■具有进行预诊断的能力,消费者可穿戴设备有可能影响后续诊断和医疗保健服务的水平。尽管如此,由于电子健康记录(EHR)中缺乏编码术语来捕获可穿戴使用,因此阻碍了对消费者可穿戴设备的上市后监控。
■我们试图开发一种基于监督的弱方法,以证明基于EHR的上市后监测对导致房颤(AF)预诊断的消费者可穿戴设备的可行性和有效性。
■我们应用了数据编程,其中标记启发式表示为基于代码的标记函数,检测房颤预诊断事件。然后使用Snorkel框架从标记函数的预测中得出标记器模型。标签器模型被应用于临床笔记,以概率标记它们,然后将标记的注释用作训练集,以微调称为Clinical-Longformer的分类器。所得到的分类器识别出具有AF预诊断的患者。进行了一项回顾性队列研究,其中将分类器识别的患者的基线特征和后续护理模式与未接受预诊断的患者进行比较.
■从标记函数得出的贴标机模型在训练集上显示出很高的准确性(0.92;F1分数=0.77)。在概率标记的注释上训练的分类器准确地识别出具有AF预诊断的患者(0.95;F1得分=0.83)。使用构建的系统进行的队列研究具有足够的统计能力来验证AppleHeart研究的关键发现,注册了更多的参与者,接受预诊断的患者往往年龄较大,男性,和白色与更高的CHA2DS2-VASc(充血性心力衰竭,高血压,年龄≥75岁,糖尿病,中风,血管疾病,年龄65-74岁,性别类别)得分(P<.001)。我们还发现,预先诊断的患者更可能使用抗凝剂(525/1037,50.63%vs5936/16,560,35.85%),并且最终诊断为AF(305/1037,29.41%vs262/16,560,1.58%)。在索引诊断时,预诊断的存在并没有根据临床特征区分患者,但确实与抗凝剂处方相关(阿哌沙班P=.004,利伐沙班P=.01)。
■我们的工作确立了基于EHR的监测系统的可行性和有效性,该系统适用于可进行AF预诊断的消费者可穿戴设备。需要进一步的工作来将这些发现推广到其他地点的患者人群。
UNASSIGNED: With the capability to render prediagnoses, consumer wearables have the potential to affect subsequent diagnoses and the level of care in the health care delivery setting. Despite this, postmarket surveillance of consumer wearables has been hindered by the lack of codified terms in electronic health records (EHRs) to capture wearable use.
UNASSIGNED: We sought to develop a weak supervision-based approach to demonstrate the feasibility and efficacy of EHR-based postmarket surveillance on consumer wearables that render atrial fibrillation (AF) prediagnoses.
UNASSIGNED: We applied data programming, where
labeling heuristics are expressed as code-based
labeling functions, to detect incidents of AF prediagnoses. A labeler model was then derived from the predictions of the labeling functions using the Snorkel framework. The labeler model was applied to clinical notes to probabilistically label them, and the labeled notes were then used as a training set to fine-tune a classifier called Clinical-Longformer. The resulting classifier identified patients with an AF prediagnosis. A retrospective cohort
study was conducted, where the baseline characteristics and subsequent care patterns of patients identified by the classifier were compared against those who did not receive a prediagnosis.
UNASSIGNED: The labeler model derived from the labeling functions showed high accuracy (0.92; F1-score=0.77) on the training set. The classifier trained on the probabilistically labeled notes accurately identified patients with an AF prediagnosis (0.95; F1-score=0.83). The cohort
study conducted using the constructed system carried enough statistical power to verify the key findings of the Apple Heart
Study, which enrolled a much larger number of participants, where patients who received a prediagnosis tended to be older, male, and White with higher CHA2DS2-VASc (congestive heart failure, hypertension, age ≥75 years, diabetes, stroke, vascular disease, age 65-74 years, sex category) scores (P<.001). We also made a novel discovery that patients with a prediagnosis were more likely to use anticoagulants (525/1037, 50.63% vs 5936/16,560, 35.85%) and have an eventual AF diagnosis (305/1037, 29.41% vs 262/16,560, 1.58%). At the index diagnosis, the existence of a prediagnosis did not distinguish patients based on clinical characteristics, but did correlate with anticoagulant prescription (P=.004 for apixaban and P=.01 for rivaroxaban).
UNASSIGNED: Our work establishes the feasibility and efficacy of an EHR-based surveillance system for consumer wearables that render AF prediagnoses. Further work is necessary to generalize these findings for patient populations at other sites.