[P1–375]: Data‐driven Diagnosis of Dementia Disorders: The Predictnd Validation Study

Bruun, Marie; Gjerum, Le; Frederiksen, Kristian Steen; Rhodius-Meester, Hanneke F.M.; Baroni, Marta; Lemstra, Evelien; Bremer, Jonne A.E.; Remes, Anne M.; Urhemaa, Timo; Tolonen, Antti; Tong, Tong; Rueckert, Daniel; Waldemar, Gunhild; Flier, Wiesje M. van der; Mecocci, Patrizia; Lötjönen, Jyrki; Hasselbalch, Steen Gregers

doi:10.1016/j.jalz.2017.06.391

Cited by 2 publications

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“…Our study demonstrates how combinations of diagnostic tests for pairwise comparison of diagnostic groups can be studied using clinical decision support system, though, still based on group-level information. However, applying classifiers like this to the diagnostic assessment in clinical practice could assist clinicians in the simultaneous evaluation of a wide range of diagnostic tests while evaluating different types of dementia on a personalized level [19] .…”

Section: Discussionmentioning

confidence: 99%

“…Maria della Misericordia” Hospital of Perugia, and 50 subjects from the Department of Neurology from the University of Eastern Finland. Data from Rigshospitalet, University of Perugia, University of Eastern Finland, and 44 subjects from VU Medical Center had been collected as part of the PredictND study [19] . The remainder of VUmc subjects was included from Amsterdam Dementia Cohort.…”

Section: Methodsmentioning

confidence: 99%

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Evaluating combinations of diagnostic tests to discriminate different dementia types

Bruun

Rhodius-Meester

Koikkalainen³

et al. 2018

Alz & Dem Diag Ass & Dis Mo

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Introduction We studied, using a data-driven approach, how different combinations of diagnostic tests contribute to the differential diagnosis of dementia. Methods In this multicenter study, we included 356 patients with Alzheimer's disease, 87 frontotemporal dementia, 61 dementia with Lewy bodies, 38 vascular dementia, and 302 controls. We used a classifier to assess accuracy for individual performance and combinations of cognitive tests, cerebrospinal fluid biomarkers, and automated magnetic resonance imaging features for pairwise differentiation between dementia types. Results Cognitive tests had good performance in separating any type of dementia from controls. Cerebrospinal fluid optimally contributed to identifying Alzheimer's disease, whereas magnetic resonance imaging features aided in separating vascular dementia, dementia with Lewy bodies, and frontotemporal dementia. Combining diagnostic tests increased the accuracy, with balanced accuracies ranging from 78% to 97%. Discussion Different diagnostic tests have their distinct roles in differential diagnostics of dementias. Our results indicate that combining different diagnostic tests may increase the accuracy further.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Evaluating combinations of diagnostic tests to discriminate different dementia types

Bruun

Rhodius-Meester

Koikkalainen³

et al. 2018

Alz & Dem Diag Ass & Dis Mo

Self Cite

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“…We recruited patients from four European memory clinics as part of the PredictND project described in detail elsewhere [ 29 ]. The patients were enrolled consecutively from March 2015 to June 2016 and followed for a minimum of 1 year.…”

Section: Methodsmentioning

confidence: 99%

“…Cognition was assessed using a standardized cognitive test battery, as presented in Table 2 and described in [ 29 ]. Lumbar puncture was performed on clinical indication ( n = 145), and Aβ42, total tau, and tau phosphorylated at threonine 181 were measured with commercially available ELISA (Innotest, Fujirebio Europe, Ghent, Belgium).…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2019

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

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[P1–375]: Data‐driven Diagnosis of Dementia Disorders: The Predictnd Validation Study

Cited by 2 publications

References 0 publications

Evaluating combinations of diagnostic tests to discriminate different dementia types

Evaluating combinations of diagnostic tests to discriminate different dementia types

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

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