Function-based dementia severity assessment for vascular cognitive impairment

Hung, Chao-Hsien; Hung, Guang-Uei; Wei, Cheng‐Yu; Tzeng, Ray-Chang; Chiu, Pai-Yi

doi:10.1016/j.jfma.2020.07.001

Cited by 11 publications

(16 citation statements)

References 37 publications

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“…CASI comprises 25 tasks selected from the Hasegawa Dementia Screening Scale, the Mini-Mental State Examination, and the Modified Mini-Mental State Test [ 11 , 25 ]. The CASI assesses nine cognitive domains, including long-term memory, short-term memory, attention, mental manipulation, orientation, abstract thinking, language, visuospatial construction, and verbal fluency.…”

Section: Methodsmentioning

confidence: 99%

Determining optimal cutoff scores of Cognitive Abilities Screening Instrument to identify dementia and mild cognitive impairment in Taiwan

Lyu,

Chiu,

Liu

et al. 2024

BMC Geriatr

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Background The early detection of dementia depends on efficient methods for the assessment of cognitive capacity. Existing cognitive screening tools are ill-suited to the differentiation of cognitive status, particularly when dealing with early-stage impairment. Methods The study included 8,979 individuals (> 50 years) with unimpaired cognitive functions, mild cognitive impairment (MCI), or dementia. This study sought to determine optimal cutoffs values for the Cognitive Abilities Screening Instrument (CASI) aimed at differentiating between individuals with or without dementia as well as between individuals with or without mild cognitive impairment. Cox proportional hazards models were used to evaluate the value of CASI tasks in predicting conversion from MCI to all-cause dementia, dementia of Alzheimer’s type (DAT), or to vascular dementia (VaD). Results Our optimized cutoff scores achieved high accuracy in differentiating between individuals with or without dementia (AUC = 0.87—0.93) and moderate accuracy in differentiating between CU and MCI individuals (AUC = 0.67 – 0.74). Among individuals without cognitive impairment, scores that were at least 1.5 × the standard deviation below the mean scores on CASI memory tasks were predictive of conversion to dementia within roughly 2 years after the first assessment (all-cause dementia: hazard ratio [HR] = 2.81 – 3.53; DAT: 1.28 – 1.49; VaD: 1.58). Note that the cutoff scores derived in this study were lower than those reported in previous studies. Conclusion Our results in this study underline the importance of establishing optimal cutoff scores for individuals with specific demographic characteristics and establishing profiles by which to guide CASI analysis.

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Section: Methodsmentioning

confidence: 99%

Determining optimal cutoff scores of Cognitive Abilities Screening Instrument to identify dementia and mild cognitive impairment in Taiwan

Lyu,

Chiu,

Liu

et al. 2024

BMC Geriatr

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“…The detailed medical history included personal history, medical history, and family history. In addition to the Clinical Dementia Rating (CDR) scale [23] neuropsychological assessments, including the Cognitive Abilities Screening Instrument (CASI) [24], Montreal Cognitive Assessment (MoCA) [25], History-based Artificial Intelligent Activities of Daily Living (HAIADL) scale [26], and 12-item Neuropsychiatric Inventory (NPI) [27], are used to determine and trace the severity of CI or dementia. The CASI, which is composed of 9 cognitive domains, was popularly applied in Taiwan and many other countries using different languages [24].…”

Section: Methods and Participantsmentioning

confidence: 99%

“…The CASI, which is composed of 9 cognitive domains, was popularly applied in Taiwan and many other countries using different languages [24]. The HAIADL, which was modified from Lawton and Brody's IADL and BI (Barthel Index), composed of 15 questions and can be applied in individuals with ADL impairment due to cognitive disorders for the determination of severity of CI stages [26]. Diagnosis of all participants were made in a consensus meeting that composed of experienced neurologists, neuropsychologists, and radiologists.…”

Section: Methods and Participantsmentioning

confidence: 99%

“…Participants with impairments in two or more cognitive domains and a decline in daily functions, with a global CDR score �0.5 plus a CDR-SB score �4.5, are considered to have dementia. An HAIADL score >8 was used for the operational diagnosis of functional impairment [26]. CASI was used to test cognitive performance.…”

Section: Diagnosis Of Predementia Stages Including Scd and MCI Or Dem...mentioning

confidence: 99%

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Emergency department visits among people with predementia highly predicts conversion to dementia

et al. 2022

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Emergency department visits (EDV) are common among older adults with and without dementia. The risk factors and demands of EDVs for people with dementia have been well studied; however, the association between EDVs and conversion to dementia among people with predementia has not been thoroughly explored. To study the predictive value of EDVs in predementia’s progression to dementia. The baseline predementia cohort registered from September 2015 to August 2017, with longitudinal follow-up in the History-based Artificial Intelligent Clinical Dementia Diagnostic System database, was retrospectively analyzed. The rates of conversion among the different EDVs were compared. Multivariate logistic regression and Cox proportional hazards analyses were applied to study the influence of EDVs on progression. Age, education, sex, neuropsychological tests, activities of daily living, neuropsychiatric symptoms, parkinsonism, and multiple vascular risk factors were adjusted for. A total of 512 participants were analyzed, including 339 (66.2%) non-converters and 173 (33.8%) converters with a mean follow-up of 3.3 (range 0.4–6.1) and 2.8 (range 0.5–5.9) years, respectively. Compared to people without EDV (EDV 0), the hazard ratios for conversion to dementia were 3.6, 5.9, and 6.9 in those with EDV once (EDV 1), twice (EDV 2), and more than twice (EDV >2), respectively. In addition, older age, lower education, poorer cognition, poorer ADL performance, and longer follow-up periods also increased the conversion rates. EDVs in the predementia stages highly predict progression to dementia. Therefore, a sound public health as well as primary healthcare system that provide strategies for better management of mental and physical condition might help prevention of EDVs among older people in the predementia stages.

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“…As dementia progresses, the affected individuals lose the ability to carry out everyday activities, including basic activities of daily living (ADLs) and instrumental ADLs (iADLs), which are fundamental skills required to independently care for oneself and serve as an indicator of a person’s functional status [ 2 ]. Assessment of ADLs and iADLs is also essential in determining the severity of dementia and the care needs of older adults [ 3 ]. This information is important in predicting a patient’s risk of mortality, long-term nursing home admission, and health care use.…”

Section: Introductionmentioning

confidence: 99%

Identifying Functional Status Impairment in People Living With Dementia Through Natural Language Processing of Clinical Documents: Cross-Sectional Study

Laurentiev,

Kim,

Mahesri

et al. 2024

J Med Internet Res

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Background Assessment of activities of daily living (ADLs) and instrumental ADLs (iADLs) is key to determining the severity of dementia and care needs among older adults. However, such information is often only documented in free-text clinical notes within the electronic health record and can be challenging to find. Objective This study aims to develop and validate machine learning models to determine the status of ADL and iADL impairments based on clinical notes. Methods This cross-sectional study leveraged electronic health record clinical notes from Mass General Brigham’s Research Patient Data Repository linked with Medicare fee-for-service claims data from 2007 to 2017 to identify individuals aged 65 years or older with at least 1 diagnosis of dementia. Notes for encounters both 180 days before and after the first date of dementia diagnosis were randomly sampled. Models were trained and validated using note sentences filtered by expert-curated keywords (filtered cohort) and further evaluated using unfiltered sentences (unfiltered cohort). The model’s performance was compared using area under the receiver operating characteristic curve and area under the precision-recall curve (AUPRC). Results The study included 10,000 key-term–filtered sentences representing 441 people (n=283, 64.2% women; mean age 82.7, SD 7.9 years) and 1000 unfiltered sentences representing 80 people (n=56, 70% women; mean age 82.8, SD 7.5 years). Area under the receiver operating characteristic curve was high for the best-performing ADL and iADL models on both cohorts (>0.97). For ADL impairment identification, the random forest model achieved the best AUPRC (0.89, 95% CI 0.86-0.91) on the filtered cohort; the support vector machine model achieved the highest AUPRC (0.82, 95% CI 0.75-0.89) for the unfiltered cohort. For iADL impairment, the Bio+Clinical bidirectional encoder representations from transformers (BERT) model had the highest AUPRC (filtered: 0.76, 95% CI 0.68-0.82; unfiltered: 0.58, 95% CI 0.001-1.0). Compared with a keyword-search approach on the unfiltered cohort, machine learning reduced false-positive rates from 4.5% to 0.2% for ADL and 1.8% to 0.1% for iADL. Conclusions In this study, we demonstrated the ability of machine learning models to accurately identify ADL and iADL impairment based on free-text clinical notes, which could be useful in determining the severity of dementia.

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Function-based dementia severity assessment for vascular cognitive impairment

Cited by 11 publications

References 37 publications

Determining optimal cutoff scores of Cognitive Abilities Screening Instrument to identify dementia and mild cognitive impairment in Taiwan

Determining optimal cutoff scores of Cognitive Abilities Screening Instrument to identify dementia and mild cognitive impairment in Taiwan

Emergency department visits among people with predementia highly predicts conversion to dementia

Identifying Functional Status Impairment in People Living With Dementia Through Natural Language Processing of Clinical Documents: Cross-Sectional Study

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