AIT Austrian Institute of Technology GmbH, Center for Health and Bioresources, Medical Signal Analysis, Giefinggasse 4, A-1210 Vienna, Austria.
TU Wien, Institute of Analysis and Scientific Computing, Wiedner Hauptstr. 8, A-1040 Vienna, Austria.
Physiol Meas. 2023 Jul 10;44(7). doi: 10.1088/1361-6579/acdfb3.
Left ventricular hypertrophy (LVH) is one of the most severe risk factors in patients with end-stage kidney disease (ESKD) regarding all-cause and cardiovascular mortality. It contributes to the risk of sudden cardiac death which accounts for approximately 25% of deaths in ESKD patients. Electrocardiography (ECG) is the least expensive way to assess whether a patient has LVH, but manual annotation is cumbersome. Thus, an automated approach has been developed to derive ECG-based LVH parameters. The aim of the current study is to compare automatic to manual measurements and to investigate their predictive value for cardiovascular and all-cause mortality.From the 12-lead 24 h ECG measurements of 301 ESKD patients undergoing haemodialysis, three different LVH parameters were calculated. Peguero-Lo Presti voltage, Cornell voltage, and Sokolow-Lyon voltage were automatically derived and compared to the manual annotations. To determine the agreement between manual and automatic measurements and their predictive value, Bland-Altman plots were created and Cox regression analysis for cardiovascular and all-cause mortality was performed.The median values for the automatic assessment were: Peguero-Lo Presti voltage 1.76 mV (IQR 1.29-2.55), Cornell voltage 1.14 mV (IQR 0.721-1.66), and Sokolow-Lyon voltage 1.66 mV (IQR 1.08-2.23). The mean differences when compared to the manual measurements were -0.027 mV (0.21 SD), 0.027 mV (0.13 SD) and -0.025 mV (0.24 SD) for Peguero-Lo Presti, Cornell, and Sokolow-Lyon voltage, respectively. The categorial LVH detection based on pre-defined thresholds differed in only 13 cases for all indices between manual and automatic assessment. Proportional hazard ratios only differed slightly in categorial LVH detection between manually and automatically determined LVH parameters; no differences could be found for continuous parameters.This study provides evidence that automatic algorithms can be as reliable in LVH parameter assessment and risk prediction as manual measurements in ESKD patients undergoing haemodialysis.
左心室肥厚（LVH）是终末期肾病（ESKD）患者全因和心血管死亡率的最重要危险因素之一。它导致心脏性猝死的风险增加，约占 ESKD 患者死亡人数的 25%。心电图（ECG）是评估患者是否存在 LVH 的最便宜方法，但手动标注很繁琐。因此，已经开发了一种自动方法来推导基于心电图的 LVH 参数。本研究的目的是比较自动和手动测量，并研究它们对心血管和全因死亡率的预测价值。
从 301 名接受血液透析的 ESKD 患者的 12 导联 24 小时 ECG 测量中，计算了三种不同的 LVH 参数。自动推导并比较了 Peguero-Lo Presti 电压、Cornell 电压和 Sokolow-Lyon 电压与手动注释。为了确定手动和自动测量之间的一致性及其对心血管和全因死亡率的预测价值，创建了 Bland-Altman 图并进行了 Cox 回归分析。
Peguero-Lo Presti 电压 1.76 mV（IQR 1.29-2.55），Cornell 电压 1.14 mV（IQR 0.721-1.66），Sokolow-Lyon 电压 1.66 mV（IQR 1.08-2.23）。与手动测量相比，平均差异分别为-0.027 mV（0.21 SD）、0.027 mV（0.13 SD）和-0.025 mV（0.24 SD），用于 Peguero-Lo Presti、Cornell 和 Sokolow-Lyon 电压。手动和自动评估之间，基于预定义阈值的分类 LVH 检测仅在所有指标上存在 13 例差异。在手动和自动确定的 LVH 参数之间，分类 LVH 检测的比例风险比仅略有不同；对于连续参数，没有发现差异。
本研究提供的证据表明，自动算法在评估 LVH 参数和预测风险方面与手动测量一样可靠，适用于接受血液透析的 ESKD 患者。
