Computerized decision support to optimally funnel patients through the diagnostic pathway for dementia

Abstract

Background: The increasing prevalence of dementia and the introduction of disease-modifying therapies (DMTs) highlight the need for efficient diagnostic pathways in memory clinics. We present a data-driven approach to efficiently guide stepwise diagnostic testing for three clinical scenarios: 1) syndrome diagnosis, 2) etiological diagnosis, and 3) eligibility for DMT.

Methods: We used data from two memory clinic cohorts (ADC, PredictND), including 504 patients with dementia (302 Alzheimer's disease, 107 frontotemporal dementia, 35 vascular dementia, 60 dementia with Lewy bodies), 191 patients with mild cognitive impairment, and 188 cognitively normal controls (CN). Tests included digital cognitive screening (cCOG), neuropsychological and functional assessment (NP), MRI with automated quantification, and CSF biomarkers. Sequential testing followed a predetermined order, guided by diagnostic certainty. Diagnostic certainty was determined using a clinical decision support system (CDSS) that generates a disease state index (DSI, 0-1), indicating the probability of the syndrome diagnosis or underlying etiology. Diagnosis was confirmed if the DSI exceeded a predefined threshold based on sensitivity/specificity cutoffs relevant to each clinical scenario. Diagnostic accuracy and the need for additional testing were assessed at each step.

Results: Using cCOG as a prescreener for 1) syndrome diagnosis has the potential to accurately reduce the need for extensive NP (42%), resulting in syndrome diagnosis in all patients, with a diagnostic accuracy of 0.71, which was comparable to using NP alone. For 2) etiological diagnosis, stepwise testing resulted in an etiological diagnosis in 80% of patients with a diagnostic accuracy of 0.77, with MRI needed in 77%, and CSF in 37%. When 3) determining DMT eligibility, stepwise testing (100% cCOG, 83% NP, 75% MRI) selected 60% of the patients for confirmatory CSF testing and eventually identified 90% of the potentially eligible patients with AD dementia.

Conclusions: Different diagnostic pathways are accurate and efficient depending on the setting. As such, a data-driven tool holds promise for assisting clinicians in selecting tests of added value across different clinical contexts. This becomes especially important with DMT availability, where the need for more efficient diagnostic pathways is crucial to maintain the accessibility and affordability of dementia diagnoses.

Keywords: Alzheimer’s disease; Data-driven diagnosis; Differential diagnosis; Eligibility.

Fig. 1

Clinical scenarios and the respective diagnostic tests used for each scenario. Note: blood/PET in light grey mean that these tests could be considered (in the future) in these steps, but they are not included in the current study. 1) Syndrome diagnosis, considering a diagnosis of CN, MCI or dementia. 2) Etiological diagnosis, considering a diagnosis of AD dementia, FTD, VaD, or DLB. 3) DMT eligibility, considering whether a patient would be eligible for DMT, based on appropriate use criteria by Cummings [6]. Abbreviations: cCOG: computerized cognitive test, MMSE: Mini-mental state examination, RAVLT: Rey-auditory verbal learning task, TMT-A/B: trail making test A/B, GDS: geriatric depression scale, DAD: disability assessment for dementia, MRI: magnetic resonance imaging, cMTA: computerized medial temporal lobe atrophy, cGCA: computerized global cortical atrophy, WMH: white matter hyperintensities, APS: anterior–posterior score, CSF: cerebrospinal fluid, Aβ42: amyloid β1-42, t-tau: total tau, p-tau: phosphorylated-tau, DMT: disease-modifying therapy

Fig. 2

Visual representation of scenario 1, syndrome diagnosis, for scenario 1A) syndrome diagnosis using NP, and scenario 1B) stepwise syndrome diagnosis, making use of letter-value plots. NOTE: The figure includes letter-value plots for DSI scores. The length of the first box represents the interquartile range (IQR), which contains the middle 50% of the data in the group, the second box (E) 12.5% at each end, the third box covers (D) 6.25% on each end, and the fourth box (C) 3.13%. The longer the box, the greater the variability. The percentages indicate the proportion of the total population that requires additional testing. Red dashed lines depict the DSI cutoff-vales. MCI: mild cognitive impairment, MRI: magnetic resonance imaging

Fig. 3

Visual representation of scenario 2, stepwise etiological syndrome diagnosis, making use of letter-value plots. NOTE: The figure includes letter-value plots for DSI scores. The length of the first box represents the interquartile range (IQR), which contains the middle 50% of the data in the group, the second box (E) 12.5% at each end, the third box covers (D) 6.25% on each end, and the fourth box (C) 3.13%. The longer the box, the greater the variability. The percentages indicate the proportion of the total population that requires additional testing. Red dashed lines depict the DSI cutoff-vales. MCI: mild cognitive impairment, MRI: magnetic resonance imaging

Fig. 4

Visual representation scenario 3, stepwise eligibility testing, making use of letter-value plots. NOTE: The figure includes letter-value plots for DSI scores. The length of the first box represents the interquartile range (IQR), which contains the middle 50% of the data in the group, the second box (E) 12.5% at each end, the third box covers (D) 6.25% on each end, and the fourth box (C) 3.13%. The longer the box, the greater the variability. Red dashed lines depict the DSI cutoff-vales. Abbreviations: AD: Alzheimer’s disease, MCI: mild cognitive impairment, MRI: magnetic resonance imaging, CSF: cerebrospinal fluid, PET: positron emission tomography

Fig. 5

Example of the stepwise approach in clinical practice. Abbreviations: DSI: disease state index, cCOG: computerized cognitive screening, NP: neuropsychological and functional assessment, CN: cognitively normal, MCI: mild cognitive impairment, MMSE: mini-mental state examination, CSF: cerebrospinal fluid, MRI: magnetic resonance imaging, AD: Alzheimer’s disease, DLB: dementia with Lewy bodies, FTD: frontotemporal dementia, VaD: vascular dementia