Alzheimer's & dementia (Amsterdam, Netherlands) · 2025
Risk prediction of mild cognitive impairment using electronic health record data.
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Li Gang, Cobb Bryan, Nelson Todd M, Devanarayan Viswanath, Borentain Stephane, Mielke Michelle M, Galvin James E, Kivipelto Miia, Tkatch Rifky, De Santi Susan, Frech Feride, Vandercappellen Jo, Nakamura Yosuke, Crislip Richard, Meyerhoff Jeffrey, Mattke Soeren, Hampel Harald
Аннотация
INTRODUCTION: Mild cognitive impairment (MCI) is underdiagnosed by primary care providers (PCPs), with detection rates as low as 6%-15%. Predictive models support the identification of individuals at risk, enabling timely intervention.
METHODS: This retrospective study was conducted on 271,054 cognitively unimpaired and 14,501 confirmed MCI cohorts from electronic health records. A machine learning model was developed with a data-driven variable selection approach based on demographics and comorbidities.
RESULTS: From 101 variables, 26 were chosen for the final model, achieving an overall area under the curve (AUC) of 86%. Age-stratified AUCs were 79.1% (40-49), 77.0% (50-64), 76.9% (65-79), and 74.4% (≥80), showing the highest predictive performance in younger age groups.
DISCUSSION: Demographic factors and comorbidities can serve as effective predictors for the risk of MCI. The model demonstrates strong predictive performance and assists as a triage tool for PCPs, facilitating the identification of individuals with MCI for early treatment.
HIGHLIGHTS: 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.
METHODS: This retrospective study was conducted on 271,054 cognitively unimpaired and 14,501 confirmed MCI cohorts from electronic health records. A machine learning model was developed with a data-driven variable selection approach based on demographics and comorbidities.
RESULTS: From 101 variables, 26 were chosen for the final model, achieving an overall area under the curve (AUC) of 86%. Age-stratified AUCs were 79.1% (40-49), 77.0% (50-64), 76.9% (65-79), and 74.4% (≥80), showing the highest predictive performance in younger age groups.
DISCUSSION: Demographic factors and comorbidities can serve as effective predictors for the risk of MCI. The model demonstrates strong predictive performance and assists as a triage tool for PCPs, facilitating the identification of individuals with MCI for early treatment.
HIGHLIGHTS: 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.