Identification of patients at risk of new onset heart failure: Utilizing a large statewide health information exchange to train and validate a risk prediction model.
<h4>Background</h4>New-onset heart failure (HF) is associated with poor prognosis and high healthcare utilization. Early identification of patients at increased risk incident-HF may allow for focused allocation of preventative care resources. Health information exchange (HIE) data span t...
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Public Library of Science (PLoS)
2021-01-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0260885&type=printable |
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| author | Son Q Duong Le Zheng Minjie Xia Bo Jin Modi Liu Zhen Li Shiying Hao Shaun T Alfreds Karl G Sylvester Eric Widen Jeffery J Teuteberg Doff B McElhinney Xuefeng B Ling |
| author_facet | Son Q Duong Le Zheng Minjie Xia Bo Jin Modi Liu Zhen Li Shiying Hao Shaun T Alfreds Karl G Sylvester Eric Widen Jeffery J Teuteberg Doff B McElhinney Xuefeng B Ling |
| author_sort | Son Q Duong |
| collection | DOAJ |
| description | <h4>Background</h4>New-onset heart failure (HF) is associated with poor prognosis and high healthcare utilization. Early identification of patients at increased risk incident-HF may allow for focused allocation of preventative care resources. Health information exchange (HIE) data span the entire spectrum of clinical care, but there are no HIE-based clinical decision support tools for diagnosis of incident-HF. We applied machine-learning methods to model the one-year risk of incident-HF from the Maine statewide-HIE.<h4>Methods and results</h4>We included subjects aged ≥ 40 years without prior HF ICD9/10 codes during a three-year period from 2015 to 2018, and incident-HF defined as assignment of two outpatient or one inpatient code in a year. A tree-boosting algorithm was used to model the probability of incident-HF in year two from data collected in year one, and then validated in year three. 5,668 of 521,347 patients (1.09%) developed incident-HF in the validation cohort. In the validation cohort, the model c-statistic was 0.824 and at a clinically predetermined risk threshold, 10% of patients identified by the model developed incident-HF and 29% of all incident-HF cases in the state of Maine were identified.<h4>Conclusions</h4>Utilizing machine learning modeling techniques on passively collected clinical HIE data, we developed and validated an incident-HF prediction tool that performs on par with other models that require proactively collected clinical data. Our algorithm could be integrated into other HIEs to leverage the EMR resources to provide individuals, systems, and payors with a risk stratification tool to allow for targeted resource allocation to reduce incident-HF disease burden on individuals and health care systems. |
| format | Article |
| id | doaj-art-ef645ee2cc074b578313005a4e2b0f43 |
| institution | DOAJ |
| issn | 1932-6203 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-ef645ee2cc074b578313005a4e2b0f432025-08-20T03:16:25ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-011612e026088510.1371/journal.pone.0260885Identification of patients at risk of new onset heart failure: Utilizing a large statewide health information exchange to train and validate a risk prediction model.Son Q DuongLe ZhengMinjie XiaBo JinModi LiuZhen LiShiying HaoShaun T AlfredsKarl G SylvesterEric WidenJeffery J TeutebergDoff B McElhinneyXuefeng B Ling<h4>Background</h4>New-onset heart failure (HF) is associated with poor prognosis and high healthcare utilization. Early identification of patients at increased risk incident-HF may allow for focused allocation of preventative care resources. Health information exchange (HIE) data span the entire spectrum of clinical care, but there are no HIE-based clinical decision support tools for diagnosis of incident-HF. We applied machine-learning methods to model the one-year risk of incident-HF from the Maine statewide-HIE.<h4>Methods and results</h4>We included subjects aged ≥ 40 years without prior HF ICD9/10 codes during a three-year period from 2015 to 2018, and incident-HF defined as assignment of two outpatient or one inpatient code in a year. A tree-boosting algorithm was used to model the probability of incident-HF in year two from data collected in year one, and then validated in year three. 5,668 of 521,347 patients (1.09%) developed incident-HF in the validation cohort. In the validation cohort, the model c-statistic was 0.824 and at a clinically predetermined risk threshold, 10% of patients identified by the model developed incident-HF and 29% of all incident-HF cases in the state of Maine were identified.<h4>Conclusions</h4>Utilizing machine learning modeling techniques on passively collected clinical HIE data, we developed and validated an incident-HF prediction tool that performs on par with other models that require proactively collected clinical data. Our algorithm could be integrated into other HIEs to leverage the EMR resources to provide individuals, systems, and payors with a risk stratification tool to allow for targeted resource allocation to reduce incident-HF disease burden on individuals and health care systems.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0260885&type=printable |
| spellingShingle | Son Q Duong Le Zheng Minjie Xia Bo Jin Modi Liu Zhen Li Shiying Hao Shaun T Alfreds Karl G Sylvester Eric Widen Jeffery J Teuteberg Doff B McElhinney Xuefeng B Ling Identification of patients at risk of new onset heart failure: Utilizing a large statewide health information exchange to train and validate a risk prediction model. PLoS ONE |
| title | Identification of patients at risk of new onset heart failure: Utilizing a large statewide health information exchange to train and validate a risk prediction model. |
| title_full | Identification of patients at risk of new onset heart failure: Utilizing a large statewide health information exchange to train and validate a risk prediction model. |
| title_fullStr | Identification of patients at risk of new onset heart failure: Utilizing a large statewide health information exchange to train and validate a risk prediction model. |
| title_full_unstemmed | Identification of patients at risk of new onset heart failure: Utilizing a large statewide health information exchange to train and validate a risk prediction model. |
| title_short | Identification of patients at risk of new onset heart failure: Utilizing a large statewide health information exchange to train and validate a risk prediction model. |
| title_sort | identification of patients at risk of new onset heart failure utilizing a large statewide health information exchange to train and validate a risk prediction model |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0260885&type=printable |
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