Abstract
Clinical decision support systems (CDSSs) hold potential for the differential diagnosis of neurodegenerative diseases. We developed a novel CDSS, the PredictND tool, designed for differential diagnosis of different types of dementia. It combines information obtained from multiple diagnostic tests such as neuropsychological tests, MRI and cerebrospinal fluid samples. Here we evaluated how the classifier used in it performs in differentiating between controls with subjective cognitive decline, dementia due to Alzheimer’s disease, vascular dementia, frontotemporal lobar degeneration and dementia with Lewy bodies. We used the multiclass Disease State Index classifier, which is the classifier used by the PredictND tool, to differentiate between controls and patients with the four different types of dementia. The multiclass Disease State Index classifier is an extension of a previously developed two-class Disease State Index classifier. As the two-class Disease State Index classifier, the multiclass Disease State Index classifier also offers a visualization of its decision making process, which makes it especially suitable for medical decision support where interpretability of the results is highly important. A subset of the Amsterdam Dementia cohort, consisting of 504 patients (age 65 ± 8 years, 44% females) with data from neuropsychological tests, cerebrospinal fluid samples and both automatic and visual MRI quantifications, was used for the evaluation. The Disease State Index classifier was highly accurate in separating the five classes from each other (balanced accuracy 82.3%). Accuracy was highest for vascular dementia and lowest for dementia with Lewy bodies. For the 50% of patients for which the classifier was most confident on the classification the balanced accuracy was 93.6%. Data-driven CDSSs can be of aid in differential diagnosis in clinical practice. The decision support system tested in this study was highly accurate in separating the different dementias and controls from each other. In addition to the predicted class, it also provides a confidence measure for the classification.
Highlights
Worldwide dementia affects over 47 million people and is one of the major causes of dependency and disability with huge social and economic impact (World Health Organization, 2016)
To reflect daily clinical practice more closely, we extended the tool to differential diagnosis of dementia
The objective of this study is to evaluate the performance of the Disease State Index (DSI) classifier for classifying patients in differential diagnosis of dementias
Summary
Worldwide dementia affects over 47 million people and is one of the major causes of dependency and disability with huge social and economic impact (World Health Organization, 2016). Progress in biomarker development has provided new disease insights and improved accuracy of dementia diagnosis This has led to an increasing role of biomarkers, such as those obtained from cerebrospinal fluid (CSF) measures and structural magnetic resonance imaging (MRI), in diagnostic criteria and guidelines (Román et al, 1993; McKhann et al, 2011; Rascovsky et al, 2011; McKeith et al, 2017). In an earlier study we presented the MRI analysis methods used in the CDSS and evaluated the classification accuracy for differentiating between patients with AD, VaD, DLB, FTLD, and controls using only structural MRI data (Koikkalainen et al, 2016). We extend the first study (Koikkalainen et al, 2016) by evaluating the DSI classifier with a more comprehensive set of data, consisting of neuropsychological tests, CSF samples, and both automatic and visual MRI ratings.
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