University of Adelaide, Roseworthy, South Australia, Australia.
Racing Victoria, Flemington, Victoria, Australia.
Aust Vet J. 2022 Mar;100(3):114-120. doi: 10.1111/avj.13137. Epub 2021 Dec 2.
AliveCor is a smartphone electrocardiography device (iECG) providing automated heart rate (HR) and rhythm determination. Atrial fibrillation (AF) in horses often is paroxysmal and rapid ECG acquisition is needed for diagnostic confirmation. iECGs were collected post-race from 15 horses with AF and 64 horses in sinus rhythm (SR). Results of manual assessment were compared to 3 commercial algorithms for HR and rhythm. Agreement between manually derived HR (HR ) and HR derived by the AliveECG Vet (HR ) and Kardia version-1 (K HR) and Kardia advanced (K HR) algorithms was quantified by the Bland-Altman limits of agreement test. Agreement between manual rhythm classification and K and K algorithms for AF and SR was calculated by the Kappa statistical coefficient. The agreement (bias, 95% limits), between HR and HR was 7.1 BPM (-29 to 43) in AF and -4.2 BPM (-38 to 30) in SR, between HR and K HR, was -0.3 BPM (-31 to 30) in AF and 0.2 BPM (-3 to 4) in SR, and between HR and K HR was 7.0 BPM (-29 to 43) in AF and 0.2 BPM (-3.9 to 4.2) in SR. Agreement between manual rhythm classification and K was 0.36 (0.13-0.59), and K was 0.84 (0.68-0.99). Sensitivity and specificity for identification of AF and SR of the K algorithm were 60, 100% and 83, 100%, respectively, and of K was 87, 100% and 93, 100% respectively. The Kardia algorithms improved precision for HR determination in SR but not AF. The advanced algorithm accurately distinguished between AF and SR. The iECG is suitable for recording episodes of AF following exercise.
AliveCor 是一款智能手机心电图设备（iECG），可自动进行心率（HR）和节律检测。马的心房颤动（AF）常呈阵发性，需要快速获取心电图以确诊。在比赛后，对 15 匹患有 AF 的马和 64 匹窦性心律（SR）的马进行了 iECG 采集。手动评估结果与 3 种商用 HR 和节律算法进行了比较。通过 Bland-Altman 协议界限检验，定量比较了手动 HR（HR ）与 AliveECG Vet（HR ）和 Kardia version-1（K HR）和 Kardia advanced（K HR）算法得出的 HR 的一致性。通过 Kappa 统计系数计算了手动节律分类与 K 和 K 算法在 AF 和 SR 中的一致性。HR 与 HR 之间的一致性（偏差，95%界限）在 AF 中为 7.1 BPM（-29 至 43），在 SR 中为-4.2 BPM（-38 至 30），HR 与 K HR 之间的一致性为-0.3 BPM（-31 至 30），在 SR 中为 0.2 BPM（-3 至 4），HR 与 K HR 之间的一致性在 AF 中为 7.0 BPM（-29 至 43），在 SR 中为 0.2 BPM（-3.9 至 4.2）。手动节律分类与 K 的一致性为 0.36（0.13-0.59），K 的一致性为 0.84（0.68-0.99）。K 算法对 AF 和 SR 的识别灵敏度和特异性分别为 60%、100%和 83%、100%，K 算法的灵敏度和特异性分别为 87%、100%和 93%、100%。Kardia 算法提高了 SR 中 HR 测定的准确性，但对 AF 无影响。高级算法能准确区分 AF 和 SR。iECG 适用于记录运动后 AF 发作的情况。
