Risk prediction of mild cognitive impairment using electronic health record data

Predictive algorithms using electronic health records (EHRs) are useful for identifying individuals at risk for mild cognitive impairment (MCI) to triage for further clinical evaluation.A machine learning model was developed using EHR data to predict those at risk for MCI.The model identified 26 variables that were able to distinguish the MCI from non-MCI cohorts.The model accurately detected MCI across the cohort (area under the curve [AUC] = 86%) and trended best for younger age groups (AUC was 77%, 77%, and 74% in 50-64, 65-79, and ≥80 age groups, respectively).Implementation of a triage tool could be used to detect MCI across aging patient populations sooner, leading to a timelier diagnosis, intervention, and treatment management.

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Publication Details

Journal
Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring
Published
2025-10-01
DOI
https://doi.org/10.1002/dad2.70209
Primary Topic
Emergency and Acute Care Studies
Type
article
Field-Weighted Citation Impact
0.00

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article

Risk prediction of mild cognitive impairment using electronic health record data

Soeren Mattke, Feride Frech, Todd D. Nelson, Susan De Santi et al.
Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring
Emergency and Acute Care Studies
article

Risk prediction of mild cognitive impairment using electronic health record data

Soeren Mattke, Feride Frech, Todd D. Nelson, Susan De Santi, Yosuke Nakamura, Harald Hampel, Viswanath Devanarayan, Jo Vandercappellen, Michelle M. Mielke, Rifky Tkatch, Gang Li, James E. Galvin, Miia Kivipelto, Bryan Cobb, Stephane Borentain, Richard Crislip, Jeffrey Meyerhoff
article en

Abstract

Predictive algorithms using electronic health records (EHRs) are useful for identifying individuals at risk for mild cognitive impairment (MCI) to triage for further clinical evaluation.A machine learning model was developed using EHR data to predict those at risk for MCI.The model identified 26 variables that were able to distinguish the MCI from non-MCI cohorts.The model accurately detected MCI across the cohort (area under the curve [AUC] = 86%) and trended best for younger age groups (AUC was 77%, 77%, and 74% in 50-64, 65-79, and ≥80 age groups, respectively).Implementation of a triage tool could be used to detect MCI across aging patient populations sooner, leading to a timelier diagnosis, intervention, and treatment management.

Alzheimer s & Dementia Diagnosis Assessment & Disease MonitoringVol. 17(4)
University of Southern California (US), University of Miami (US), Karolinska Institutet (SE), University of Miami Health System (US), Optum (United States) (US), MemorialCare Health System (US), Eisai (United States) (US), Wake Forest University (US)
Eisai
Openalex Percentile: Top 48%
Emergency and Acute Care Studies
DOIOpen Access PDFOpenAlex
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