Global Clinical Dementia Rating Score of 0.5 May Not Be an Accurate Criterion to Identify Individuals with Mild Cognitive Impairment

Disentangling and quantifying the relative cognitive impact of concurrent mixed neurodegenerative pathologies

Neurodegenerative pathologies such as Alzheimer disease neuropathologic change (ADNC), Lewy body disease (LBD), limbic-predominant age-related TDP-43 encephalopathy neuropathologic change (LATE-NC), and cerebrovascular disease (CVD) frequently coexist, but little is known about the exact contribution of each pathology to cognitive decline and dementia in subjects with mixed pathologies. We explored the relative cognitive impact of concurrent common and rare neurodegenerative pathologies employing multivariate logistic regression analysis adjusted for age, gender, and level of education. We analyzed a cohort of 6,262 subjects from the National Alzheimer's Coordinating Center database, ranging from 0 to 6 comorbid neuropathologic findings per individual, where 95.7% of individuals had at least 1 neurodegenerative finding at autopsy and 75.5% had at least 2 neurodegenerative findings. We identified which neuropathologic entities correlate most frequently with one another and demonstrated that the total number of pathologies per individual was directly correlated with cognitive performance as assessed by Clinical Dementia Rating (CDR®) and Mini-Mental State Examination (MMSE). We show that ADNC, LBD, LATE-NC, CVD, hippocampal sclerosis, Pick disease, and FTLD-TDP significantly impact overall cognition as independent variables. More specifically, ADNC significantly affected all assessed cognitive domains, LBD affected attention, processing speed, and language, LATE-NC primarily affected tests related to logical memory and language, while CVD and other less common pathologies (including Pick disease, progressive supranuclear palsy, and corticobasal degeneration) had more variable neurocognitive effects. Additionally, ADNC, LBD, and higher numbers of comorbid neuropathologies were associated with the presence of at least one APOE ε4 allele, and ADNC and higher numbers of neuropathologies were inversely correlated with APOE ε2 alleles. Understanding the mechanisms by which individual and concomitant neuropathologies affect cognition and the degree to which each contributes is an imperative step in the development of biomarkers and disease-modifying therapeutics, particularly as these medical interventions become more targeted and personalized.

Role of Cognitive Frailty in Older Adults With Cardiovascular Disease

As the older adult population expands, an increasing number of patients affected by geriatric syndromes are seen by cardiovascular clinicians. One such syndrome that has been associated with poor outcomes is cognitive frailty: the simultaneous presence of cognitive impairment, without evidence of dementia, and physical frailty, which results in decreased cognitive reserve. Driven by common pathophysiologic underpinnings (eg, inflammation and neurohormonal dysregulation), cardiovascular disease, cognitive impairment, and frailty also share the following risk factors: hypertension, diabetes, obesity, sedentary behavior, and tobacco use. Cardiovascular disease has been associated with the onset and progression of cognitive frailty, which may be reversible in early stages, making it essential for clinicians to diagnose the condition in a timely manner and prescribe appropriate interventions. Additional research is required to elucidate the mechanisms underlying the development of cognitive frailty, establish preventive and therapeutic strategies to address the needs of older patients with cardiovascular disease at risk for cognitive frailty, and ultimately facilitate targeted intervention studies.

Brain Age Is Associated with Cognitive Outcomes of Cholinesterase Inhibitor Treatment in Patients with Mild Cognitive Impairment

Background

The effect of cholinesterase inhibitor (ChEI) on mild cognitive impairment (MCI) is controversial. Brain age has been shown to predict Alzheimer's disease conversion from MCI.

Objective

The study aimed to show that brain age is related to cognitive outcomes of ChEI treatment in MCI.

Methods

Brain MRI, the Clinical Dementia Rating (CDR) and Mini-Mental State Exam (MMSE) scores were retrospectively retrieved from a ChEI treatment database. Patients who presented baseline CDR of 0.5 and received ChEI treatment for at least 2 years were selected. Patients with stationary or improved cognition as verified by the CDR and MMSE were categorized to the ChEI-responsive group, and those with worsened cognition were assigned to the ChEI-unresponsive group. A gray matter brain age model was built with a machine learning algorithm by training T1-weighted MRI data of 362 healthy participants. The model was applied to each patient to compute predicted age difference (PAD), i.e. the difference between brain age and chronological age. The PADs were compared between the two groups.

Results

58 patients were found to fit the ChEI-responsive criteria in the patient data, and 58 matched patients that fit the ChEI-unresponsive criteria were compared. ChEI-unresponsive patients showed significantly larger PAD than ChEI-responsive patients (8.44±8.78 years versus 3.87±9.02 years, p = 0.0067).

Conclusions

Gray matter brain age is associated with cognitive outcomes after 2 years of ChEI treatment in patients with the CDR of 0.5. It might facilitate the clinical trials of novel therapeutics for MCI.

Memory performance mediates subjective sleep quality associations with cerebrospinal fluid Alzheimer's disease biomarker levels and hippocampal volume among individuals with mild cognitive symptoms

Sleep disturbances are prevalent in Alzheimer's disease (AD), affecting individuals during its early stages. We investigated associations between subjective sleep measures and cerebrospinal fluid (CSF) biomarkers of AD in adults with mild cognitive symptoms from the European Prevention of Alzheimer's Dementia Longitudinal Cohort Study, considering the influence of memory performance. A total of 442 participants aged >50 years with a Clinical Dementia Rating (CDR) score of 0.5 completed the Pittsburgh Sleep Quality Index questionnaire and underwent neuropsychological assessment, magnetic resonance imaging acquisition, and CSF sampling. We analysed the relationship of sleep quality with CSF AD biomarkers and cognitive performance in separated multivariate linear regression models, adjusting for covariates. Poorer cross-sectional sleep quality was associated with lower CSF levels of phosphorylated tau and total tau alongside better immediate and delayed memory performance. After adjustment for delayed memory scores, associations between CSF biomarkers and sleep quality became non-significant, and further analysis revealed that memory performance mediated this relationship. In post hoc analyses, poorer subjective sleep quality was associated with lesser hippocampal atrophy, with memory performance also mediating this association. In conclusion, worse subjective sleep quality is associated with less altered AD biomarkers in adults with mild cognitive symptoms (CDR score 0.5). These results could be explained by a systematic recall bias affecting subjective sleep assessment in individuals with incipient memory impairment. Caution should therefore be exercised when interpreting subjective sleep quality measures in memory-impaired populations, emphasising the importance of complementing subjective measures with objective assessments.

A Remotely Coached Multimodal Lifestyle Intervention for Alzheimer's Disease Ameliorates Functional and Cognitive Outcomes

BACKGROUND

Comprehensive treatment of Alzheimer's disease and related dementias (ADRD) requires not only pharmacologic treatment but also management of existing medical conditions and lifestyle modifications including diet, cognitive training, and exercise. Personalized, multimodal therapies are needed to best prevent and treat Alzheimer's disease (AD).

OBJECTIVE

The Coaching for Cognition in Alzheimer's (COCOA) trial was a prospective randomized controlled trial to test the hypothesis that a remotely coached multimodal lifestyle intervention would improve early-stage AD.

METHODS

Participants with early-stage AD were randomized into two arms. Arm 1 (N = 24) received standard of care. Arm 2 (N = 31) additionally received telephonic personalized coaching for multiple lifestyle interventions. The primary outcome was a test of the hypothesis that the Memory Performance Index (MPI) change over time would be better in the intervention arm than in the control arm. The Functional Assessment Staging Test was assessed for a secondary outcome. COCOA collected psychometric, clinical, lifestyle, genomic, proteomic, metabolomic, and microbiome data at multiple timepoints (dynamic dense data) across two years for each participant.

RESULTS

The intervention arm ameliorated 2.1 [1.0] MPI points (mean [SD], p = 0.016) compared to the control over the two-year intervention. No important adverse events or side effects were observed.

CONCLUSION

Multimodal lifestyle interventions are effective for ameliorating cognitive decline and have a larger effect size than pharmacological interventions. Dietary changes and exercise are likely to be beneficial components of multimodal interventions in many individuals. Remote coaching is an effective intervention for early stage ADRD. Remote interventions were effective during the COVID pandemic.

Effect of a multimodal non-pharmacological intervention on older people with dementia: a single-case experimental design study

BACKGROUND

Older people with dementia (PWD) in nursing homes (NHs) tend to have decreased cognitive function, which may cause behavioral and psychological symptoms of dementia (BPSDs) and hinder activities of daily living (ADLs). Therefore, taking measures against the cognitive decline of PWD in NH and, in turn, the decline of BPSDs and ADLs is crucial. The purpose of this study was to test whether a multimodal non-pharmacological intervention (MNPI) is effective in maintaining and improving global cognitive function, BPSDs, and ADLs in PWD in NHs.

METHODS

An intervention study using a single-case AB design was conducted in three subjects in NHs. During the non-intervention phase, participants underwent follow-up assessments, and during the intervention phase, they participated in an MNPI. The ABC Dementia Scale (which concurrently assesses ADLs ["A"], BPSDs ["B"], and cognitive function ["C"]) was used for the assessment.

RESULTS

One of the three patients showed improvement in dementia severity, global cognitive function, ADLs, and BPSDs. However, the other two participants showed no improvement following the MNPI, although the possibility of a maintenance effect remained.

CONCLUSION

Although there is room for improvement of the MNPI, it may be effective in maintaining and improving cognitive function, ADLs, and BPSD, in PWD in NHs.

TRIAL REGISTRATION

The University Hospital Medical Information Network Clinical Trials Registry ( http://www.umin.ac.jp/ , No. UMIN000045858, registration date: November 1, 2021).

Deep learning algorithm reveals probabilities of stage-specific time to conversion in individuals with neurodegenerative disease LATE

Introduction

Limbic-predominant age-related TAR DNA-binding protein 43 (TDP-43) encephalopathy (LATE) is a recently defined neurodegenerative disease. Currently, there is no effective way to make a prognosis of time to stage-specific future conversions at an individual level.

Methods

After using the Kaplan-Meier estimation and log-rank test to confirm the heterogeneity of LATE progression, we developed a deep learning-based approach to assess the stage-specific probabilities of time to LATE conversions for different subjects.

Results

Our approach could accurately estimate the disease incidence and transition to next stages: the concordance index was at least 82% and the integrated Brier score was less than 0.14. Moreover, we identified the top 10 important predictors for each disease conversion scenario to help explain the estimation results, which were clinicopathologically meaningful and most were also statistically significant.

Discussion

Our study has the potential to provide individualized assessment for future time courses of LATE conversions years before their actual occurrence.

Memory markers in the continuum of the Alzheimer's clinical syndrome

BACKGROUND

The individual and complementary value of the Visual Short-Term Memory Binding Test (VSTMBT) and the Free and Cued Selective Reminding Test (FCSRT) as markers to trace the AD continuum was investigated. It was hypothesised that the VSTMBT would be an early indicator while the FCSRT would inform on imminent progression.

METHODS

Healthy older adults (n=70) and patients with mild cognitive impairment (MCI) (n=80) were recruited and followed up between 2012 and 2017. Participants with at least two assessment points entered the study. Using baseline and follow-up assessments four groups were defined: Older adults who were healthy (HOA), with very mild cognitive but not functional impairment (eMCI), and with MCI who did and did not convert to dementia (MCI converters and non-converters).

RESULTS

Only the VSTMBT predicted group membership in the very early stages (HOA vs eMCI). As the disease progressed, the FCSRT became a strong predictor excluding the VSTMB from the models. Their complementary value was high during the mid-prodromal stages and decreased in stages closer to dementia.

DISCUSSION

The study supports the notion that neuropsychological assessment for AD needs to abandon the notion of one-size-fits-all. A memory toolkit for AD needs to consider tools that are early indicators and tools that suggest imminent progression. The VSTMBT and the FSCRT are such tools.

Clinically Relevant Changes for Cognitive Outcomes in Preclinical and Prodromal Cognitive Stages: Implications for Clinical Alzheimer Trials

BACKGROUND AND OBJECTIVES

Identifying a clinically meaningful change in cognitive test score is essential when using cognition as an outcome in clinical trials. This is especially relevant because clinical trials increasingly feature novel composites of cognitive tests. Our primary objective was to establish minimal clinically important differences (MCIDs) for commonly used cognitive tests, using anchor-based and distribution-based methods, and our secondary objective was to investigate a composite cognitive measure that best predicts a minimal change in the Clinical Dementia Rating-Sum of Boxes (CDR-SB).

METHODS

From the Swedish BioFINDER cohort study, we consecutively included cognitively unimpaired (CU) individuals with and without subjective or mild cognitive impairment (MCI). We calculated MCIDs associated with a change of ≥0.5 or ≥1.0 on CDR-SB for Mini-Mental State Examination (MMSE), ADAS-Cog delayed recall 10-word list, Stroop, Letter S Fluency, Animal Fluency, Symbol Digit Modalities Test (SDMT) and Trailmaking Test (TMT) A and B, and triangulated MCIDs for clinical use for CU, MCI, and amyloid-positive CU participants. For investigating cognitive measures that best predict a change in CDR-SB of ≥0.5 or ≥1.0 point, we conducted receiver operating characteristic analyses.

RESULTS

Our study included 451 cognitively unimpaired individuals, 90 with subjective cognitive decline and 361 without symptoms of cognitive decline (pooled mean follow-up time 32.4 months, SD 26.8, range 12-96 months), and 292 people with MCI (pooled mean follow-up time 19.2 months, SD 19.0, range 12-72 months). We identified potential triangulated MCIDs (cognitively unimpaired; MCI) on a range of cognitive test outcomes: MMSE -1.5, -1.7; ADAS delayed recall 1.4, 1.1; Stroop 5.5, 9.3; Animal Fluency: -2.8, -2.9; Letter S Fluency -2.9, -1.8; SDMT: -3.5, -3.8; TMT A 11.7, 13.0; and TMT B 24.4, 20.1. For amyloid-positive CU, we found the best predicting composite cognitive measure included gender and changes in ADAS delayed recall, MMSE, SDMT, and TMT B. This produced an AUC of 0.87 (95% CI 0.79-0.94, sensitivity 75%, specificity 88%).

DISCUSSION

Our MCIDs may be applied in clinical practice or clinical trials for identifying whether a clinically relevant change has occurred. The composite measure can be useful as a clinically relevant cognitive test outcome in preclinical AD trials.

Fecal microbiota transplantation can improve cognition in patients with cognitive decline and Clostridioides difficile infection

After fecal microbiota transplantation (FMT) to treat Clostridioides difficile infection (CDI), cognitive improvement is noticeable, suggesting an essential association between the gut microbiome and neural function. Although the gut microbiome has been associated with cognitive function, it remains to be elucidated whether fecal microbiota transplantation can improve cognition in patients with cognitive decline. The study included 10 patients (age range, 63-90 years; female, 80%) with dementia and severe CDI who were receiving FMT. Also, 10 patients (age range, 62-91; female, 80%) with dementia and severe CDI who were not receiving FMT. They were evaluated using cognitive function tests (Mini-Mental State Examination [MMSE] and Clinical Dementia Rating scale Sum of Boxes [CDR-SB]) at 1 month before and after FMT or antibiotics treatment (control group). The patients' fecal samples were analyzed to compare the composition of their gut microbiota before and 3 weeks after FMT or antibiotics treatment. Ten patients receiving FMT showed significantly improvements in clinical symptoms and cognitive functions compared to control group. The MMSE and CDR-SB of FMT group were improved compare to antibiotics treatment (MMSE: 16.00, median, 13.00-18.00 [IQR] vs. 10.0, median, 9.8-15.3 [IQR]); CDR-SB: 5.50, median, 4.00-8.00 [IQR]) vs. 8.0, median, 7.9-12.5, [IQR]). FMT led to changes in the recipient's gut microbiota composition, with enrichment of Proteobacteria and Bacteroidetes. Alanine, aspartate, and glutamate metabolism pathways were also significantly different after FMT. This study revealed important interactions between the gut microbiome and cognitive function. Moreover, it suggested that FMT may effectively delay cognitive decline in patients with dementia.

Screening for Mild Cognitive Impairment in the Preoperative Setting: A Narrative Review

Cognitive impairment predisposes patients to the development of delirium and postoperative cognitive dysfunction. In particular, in older patients, the adverse sequelae of cognitive decline in the perioperative period may contribute to adverse outcomes after surgical procedures. Subtle signs of cognitive impairment are often not previously diagnosed. Therefore, the aim of this review is to describe the available cognitive screeners suitable for preoperative screening and their psychometric properties for identifying mild cognitive impairment, as preoperative workup may improve perioperative care for patients at risk for postoperative cognitive dysfunction. Electronic systematic and snowball searches of PubMed, PsycInfo, ClinicalKey, and ScienceDirect were conducted for the period 2015–2020. Major inclusion criteria for articles included those that discussed a screener that included the cognitive domain ‘memory’, that had a duration time of less than 15 min, and that reported sensitivity and specificity to detect mild cognitive impairment. Studies about informant-based screeners were excluded. We provided an overview of the characteristics of the cognitive screener, such as interrater and test-retest reliability correlations, sensitivity and specificity for mild cognitive impairment and cognitive impairment, and duration of the screener and cutoff points. Of the 4775 identified titles, 3222 were excluded from further analysis because they were published prior to 2015. One thousand four hundred and forty-eight titles did not fulfill the inclusion criteria. All abstracts of 52 studies on 45 screeners were examined of which 10 met the inclusion criteria. For these 10 screeners, a further snowball search was performed to obtain related studies, resulting in 20 articles. Screeners included in this review were the Mini-Cog, MoCA, O3DY, AD8, SAGE, SLUMS, TICS(-M), QMCI, MMSE2, and Mini-ACE. The sensitivity and specificity range to detect MCI in an older population is the highest for the MoCA, with a sensitivity range of 81–93% and a specificity range of 74–89%. The MoCA, with the highest combination of sensitivity and specificity, is a feasible and valid routine screening of pre-surgical cognitive function. This warrants further implementation and validation studies in surgical pathways with a large proportion of older patients.

A Study on Verification of Equivalence and Effectiveness of Non-Pharmacologic Dementia Prevention and Early Detection Contents : Non-Randomly Equivalent Design

Objective

The aim of this study was to verify the equivalence and effectiveness of the tablet-administered Korean Repeatable Battery for the Assessment of Neuropsychological Status (K-RBANS) for the prevention and early detection of dementia.

Methods

Data from 88 psychiatry and neurology patient samples were examined to evaluate the equivalence between tablet and paper administrations of the K-RBANS using a non-randomly equivalent group design. We calculated the prediction scores of the tablet-administered K-RBANS based on demographics and covariate-test scores for focal tests using norm samples and tested format effects. In addition, we compared the receiver operating characteristic curves to confirm the effectiveness of the K-RBANS for preventing and detecting dementia.

Results

In the analysis of raw scores, line orientation showed a significant difference (t=-2.94, p<0.001), and subtests showed small to large effect sizes (0.04–0.86) between paper- and tablet-administered K-RBANS. To investigate the format effect, we compared the predicted scaled scores of the tablet sample to the scaled scores of the norm sample. Consequently, a small effect size (d≤0.20) was observed in most of the subtests, except word list and story recall, which showed a medium effect size (d=0.21), while picture naming and subtests of delayed memory showed significant differences in the one-sample t-test. In addition, the area under the curve of the total scale index (TSI) (0.827; 95% confidence interval, 0.738–0.916) was higher than that of the five indices, ranging from 0.688 to 0.820. The sensitivity and specificity of TSI were 80% and 76%, respectively.

Conclusion

The overall results of this study suggest that the tablet-administered K-RBANS showed significant equivalence to the norm sample, although some subtests showed format effects, and it may be used as a valid tool for the brief screening of patients with neuropsychological disorders in Korea.

Machine Learning Approach Predicts Probability of Time to Stage-Specific Conversion of Alzheimer's Disease

BACKGROUND

The progression of Alzheimer's disease (AD) varies in different patients at different stages, which makes predicting the time of disease conversions challenging.

OBJECTIVE

We established an algorithm by leveraging machine learning techniques to predict the probability of the conversion time to next stage for different subjects during a given period.

METHODS

Firstly, we used Kaplan-Meier (KM) estimation to get the transition curves of different AD stages, and calculated Log-rank statistics to test whether the progression rate between different stages was identical. This quantitatively confirmed the progression rates known in the literature. Then, we developed an approach based on deep learning model, DeepSurv, to predict the probabilities of time-to-conversion. Finally, to help interpret the deep learning model in our approach, we identified important variables contributing the most to the DeepSurv prediction, whose significance were validated with the analysis of variance (ANOVA).

RESULTS

Our machine learning approach predicted the time to conversion with a high accuracy. For each of the different stages, the concordance index (CI) of our approach was at least 86%, and the integrated Brier score (IBS) was less than 0.1. To facilitate interpretability of the prediction results, our approach identified the top 10 variables for each disease conversion scenario, which were clinicopathologically meaningful, and most of them were also statistically significant.

CONCLUSION

Our study has the potential to provide individualized prediction for future time course of AD conversions years before their actual occurrence, thus facilitating personalized prevention and intervention strategies to slow down the progression of AD.

ABC Dementia Scale Classifies Alzheimer's Disease Patients into Subgroups Characterized by Activities of Daily Living, Behavioral and Psychological Symptoms of Dementia, and Cognitive Function

The course of Alzheimer’s disease (AD) varies between individuals, and the relationship between cognitive and functional decline and the deterioration of behavioral and psychological symptoms of dementia (BPSD) is still poorly understood. Until recently, it was challenging to monitor subsequent changes in these symptoms because there was no single composite scale available that could simultaneously evaluate activities of daily living (ADL), BPSD, and cognitive function (CF) states. The present authors developed a new, brief assessment scale, the “ABC Dementia Scale” (ABC-DS), which is based on item response theory and facilitates concurrent measurement of ADL, BPSD, and CF states. We previously presented the reliability, construct validity, concurrent validity, and responsiveness of the ABC-DS. We obtained the evidence through three clinical trials featuring 1,400 subjects in total. In the present study, we performed a secondary analysis of the data obtained in the previous study. We conducted hierarchical cluster analyses that allowed us to classify 197 AD patients in terms of similarities regarding ADL, BPSD, and CF domain scores, as measured by the ABC-DS. Consequently, the scale identified subgroups of patients with global clinical dementia ratings of 1, 2, and 3. Considering our results in conjunction with the clinical experiences of the AD expert among the present authors regarding longitudinal changes in ADL, BPSD, and CF, we were able to propose potential progression pathways of AD in the form of a hypothetical roadmap.

Screening for Early-Stage Alzheimer's Disease Using Optimized Feature Sets and Machine Learning

BACKGROUND

Detecting early-stage Alzheimer's disease in clinical practice is difficult due to a lack of efficient and easily administered cognitive assessments that are sensitive to very mild impairment, a likely contributor to the high rate of undetected dementia.

OBJECTIVE

We aim to identify groups of cognitive assessment features optimized for detecting mild impairment that may be used to improve routine screening. We also compare the efficacy of classifying impairment using either a two-class (impaired versus non-impaired) or three-class using the Clinical Dementia Rating (CDR 0 versus CDR 0.5 versus CDR 1) approach.

METHODS

Supervised feature selection methods generated groups of cognitive measurements targeting impairment defined at CDR 0.5 and above. Random forest classifiers then generated predictions of impairment for each group using highly stochastic cross-validation, with group outputs examined using general linear models.

RESULTS

The strategy of combining impairment levels for two-class classification resulted in significantly higher sensitivities and negative predictive values, two metrics useful in clinical screening, compared to the three-class approach. Four features (delayed WAIS Logical Memory, trail-making, patient and informant memory questions), totaling about 15 minutes of testing time (∼30 minutes with delay), enabled classification sensitivity of 94.53% (88.43% positive predictive value, PPV). The addition of four more features significantly increased sensitivity to 95.18% (88.77% PPV) when added to the model as a second classifier.

CONCLUSION

The high detection rate paired with the minimal assessment time of the four identified features may act as an effective starting point for developing screening protocols targeting cognitive impairment defined at CDR 0.5 and above.

On-Road Behavior in Older Drivers With Mild Cognitive Impairment

OBJECTIVES

Dementia increases the risk of unsafe driving, but this is less apparent in preclinical stages such as mild cognitive impairment (MCI). There is, however, limited detailed data on the patterns of driving errors associated with MCI. Here, we examined whether drivers with MCI exhibited different on-road error profiles compared with cognitively normal (CN) older drivers.

DESIGN

Observational.

SETTING AND PARTICIPANTS

A total of 296 licensed older drivers [mean age 75.5 (SD = 6.2) years, 120 (40.5%) women] recruited from the community.

METHOD

Participants completed a health and driving history survey, a neuropsychological test battery, and an on-road driving assessment including driver-instructed and self-navigation components. Driving assessors were blind to participant cognitive status. Participants were categorized as safe or unsafe based on a validated on-road safety scale, and as having MCI based on International Working Group diagnostic criteria. Proportion of errors incurred as a function of error type and traffic context were compared across safe and unsafe MCI and CN drivers.

RESULTS

Compared with safe CN drivers (n = 225), safe MCI drivers (n = 45) showed a similar pattern of errors in different traffic contexts. Compared with safe CN drivers, unsafe CN drivers (n = 17) were more likely to make errors in observation, speed control, lane position, and approach, and at stop/give-way signs, lane changes, and curved driving. Unsafe MCI drivers (n = 9) had additional difficulties at intersections, roundabouts, parking, straight driving, and under self-navigation conditions. A higher proportion of unsafe MCI drivers had multidomain subtype [n = 6 (67%)] than safe MCI drivers [n = 11 (25%)], odds ratio 6.2 (95% confidence interval, 1.4-29.6).

CONCLUSION AND IMPLICATIONS

Among safe drivers, MCI and CN drivers exhibit similar on-road error profiles, suggesting driver restrictions based on MCI status alone are unwarranted. However, formal evaluation is recommended in such cases, as there is evidence drivers with multiple domains of cognitive impairment may require additional interventions to support safe driving.