Prevalence of frailty in 62 countries across the world: a systematic review and meta-analysis of population-level studies.

INTRODUCTION: The prevalence of frailty at population level is unclear. We examined this in population-based studies, investigating sources of heterogeneity. METHODS: PubMed, Embase, CINAHL and Cochrane Library databases were searched for observational population-level studies published between 1 January 1998 and 1 April 2020, including individuals aged ≥50 years, identified using any frailty measure. Prevalence estimates were extracted independently, assessed for bias and analysed using a random-effects model. RESULTS: In total, 240 studies reporting 265 prevalence proportions from 62 countries and territories, representing 1,755,497 participants, were included. Pooled prevalence in studies using physical frailty measures was 12% (95% CI = 11-13%; n = 178), compared with 24% (95% CI = 22-26%; n = 71) for the deficit accumulation model (those using a frailty index, FI). For pre-frailty, this was 46% (95% CI = 45-48%; n = 147) and 49% (95% CI = 46-52%; n = 29), respectively. For physical frailty, the prevalence was higher among females, 15% (95% CI = 14-17%; n = 142), than males, 11% (95% CI = 10-12%; n = 144). For studies using a FI, the prevalence was also higher in females, 29% (95% CI = 24-35%; n = 34) versus 20% (95% CI = 16-24%; n = 34), for males. These values were similar for pre-frailty. Prevalence increased according to the minimum age at study inclusion. Analysing only data from nationally representative studies gave a frailty prevalence of 7% (95% CI = 5-9%; n = 46) for physical frailty and 24% (95% CI = 22-26%; n = 44) for FIs. CONCLUSIONS: Population-level frailty prevalence varied by classification and sex. Data were heterogenous and limited, particularly from nationally representative studies making the interpretation of differences by geographic region challenging. Common methodological approaches to gathering data are required to improve the accuracy of population-level prevalence estimates. PROTOCOL REGISTRATION: PROSPERO-CRD42018105431.

Informal caregiving for dementia patients: the contribution of patient characteristics and behaviours to caregiver burden.

OBJECTIVES: The burden often associated with informal caregiving for patients with dementia is associated with negative effects on health, both physiologically and in terms of caregiver cognition. There is wide variation in the level of burden experienced by dementia caregivers. To better understand caregiver burden, it is thus important to understand the factors associated with level of burden. METHODS: In the current study, we collected carer burden and putative associated factors at baseline, 6 and 12 months. Hierarchical regression was used to assess the impact of these factors on caregiver burden. We assessed self-reported carer burden, patient behavioural and safety issues, and level of difficulty associated with providing assistance with activities of daily living (ADL). Patients' age was also recorded, and trained nurses assessed patient cognitive performance using the quick mild cognitive impairment screen. RESULTS: At baseline, patients' age, cognition and ADLs were associated with burden, and safety and challenging behaviour were both significantly associated with burden independent of the other factors. Change in burden was associated with change in carer-reported safety at 6-month follow-up, and with change in safety and change in carer-reported challenging behaviours at 12-month follow-up. CONCLUSIONS: Safety issues and challenging behaviours are associated with carer burden, even after accounting for cognitive and functional impairment in the person with dementia. As dementia progresses, monitoring these factors may help to inform stress-management strategies for caregivers.

Nilvadipine in mild to moderate Alzheimer disease: A randomised controlled trial.

BACKGROUND: This study reports the findings of the first large-scale Phase III investigator-driven clinical trial to slow the rate of cognitive decline in Alzheimer disease with a dihydropyridine (DHP) calcium channel blocker, nilvadipine. Nilvadipine, licensed to treat hypertension, reduces amyloid production, increases regional cerebral blood flow, and has demonstrated anti-inflammatory and anti-tau activity in preclinical studies, properties that could have disease-modifying effects for Alzheimer disease. We aimed to determine if nilvadipine was effective in slowing cognitive decline in subjects with mild to moderate Alzheimer disease. METHODS AND FINDINGS: NILVAD was an 18-month, randomised, placebo-controlled, double-blind trial that randomised participants between 15 May 2013 and 13 April 2015. The study was conducted at 23 academic centres in nine European countries. Of 577 participants screened, 511 were eligible and were randomised (258 to placebo, 253 to nilvadipine). Participants took a trial treatment capsule once a day after breakfast for 78 weeks. Participants were aged >50 years, meeting National Institute of Neurological and Communicative Disorders and Stroke/Alzheimer's disease Criteria (NINCDS-ADRDA) for diagnosis of probable Alzheimer disease, with a Standardised Mini-Mental State Examination (SMMSE) score of ≥12 and <27. Participants were randomly assigned to 8 mg sustained-release nilvadipine or matched placebo. The a priori defined primary outcome was progression on the Alzheimer's Disease Assessment Scale Cognitive Subscale-12 (ADAS-Cog 12) in the modified intention-to-treat (mITT) population (n = 498), with the Clinical Dementia Rating Scale sum of boxes (CDR-sb) as a gated co-primary outcome, eligible to be promoted to primary end point conditional on a significant effect on the ADAS-Cog 12. The analysis set had a mean age of 73 years and was 62% female. Baseline demographic and Alzheimer disease-specific characteristics were similar between treatment groups, with reported mean of 1.7 years since diagnosis and mean SMMSE of 20.4. The prespecified primary analyses failed to show any treatment benefit for nilvadipine on the co-primary outcome (p = 0.465). Decline from baseline in ADAS-Cog 12 on placebo was 0.79 (95% CI, -0.07-1.64) at 13 weeks, 6.41 (5.33-7.49) at 52 weeks, and 9.63 (8.33-10.93) at 78 weeks and on nilvadipine was 0.88 (0.02-1.74) at 13 weeks, 5.75 (4.66-6.85) at 52 weeks, and 9.41 (8.09-10.73) at 78 weeks. Exploratory analyses of the planned secondary outcomes showed no substantial effects, including on the CDR-sb or the Disability Assessment for Dementia. Nilvadipine appeared to be safe and well tolerated. Mortality was similar between groups (3 on nilvadipine, 4 on placebo); higher counts of adverse events (AEs) on nilvadipine (1,129 versus 1,030), and serious adverse events (SAEs; 146 versus 101), were observed. There were 14 withdrawals because of AEs. Major limitations of this study were that subjects had established dementia and the likelihood that non-Alzheimer subjects were included because of the lack of biomarker confirmation of the presence of brain amyloid. CONCLUSIONS: The results do not suggest benefit of nilvadipine as a treatment in a population spanning mild to moderate Alzheimer disease. TRIAL REGISTRATION: Clinicaltrials.gov NCT02017340, EudraCT number 2012-002764-27.

Comparison of the Quick Mild Cognitive Impairment (Qmci) screen to the Montreal Cognitive Assessment (MoCA) in an Australian geriatrics clinic.

INTRODUCTION: The Montreal Cognitive Assessment (MoCA) accurately differentiates mild cognitive impairment (MCI) from mild dementia and normal controls (NC). While the MoCA is validated in multiple clinical settings, few studies compare it with similar tests also designed to detect MCI. We sought to investigate how the shorter Quick Mild Cognitive Impairment (Qmci) screen compares with the MoCA. METHODS: Consecutive referrals presenting with cognitive complaints to a teaching hospital geriatric clinic (Fremantle, Western Australia) underwent a comprehensive assessment and were classified as MCI (n = 72) or dementia (n = 109). NC (n = 41) were a sample of convenience. The Qmci and MoCA were scored by trained geriatricians, in random order, blind to the diagnosis. RESULTS: Median Qmci scores for NC, MCI and dementia were 69 (+/-19), 52.5 (+/-12) and 36 (+/-14), respectively, compared with 27 (+/-5), 22 (+/-4) and 15 (+/-7) for the MoCA. The Qmci more accurately identified cognitive impairment (MCI or dementia), area under the curve (AUC) 0.97, than the MoCA (AUC 0.92), p = 0.04. The Qmci was non-significantly more accurate in distinguishing MCI from controls (AUC 0.91 vs 0.84, respectively = 0.16). Both instruments had similar accuracy for differentiating MCI from dementia (AUC of 0.91 vs 0.88, p = 0.35). At the optimal cut-offs, calculated from receiver operating characteristic curves, the Qmci (≤57) had a sensitivity of 91% and specificity of 93% for cognitive impairment, compared with 87% sensitivity and 80% specificity for the MoCA (≤23). CONCLUSION: While both instruments are accurate in detecting MCI, the Qmci is shorter and arguably easier to complete, suggesting that it is a useful instrument in an Australian geriatric outpatient population. Copyright © 2016 John Wiley & Sons, Ltd.

Challenges in implementing an advance care planning programme in long-term care.

BACKGROUND: A high prevalence of cognitive impairment and frailty complicates the feasibility of advance care planning in the long-term-care population. Research aim: To identify challenges in implementing the 'Let Me Decide' advance care planning programme in long-term-care. RESEARCH DESIGN: This feasibility study had two phases: (1) staff education on advance care planning and (2) structured advance care planning by staff with residents and families. Participants and research context: long-term-care residents in two nursing homes and one community hospital. Ethical considerations: The local research ethics committee granted ethical approval. FINDINGS: Following implementation, over 50% of all residents had completed some form of end-of-life care plan. Of the 70 residents who died in the post-implementation period, 14% had no care plan, 10% (with capacity) completed an advance care directive and lacking such capacity, 76% had an end-of-life care plan completed for them by the medical team, following discussions with the resident (if able) and family. The considerable logistical challenge of releasing staff for training triggered development of an e-learning programme to facilitate training. DISCUSSION: The challenges encountered were largely concerned with preserving resident's autonomy, avoiding harm and suboptimal or crisis decision-making, and ensuring residents were treated fairly through optimisation of finite resources. CONCLUSIONS: Although it may be too late for many long-term-care residents to complete their own advance care directive, the ' Let Me Decide' programme includes a feasible and acceptable option for structured end-of-life care planning for residents with variable capacity to complete an advance care directive, involving discussion with the resident (to the extent they were able) and their family. While end-of-life care planning was time-consuming to deliver, nursing staff were willing to overcome this and take ownership of the programme, once the benefits in improved communication and enhanced peace of mind among all parties involved became apparent in practice.

Nurses' knowledge of advance directives and perceived confidence in end-of-life care: a cross-sectional study in five countries.

Nurses' knowledge regarding advance directives may affect their administration and completion in end-of-life care. Confidence among nurses is a barrier to the provision of quality end-of-life care. This study investigated nurses' knowledge of advance directives and perceived confidence in end-of-life care, in Hong Kong, Ireland, Israel, Italy and the USA using a cross-sectional descriptive design (n = 1089). In all countries, older nurses and those who had more professional experience felt more confident managing patients' symptoms at end-of-life and more comfortable stopping preventive medications at end-of-life. Nurses in the USA reported that they have more knowledge and experience of advance directives compared with other countries. In addition, they reported the highest levels of confidence and comfort in dealing with end-of-life care. Although legislation for advance directives does not yet exist in Ireland, nurses reported high levels of confidence in end-of-life care.

The inter-rater reliability of the Risk Instrument for Screening in the Community.

Predicting risk of adverse healthcare outcomes is important to enable targeted delivery of interventions. The Risk Instrument for Screening in the Community (RISC), designed for use by public health nurses (PHNs), measures the 1-year risk of hospitalisation, institutionalisation and death in community-dwelling older adults according to a five-point global risk score: from low (score 1,2) to medium (3) to high (4,5). We examined the inter-rater reliability (IRR) of the RISC between student PHNs (n=32) and expert raters using six cases (two low, medium and high-risk), scored before and after RISC training. Correlations increased for each adverse outcome, statistically significantly for institutionalisation (r=0.72 to 0.80, p=0.04) and hospitalisation (r=0.51 to 0.71, p<0.01) but not death. Training improved accuracy for low-risk but not all high-risk cases. Overall, the RISC showed good IRR, which increased after RISC training. That reliability fell for some high-risk cases suggests that the training programme requires adjustment to improve IRR further.

Validation of the Dutch version of the quick mild cognitive impairment screen (Qmci-D).

BACKGROUND: Differentiating mild cognitive impairment (MCI) from dementia is important, as treatment options differ. There are few short (<5 min) but accurate screening tools that discriminate between MCI, normal cognition (NC) and dementia, in the Dutch language. The Quick Mild Cognitive Impairment (Qmci) screen is sensitive and specific in differentiating MCI from NC and mild dementia. Given this, we adapted the Qmci for use in Dutch-language countries and validated the Dutch version, the Qmci-D, against the Dutch translation of the Standardised Mini-Mental State Examination (SMMSE-D). METHOD: The Qmci was translated into Dutch with a combined qualitative and quantitative approach. In all, 90 participants were recruited from a hospital geriatric clinic (25 with dementia, 30 with MCI, 35 with NC). The Qmci-D and SMMSE-D were administered sequentially but randomly by the same trained rater, blind to the diagnosis. RESULTS: The Qmci-D was more sensitive than the SMMSE-D in discriminating MCI from dementia, with a significant difference in the area under the curve (AUC), 0.73 compared to 0.60 (p = 0.024), respectively, and in discriminating dementia from NC, with an AUC of 0.95 compared to 0.89 (p = 0.006). Both screening instruments discriminated MCI from NC with an AUC of 0.86 (Qmci-D) and 0.84 (SMMSE-D). CONCLUSION: The Qmci-D shows similar,(good) accuracy as the SMMSE-D in separating NC from MCI; greater,(albeit fair), accuracy differentiating MCI from dementia, and significantly greater accuracy in separating dementia from NC. Given its brevity and ease of administration, the Qmci-D seems a useful cognitive screen in a Dutch population. Further study with a suitably powered sample against more sensitive screens is now required.

The Risk Instrument for Screening in the Community (RISC): a new instrument for predicting risk of adverse outcomes in community dwelling older adults.

BACKGROUND: Predicting risk of adverse healthcare outcomes, among community dwelling older adults, is difficult. The Risk Instrument for Screening in the Community (RISC) is a short (2-5 min), global subjective assessment of risk created to identify patients' 1-year risk of three outcomes:institutionalisation, hospitalisation and death. METHODS: We compared the accuracy and predictive ability of the RISC, scored by Public Health Nurses (PHN), to the Clinical Frailty Scale (CFS) in a prospective cohort study of community dwelling older adults (n = 803), in two Irish PHN sectors. The area under the curve (AUC), from receiver operating characteristic curves and binary logistic regression models, with odds ratios (OR), compared the discriminatory characteristics of the RISC and CFS. RESULTS: Follow-up data were available for 801 patients. The 1-year incidence of institutionalisation, hospitalisation and death were 10.2, 17.7 and 15.6 % respectively. Patients scored maximum-risk (RISC score 3,4 or 5/5) at baseline had a significantly greater rate of institutionalisation (31.3 and 7.1 %, p < 0.001), hospitalisation (25.4 and 13.2 %, p < 0.001) and death (33.5 and 10.8 %, p < 0.001), than those scored minimum-risk (score 1 or 2/5). The RISC had comparable accuracy for 1-year risk of institutionalisation (AUC of 0.70 versus 0.63), hospitalisation (AUC 0.61 versus 0.55), and death (AUC 0.70 versus 0.67), to the CFS. The RISC significantly added to the predictive accuracy of the regression model for institutionalisation (OR 1.43, p = 0.01), hospitalisation (OR 1.28, p = 0.01), and death (OR 1.58, p = 0.001). CONCLUSION: Follow-up outcomes matched well with baseline risk. The RISC, a short global subjective assessment, demonstrated satisfactory validity compared with the CFS.

Evaluating the systematic implementation of the 'Let Me Decide' advance care planning programme in long term care through focus groups: staff perspectives.

BACKGROUND: The 'Let Me Decide' Advance Care Planning (LMD-ACP) programme offers a structured approach to End-of-Life (EoL) care planning in long-term care for residents with and without capacity to complete an advance care directive/plan. The programme was implemented in three homes in the South of Ireland, with a view to improving quality of care at end of life. This paper will present an evaluation of the systematic implementation of the LMD-ACP programme in the homes. METHODS: Focus groups were conducted with 15 Clinical Nurse Managers and two Directors of Nursing where the programme had been implemented. A semi-structured topic guide was used to direct questions that addressed implementation process, challenges implementing advance care planning, advantages/disadvantages and recommendations for the future. Data was analysed using manifest content analysis. RESULTS: Five key categories emerged, with 16 corresponding subcategories. These subcategories emerged as a result of 37 codes. Key benefits of the programme included enhancing communication, changing the care culture, promoting preference-based care and avoiding crisis decision making. Establishing capacity among residents and indecision were among the main challenges reported by staff. DISCUSSION: A number of recommendations were proposed by participants and included multi-disciplinary team involvement, and a blended approach to education on the topic. According to participants relationships with residents deepened, there was a more open and honest environment with family, end of life care focused more on symptom management, comfort and addressing spiritual care needs as opposed to crisis decision making and family conflict. CONCLUSION: The introduction of the LMD-ACP programme enhanced the delivery of care in the long-term care sites and led to a more open and positive care environment.

A multicenter, blinded, randomized, factorial controlled trial of doxycycline and rifampin for treatment of Alzheimer's disease: the DARAD trial.

OBJECTIVES: Preliminary evidence suggested that doxycycline and rifampin might stop or slow the progression of Alzheimer's disease (AD). We carried out a randomized trial to confirm or refute these findings. METHODS: A multicenter, blinded, randomized, 2 × 2 factorial controlled trial, set at 14 geriatric outpatient clinics in Canada. Four hundred and six patients with mild to moderate AD (standardized mini mental state examination (SMMSE) score 14-26) participated. The intervention was 12 months' treatment with doxycycline 100 mg twice daily + rifampin 300 mg daily or doxycycline 100 mg twice daily + placebo-rifampin daily or rifampin 300 mg daily + placebo-doxycycline twice daily or placebo-doxycycline twice daily + placebo-rifampin daily. Coprimary outcomes were the Standardized Alzheimer's Disease Assessment Scale-Cognitive Subscale (SADAS-cog) and the Clinical Dementia Rating Scale-Sum of the Boxes (CDR-SB). Secondary outcomes were the SMMSE, Quick mild cognitive impairment screen, Geriatric Depression Scale, Cornell Scale for Depression in Dementia, activities of daily living (Lawton Scale), and the Dysfunctional Behavior Rating Instrument frequency and reaction subscales. RESULTS: There was a significant deterioration in SADAS-cog over time with both rifampin and doxycycline in comparison with placebo. When the two were used together, there was no statistically significant decline/deterioration in comparison with placebo (n = 305). For the CDR-SB, there were no significant effects of either rifampin or doxycycline. Secondary outcome results followed similar patterns. CONCLUSION: Twelve months' treatment with doxycycline or rifampin, alone or in combination, has no beneficial effects on cognition or function in AD.

Which part of the Quick mild cognitive impairment screen (Qmci) discriminates between normal cognition, mild cognitive impairment and dementia?

INTRODUCTION: the Qmci is a sensitive and specific test to differentiate between normal cognition (NC), mild cognitive impairment (MCI) and dementia. We compared the sensitivity and specificity of the subtests of the Qmci to determine which best discriminated NC, MCI and dementia. OBJECTIVE: the objective was to determine the contribution each subtest of the Qmci makes, to its sensitivity and specificity in differentiating MCI from NC and dementia, to refine and shorten the instrument. METHODS: existing data from our previous study of 965 subjects, testing the Qmci, was analysed to compare the sensitivity and specificity of the Qmci subtests. RESULTS: all the subtests of the Qmci differentiated MCI from NC. Logical memory (LM) performed the best (area under the receiver operating curve of 0.80), registration the worst, (0.56). LM and verbal fluency had the largest median differences (expressed as percentage of total score) between MCI and NC, 20 and 25%, respectively. Other subtests did not have clinically useful differences. LM was best at differentiating MCI from NC, irrespective of age or educational status. CONCLUSION: the Qmci incorporates several important cognitive domains making it useful across the spectrum of cognitive impairment. LM is the best performing subtest for differentiating MCI from NC.

Comparison of the Six Item Cognitive Impairment Test (6CIT) to Commonly-Used Short Cognitive Screening Instruments in a Memory Clinic Population.

Background: Short cognitive screening instruments (CSI) are required to identify cognitive impairment in busy outpatient clinics. While the Six Item Cognitive Impairment Test (6CIT) is commonly used, its accuracy in those with mild cognitive impairment (MCI) and subjective cognitive decline (SCD) and against more widely-used CSIs is less well established. Objective: To examine the diagnostic accuracy of the 6CIT against the Montreal Cognitive Assessment (MoCA) and Quick Mild Cognitive Impairment (Qmci) screen across the cognitive spectrum in a memory clinic population. Methods: In total, 142 paired assessments were available (21 with SCD, 32 MCI, and 89 with dementia). Consecutive patients underwent a comprehensive assessment and were screened using the 6CIT, Qmci, and MoCA. Accuracy was determined from the area under receiver operating characteristic curves (AUC). Results: The median age of patients was 76 (±11) years; 68% were female. The median 6CIT score was 10/28 (±14). The 6CIT was strongly, negatively, and statistically significantly correlated with the Qmci (r = -0.84) and MoCA (r = -0.86). The 6CIT had good accuracy for separating cognitive impairment (MCI or dementia) from SCD, (AUC:0.88; 0.82-0.94), similar to the MoCA (AUC:0.92; 0.87-0.97, p = 0.308), but statistically lower than the Qmci (AUC:0.96; 0.94-0.99, p = 0.01). The 6CIT was faster to administer, median time 2.05 minutes versus 4.38 and 9.5 for the Qmci and MoCA, respectively. Conclusion: While the Qmci was more accurate than the 6CIT, the shorter administration time of the 6CIT, suggests it may be useful when assessing or monitoring cognitive impairment in busy memory clinics, though larger samples are required to evaluate.

Comparison of the quick mild cognitive impairment (Qmci) screen and the SMMSE in screening for mild cognitive impairment.

INTRODUCTION: differentiating mild cognitive impairment (MCI) from normal cognition (NC) is difficult. The AB Cognitive Screen (ABCS) 135, sensitive in differentiating MCI from dementia, was modified to improve sensitivity and specificity, producing the quick mild cognitive impairment (Qmci) screen. OBJECTIVE: this study compared the sensitivity and specificity of the Qmci with the Standardised MMSE and ABCS 135, to differentiate NC, MCI and dementia. METHODS: weightings and subtests of the ABCS 135 were changed and a new section 'logical memory' added, creating the Qmci. From four memory clinics in Ontario, Canada, 335 subjects (154 with MCI, 181 with dementia) were recruited and underwent comprehensive assessment. Caregivers, attending with the subjects, without cognitive symptoms, were recruited as controls (n = 630). RESULTS: the Qmci was more sensitive than the SMMSE and ABCS 135, in differentiating MCI from NC, with an area under the curve (AUC) of 0.86 compared with 0.67 and 0.83, respectively, and in differentiating MCI from mild dementia, AUC of 0.92 versus 0.91 and 0.91. The ability of the Qmci to identify MCI was better for those over 75 years. CONCLUSION: the Qmci is more sensitive than the SMMSE in differentiating MCI and NC, making it a useful test, for MCI in clinical practice, especially for older adults.

Screening for Cognitive Impairment with the Quick Memory Check: Validation of a Caregiver Administered Cognitive Screen.

BACKGROUND: Self or home-administered cognitive screening instruments (CSIs) can reduce barriers to the early detection of mild cognitive impairment (MCI) and dementia. OBJECTIVE: To examine the acceptability and diagnostic accuracy of a caregiver-administered CSI, the Quick Memory Check (QMC). METHODS: Components of the Quick Mild Cognitive impairment (Qmci) screen (orientation, verbal fluency, and logical memory) were re-weighted to create the QMC, scored out of 100 points. Participants, attending a university hospital memory clinic, were provided administration instructions beforehand. Area under the curve (AUC) scores, adjusted for age and education, were compared with the Qmci screen and Montreal Cognitive Assessment (MoCA). Caregivers or family scored the QMC. RESULTS: In all, 366 participants were recruited; 53 with subjective memory complaints (SMC), 74 with MCI, 193 with dementia, and 46 normal controls. Median QMC scores for controls were 70±13 versus 60±20 for SMC, 52±18 for MCI, and 31±21 for dementia. The QMC had excellent accuracy (AUC 0.97) for cognitive impairment (MCI/dementia from controls), similar to the Qmci screen (AUC 0.98, p = 0.17) and MoCA (AUC 0.95, p = 0.13). At a cut-off of <52/100, the QMC had 83% sensitivity and 100% specificity for cognitive impairment. The QMC had lower accuracy differentiating MCI from SMC (AUC 0.73), albeit similar to the MoCA (AUC 0.70). CONCLUSION: The QMC, administered by caregivers in advance of clinic, compared favorably to established CSIs scored by trained raters. This caregiver, home-administered CSI is acceptable and can identify cognitive impairment, potentially improving efficiency by reducing testing time and patient stress in busy clinical settings.

Prevalence and Predictors of Caregiver Burden in a Memory Clinic Population.

BACKGROUND: Although caregiver burden is common among carers of people with dementia, little is known about its prevalence and predictors among caregivers of patients attending memory clinics. OBJECTIVE: To examine carer and patient-specific characteristics associated with caregiver burden across the cognitive spectrum in a memory clinic population. METHODS: Consecutive patients referred to a university hospital geriatric memory clinic were included. Caregiver burden was scored using the Caregiver Burden Score (CBS), (modified Zarit), with scores≥15/30 suggesting burden. BPSD were measured with the dysfunctional behaviour rating instrument (DBRI). Cognition was screened using the Montreal Cognitive Assessment (MoCA) and Quick Mild Cognitive Impairment (Qmci) screen. RESULTS: In all, 351 patients were included, median age 77 (±11) years; 65.5% were female. The prevalence of caregiver burden was 33.6% overall, increasing from 10.8% in subjective cognitive decline (SCD), to 15% in mild cognitive impairment (MCI) and 43% in dementia; CBS scores were significantly higher in dementia (p < 0.001). Caregivers with burden were significantly younger (p = 0.045) and were more likely to be adult children (p = 0.007). The CBS weakly correlated with the stage of cognitive impairment (r = 0.16) but had moderate correlation with MoCA (r = -0.54) and Qmci scores (r = -0.60). After adjustment for co-variates, DBRI scores alone independently predicted burden (odds ratio 1.23;1.11-1.35, p < 0.001). CONCLUSION: Caregiver burden is associated with the stage of cognitive impairment, with higher prevalence proportions in those with dementia compared with MCI and SCD. Only the severity of neuropsychiatric symptoms independently predicted caregiver burden in this population and its presence should prompt assessment for burden.

Comparison of the Greek Version of the Quick Mild Cognitive Impairment Screen and Standardised Mini-Mental State Examination.

INTRODUCTION: Short cognitive screening instruments (CSIs) are widely used to stratify patients presenting with cognitive symptoms. The Quick Mild Cognitive Impairment (Qmci) screen is a new, brief (<5mins) CSI designed to identify mild cognitive impairment (MCI), which can be used across the spectrum of cognitive decline. Here we present the translation of the Qmci into Greek (Qmci-Gr) and its validation against the widely-used Standardised Mini-Mental State Examination (SMMSE). METHODS: Consecutive patients aged ≥55 years presenting with cognitive complaints were recruited from two outpatient clinics in Greece. All patients completed the Qmci-Gr and SMMSE and underwent an independent detailed neuropsychological assessment to determine a diagnostic classification. RESULTS: In total, 140 patients, median age 75 years, were included; 30 with mild dementia (median SMMSE 23/30), 76 with MCI and 34 with subjective memory complaints (SMC) but normal cognition. The Qmci-Gr had similar accuracy in differentiating SMC from cognitive impairment (MCI & mild dementia) compared with SMMSE, area under the curve (AUC) of 0.84 versus 0.79, respectively; while accuracy was higher for the Qmci-Gr, this finding was not significantly different, (p = .19). Similarly, the Qmci-Gr had similar accuracy in separating SMC from MCI, AUC of 0.79 versus 0.73 (p = .23). CONCLUSIONS: The Qmci-Gr compared favorably with the SMMSE. Further research with larger samples and comparison with other instruments such as the Montreal Cognitive Assessment is needed to confirm these findings but given its established brevity, it may be a better choice in busy clinical practice in Greece.

Screening for Cognitive Impairment After Stroke: Validation of the Chinese Version of the Quick Mild Cognitive Impairment Screen.

Background: Screening for post-stroke cognitive impairment (PSCI) is necessary because stroke increases the incidence of and accelerates premorbid cognitive decline. The Quick Mild Cognitive Impairment (Qmci) screen is a short, reliable and accurate cognitive screening instrument but is not yet validated in PSCI. We compared the diagnostic accuracy of a Chinese version of the Qmci screen (Qmci-CN) compared with the widely-used Chinese versions of the Montreal Cognitive Assessment (MoCA-CN) and Mini-Mental State Examination (MMSE-CN). Methods: We recruited 34 patients who had recovered from a stroke in rehabilitation unit clinics in 2 university hospitals in China: 11 with post-stroke dementia (PSD), 15 with post-stroke cognitive impairment no dementia (PSCIND), and 8 with normal cognition (NC). Classification was made based on clinician assessment supported by a neuropsychological battery, independent of the screening test scores. The Qmci-CN, MoCA-CN, and MMSE-CN screens were administered randomly by a trained rater, blind to the diagnosis. Results: The mean age of the sample was 63 ± 13 years and 61.8% were male. The Qmci-CN had statistically similar diagnostic accuracy in differentiating PSD from NC, an area under the curve (AUC) of 0.94 compared to 0.99 for the MoCA-CN (p = 0.237) and 0.99 for the MMSE-CN (p = 0.293). The Qmci-CN (AUC 0.91), MoCA-CN (AUC 0.94), and MMSE-CN (AUC 0.79) also had statistically similar accuracy in separating PSD from PSCIND. The MoCA-CN more accurately distinguished between PSCIND and normal cognition than the Qmci-CN (p = 0.015). Compared to the MoCA-CN, the administration times of the Qmci-CN (329s vs. 611s, respectively, p < 0.0001) and MMSE-CN (280 vs. 611s, respectively, p < 0.0001) were significantly shorter. Conclusion: The Qmci-CN is accurate in identifying PSD and separating PSD from PSCIND in patients post-stroke following rehabilitation and is comparable to the widely-used MoCA-CN, albeit with a significantly shorter administration time. The Qmci-CN had relatively poor accuracy in identifying PSCIND from NC and hence may lack accuracy for certain subgroups. However, given the small sample size, the study is under-powered to show superiority of one instrument over another. Further study is needed to confirm these findings in a larger sample size and in other settings (countries and languages).

Screening for Cognitive Frailty Using Short Cognitive Screening Instruments: Comparison of the Chinese Versions of the MoCA and Qmci Screen.

BACKGROUND: Cognitive frailty describes cognitive impairment associated with physical decline. Few studies have explored whether short cognitive screens identify frailty. We examined the diagnostic accuracy of the Chinese versions of the Quick Mild Cognitive Impairment (Qmci-CN) screen and Montreal Cognitive Assessment (MoCA-CN) in identifying cognitive frailty. METHODS: Ninety-five participants with cognitive symptoms [47 with mild cognitive impairment (MCI), 34 with subjective cognitive disorder, and 14 with dementia] were included from two outpatient rehabilitation clinics. Energy (work intensity) and physical activity levels were recorded. Cognitive frailty was diagnosed by an interdisciplinary team using the IANA/IAGG consensus criteria, stratified on the Clinical Frailty Scale (CFS). Instruments were administered sequentially and randomly by trained assessors, blind to the diagnosis. RESULTS: The mean age of the sample was 62.6 ± 10.2 years; median CFS score was 4 ± 1 and 36 (38%) were cognitively frail. The Qmci-CN had similar accuracy in differentiating the non-frail from cognitively frail compared to the MoCA-CN, AUC 0.82 versus 0.74, respectively (p = 0.19). At its optimal cut-off (≤55/100), the Qmci-CN provided a sensitivity of 83% and specificity of 67% versus 91% and 51%, respectively, for the MoCA-CN (≤23/30). Neither was accurate in separating MCI from cognitive frailty but both accurately separated cognitive frailty from dementia. CONCLUSION: Established short cognitive screens may be useful in identifying cognitive frailty in Chinese adults with cognitive complaints but not in separating MCI from cognitive frailty. The Qmci-CN had similar accuracy to the MoCA-CN and a shorter administration time in this small and under-powered study, necessitating the need for adequately powered studies in different healthcare settings.

Comparing the Diagnostic Accuracy of Two Cognitive Screening Instruments in Different Dementia Subtypes and Clinical Depression.

Short but accurate cognitive screening instruments are required in busy clinical practice. Although widely-used, the diagnostic accuracy of the standardised Mini-Mental State Examination (SMMSE) in different dementia subtypes remains poorly characterised. We compared the SMMSE to the Quick Mild Cognitive Impairment (Qmci) screen in patients (n = 3020) pooled from three memory clinic databases in Canada including those with mild cognitive impairment (MCI) and Alzheimer's, vascular, mixed, frontotemporal, Lewy Body and Parkinson's dementia, with and without co-morbid depression. Caregivers (n = 875) without cognitive symptoms were included as normal controls. The median age of patients was 77 (Interquartile = ±9) years. Both instruments accurately differentiated cognitive impairment (MCI or dementia) from controls. The SMMSE most accurately differentiated Alzheimer's (AUC 0.94) and Lewy Body dementia (AUC 0.94) and least accurately identified MCI (AUC 0.73), vascular (AUC 0.74), and Parkinson's dementia (AUC 0.81). The Qmci had statistically similar or greater accuracy in distinguishing all dementia subtypes but particularly MCI (AUC 0.85). Co-morbid depression affected accuracy in those with MCI. The SMMSE and Qmci have good-excellent accuracy in established dementia. The SMMSE is less suitable in MCI, vascular and Parkinson's dementia, where alternatives including the Qmci screen may be used. The influence of co-morbid depression on scores merits further investigation.