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

Marie Bruun (Corresponding Author), Kristian S. Frederiksen, Hanneke F.M. Rhodius-Meester, Marta Baroni, Le Gjerum, Juha Koikkalainen, Timo Urhemaa, Antti Tolonen, Mark van Gils, Daniel Rueckert, Nadia Dyremose, Birgitte B. Andersen, Afina W. Lemstra, Merja Hallikainen, Sudhir Kurl, Sanna Kaisa Herukka, Anne M. Remes, Gunhild Waldemar, Hilkka Soininen, Patrizia MecocciWiesje M. Van Der Flier, Jyrki Lötjönen, Steen G. Hasselbalch

    Research output: Contribution to journalArticleScientificpeer-review

    21 Citations (Scopus)


    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.

    Original languageEnglish
    Article number25
    JournalAlzheimer's Research and Therapy
    Issue number1
    Publication statusPublished - 20 Mar 2019
    MoE publication typeA1 Journal article-refereed


    This work was co-funded by the European Commission under grant agreement 611005 (PredictND). For development of the PredictND tool, VTT Technical Research Center of Finland Ltd has received funding from European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreements 601055 (VPH-DARE@IT), 224328 and 611005. Furthermore, the work was supported by the Ellen Mørch's foundation and the family Hede Nielsen's foundation.


    • Alzheimer's disease
    • CDSS
    • Computer-assisted
    • Conversion
    • Dementia
    • Mild cognitive impairment
    • Progression
    • Subjective cognitive decline


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