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

Marie BruunEmail author View ORCID ID profile,
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 Mecocci,
Wiesje M. van der Flier,
Jyrki Lötjönen and
Steen G. Hasselbalch

Alzheimer's Research & Therapy201911:25

https://doi.org/10.1186/s13195-019-0482-3

Received: 21 November 2018
Accepted: 11 March 2019
Published: 20 March 2019

Abstract

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.