Using Atrial Fibrillation Burden Trends and Machine Learning to Predict Near-Term Risk of Cardiovascular Hospitalization.

IF 9.1 1区 医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS Circulation. Arrhythmia and electrophysiology Pub Date : 2024-11-01 Epub Date: 2024-10-24 DOI:10.1161/CIRCEP.124.012991
James Peacock, Evan J Stanelle, Lawrence C Johnson, Elaine M Hylek, Rahul Kanwar, Dhanunjaya R Lakkireddy, Suneet Mittal, Rod S Passman, Andrea M Russo, Dana Soderlund, Mellanie True Hills, Jonathan P Piccini
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Abstract

Background: Atrial fibrillation is associated with an increased risk of cardiovascular hospitalization (CVH), which may be triggered by changes in daily burden. Machine learning of dynamic trends in atrial fibrillation burden, as measured by insertable cardiac monitors (ICMs), may be useful in predicting near-term CVH.

Methods: Using Optum's deidentified Clinformatics Data Mart Database (2007-2019), linked with the Medtronic CareLink ICM database, we identified patients with >1 days of ICM-detected atrial fibrillation. ICM-detected diagnostic parameters were transformed into simple moving averages over different periods for daily follow-up. A diagnostic trend was defined as the comparison of 2 simple moving averages of different periods for each diagnostic parameter. CVH was defined as any hospital, emergency department, or ambulatory surgical center encounter with a cardiovascular diagnosis-related group or diagnosis code. Machine learning was used to determine which diagnostic trends could best predict patient risk 5 days before CVH.

Results: A total of 2616 patients with ICMs met the inclusion criteria (71±11 years; 55% male). Among them, 1998 (76%) had a planned or unplanned CVH over 605 363 days. Machine learning revealed distinct groups: (A) sinus rhythm (reference), (B) below-average burden, (C) above-average burden, and (D) above-average burden with decreasing patient activity. The relative risk was increased in all groups versus the reference (B, 4.49 [95% CI, 3.74-5.40]; C, 8.41 [95% CI, 7.00-10.11]; D, 11.15 [95% CI, 9.10-13.65]), including a 21% increase in CVH detection over prespecified burden thresholds of duration (≥1 hour) and quantity (≥5%). The area under the receiver operating characteristic curve increased from 0.55 when using hourly burden amounts to 0.66 when using burden trends and decreasing patient activity (P<0.001), a 20% increase in predictive power.

Conclusions: Trends in atrial fibrillation were strongly associated with near-term CVH, especially above-average burden coupled with low patient activity. This approach could provide actionable information to guide treatment and reduce CVH.

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利用心房颤动负担趋势和机器学习预测心血管病住院的近期风险。
背景:心房颤动与心血管住院(CVH)风险的增加有关,而日常负担的变化可能会引发CVH。通过插入式心脏监护仪(ICM)测量的心房颤动负担动态趋势的机器学习可能有助于预测近期的 CVH:我们使用 Optum 的去标识临床信息学数据集市数据库(2007-2019 年),并与美敦力 CareLink ICM 数据库链接,确定了 ICM 检测出心房颤动时间大于 1 天的患者。将 ICM 检测到的诊断参数转化为每日随访不同时期的简单移动平均值。诊断趋势定义为每个诊断参数的两个不同时期简单移动平均值的比较。CVH定义为任何医院、急诊科或门诊手术中心遇到的心血管诊断相关组或诊断代码。机器学习用于确定哪些诊断趋势最能预测 CVH 前 5 天的患者风险:共有 2616 名 ICM 患者符合纳入标准(71±11 岁;55% 为男性)。其中,1998 人(76%)在 605 363 天内进行过计划内或计划外的 CVH。机器学习显示了不同的组别:(A) 窦性心律(参考),(B) 低于平均负担,(C) 高于平均负担,(D) 高于平均负担且患者活动减少。与参照组相比,所有组的相对风险都有所增加(B 组,4.49 [95% CI,3.74-5.40];C 组,8.41 [95% CI,7.00-10.11];D 组,11.15 [95% CI,9.10-13.65]),其中 CVH 检出率比预设的持续时间(≥1 小时)和数量(≥5%)负担阈值高 21%。接收器工作特征曲线下的面积从使用每小时负荷量时的 0.55 增加到使用负荷趋势和患者活动减少时的 0.66(PConclusions:心房颤动的趋势与近期 CVH 密切相关,尤其是高于平均水平的负担和低患者活动。这种方法可以提供可操作的信息来指导治疗并降低 CVH。
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来源期刊
CiteScore
13.70
自引率
4.80%
发文量
187
审稿时长
4-8 weeks
期刊介绍: Circulation: Arrhythmia and Electrophysiology is a journal dedicated to the study and application of clinical cardiac electrophysiology. It covers a wide range of topics including the diagnosis and treatment of cardiac arrhythmias, as well as research in this field. The journal accepts various types of studies, including observational research, clinical trials, epidemiological studies, and advancements in translational research.
期刊最新文献
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