Alzheimer’s Disease Assessments Optimized for Diagnostic Accuracy and Administration Time

RECOVER-NEURO: study protocol for a multi-center, multi-arm, phase 2, randomized, active comparator trial evaluating three interventions for cognitive dysfunction in post-acute sequelae of SARS-CoV-2 infection (PASC)

BACKGROUND

Post-acute sequelae of SARS-CoV-2 infection (PASC) symptoms have broad impact, and may affect individuals regardless of COVID-19 severity, socioeconomic status, race, ethnicity, or age. A prominent PASC symptom is cognitive dysfunction, colloquially referred to as "brain fog" and characterized by declines in short-term memory, attention, and concentration. Cognitive dysfunction can severely impair quality of life by impairing daily functional skills and preventing timely return to work.

METHODS

RECOVER-NEURO is a prospective, multi-center, multi-arm, phase 2, randomized, active-comparator design investigating 3 interventions: (1) BrainHQ is an interactive, online cognitive training program; (2) PASC-Cognitive Recovery is a cognitive rehabilitation program specifically designed to target frequently reported challenges among individuals with brain fog; (3) transcranial direct current stimulation (tDCS) is a noninvasive form of mild electrical brain stimulation. The interventions will be combined to establish 5 arms: (1) BrainHQ; (2) BrainHQ + PASC-Cognitive Recovery; (3) BrainHQ + tDCS-active; (4) BrainHQ + tDCS-sham; and (5) Active Comparator. The interventions will occur for 10 weeks. Assessments will be completed at baseline and at the end of intervention and will include cognitive testing and patient-reported surveys. All study activities can be delivered in Spanish and English.

DISCUSSION

This study is designed to test whether cognitive dysfunction symptoms can be alleviated by the use of pragmatic and established interventions with different mechanisms of action and with prior evidence of improving cognitive function in patients with neurocognitive disorder. If successful, results will provide beneficial treatments for PASC-related cognitive dysfunction.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05965739. Registered on July 25, 2023.

Are Advanced Clinical Practitioners perfectly placed to re-report neuroimages to support clinical diagnosis of dementia?

With the ageing population, the prevalence of dementia is increasing worldwide. There is an emphasis on early, timely diagnosis and treatment options for people with a dementia yet wait times from referral to diagnosis have increased. Neuroimaging performed by radiologists is utilised to support dementia diagnosis and some patients will already have a CT scan from a pre-existing condition such as stroke. The purpose of this commentary is to consider whether ACPs who specialise in dementia, are perfectly placed to re-report on pre-existing neuroimages to support the clinical diagnosis of dementia.

Hierarchical Two-Stage Cost-Sensitive Clinical Decision Support System for Screening Prodromal Alzheimer's Disease and Related Dementias

BACKGROUND

The detection of subtle cognitive impairment in a clinical setting is difficult. Because time is a key factor in small clinics and research sites, the brief cognitive assessments that are relied upon often misclassify patients with very mild impairment as normal.

OBJECTIVE

In this study, we seek to identify a parsimonious screening tool in one stage, followed by additional assessments in an optional second stage if additional specificity is desired, tested using a machine learning algorithm capable of being integrated into a clinical decision support system.

METHODS

The best primary stage incorporated measures of short-term memory, executive and visuospatial functioning, and self-reported memory and daily living questions, with a total time of 5 minutes. The best secondary stage incorporated a measure of neurobiology as well as additional cognitive assessment and brief informant report questionnaires, totaling 30 minutes including delayed recall. Combined performance was evaluated using 25 sets of models, trained on 1,181 ADNI participants and tested on 127 patients from a memory clinic.

RESULTS

The 5-minute primary stage was highly sensitive (96.5%) but lacked specificity (34.1%), with an AUC of 87.5% and diagnostic odds ratio of 14.3. The optional secondary stage increased specificity to 58.6%, resulting in an overall AUC of 89.7% using the best model combination of logistic regression and gradient-boosted machine.

CONCLUSION

The primary stage is brief and effective at screening, with the optional two-stage technique further increasing specificity. The hierarchical two-stage technique exhibited similar accuracy but with reduced costs compared to the more common single-stage paradigm.

Multiple Cost Optimisation for Alzheimer's Disease Diagnosis

Current machine learning techniques for dementia diagnosis often do not take into account real-world practical constraints, which may include, for example, the cost of diagnostic assessment time and financial budgets. In this work, we built on previous cost-sensitive feature selection approaches by generalising to multiple cost types, while taking into consideration that stakeholders attempting to optimise the dementia care pathway might face multiple non-fungible budget constraints. Our new optimisation algorithm involved the searching of cost-weighting hyperparameters while constrained by total budgets. We then provided a proof of concept using both assessment time cost and financial budget cost. We showed that budget constraints could control the feature selection process in an intuitive and practical manner, while adjusting the hyperparameter increased the range of solutions selected by feature selection. We further showed that our budget-constrained cost optimisation framework could be implemented in a user-friendly graphical user interface sandbox tool to encourage non-technical users and stakeholders to adopt and to further explore and audit the model - a humans-in-the-loop approach. Overall, we suggest that setting budget constraints initially and then fine tuning the cost-weighting hyperparameters can be an effective way to perform feature selection where multiple cost constraints exist, which will in turn lead to more realistic optimising and redesigning of dementia diagnostic assessments. Clinical Relevance-By optimising diagnostic accuracy against various costs (e.g. assessment administration time and financial budget) predictive yet practical dementia diagnostic assessments can be redesigned to suit clinical use.