Automated identification of older adults at risk for cognitive decline
Abstract INTRODUCTION Automated models that predict cognitive risk in older adults can aid decisions about which patients to screen in busy primary care settings. METHODS In this retrospective prediction model development study, we conducted formal cognitive testing on 337 older primary care patient...
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| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-04-01
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| Series: | Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring |
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| Online Access: | https://doi.org/10.1002/dad2.70136 |
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| author | Darlene P. Floden Olivia Hogue Saket A. Saxena Anita D. Misra‐Hebert Alex Milinovich Michael B. Rothberg Elizabeth R. Pfoh Robyn M. Busch Kamini Krishnan Robert J. Fox Michael W. Kattan |
| author_facet | Darlene P. Floden Olivia Hogue Saket A. Saxena Anita D. Misra‐Hebert Alex Milinovich Michael B. Rothberg Elizabeth R. Pfoh Robyn M. Busch Kamini Krishnan Robert J. Fox Michael W. Kattan |
| author_sort | Darlene P. Floden |
| collection | DOAJ |
| description | Abstract INTRODUCTION Automated models that predict cognitive risk in older adults can aid decisions about which patients to screen in busy primary care settings. METHODS In this retrospective prediction model development study, we conducted formal cognitive testing on 337 older primary care patients to establish cognitive status. We used up to 5 years of prior discrete‐field electronic health record (EHR) data to develop a multivariable prediction model that differentiates patients with impaired versus intact cognition. RESULTS The final model included seven easily extractable variables with known associations to cognitive decline: age, race, pulse, systolic blood pressure, non‐steroidal anti‐inflammatory use, history of mood disorder, and family history of neurological disease. The model demonstrated good discrimination of cognitive status (concordance statistic = 0.72). DISCUSSION The cognitive risk model may be useful clinically to prompt for objective cognitive screening in high‐risk patients. The use of common, discrete variables ensures relative ease of implementation in EHRs. Highlights 337 older primary care patients completed full neuropsychological assessment. Risk modeling used data available in a typical primary care record. The model successfully differentiated patients with/without cognitive impairment. This EHR model offers a passive workflow to identify patients at cognitive risk. |
| format | Article |
| id | doaj-art-db2c4207bdfa422796eea142461d9d1e |
| institution | DOAJ |
| issn | 2352-8729 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring |
| spelling | doaj-art-db2c4207bdfa422796eea142461d9d1e2025-08-20T03:23:57ZengWileyAlzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring2352-87292025-04-01172n/an/a10.1002/dad2.70136Automated identification of older adults at risk for cognitive declineDarlene P. Floden0Olivia Hogue1Saket A. Saxena2Anita D. Misra‐Hebert3Alex Milinovich4Michael B. Rothberg5Elizabeth R. Pfoh6Robyn M. Busch7Kamini Krishnan8Robert J. Fox9Michael W. Kattan10Section of Neuropsychology Neurological Institute Cleveland Clinic Cleveland Ohio USAQuantitative Health Sciences Lerner Research Institute Cleveland Clinic Cleveland Ohio USACenter for Geriatric Medicine Primary Care Institute Cleveland Clinic Cleveland Ohio USAQuantitative Health Sciences Lerner Research Institute Cleveland Clinic Cleveland Ohio USAQuantitative Health Sciences Lerner Research Institute Cleveland Clinic Cleveland Ohio USADepartment of Internal Medicine Primary Care Institute Cleveland Clinic Cleveland Ohio USADepartment of Internal Medicine Primary Care Institute Cleveland Clinic Cleveland Ohio USASection of Neuropsychology Neurological Institute Cleveland Clinic Cleveland Ohio USASection of Neuropsychology Neurological Institute Cleveland Clinic Cleveland Ohio USAMellen Center for Multiple Sclerosis Neurological Institute Research Office Neurological Institute Cleveland Clinic Cleveland Ohio USAQuantitative Health Sciences Lerner Research Institute Cleveland Clinic Cleveland Ohio USAAbstract INTRODUCTION Automated models that predict cognitive risk in older adults can aid decisions about which patients to screen in busy primary care settings. METHODS In this retrospective prediction model development study, we conducted formal cognitive testing on 337 older primary care patients to establish cognitive status. We used up to 5 years of prior discrete‐field electronic health record (EHR) data to develop a multivariable prediction model that differentiates patients with impaired versus intact cognition. RESULTS The final model included seven easily extractable variables with known associations to cognitive decline: age, race, pulse, systolic blood pressure, non‐steroidal anti‐inflammatory use, history of mood disorder, and family history of neurological disease. The model demonstrated good discrimination of cognitive status (concordance statistic = 0.72). DISCUSSION The cognitive risk model may be useful clinically to prompt for objective cognitive screening in high‐risk patients. The use of common, discrete variables ensures relative ease of implementation in EHRs. Highlights 337 older primary care patients completed full neuropsychological assessment. Risk modeling used data available in a typical primary care record. The model successfully differentiated patients with/without cognitive impairment. This EHR model offers a passive workflow to identify patients at cognitive risk.https://doi.org/10.1002/dad2.70136decision supportdementiaearly diagnosiselectronic health recordmachine learningmild cognitive impairment |
| spellingShingle | Darlene P. Floden Olivia Hogue Saket A. Saxena Anita D. Misra‐Hebert Alex Milinovich Michael B. Rothberg Elizabeth R. Pfoh Robyn M. Busch Kamini Krishnan Robert J. Fox Michael W. Kattan Automated identification of older adults at risk for cognitive decline Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring decision support dementia early diagnosis electronic health record machine learning mild cognitive impairment |
| title | Automated identification of older adults at risk for cognitive decline |
| title_full | Automated identification of older adults at risk for cognitive decline |
| title_fullStr | Automated identification of older adults at risk for cognitive decline |
| title_full_unstemmed | Automated identification of older adults at risk for cognitive decline |
| title_short | Automated identification of older adults at risk for cognitive decline |
| title_sort | automated identification of older adults at risk for cognitive decline |
| topic | decision support dementia early diagnosis electronic health record machine learning mild cognitive impairment |
| url | https://doi.org/10.1002/dad2.70136 |
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