BHA-CS: A novel cognitive composite for Alzheimer's disease and related disorders

Gaps in cognitive care among older patients undergoing spine surgery

INTRODUCTION

Among older adults undergoing surgery, postoperative delirium is the most common complication. Cognitive impairment and dementia are major risk factors for postoperative delirium, yet they are frequently under-recognized. It is well established that applying delirium preventive interventions to at-risk individuals can reduce the likelihood of delirium by up to 40%. The aim of this study was to evaluate how often delirium preventive interventions are missing in patients at risk for delirium due to baseline cognitive impairment.

METHODS

We conducted a retrospective study using data from the observational study Perioperative Anesthesia Neurocognitive Disorder Assessment-Geriatric (PANDA-G) and clinical data from the University of California San Francisco delirium prevention bundle. Patients age 65+ received preoperative multidomain cognitive assessment as part of a research protocol prior to undergoing inpatient spine surgery at a single major academic institution. Results of the cognitive testing were not available to clinical teams. Using electronic medical records, we evaluated if patients who were cognitively impaired at baseline received delirium prevention orders, sleep orders, and avoidance of AGS Beers Criteria® potentially inappropriate medications.

RESULTS

Of the 245 patients included in the study, 42% were women. The mean [SD] age was 72 [5.2] years. Preoperative cognitive impairment was identified in 40% of participants (N = 98), and of these, 34% had postoperative delirium. Of patients with preoperative cognitive impairment, 45% did not receive delirium preventive orders, 43% received PIMs, and 49% were missing sleep orders. At least one of the three delirium preventive interventions was missing in 70% of the patients.

DISCUSSION

Undiagnosed preoperative cognitive impairment among older adults undergoing spine surgery is common. When cognitive test results were not available to clinicians, patients with baseline cognitive impairment frequently did not receive evidence-based delirium preventive interventions. These findings highlight an opportunity to improve perioperative brain health care via preoperative cognitive assessment and clinical communication.

Scalable plasma and digital cognitive markers for diagnosis and prognosis of Alzheimer's disease and related dementias

INTRODUCTION

With emergence of disease-modifying therapies, efficient diagnostic pathways are critically needed to identify treatment candidates, evaluate disease severity, and support prognosis. A combination of plasma biomarkers and brief digital cognitive assessments could provide a scalable alternative to current diagnostic work-up.

METHODS

We examined the accuracy of plasma biomarkers and a 10-minute supervised tablet-based cognitive assessment (Tablet-based Cognitive Assessment Tool Brain Health Assessment [TabCAT-BHA]) in predicting amyloid β positive (Aβ+) status on positron emission tomography (PET), concurrent disease severity, and functional decline in 309 older adults with subjective cognitive impairment (n = 49), mild cognitive impairment (n = 159), and dementia (n = 101).

RESULTS

Combination of plasma pTau181, Aβ42/40, neurofilament light (NfL), and TabCAT-BHA was optimal for predicting Aβ-PET positivity (AUC = 0.962). Whereas NfL and TabCAT-BHA optimally predicted concurrent disease severity, combining these with pTau181 and glial fibrillary acidic protein was most accurate in predicting functional decline.

DISCUSSION

Combinations of plasma and digital cognitive markers show promise for scalable diagnosis and prognosis of ADRD.

HIGHLIGHTS

The need for cost-efficient diagnostic and prognostic markers of AD is urgent. Plasma and digital cognitive markers provide complementary diagnostic contributions. Combination of these markers holds promise for scalable diagnosis and prognosis. Future validation in community cohorts is needed to inform clinical implementation.

Digital Cognitive Assessment Tests for Older Adults: Systematic Literature Review

BACKGROUND

The global health pandemic has affected the increasing older adult population, especially those with mental illnesses. It is necessary to prevent cases of cognitive impairment in adults early on, and this requires the support of information and communication technologies for evaluating and training cognitive functions. This can be achieved through computer applications designed for cognitive assessment.

OBJECTIVE

In this review, we aimed to assess the state of the art of the current platforms and digital test applications for cognitive evaluation, with a focus on older adults.

METHODS

A systematic literature search was conducted on 3 databases (Web of Science, PubMed, and Scopus) to retrieve recent articles on the applications of digital tests for cognitive assessment and analyze them based on the methodology used. Four research questions were considered. Through the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, following the application of inclusion and exclusion criteria, a total of 20 articles were finally reviewed.

RESULTS

Some gaps and trends were identified regarding the types of digital applications and technologies used, the evaluated effects on cognitive domains, and the psychometric parameters and personal characteristics considered for validation.

CONCLUSIONS

Computerized tests (similar to paper-and-pencil tests) and test batteries (on computers, tablets, or web platforms) were the predominant types of assessments. Initial studies with simulators, virtual environments, and daily-life activity games were also conducted. Diverse validation methods and psychometric properties were observed; however, there was a lack of evaluations that involved specific populations with diverse education levels, cultures, and degrees of technology acceptance. In addition, these evaluations should consider emotional and usability aspects.

XGBoost-SHAP-based interpretable diagnostic framework for alzheimer's disease

BACKGROUND

Due to the class imbalance issue faced when Alzheimer's disease (AD) develops from normal cognition (NC) to mild cognitive impairment (MCI), present clinical practice is met with challenges regarding the auxiliary diagnosis of AD using machine learning (ML). This leads to low diagnosis performance. We aimed to construct an interpretable framework, extreme gradient boosting-Shapley additive explanations (XGBoost-SHAP), to handle the imbalance among different AD progression statuses at the algorithmic level. We also sought to achieve multiclassification of NC, MCI, and AD.

METHODS

We obtained patient data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, including clinical information, neuropsychological test results, neuroimaging-derived biomarkers, and APOE-ε4 gene statuses. First, three feature selection algorithms were applied, and they were then included in the XGBoost algorithm. Due to the imbalance among the three classes, we changed the sample weight distribution to achieve multiclassification of NC, MCI, and AD. Then, the SHAP method was linked to XGBoost to form an interpretable framework. This framework utilized attribution ideas that quantified the impacts of model predictions into numerical values and analysed them based on their directions and sizes. Subsequently, the top 10 features (optimal subset) were used to simplify the clinical decision-making process, and their performance was compared with that of a random forest (RF), Bagging, AdaBoost, and a naive Bayes (NB) classifier. Finally, the National Alzheimer's Coordinating Center (NACC) dataset was employed to assess the impact path consistency of the features within the optimal subset.

RESULTS

Compared to the RF, Bagging, AdaBoost, NB and XGBoost (unweighted), the interpretable framework had higher classification performance with accuracy improvements of 0.74%, 0.74%, 1.46%, 13.18%, and 0.83%, respectively. The framework achieved high sensitivity (81.21%/74.85%), specificity (92.18%/89.86%), accuracy (87.57%/80.52%), area under the receiver operating characteristic curve (AUC) (0.91/0.88), positive clinical utility index (0.71/0.56), and negative clinical utility index (0.75/0.68) on the ADNI and NACC datasets, respectively. In the ADNI dataset, the top 10 features were found to have varying associations with the risk of AD onset based on their SHAP values. Specifically, the higher SHAP values of CDRSB, ADAS13, ADAS11, ventricle volume, ADASQ4, and FAQ were associated with higher risks of AD onset. Conversely, the higher SHAP values of LDELTOTAL, mPACCdigit, RAVLT_immediate, and MMSE were associated with lower risks of AD onset. Similar results were found for the NACC dataset.

CONCLUSIONS

The proposed interpretable framework contributes to achieving excellent performance in imbalanced AD multiclassification tasks and provides scientific guidance (optimal subset) for clinical decision-making, thereby facilitating disease management and offering new research ideas for optimizing AD prevention and treatment programs.

A novel approach for the detection of cognitive impairment and delirium risk in older patients undergoing spine surgery

BACKGROUND

Postoperative delirium is a common postsurgical complication in older patients and is associated with high morbidity and mortality. The objective of this study was to determine whether a digital cognitive assessment and patient characteristics could identify those at-risk.

METHODS

Patients 65 years and older undergoing spine surgeries ≥3 h were evaluated as part of a single-center prospective observational cohort study at an academic medical center, from January 1, 2019, to December 31, 2020. Of 220 eligible patients, 161 were enrolled and 152 completed the study. The primary outcome of postoperative delirium was measured by the Confusion Assessment Method for the Intensive Care Unit or the Nursing Delirium Screening Scale, administered by trained nursing staff independent from the study protocol. Baseline cognitive impairment was identified using the tablet-based TabCAT Brain Health Assessment.

RESULTS

Of the 152 patients included in this study, 46% were women. The mean [SD] age was 72 [5.4] years. Baseline cognitive impairment was identified in 38% of participants, and 26% had postoperative delirium. In multivariable analysis, impaired Brain Health Assessment Cognitive Score (OR 2.45; 95% CI, 1.05-5.67; p = 0.037), depression (OR 4.54; 95% CI, 1.73-11.89; p = 0.002), and higher surgical complexity Tier 4 (OR 5.88; 95% CI, 1.55-22.26; p = 0.009) were associated with postoperative delirium. The multivariate model was 72% accurate for predicting postoperative delirium, compared to 45% for the electronic medical record-based risk stratification model currently in use.

CONCLUSION

In this prospective cohort study of spine surgery patients, age, cognitive impairment, depression, and surgical complexity identified patients at high risk for postoperative delirium. Integration of scalable digital assessments into preoperative workflows could identify high-risk patients, automate decision support for timely interventions that can improve patient outcomes and lower hospital costs, and provide a baseline cognitive assessment to monitor for postoperative cognitive change.

Cognitive composites for genetic frontotemporal dementia: GENFI-Cog

Background

Clinical endpoints for upcoming therapeutic trials in frontotemporal dementia (FTD) are increasingly urgent. Cognitive composite scores are often used as endpoints but are lacking in genetic FTD. We aimed to create cognitive composite scores for genetic frontotemporal dementia (FTD) as well as recommendations for recruitment and duration in clinical trial design.

Methods

A standardized neuropsychological test battery covering six cognitive domains was completed by 69 C9orf72, 41 GRN, and 28 MAPT mutation carriers with CDR® plus NACC-FTLD ≥ 0.5 and 275 controls. Logistic regression was used to identify the combination of tests that distinguished best between each mutation carrier group and controls. The composite scores were calculated from the weighted averages of test scores in the models based on the regression coefficients. Sample size estimates were calculated for individual cognitive tests and composites in a theoretical trial aimed at preventing progression from a prodromal stage (CDR® plus NACC-FTLD 0.5) to a fully symptomatic stage (CDR® plus NACC-FTLD ≥ 1). Time-to-event analysis was performed to determine how quickly mutation carriers progressed from CDR® plus NACC-FTLD = 0.5 to ≥ 1 (and therefore how long a trial would need to be).

Results

The results from the logistic regression analyses resulted in different composite scores for each mutation carrier group (i.e. C9orf72, GRN, and MAPT). The estimated sample size to detect a treatment effect was lower for composite scores than for most individual tests. A Kaplan-Meier curve showed that after 3 years, ~ 50% of individuals had converted from CDR® plus NACC-FTLD 0.5 to ≥ 1, which means that the estimated effect size needs to be halved in sample size calculations as only half of the mutation carriers would be expected to progress from CDR® plus NACC FTLD 0.5 to ≥ 1 without treatment over that time period.

Discussion

We created gene-specific cognitive composite scores for C9orf72, GRN, and MAPT mutation carriers, which resulted in substantially lower estimated sample sizes to detect a treatment effect than the individual cognitive tests. The GENFI-Cog composites have potential as cognitive endpoints for upcoming clinical trials. The results from this study provide recommendations for estimating sample size and trial duration.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13195-022-00958-0.

Assessing Cognitive Function in Multiple Sclerosis With Digital Tools: Observational Study

Background

Cognitive impairment (CI) is one of the most prevalent symptoms of multiple sclerosis (MS). However, it is difficult to include cognitive assessment as part of MS standard care since the comprehensive neuropsychological examinations are usually time-consuming and extensive.

Objective

To improve access to CI assessment, we evaluated the feasibility and potential assessment sensitivity of a tablet-based cognitive battery in patients with MS.

Methods

In total, 53 participants with MS (24 [45%] with CI and 29 [55%] without CI) and 24 non-MS participants were assessed with a tablet-based cognitive battery (Adaptive Cognitive Evaluation [ACE]) and standard cognitive measures, including the Symbol Digit Modalities Test (SDMT) and the Paced Auditory Serial Addition Test (PASAT). Associations between performance in ACE and the SDMT/PASAT were explored, with group comparisons to evaluate whether ACE modules can capture group-level differences.

Results

Correlations between performance in ACE and the SDMT (R=–0.57, P<.001), as well as PASAT (R=–0.39, P=.01), were observed. Compared to non-MS and non-CI MS groups, the CI MS group showed a slower reaction time (CI MS vs non-MS: P<.001; CI MS vs non-CI MS: P=.004) and a higher attention cost (CI MS vs non-MS: P=.02; CI MS vs non-CI MS: P<.001).

Conclusions

These results provide preliminary evidence that ACE, a tablet-based cognitive assessment battery, provides modules that could potentially serve as a digital cognitive assessment for people with MS.

Trial Registration

ClinicalTrials.gov NCT03569618; https://clinicaltrials.gov/ct2/show/NCT03569618

Detecting Alzheimer's disease biomarkers with a brief tablet-based cognitive battery: sensitivity to Aβ and tau PET

Background

β-amyloid (Aβ) and tau positron emission tomography (PET) detect the pathological changes that define Alzheimer’s disease (AD) in living people. Cognitive measures sensitive to Aβ and tau burden may help streamline identification of cases for confirmatory AD biomarker testing.

Methods

We examined the association of Brain Health Assessment (BHA) tablet-based cognitive measures with dichotomized Aβ -PET status using logistic regression models in individuals with mild cognitive impairment (MCI) or dementia (N = 140; 43 Aβ-, 97 Aβ+). We also investigated the relationship between the BHA tests and regional patterns of tau-PET signal using voxel-wise regression analyses in a subsample of 60 Aβ+ individuals with MCI or dementia.

Results

Favorites (associative memory), Match (executive functions and speed), and Everyday Cognition Scale scores were significantly associated with Aβ positivity (area under the curve [AUC] = 0.75 [95% CI 0.66–0.85]). We found significant associations with tau-PET signal in mesial temporal regions for Favorites, frontoparietal regions for Match, and occipitoparietal regions for Line Orientation (visuospatial skills) in a subsample of individuals with MCI and dementia.

Conclusion

The BHA measures are significantly associated with both Aβ and regional tau in vivo imaging markers and could be used for the identification of patients with suspected AD pathology in clinical practice.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13195-021-00776-w.

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.