Machine learning models identify predictive features of patient mortality across dementia types
Abstract Background Dementia care is challenging due to the divergent trajectories in disease progression and outcomes. Predictive models are needed to flag patients at risk of near-term mortality and identify factors contributing to mortality risk across different dementia types. Methods Here, we d...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-02-01
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| Series: | Communications Medicine |
| Online Access: | https://doi.org/10.1038/s43856-024-00437-7 |
| _version_ | 1797273468921708544 |
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| author | Jimmy Zhang Luo Song Zachary Miller Kwun C. G. Chan Kuan-lin Huang |
| author_facet | Jimmy Zhang Luo Song Zachary Miller Kwun C. G. Chan Kuan-lin Huang |
| author_sort | Jimmy Zhang |
| collection | DOAJ |
| description | Abstract Background Dementia care is challenging due to the divergent trajectories in disease progression and outcomes. Predictive models are needed to flag patients at risk of near-term mortality and identify factors contributing to mortality risk across different dementia types. Methods Here, we developed machine-learning models predicting dementia patient mortality at four different survival thresholds using a dataset of 45,275 unique participants and 163,782 visit records from the U.S. National Alzheimer’s Coordinating Center (NACC). We built multi-factorial XGBoost models using a small set of mortality predictors and conducted stratified analyses with dementiatype-specific models. Results Our models achieved an area under the receiver operating characteristic curve (AUC-ROC) of over 0.82 utilizing nine parsimonious features for all 1-, 3-, 5-, and 10-year thresholds. The trained models mainly consisted of dementia-related predictors such as specific neuropsychological tests and were minimally affected by other age-related causes of death, e.g., stroke and cardiovascular conditions. Notably, stratified analyses revealed shared and distinct predictors of mortality across eight dementia types. Unsupervised clustering of mortality predictors grouped vascular dementia with depression and Lewy body dementia with frontotemporal lobar dementia. Conclusions This study demonstrates the feasibility of flagging dementia patients at risk of mortality for personalized clinical management. Parsimonious machine-learning models can be used to predict dementia patient mortality with a limited set of clinical features, and dementiatype-specific models can be applied to heterogeneous dementia patient populations. |
| first_indexed | 2024-03-07T14:44:45Z |
| format | Article |
| id | doaj.art-74174c24a76c4e8ba25cfad62f3a50f5 |
| institution | Directory Open Access Journal |
| issn | 2730-664X |
| language | English |
| last_indexed | 2024-03-07T14:44:45Z |
| publishDate | 2024-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Medicine |
| spelling | doaj.art-74174c24a76c4e8ba25cfad62f3a50f52024-03-05T20:05:33ZengNature PortfolioCommunications Medicine2730-664X2024-02-014111310.1038/s43856-024-00437-7Machine learning models identify predictive features of patient mortality across dementia typesJimmy Zhang0Luo Song1Zachary Miller2Kwun C. G. Chan3Kuan-lin Huang4Department of Genetics and Genomic Sciences, Center for Transformative Disease Modeling, Tisch Cancer Institute, Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount SinaiSchool of Medicine, The University of QueenslandNational Alzheimer’s Coordinating Center, University of WashingtonNational Alzheimer’s Coordinating Center, University of WashingtonDepartment of Genetics and Genomic Sciences, Center for Transformative Disease Modeling, Tisch Cancer Institute, Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount SinaiAbstract Background Dementia care is challenging due to the divergent trajectories in disease progression and outcomes. Predictive models are needed to flag patients at risk of near-term mortality and identify factors contributing to mortality risk across different dementia types. Methods Here, we developed machine-learning models predicting dementia patient mortality at four different survival thresholds using a dataset of 45,275 unique participants and 163,782 visit records from the U.S. National Alzheimer’s Coordinating Center (NACC). We built multi-factorial XGBoost models using a small set of mortality predictors and conducted stratified analyses with dementiatype-specific models. Results Our models achieved an area under the receiver operating characteristic curve (AUC-ROC) of over 0.82 utilizing nine parsimonious features for all 1-, 3-, 5-, and 10-year thresholds. The trained models mainly consisted of dementia-related predictors such as specific neuropsychological tests and were minimally affected by other age-related causes of death, e.g., stroke and cardiovascular conditions. Notably, stratified analyses revealed shared and distinct predictors of mortality across eight dementia types. Unsupervised clustering of mortality predictors grouped vascular dementia with depression and Lewy body dementia with frontotemporal lobar dementia. Conclusions This study demonstrates the feasibility of flagging dementia patients at risk of mortality for personalized clinical management. Parsimonious machine-learning models can be used to predict dementia patient mortality with a limited set of clinical features, and dementiatype-specific models can be applied to heterogeneous dementia patient populations.https://doi.org/10.1038/s43856-024-00437-7 |
| spellingShingle | Jimmy Zhang Luo Song Zachary Miller Kwun C. G. Chan Kuan-lin Huang Machine learning models identify predictive features of patient mortality across dementia types Communications Medicine |
| title | Machine learning models identify predictive features of patient mortality across dementia types |
| title_full | Machine learning models identify predictive features of patient mortality across dementia types |
| title_fullStr | Machine learning models identify predictive features of patient mortality across dementia types |
| title_full_unstemmed | Machine learning models identify predictive features of patient mortality across dementia types |
| title_short | Machine learning models identify predictive features of patient mortality across dementia types |
| title_sort | machine learning models identify predictive features of patient mortality across dementia types |
| url | https://doi.org/10.1038/s43856-024-00437-7 |
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