Application of the PredictAD software tool to predict progression in patients with mild cognitive impairment

Impact of a Clinical Decision Support Tool on Dementia Diagnostics in Memory Clinics: The PredictND Validation Study

BACKGROUND

Determining the underlying etiology of dementia can be challenging. Computer- based Clinical Decision Support Systems (CDSS) have the potential to provide an objective comparison of data and assist clinicians.

OBJECTIVES

To assess the diagnostic impact of a CDSS, the PredictND tool, for differential diagnosis of dementia in memory clinics.

METHODS

In this prospective multicenter study, we recruited 779 patients with either subjective cognitive decline (n=252), mild cognitive impairment (n=219) or any type of dementia (n=274) and followed them for minimum 12 months. Based on all available patient baseline data (demographics, neuropsychological tests, cerebrospinal fluid biomarkers, and MRI visual and computed ratings), the PredictND tool provides a comprehensive overview and analysis of the data with a likelihood index for five diagnostic groups; Alzheimer´s disease, vascular dementia, dementia with Lewy bodies, frontotemporal dementia and subjective cognitive decline. At baseline, a clinician defined an etiological diagnosis and confidence in the diagnosis, first without and subsequently with the PredictND tool. The follow-up diagnosis was used as the reference diagnosis.

RESULTS

In total, 747 patients completed the follow-up visits (53% female, 69±10 years). The etiological diagnosis changed in 13% of all cases when using the PredictND tool, but the diagnostic accuracy did not change significantly. Confidence in the diagnosis, measured by a visual analogue scale (VAS, 0-100%) increased (ΔVAS=3.0%, p<0.0001), especially in correctly changed diagnoses (ΔVAS=7.2%, p=0.0011).

CONCLUSION

Adding the PredictND tool to the diagnostic evaluation affected the diagnosis and increased clinicians' confidence in the diagnosis indicating that CDSSs could aid clinicians in the differential diagnosis of dementia.

Impact of a clinical decision support tool on prediction of progression in early-stage dementia: a prospective validation study

Background

In clinical practice, it is often difficult to predict which patients with cognitive complaints or impairment will progress or remain stable. We assessed the impact of using a clinical decision support system, the PredictND tool, to predict progression in patients with subjective cognitive decline (SCD) and mild cognitive impairment (MCI) in memory clinics.

Methods

In this prospective multicenter study, we included 429 patients with SCD (n = 230) and MCI (n = 199) (female 54%, age 67 ± 9, MMSE 28 ± 2) and followed them for at least 12 months. Based on all available patient baseline data (demographics, cognitive tests, cerebrospinal fluid biomarkers, and MRI), the PredictND tool provides a comprehensive overview of the data and a classification defining the likelihood of progression. At baseline, a clinician defined an expected follow-up diagnosis and estimated the level of confidence in their prediction using a visual analogue scale (VAS, 0–100%), first without and subsequently with the PredictND tool. As outcome measure, we defined clinical progression as progression from SCD to MCI or dementia, and from MCI to dementia. Correspondence between the expected and the actual clinical progression at follow-up defined the prognostic accuracy.

Results

After a mean follow-up time of 1.7 ± 0.4 years, 21 (9%) SCD and 63 (32%) MCI had progressed. When using the PredictND tool, the overall prognostic accuracy was unaffected (0.4%, 95%CI − 3.0%; + 3.9%; p = 0.79). However, restricting the analysis to patients with more certain classifications (n = 203), we found an increase of 3% in the accuracy (95%CI − 0.6%; + 6.5%; p = 0.11). Furthermore, for this subgroup, the tool alone showed a statistically significant increase in the prognostic accuracy compared to the evaluation without tool (6.4%, 95%CI 2.1%; 10.7%; p = 0.004). Specifically, the negative predictive value was high. Moreover, confidence in the prediction increased significantly (∆VAS = 4%, p < .0001).

Conclusions

Adding the PredictND tool to the clinical evaluation increased clinicians’ confidence. Furthermore, the results indicate that the tool has the potential to improve prediction of progression for patients with more certain classifications.

Electronic supplementary material

The online version of this article (10.1186/s13195-019-0482-3) contains supplementary material, which is available to authorized users.

Development of a Late-Life Dementia Prediction Index with Supervised Machine Learning in the Population-Based CAIDE Study

BACKGROUND AND OBJECTIVE

This study aimed to develop a late-life dementia prediction model using a novel validated supervised machine learning method, the Disease State Index (DSI), in the Finnish population-based CAIDE study.

METHODS

The CAIDE study was based on previous population-based midlife surveys. CAIDE participants were re-examined twice in late-life, and the first late-life re-examination was used as baseline for the present study. The main study population included 709 cognitively normal subjects at first re-examination who returned to the second re-examination up to 10 years later (incident dementia n = 39). An extended population (n = 1009, incident dementia 151) included non-participants/non-survivors (national registers data). DSI was used to develop a dementia index based on first re-examination assessments. Performance in predicting dementia was assessed as area under the ROC curve (AUC).

RESULTS

AUCs for DSI were 0.79 and 0.75 for main and extended populations. Included predictors were cognition, vascular factors, age, subjective memory complaints, and APOE genotype.

CONCLUSION

The supervised machine learning method performed well in identifying comprehensive profiles for predicting dementia development up to 10 years later. DSI could thus be useful for identifying individuals who are most at risk and may benefit from dementia prevention interventions.

Using the Disease State Fingerprint Tool for Differential Diagnosis of Frontotemporal Dementia and Alzheimer's Disease

BACKGROUND

Disease State Index (DSI) and its visualization, Disease State Fingerprint (DSF), form a computer-assisted clinical decision making tool that combines patient data and compares them with cases with known outcomes.

AIMS

To investigate the ability of the DSI to diagnose frontotemporal dementia (FTD) and Alzheimer's disease (AD).

METHODS

The study cohort consisted of 38 patients with FTD, 57 with AD and 22 controls. Autopsy verification of FTD with TDP-43 positive pathology was available for 14 and AD pathology for 12 cases. We utilized data from neuropsychological tests, volumetric magnetic resonance imaging, single-photon emission tomography, cerebrospinal fluid biomarkers and the APOE genotype. The DSI classification results were calculated with a combination of leave-one-out cross-validation and bootstrapping. A DSF visualization of a FTD patient is presented as an example.

RESULTS

The DSI distinguishes controls from FTD (area under the receiver-operator curve, AUC = 0.99) and AD (AUC = 1.00) very well and achieves a good differential diagnosis between AD and FTD (AUC = 0.89). In subsamples of autopsy-confirmed cases (AUC = 0.97) and clinically diagnosed cases (AUC = 0.94), differential diagnosis of AD and FTD performs very well.

CONCLUSIONS

DSI is a promising computer-assisted biomarker approach for aiding in the diagnostic process of dementing diseases. Here, DSI separates controls from dementia and differentiates between AD and FTD.

2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception

The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's disease (AD). The initial study, ADNI-1, enrolled 400 subjects with early mild cognitive impairment (MCI), 200 with early AD, and 200 cognitively normal elderly controls. ADNI-1 was extended by a 2-year Grand Opportunities grant in 2009 and by a competitive renewal, ADNI-2, which enrolled an additional 550 participants and will run until 2015. This article reviews all papers published since the inception of the initiative and summarizes the results to the end of 2013. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are largely consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimer's Dis 2006;9(Suppl 3):151-3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers select and combine optimum features from multiple modalities, including MRI, [(18)F]-fluorodeoxyglucose-PET, amyloid PET, CSF biomarkers, and clinical tests; (4) the development of blood biomarkers for AD as potentially noninvasive and low-cost alternatives to CSF biomarkers for AD diagnosis and the assessment of α-syn as an additional biomarker; (5) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects and are leading candidates for the detection of AD in its preclinical stages; (6) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Multimodal methods incorporating APOE status and longitudinal MRI proved most highly predictive of future decline. Refinements of clinical tests used as outcome measures such as clinical dementia rating-sum of boxes further reduced sample sizes; (7) the pioneering of genome-wide association studies that leverage quantitative imaging and biomarker phenotypes, including longitudinal data, to confirm recently identified loci, CR1, CLU, and PICALM and to identify novel AD risk loci; (8) worldwide impact through the establishment of ADNI-like programs in Japan, Australia, Argentina, Taiwan, China, Korea, Europe, and Italy; (9) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker and clinical data to stimulate research that will resolve controversies about competing hypotheses on the etiopathogenesis of AD, thereby advancing efforts to find disease-modifying drugs for AD; and (10) the establishment of infrastructure to allow sharing of all raw and processed data without embargo to interested scientific investigators throughout the world.

Application of the PredictAD decision support tool to a Danish cohort of patients with Alzheimer's disease and other dementias

BACKGROUND

The diagnosis of Alzheimer's disease (AD) is based on an ever-increasing body of data and knowledge making it a complex task. The PredictAD tool integrates heterogeneous patient data using an interactive user interface to provide decision support. The aim of this project was to investigate the performance of the tool in distinguishing AD from non-AD dementia using a realistic clinical dataset.

METHODS

We retrieved clinical data from a group of patients diagnosed with AD (n = 72), vascular dementia (VaD, n = 30), frontotemporal dementia (FTD, n = 25) or dementia with Lewy bodies (DLB, n = 14) at the Copenhagen Memory Clinic at Rigshospitalet. Three classification methods were applied to the data in order to differentiate between AD and a group of non-AD dementias. The methods were the PredictAD tool's Disease State Index (DSI), the naïve Bayesian classifier and the random forest.

RESULTS

The DSI performed best for this realistic dataset with an accuracy of 76.6% compared to the accuracies for the naïve Bayesian classifier and random forest of 67.4 and 66.7%, respectively. Furthermore, the DSI differentiated between the four diagnostic groups with a p value of <0.0001.

CONCLUSION

In this dataset, the DSI method used by the PredictAD tool showed a superior performance for the differentiation between patients with AD and those with other dementias. However, the methods need to be refined further in order to optimize the differential diagnosis between AD, FTD, VaD and DLB.