Hakacova Nina, Steding Katarina, Engblom Henrik, Sjögren Jane, Maynard Charles, Pahlm Olle
Department of Clinical Physiology, Lund University Hospital, Sweden.
Ann Noninvasive Electrocardiol. 2010 Apr;15(2):124-9. doi: 10.1111/j.1542-474X.2010.00352.x.
The knowledge of the case-specific normal QRS duration in each individual is needed when determining the onset, severity and progression of the heart disease. However, large interindividual variability even of the normal QRS duration exists. The aims of the study were to develop a model for prediction of normal QRS complex duration and to test it on healthy individuals.
The study population of healthy adult volunteers was divided into a sample for development of a prediction model (n = 63) and a testing sample (n = 30). Magnetic resonance imaging data were used to assess anatomical characteristics of the left ventricle: the angle between papillary muscles (PM(A)), the length of the left ventricle (LV(L)) and left ventricular mass (LV(M)). Twelve-lead electrocardiogram (ECG) was used for measurement of the QRS duration. Multiple linear regression analysis was used to develop a prediction model to estimate the QRS duration. The accuracy of the prediction model was assessed by comparing predicted with measured QRS duration in the test set.
The angle between PM(A) and the length of the LV(L) were statistically significant predictors of QRS duration. Correlation between QRS duration and PM(A) and LV(L) was r = 0.57, P = 0.0001 and r = 0.45, P = 0.0002, respectively. The final model for prediction of the QRS was: QRS(Predicted)= 97 + (0.35 x LV(L)) - (0.45 x PM(A)). The predicted and real QRS duration differed with median 1 ms.
The model for prediction of QRS duration opens the ability to predict case-specific normal QRS duration. This knowledge can have clinical importance, when determining the normality on case-specific basis.
在确定心脏病的发作、严重程度和进展时，需要了解每个个体特定病例的正常QRS时限。然而，即使是正常QRS时限也存在很大的个体间差异。本研究的目的是建立一个预测正常QRS波群时限的模型，并在健康个体上进行测试。
健康成年志愿者的研究人群被分为一个用于建立预测模型的样本（n = 63）和一个测试样本（n = 30）。磁共振成像数据用于评估左心室的解剖特征：乳头肌之间的角度（PM(A)）、左心室长度（LV(L)）和左心室质量（LV(M)）。使用十二导联心电图（ECG）测量QRS时限。采用多元线性回归分析建立预测模型以估计QRS时限。通过比较测试集中预测的和测量的QRS时限来评估预测模型的准确性。
PM(A)与LV(L)的长度是QRS时限的统计学显著预测因子。QRS时限与PM(A)和LV(L)的相关性分别为r = 0.57，P = 0.0001和r = 0.45，P = 0.0002。预测QRS的最终模型为：QRS(预测值)= 97 + (0.35 × LV(L)) - (0.45 × PM(A))。预测的和实际的QRS时限中位数相差1 ms。
QRS时限预测模型开启了预测特定病例正常QRS时限的能力。在基于特定病例确定正常情况时，这一知识可能具有临床重要性。
