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用于心力衰竭风险分层的简化杜克活动状态指数

Abbreviated Duke Activity Status Index for Risk Stratification in Heart Failure.

作者信息

Augusto Silvio Nunes, Wu Yuping, Chaikijurajai Thanat, Hazen Stanley L, Tang W H Wilson

机构信息

Cardiovascular and Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio.

Cardiovascular and Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio; Cleveland State University, Cleveland, Ohio.

出版信息

Am J Cardiol. 2025 Feb 15;237:54-59. doi: 10.1016/j.amjcard.2024.11.029. Epub 2024 Nov 26.

DOI:10.1016/j.amjcard.2024.11.029
PMID:39603580
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11761384/
Abstract

The Duke activity status index (DASI), a self-administered 12-item questionnaire has been used to estimate functional capacity and recently demonstrated prognostic information. We aimed to develop an abbreviated version for clinical applications. Leveraging the Cleveland Clinic GeneBank Study, we developed an abbreviated DASI questionnaire (aDASI) with the machine learning XGBoost algorithm, with the goal of maintaining the accuracy and reliability of the original DASI. We validated the prognostic value of aDASI in a subset of patients with heart failure from GeneBank and an independent data set from the GUIDE-IT (Guiding Evidence Based Therapy Using Biomarker Intensified Treatment in Heart Failure; ClinicalTrials.gov NCT01685840) trial. The results confirmed the congruence and accuracy between the original and the abbreviated scores while reducing the number of questions (R = 0.97, p <0.001). The original DASI score and the aDASI exhibited a strong correlation in the GeneBank and predictive value for all-cause mortality at different time points in the GUIDE-IT cohort. In conclusion, the abbreviated DASI tracks with original DASI assessment and performs comparably to the original DASI questionnaire in predicting all-cause mortality.

摘要

杜克活动状态指数(DASI)是一份包含12个条目的自填式问卷,已被用于评估功能能力,且最近显示出具有预后信息。我们旨在开发一个用于临床应用的简化版本。利用克利夫兰诊所基因库研究,我们使用机器学习XGBoost算法开发了一个简化的DASI问卷(aDASI),目标是保持原始DASI的准确性和可靠性。我们在基因库中的一部分心力衰竭患者以及来自GUIDE-IT(心力衰竭中使用生物标志物强化治疗指导循证治疗;ClinicalTrials.gov NCT01685840)试验的独立数据集中验证了aDASI的预后价值。结果证实了原始分数与简化分数之间的一致性和准确性,同时减少了问题数量(R = 0.97,p <0.001)。在基因库中,原始DASI分数与aDASI表现出很强的相关性,并且在GUIDE-IT队列的不同时间点对全因死亡率具有预测价值。总之,简化的DASI与原始DASI评估结果一致,在预测全因死亡率方面与原始DASI问卷表现相当。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c530/11761384/4597785e7620/nihms-2038196-f0006.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c530/11761384/66cf6e517a52/nihms-2038196-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c530/11761384/4597785e7620/nihms-2038196-f0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c530/11761384/d25deb3e9b78/nihms-2038196-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c530/11761384/b08a01abb2f1/nihms-2038196-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c530/11761384/d6f57fd225a9/nihms-2038196-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c530/11761384/72f478515707/nihms-2038196-f0004.jpg
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本文引用的文献

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Exercise Physiology and Cardiopulmonary Exercise Testing.运动生理学与心肺运动测试
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Using machine-learning strategies to solve psychometric problems.运用机器学习策略解决心理计量学问题。
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Cardiopulmonary Exercise Test Parameters in Athletic Population: A Review.运动员群体的心肺运动试验参数:综述
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Clinical Interpretation of Cardiopulmonary Exercise Testing: Current Pitfalls and Limitations.心肺运动试验的临床解读:当前的缺陷与局限
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