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.
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.
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DOI：
10.1371/journal.pone.0260885
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发表时间：
2021
期刊：
影响因子：
3.7
通讯作者：
Ling XB
中科院分区：
文献类型：
--
作者：
Duong SQ;Zheng L;Xia M;Jin B;Liu M;Li Z;Hao S;Alfreds ST;Sylvester KG;Widen E;Teuteberg JJ;McElhinney DB;Ling XB
新发心力衰竭（HF）与预后不良和高医疗利用相关。早期识别发生HF风险增加的患者，可以集中分配预防性护理资源。健康信息交换（HIE）数据跨越整个临床护理范围，但没有基于HIE的临床决策支持工具用于诊断意外HF。我们应用机器学习方法对缅因州HIE的一年意外HF风险进行建模。我们纳入了2015年至2018年3年期间年龄≥ 40岁且既往无HF ICD 9/10代码的受试者，HF事件定义为一年内分配了2个门诊或1个住院代码。一个树提升算法被用来模拟事件HF的概率在第二年从第一年收集的数据，然后在第三年进行验证。在验证队列中，521，347例患者中有5，668例（1.09%）发生了意外HF。在验证队列中，模型c-统计量为0.824，在临床预定的风险阈值下，在缅因州，通过模型识别的10%的患者发生意外HF，所有意外HF病例的29%被识别。利用被动收集的临床HIE数据的机器学习建模技术，我们开发并验证了一种事件-HF预测工具，该工具与需要主动收集临床数据的其他模型具有相同的性能。我们的算法可以集成到其他HIE中，以利用EMR资源为个人、系统和支付者提供风险分层工具，从而实现有针对性的资源分配，以减少个人和医疗保健系统的偶发HF疾病负担。
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. 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. 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.
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DOI：
10.1161/circheartfailure.110.938175
发表时间：
2010-11
期刊：
Circulation. Heart failure
影响因子：
--
作者：
Goyal A;Norton CR;Thomas TN;Davis RL;Butler J;Ashok V;Zhao L;Vaccarino V;Wilson PW
通讯作者：
Wilson PW
影响因子：
37.8
作者：
Lloyd-Jones, DM;Larson, MG;Levy, D
通讯作者：
Levy, D
DOI：
10.1016/0735-1097(93)90455-a
发表时间：
1993-10-01
影响因子：
24
作者：
HO, KKL;PINSKY, JL;LEVY, D
通讯作者：
LEVY, D
影响因子：
9.3
作者：
Nambi, Vijay;Liu, Xiaoxi;Ballantyne, Christie M.
通讯作者：
Ballantyne, Christie M.
影响因子：
--
作者：
Le NQK;Do DT;Chiu FY;Yapp EKY;Yeh HY;Chen CY
通讯作者：
Chen CY