dementia and to determine whether survival after dementia diagnosis can be predicted with good accuracy. METHODS We conducted a nationwide registry-linkage study including 829 health centers, i.e., all memory clinics and ≈75% of primary care facilities, across Sweden. Data including cognitive function from 50,076 people with incident dementia diagnoses ≥65 years of age and registered with the Swedish Dementia Register in 2007 to 2015 were used, with a maximum follow-up of 9.7 years for survival until 2016. Sociodemographic factors, comorbidity burden, medication use, and dates of death were obtained from nationwide registries. Cox proportional hazards regression models were used to create tables depicting 3-year survival probabilities for different risk factor profiles. RESULTS By August 2016, 20,828 (41.6%) patients in our cohort had died. Median survival time from diagnosis of dementia was 5.1 (interquartile range 2.9-8.0) years for women and 4.3 (interquartile range 2.3-7.0) years for men. Predictors of mortality were higher age, male sex, increased comorbidity burden and lower cognitive function at diagnosis, a diagnosis of non-Alzheimer dementia, living alone, and using more medications. The developed prediction tables yielded c indexes of 0.70 (95% confidence interval [CI] 0.69-0.71) to 0.72 (95% CI 0.71-0.73) and showed good calibration. CONCLUSIONS Three-year survival after dementia diagnosis can be predicted with good accuracy. The survival prediction tables developed in this study may aid clinicians and patients in shared decision-making and advance care planning.
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Affiliation(s)
- Miriam L Haaksma
- From the Department of Geriatric Medicine (M.L.H., M.G.M.O.R., R.J.F.M.), Radboudumc Alzheimer Center, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, the Netherlands; Aging Research Center (M.L.H., D.R.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Solna; Division of Clinical Geriatrics (M.E., S.G.-P.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet; Theme Aging (M.E., S.G.-P.), Karolinska University Hospital, Huddinge, Sweden; Department of Psychiatry (J.-M.S.L.), Division of Geriatric Psychiatry and Neuropsychiatry, Johns Hopkins University School of Medicine, Baltimore, MD; Radboud University Medical Center (M.G.M.O.R.), Donders Institute for Brain, Cognition and Behaviour, Department of Geriatric Medicine, Radboudumc Alzheimer Center, Nijmegen, the Netherlands; and Department of Internal Medicine (S.G.-P.), Section for Neurology, Södersjukhuset Stockholm, Sweden
| | - Maria Eriksdotter
- From the Department of Geriatric Medicine (M.L.H., M.G.M.O.R., R.J.F.M.), Radboudumc Alzheimer Center, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, the Netherlands; Aging Research Center (M.L.H., D.R.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Solna; Division of Clinical Geriatrics (M.E., S.G.-P.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet; Theme Aging (M.E., S.G.-P.), Karolinska University Hospital, Huddinge, Sweden; Department of Psychiatry (J.-M.S.L.), Division of Geriatric Psychiatry and Neuropsychiatry, Johns Hopkins University School of Medicine, Baltimore, MD; Radboud University Medical Center (M.G.M.O.R.), Donders Institute for Brain, Cognition and Behaviour, Department of Geriatric Medicine, Radboudumc Alzheimer Center, Nijmegen, the Netherlands; and Department of Internal Medicine (S.G.-P.), Section for Neurology, Södersjukhuset Stockholm, Sweden
| | - Debora Rizzuto
- From the Department of Geriatric Medicine (M.L.H., M.G.M.O.R., R.J.F.M.), Radboudumc Alzheimer Center, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, the Netherlands; Aging Research Center (M.L.H., D.R.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Solna; Division of Clinical Geriatrics (M.E., S.G.-P.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet; Theme Aging (M.E., S.G.-P.), Karolinska University Hospital, Huddinge, Sweden; Department of Psychiatry (J.-M.S.L.), Division of Geriatric Psychiatry and Neuropsychiatry, Johns Hopkins University School of Medicine, Baltimore, MD; Radboud University Medical Center (M.G.M.O.R.), Donders Institute for Brain, Cognition and Behaviour, Department of Geriatric Medicine, Radboudumc Alzheimer Center, Nijmegen, the Netherlands; and Department of Internal Medicine (S.G.-P.), Section for Neurology, Södersjukhuset Stockholm, Sweden
| | - Jeannie-Marie S Leoutsakos
- From the Department of Geriatric Medicine (M.L.H., M.G.M.O.R., R.J.F.M.), Radboudumc Alzheimer Center, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, the Netherlands; Aging Research Center (M.L.H., D.R.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Solna; Division of Clinical Geriatrics (M.E., S.G.-P.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet; Theme Aging (M.E., S.G.-P.), Karolinska University Hospital, Huddinge, Sweden; Department of Psychiatry (J.-M.S.L.), Division of Geriatric Psychiatry and Neuropsychiatry, Johns Hopkins University School of Medicine, Baltimore, MD; Radboud University Medical Center (M.G.M.O.R.), Donders Institute for Brain, Cognition and Behaviour, Department of Geriatric Medicine, Radboudumc Alzheimer Center, Nijmegen, the Netherlands; and Department of Internal Medicine (S.G.-P.), Section for Neurology, Södersjukhuset Stockholm, Sweden
| | - Marcel G M Olde Rikkert
- From the Department of Geriatric Medicine (M.L.H., M.G.M.O.R., R.J.F.M.), Radboudumc Alzheimer Center, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, the Netherlands; Aging Research Center (M.L.H., D.R.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Solna; Division of Clinical Geriatrics (M.E., S.G.-P.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet; Theme Aging (M.E., S.G.-P.), Karolinska University Hospital, Huddinge, Sweden; Department of Psychiatry (J.-M.S.L.), Division of Geriatric Psychiatry and Neuropsychiatry, Johns Hopkins University School of Medicine, Baltimore, MD; Radboud University Medical Center (M.G.M.O.R.), Donders Institute for Brain, Cognition and Behaviour, Department of Geriatric Medicine, Radboudumc Alzheimer Center, Nijmegen, the Netherlands; and Department of Internal Medicine (S.G.-P.), Section for Neurology, Södersjukhuset Stockholm, Sweden
| | - René J F Melis
- From the Department of Geriatric Medicine (M.L.H., M.G.M.O.R., R.J.F.M.), Radboudumc Alzheimer Center, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, the Netherlands; Aging Research Center (M.L.H., D.R.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Solna; Division of Clinical Geriatrics (M.E., S.G.-P.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet; Theme Aging (M.E., S.G.-P.), Karolinska University Hospital, Huddinge, Sweden; Department of Psychiatry (J.-M.S.L.), Division of Geriatric Psychiatry and Neuropsychiatry, Johns Hopkins University School of Medicine, Baltimore, MD; Radboud University Medical Center (M.G.M.O.R.), Donders Institute for Brain, Cognition and Behaviour, Department of Geriatric Medicine, Radboudumc Alzheimer Center, Nijmegen, the Netherlands; and Department of Internal Medicine (S.G.-P.), Section for Neurology, Södersjukhuset Stockholm, Sweden
| | - Sara Garcia-Ptacek
- From the Department of Geriatric Medicine (M.L.H., M.G.M.O.R., R.J.F.M.), Radboudumc Alzheimer Center, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, the Netherlands; Aging Research Center (M.L.H., D.R.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Solna; Division of Clinical Geriatrics (M.E., S.G.-P.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet; Theme Aging (M.E., S.G.-P.), Karolinska University Hospital, Huddinge, Sweden; Department of Psychiatry (J.-M.S.L.), Division of Geriatric Psychiatry and Neuropsychiatry, Johns Hopkins University School of Medicine, Baltimore, MD; Radboud University Medical Center (M.G.M.O.R.), Donders Institute for Brain, Cognition and Behaviour, Department of Geriatric Medicine, Radboudumc Alzheimer Center, Nijmegen, the Netherlands; and Department of Internal Medicine (S.G.-P.), Section for Neurology, Södersjukhuset Stockholm, Sweden.
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