背景:心力衰竭(HF)对发病率有很大贡献,死亡率,和全世界的医疗保健费用。密切跟踪医院再入院率,并确定联邦报销美元。当前的模态或技术不允许在动态中精确测量相关的HF参数,农村,或服务不足的设置。这限制了远程医疗在非卧床患者中诊断或监测HF的使用。
目的:本研究描述了一种使用标准手机录音的新型HF诊断技术。
方法:这项声学麦克风录音的前瞻性研究纳入了来自美国2个不同地区2个不同临床地点的患者的便利样本。在患者直立的情况下在主动脉(第二肋间)部位获得记录。该团队使用录音来创建基于物理(而不是神经网络)模型的预测算法。分析将手机声学数据与超声心动图评估的射血分数(EF)和每搏输出量(SV)相匹配。使用基于物理的方法来确定特征,完全消除了对神经网络和过拟合策略的需求,可能在数据效率方面提供优势,模型稳定性,监管可见性,和身体上的洞察力。
结果:记录来自113名参与者。由于背景噪音或任何其他原因,没有记录被排除。参与者具有不同的种族背景和体表区域。113例患者的EF和65例患者的SV均可获得可靠的超声心动图数据。EF队列的平均年龄为66.3(SD13.3)岁,女性患者占该组的38.3%(43/113)。使用≤40%与>40%的EF截止值,该模型(使用4个特征)的受试者工作曲线下面积(AUROC)为0.955,灵敏度为0.952,特异性为0.958,准确度为0.956.SV队列的平均年龄为65.5(SD12.7)岁,女性患者占该组的34%(38/65)。使用<50mL与>50mL的临床相关SV截止值,该模型(使用3个特征)的AUROC为0.922,敏感性为1.000,特异性为0.844,准确性为0.923.观察到与SV相关的声学频率高于与EF相关的声学频率,因此,不太可能穿过组织而不变形。
结论:这项工作描述了使用未改变的蜂窝麦克风获得的移动电话听诊录音的使用。该分析以令人印象深刻的准确性再现了EF和SV的估计。这项技术将进一步发展成为一个移动应用程序,可以将HF的筛查和监测带到几个临床环境中,比如家庭或远程医疗,农村,远程,以及全球服务不足的地区。这将使用他们已经拥有的设备以及在不存在其他诊断和监测选项的情况下,为HF患者带来高质量的诊断方法。
BACKGROUND: Heart failure (HF) contributes greatly to morbidity, mortality, and health care costs worldwide. Hospital readmission rates are tracked closely and determine federal reimbursement dollars. No current modality or technology allows for accurate measurement of relevant HF parameters in ambulatory, rural, or underserved settings. This limits the use of telehealth to diagnose or monitor HF in ambulatory patients.
OBJECTIVE: This study describes a novel HF diagnostic technology using audio recordings from a standard mobile phone.
METHODS: This prospective study of acoustic microphone recordings enrolled convenience samples of patients from 2 different clinical sites in 2 separate areas of the United States. Recordings were obtained at the aortic (second intercostal) site with the patient sitting upright. The team used recordings to create predictive algorithms using physics-based (not neural networks) models. The analysis matched mobile phone acoustic data to ejection fraction (EF) and stroke volume (SV) as evaluated by echocardiograms. Using the physics-based approach to determine features eliminates the need for neural networks and overfitting strategies entirely, potentially offering advantages in data efficiency, model stability, regulatory visibility, and physical insightfulness.
RESULTS: Recordings were obtained from 113 participants. No recordings were excluded due to background noise or for any other reason. Participants had diverse racial backgrounds and body surface areas. Reliable echocardiogram data were available for EF from 113 patients and for SV from 65 patients. The mean age of the EF cohort was 66.3 (SD 13.3) years, with female patients comprising 38.3% (43/113) of the group. Using an EF cutoff of ≤40% versus >40%, the model (using 4 features) had an area under the receiver operating curve (AUROC) of 0.955, sensitivity of 0.952, specificity of 0.958, and accuracy of 0.956. The mean age of the SV cohort was 65.5 (SD 12.7) years, with female patients comprising 34% (38/65) of the group. Using a clinically relevant SV cutoff of <50 mL versus >50 mL, the model (using 3 features) had an AUROC of 0.922, sensitivity of 1.000, specificity of 0.844, and accuracy of 0.923. Acoustics frequencies associated with SV were observed to be higher than those associated with EF and, therefore, were less likely to pass through the tissue without distortion.
CONCLUSIONS: This work describes the use of mobile phone auscultation recordings obtained with unaltered cellular microphones. The analysis reproduced the estimates of EF and SV with impressive accuracy. This technology will be further developed into a mobile app that could bring screening and monitoring of HF to several clinical settings, such as home or telehealth, rural, remote, and underserved areas across the globe. This would bring high-quality diagnostic methods to patients with HF using equipment they already own and in situations where no other diagnostic and monitoring options exist.