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 (Δ 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 language | English |
|---|---|
| Article number | 25 |
| Journal | Alzheimer's Research and Therapy |
| Volume | 11 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 20 Mar 2019 |
| MoE publication type | A1 Journal article-refereed |
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Keywords
- Alzheimer's disease
- CDSS
- Computer-assisted
- Conversion
- Dementia
- Mild cognitive impairment
- Progression
- Subjective cognitive decline
Cite this
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Impact of a clinical decision support tool on prediction of progression in early-stage dementia : A prospective validation study. / Bruun, Marie (Corresponding Author); Frederiksen, Kristian S.; Rhodius-Meester, Hanneke F.M.; Baroni, Marta; Gjerum, Le; Koikkalainen, Juha; Urhemaa, Timo; Tolonen, Antti; van Gils, Mark; Rueckert, Daniel; Dyremose, Nadia; Andersen, Birgitte B.; Lemstra, Afina W.; Hallikainen, Merja; Kurl, Sudhir; Herukka, Sanna Kaisa; Remes, Anne M.; Waldemar, Gunhild; Soininen, Hilkka; Mecocci, Patrizia; Van Der Flier, Wiesje M.; Lötjönen, Jyrki; Hasselbalch, Steen G.
In: Alzheimer's Research and Therapy, Vol. 11, No. 1, 25, 20.03.2019.Research output: Contribution to journal › Article › Scientific › peer-review
TY - JOUR
T1 - Impact of a clinical decision support tool on prediction of progression in early-stage dementia
T2 - A prospective validation study
AU - Bruun, Marie
AU - Frederiksen, Kristian S.
AU - Rhodius-Meester, Hanneke F.M.
AU - Baroni, Marta
AU - Gjerum, Le
AU - Koikkalainen, Juha
AU - Urhemaa, Timo
AU - Tolonen, Antti
AU - van Gils, Mark
AU - Rueckert, Daniel
AU - Dyremose, Nadia
AU - Andersen, Birgitte B.
AU - Lemstra, Afina W.
AU - Hallikainen, Merja
AU - Kurl, Sudhir
AU - Herukka, Sanna Kaisa
AU - Remes, Anne M.
AU - Waldemar, Gunhild
AU - Soininen, Hilkka
AU - Mecocci, Patrizia
AU - Van Der Flier, Wiesje M.
AU - Lötjönen, Jyrki
AU - Hasselbalch, Steen G.
PY - 2019/3/20
Y1 - 2019/3/20
N2 - 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 (Δ 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.
AB - 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 (Δ 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.
KW - Alzheimer's disease
KW - CDSS
KW - Computer-assisted
KW - Conversion
KW - Dementia
KW - Mild cognitive impairment
KW - Progression
KW - Subjective cognitive decline
UR - http://www.scopus.com/inward/record.url?scp=85063294881&partnerID=8YFLogxK
U2 - 10.1186/s13195-019-0482-3
DO - 10.1186/s13195-019-0482-3
M3 - Article
AN - SCOPUS:85063294881
VL - 11
JO - Alzheimer's Research and Therapy
JF - Alzheimer's Research and Therapy
SN - 1758-9193
IS - 1
M1 - 25
ER -