Karen C Schliep, Jeffrey Thornhill, JoAnn Tschanz, Julio C Facelli, Truls Østbye, Michelle K Sorweid, Ken R Smith, Michael Varner, Richard D Boyce, Christine J Cliatt Brown, Huong Meeks, Samir Abdelrahman
{"title":"利用电子健康记录预测阿尔茨海默病和相关痴呆症的发病：卡奇县老年记忆研究（1995-2008 年）》。","authors":"Karen C Schliep, Jeffrey Thornhill, JoAnn Tschanz, Julio C Facelli, Truls Østbye, Michelle K Sorweid, Ken R Smith, Michael Varner, Richard D Boyce, Christine J Cliatt Brown, Huong Meeks, Samir Abdelrahman","doi":"10.21203/rs.3.rs-4414498/v1","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>Clinical notes, biomarkers, and neuroimaging have been proven valuable in dementia prediction models. Whether commonly available structured clinical data can predict dementia is an emerging area of research. We aimed to predict Alzheimer's disease (AD) and Alzheimer's disease related dementias (ADRD) in a well-phenotyped, population-based cohort using a machine learning approach.</p><p><strong>Methods: </strong>Administrative healthcare data (k=163 diagnostic features), in addition to Census/vital record sociodemographic data (k = 6 features), were linked to the Cache County Study (CCS, 1995-2008).</p><p><strong>Results: </strong>Among successfully linked UPDB-CCS participants (n=4206), 522 (12.4%) had incident AD/ADRD as per the CCS \"gold standard\" assessments. Random Forest models, with a 1-year prediction window, achieved the best performance with an Area Under the Curve (AUC) of 0.67. Accuracy declined for dementia subtypes: AD/ADRD (AUC = 0.65); ADRD (AUC = 0.49).</p><p><strong>Discussion: </strong>Commonly available structured clinical data (without labs, notes, or prescription information) demonstrate modest ability to predict AD/ADRD, corroborated by prior research.</p>","PeriodicalId":94282,"journal":{"name":"Research square","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11177999/pdf/","citationCount":"0","resultStr":"{\"title\":\"Predicting the onset of Alzheimer's disease and related dementia using Electronic Health Records: Findings from the Cache County Study on Memory in Aging (1995-2008).\",\"authors\":\"Karen C Schliep, Jeffrey Thornhill, JoAnn Tschanz, Julio C Facelli, Truls Østbye, Michelle K Sorweid, Ken R Smith, Michael Varner, Richard D Boyce, Christine J Cliatt Brown, Huong Meeks, Samir Abdelrahman\",\"doi\":\"10.21203/rs.3.rs-4414498/v1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>Clinical notes, biomarkers, and neuroimaging have been proven valuable in dementia prediction models. Whether commonly available structured clinical data can predict dementia is an emerging area of research. We aimed to predict Alzheimer's disease (AD) and Alzheimer's disease related dementias (ADRD) in a well-phenotyped, population-based cohort using a machine learning approach.</p><p><strong>Methods: </strong>Administrative healthcare data (k=163 diagnostic features), in addition to Census/vital record sociodemographic data (k = 6 features), were linked to the Cache County Study (CCS, 1995-2008).</p><p><strong>Results: </strong>Among successfully linked UPDB-CCS participants (n=4206), 522 (12.4%) had incident AD/ADRD as per the CCS \\\"gold standard\\\" assessments. Random Forest models, with a 1-year prediction window, achieved the best performance with an Area Under the Curve (AUC) of 0.67. Accuracy declined for dementia subtypes: AD/ADRD (AUC = 0.65); ADRD (AUC = 0.49).</p><p><strong>Discussion: </strong>Commonly available structured clinical data (without labs, notes, or prescription information) demonstrate modest ability to predict AD/ADRD, corroborated by prior research.</p>\",\"PeriodicalId\":94282,\"journal\":{\"name\":\"Research square\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-06-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11177999/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Research square\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.21203/rs.3.rs-4414498/v1\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Research square","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21203/rs.3.rs-4414498/v1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Predicting the onset of Alzheimer's disease and related dementia using Electronic Health Records: Findings from the Cache County Study on Memory in Aging (1995-2008).
Introduction: Clinical notes, biomarkers, and neuroimaging have been proven valuable in dementia prediction models. Whether commonly available structured clinical data can predict dementia is an emerging area of research. We aimed to predict Alzheimer's disease (AD) and Alzheimer's disease related dementias (ADRD) in a well-phenotyped, population-based cohort using a machine learning approach.
Methods: Administrative healthcare data (k=163 diagnostic features), in addition to Census/vital record sociodemographic data (k = 6 features), were linked to the Cache County Study (CCS, 1995-2008).
Results: Among successfully linked UPDB-CCS participants (n=4206), 522 (12.4%) had incident AD/ADRD as per the CCS "gold standard" assessments. Random Forest models, with a 1-year prediction window, achieved the best performance with an Area Under the Curve (AUC) of 0.67. Accuracy declined for dementia subtypes: AD/ADRD (AUC = 0.65); ADRD (AUC = 0.49).
Discussion: Commonly available structured clinical data (without labs, notes, or prescription information) demonstrate modest ability to predict AD/ADRD, corroborated by prior research.
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