Advertisement
Original Article|Articles in Press

Elicited Clinician Knowledge Did Not Improve Dementia Risk Prediction in Individuals with Mild Cognitive Impairment

Published:March 15, 2023DOI:https://doi.org/10.1016/j.jclinepi.2023.03.009
Elicited Clinician Knowledge Did Not Improve Dementia Risk Prediction in Individuals with Mild Cognitive Impairment
Previous ArticleOverinterpretation of findings in machine learning prediction model studies in oncology: a systematic review
Next ArticleA Scoping Review described diversity in methods of randomization and reporting of baseline balance in Stepped-Wedge Cluster Randomized Trials
      This paper is only available as a PDF. To read, Please Download here.

      Abstract

      Objective

      This study aims to develop and validate a Bayesian risk prediction model that combines research cohort data with elicited expert knowledge to predict dementia progression in people with mild cognitive impairment (MCI).

      Study Design and Setting

      This is a prognostic risk prediction modeling study based on cohort data (Alzheimer’s Disease Neuroimaging Initiative [ADNI]; n=365) of research participants with MCI and elicited expert data. Bayesian Cox models were used to combine expert knowledge and ADNI data to predict dementia progression in people with MCI. Posterior distributions were obtained based on Gibbs sampler and the predictive performance was evaluated using ten-fold cross-validation via c-index, integrated calibration index (ICI), and integrated brier score (IBS).

      Results

      365 people with MCI were included, mean age was 73 years (SD=7.5) and 39% developed dementia within 3 years. When expert knowledge was incorporated, the c-index, ICI, and IBS values were 0.74 (95% CI 0.70-0.79), 0.06 (95% CI 0.05-0.08), and 0.17 (95% CI 0.14-0.19), respectively. These were similar to the model without expert knowledge data.

      Conclusion

      The addition of expert knowledge did not improve model accuracy in this ADNI sample to predict dementia progression in individuals with MCI.

      Key words

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Journal of Clinical Epidemiology
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Register: Create an account
      Institutional Access: Sign in to ScienceDirect

      Article info

      Publication history

      Accepted: March 13, 2023
      Received in revised form: March 10, 2023
      Received: January 10, 2023

      Publication stage

      In Press Journal Pre-Proof

      Identification

      DOI: https://doi.org/10.1016/j.jclinepi.2023.03.009

      Copyright

      © 2023 Elsevier Inc. All rights reserved.

      ScienceDirect

      Access this article on ScienceDirect
      We use cookies to help provide and enhance our service and tailor content. To update your cookie settings, please visit the for this site.
      Copyright © 2023 Elsevier Inc. except certain content provided by third parties. The content on this site is intended for healthcare professionals.


      RELX