Stanford Center for Clinical Research, Stanford University School of Medicine, Palo Alto, California, USA.
Heart Center, Turku University Hospital, Turku, Finland; University of Turku, Turku, Finland.
JACC Heart Fail. 2024 Jun;12(6):1030-1040. doi: 10.1016/j.jchf.2024.01.022. Epub 2024 Apr 3.
Heart failure (HF) is the leading cause of hospitalization in individuals over 65 years of age. Identifying noninvasive methods to detect HF may address the epidemic of HF. Seismocardiography which measures cardiac vibrations transmitted to the chest wall has recently emerged as a promising technology to detect HF.
In this multicenter study, the authors examined whether seismocardiography using commercially available smartphones can differentiate control subjects from patients with stage C HF.
Both inpatients and outpatients with HF were enrolled from Finland and the United States. Inpatients with HF were assessed within 2 days of admission, and outpatients were assessed in the ambulatory setting. In a prespecified pooled data analysis, algorithms were derived using logistic regression and then validated using a bootstrap aggregation method.
A total of 217 participants with HF (174 inpatients and 172 outpatients) and 786 control subjects from cardiovascular clinics were enrolled. The mean age of participants with acute HF was 64 ± 13 years, 64.9% were male, left ventricular ejection fraction was 39% ± 15%, and median N-terminal pro-B-type natriuretic peptide was 5,778 ng/L (Q1-Q3: 1,933-6,703). The majority (74%) of participants with HF had reduced EF, and 38% had atrial fibrillation. Across both HF cohorts, the algorithms had an area under the receiver operating characteristic curve of 0.95 with a sensitivity of 85%, specificity of 90%, and accuracy of 89% for the detection of HF, with a decision threshold of 0.5. The positive and negative likelihood ratios were 8.50 and 0.17, respectively. The accuracy of the algorithms was not significantly different in subgroups based on age, sex, body mass index, and atrial fibrillation.
Smartphone-based assessment of cardiac function using seismocardiography is feasible and differentiates patients with HF from control subjects with high diagnostic accuracy. (Recognition of Heart Failure With Micro Electro-mechanical Sensors FI; NCT04444583; Recognition of Heart Failure With Micro Electro-mechanical Sensors [NCT04378179]; Detection of Coronary Artery Disease With Micro Electro-mechanical Sensors; NCT04290091).
心力衰竭（HF）是 65 岁以上人群住院的主要原因。寻找非侵入性方法来检测 HF 可能有助于应对 HF 的流行。最近，一种测量心脏振动并将其传输到胸壁的新兴技术——心震描记术，已被证明是一种有前途的检测 HF 的方法。
在这项多中心研究中，作者研究了使用商业上可用的智能手机进行心震描记术是否可以区分 C 期 HF 患者与对照组。
该研究从芬兰和美国招募了 HF 住院患者和门诊患者。HF 住院患者在入院后 2 天内进行评估，门诊患者在门诊环境中进行评估。在预先指定的汇总数据分析中，使用逻辑回归推导算法，然后使用自助聚合方法进行验证。
共纳入 217 名 HF 患者（174 名住院患者和 172 名门诊患者）和 786 名心血管诊所的对照组。急性 HF 患者的平均年龄为 64±13 岁，64.9%为男性，左心室射血分数为 39%±15%，中位 N 末端 B 型利钠肽前体为 5778ng/L（Q1-Q3：1933-6703）。大多数（74%）HF 患者射血分数降低，38%的患者患有心房颤动。在 HF 两个队列中，该算法的接受者操作特征曲线下面积为 0.95，敏感性为 85%，特异性为 90%，准确性为 89%，检测 HF 的决策阈值为 0.5。阳性和阴性似然比分别为 8.50 和 0.17。基于年龄、性别、体重指数和心房颤动的亚组分析显示，该算法的准确性无显著差异。
使用心震描记术通过智能手机评估心脏功能是可行的，并且可以以较高的诊断准确性区分 HF 患者和对照组。（使用微机电传感器识别心力衰竭[FI]；NCT04444583；使用微机电传感器识别心力衰竭[NCT04378179]；使用微机电传感器检测冠状动脉疾病；NCT04290091）。
