Table 3 Summary of tools and technologies to support PCPs in providing dementia care that considers comorbidities
From: Dementia and comorbidities in primary care: a scoping review
Stage of Patient Pathway | Dementia Type | Tools & Technologies | Description | Stage of Development | Citation |
|---|---|---|---|---|---|
Risk Assessment and Prevention | Dementia | Dementia risk prediction metrics | This meta-analysis identified factors (e.g. depression, history of stroke and orthostatic hypotension, diabetes) that are consistently associated with dementia, which should be further explored when developing a dementia risk prediction tool for primary care for PCPs to implement early interventions to slow the progression of dementia | Predictors were selected for a dementia risk prediction tool; the tool was not developed | [41] |
Cognitive decline in MCI | Dementia risk prediction metrics | This randomized clinical trial (RCT) conducted in China with a cohort of individuals aged 55–65 found that right-sided hearing loss may be a predictor of cognitive decline in older adults. This morbidity can be utilized as a marker to support PCPs in identifying high-risk individuals for early prevention of cognitive decline | Risk predictor suggested; the tool was not developed | [42] | |
Dementia | Dementia risk prediction metrics | This observational study conducted in Portugal identified gastrointestinal diseases as potential risk factors when screening for dementia in a younger cohort aged 50 and older, which could be readily implemented in primary care. However, gastrointestinal diseases are only demonstrated to be highly correlated with dementia risk when used in a multivariate model with other factors such as gender, age, APOE ε4 allele, female gender, and low education, and needs to be further validated | Risk predictor suggested; the tool was not developed | [43] | |
Dementia | MyHealthNetwork | MyHealthNetwork is a digital health tool to monitor brain health to lower dementia risk, especially given the positive association between hypertension and the risk of future dementia. The tool is designed for patients to record their blood pressure readings and readily share the data with their PCPs to monitor and detect hypertension and ultimately potential changes to brain health. Future developments of this platform will capture additional metrics related to brain health (e.g. blood glucose level, sleep patterns) to provide more comprehensive insights into one’s brain health | Prototype development | [44] | |
Dementia | Dementia prediction model | This multivariable risk prediction model incorporates metrics easily collected in the primary care setting (e.g. age, sex, hypertension, education level, diabetes, body mass index (BMI), history of stroke, smoking status, and sedentariness) to identify individuals at high risk of developing dementia within 10 years of the assessment. Assessment results can guide PCPs in providing risk factor-based interventions to lower the risk of future dementia | Developed with a sample of 795 participants aged 65 + | [45] | |
AD | Zaragoza Dementia and Depression Project (ZARADEMP) Alzheimer Dementia Risk Score | The ZARADEMP risk score incorporates socio-demographic (age, sex, marital status, educational level), psychological (depression and anxiety), behavioral (obesity and alcohol and tobacco use), and medical (diabetes, hypertension, stroke, acute myocardial infarction, history of angina, and hearing loss) factors to predict the risk of developing AD within 5 years of the assessment. This score can identify at-risk individuals in primary care settings and implement preventative and therapeutic strategies in earlier disease stages | Developed with a sample of 3044 participants aged 65 + , not yet validated | [46] | |
MCI and dementia | Singapore Longitudinal Ageing Study (SLAS) MCI risk prediction index | The SLAS risk index incorporates 20 indicators commonly measured in primary care settings, including psychosocial (e.g. life satisfaction, living alone), lifestyle (e.g. education level, frequency of physical and social activities), and health risk factors (e.g. sex, age, smoking history and status, cardio-metabolic and vascular risk factors) to predict the risk of MCI or dementia within 4.5 years of the assessment. This assessment is to identify individuals in community settings at the pre-dementia stage for preventative interventions | Validated with a sample of ~ 4374 participants aged 55 + | [47] | |
Dementia | The Rapid Assessment of Dementia Risk (RaDaR) | The RADaR score uses predictors capturing demographic factors (e.g. sex, age), clinical predictors (e.g. blood pressure, stroke, medications), medical history (e.g. cancer, smoking, hypertension, diabetes), memory complaints, functional disability, psychological factors, cognitive testing, and motor evaluations to measure the risk of developing dementia within 3 years of the assessment for individuals over the age of 65 years. This tool can be utilized in primary care to identify screening frequency for high-risk individuals | Validated with a sample of 1308 participants (average age of 65 +) | [48] | |
Dementia | Dementia risk prediction model | This multivariate dementia risk prediction model used predictors such as sex, age, education level, history of diabetes, systolic blood pressure, history of stroke, smoking status, parental history of dementia, and presence of depressive symptoms to predict the risk of developing dementia within 10 years of the assessment to identify individuals in primary care to be referred to specialized care settings for diagnosis and to stratify high-risk individuals to inform clinical trial design targeting early dementia interventions | Validated with a sample of 742 participants aged 60 + | [49] | |
AD | AD prediction score | The AD prediction score is to be used in primary care to classify patients who will develop AD within 15 years of the evaluation. The prediction score incorporates the symptomatic and drug use predictors, including the presence of memory disorders, hallucinations, anxiety, and depression, and the use of non-steroidal anti-inflammatory drugs. This score can allow PCPs to screen for at-risk individuals in primary care settings to implement preventative and therapeutic strategies | Validated with a sample of 66,659 participants aged 60 + | [50] | |
MCI | mCAIDE | The modified Cardiovascular Risk Factors, Aging, and Dementia (mCAIDE) score includes easily assessed and self-reported metrics (e.g. sex, age, education, history of cholesterol), non-invasive clinical indicators of chronic illness (e.g. systolic blood pressure), and physical performance-based indicators (e.g. mini Physical Performance Testing [mPPT]), all which can be readily implemented in the community and primary care to screen for cognitive impairment and identify at-risk patients to target for interventions to delay dementia progression | Validated with a sample of 219 participants aged 65 + | [51] | |
Dementia | Anticholinergic Cognitive Burden Scale (ACB) | The compounded use of anticholinergic medications in older adults for chronic conditions (e.g. cardiovascular drugs, anti-histamines, drugs affecting the nervous system) has increased the vulnerability of the patients to anticholinergic adverse events, such as cognitive impairment which may develop into dementia. The use of ACB in primary care can minimize suboptimal prescribing and choose medications to reduce the anticholinergic burden and hence lower dementia risk | An analysis of 116,043 older adults demonstrated the effectiveness of the ACB scale at predicting the risk of incidence dementia in individuals aged 65–84; the tool can be used to guide PCP prescriptions | [52] | |
Screening, Detection, and Diagnosis | MCI, dementia | Diagnostic algorithm | This diagnostic algorithm assists PCPs in choosing the most suitable cognitive test (e.g. clock drawing test, MoCA, MMSE) per suspected case of cognitive impairment, which depends on the likelihood of the cognitive impairment after history taking and an informant interview. The algorithm considers other chronic conditions (e.g. depression, diabetes, cardiovascular factors) as risk factors when determining whether or not cognitive impairment is a potential diagnosis before selecting the appropriate cognitive test | Framework for algorithm is proposed, but not yet developed | [53] |
Management | Dementia | CASEPLUS-SimPat | CASEPLUS-SimPat is a web-based case management system that allows different healthcare professionals (e.g. PCPs and hospital employees) to access patient data to coordinate treatment, which will be particularly valuable for PwD with multimorbidity as they will require more complex care situations | Developed, but not yet validated | [54] |