El-Segaier Milad, Pesonen Erkki, Lukkarinen Sakari, Peters Kristoffer, Sörnmo Leif, Sepponen Raimo
Department of Paediatrics, Division of Paediatric Cardiology, Lund University Hospital, Lund, Sweden.
Acta Paediatr. 2007 Jul;96(7):1036-42. doi: 10.1111/j.1651-2227.2007.00318.x. Epub 2007 May 24.
To develop an objective diagnostic method that facilitates detection of noncyanotic congenital heart diseases.
Heart sounds and murmurs were recorded from 60 healthy children and 173 children with noncyanotic congenital heart disease. Time intervals were measured and spectrum of the systolic murmurs analyzed. Stepwise logistic regression analysis was used to distinguish physiological from pathological signals. The receiver operating characteristic (ROC) curve was plotted to show the classification performance of the model and the area under the curve (AUC) was calculated. The probability cut-off points for calculation of sensitivities and specificities were estimated.
The distinguishing variables were the interval from the end of the first heart sound (S(1)) and the beginning of the systolic murmur, respiratory variation of the splitting of the second heart sound, intensity of the systolic murmur, and standard deviation of the interval from the end of the S(1) to the maximum intensity of the murmur. The AUC was 0.95, indicating an excellent classification performance of the model. The sensitivity of 95% and specificity of 72% was achieved at a probability cut-off point of 0.45. Significant cardiac defects were correctly classified.
Interval measurements and spectral analysis can be used to confirm significant noncyanotic congenital heart diseases. Further development of the method is necessary to detect also insignificant heart defects.
开发一种有助于检测非青紫型先天性心脏病的客观诊断方法。
记录了60名健康儿童和173名非青紫型先天性心脏病儿童的心音和杂音。测量了时间间隔并分析了收缩期杂音的频谱。采用逐步逻辑回归分析来区分生理信号和病理信号。绘制了受试者工作特征（ROC）曲线以显示模型的分类性能，并计算了曲线下面积（AUC）。估计了用于计算敏感性和特异性的概率截断点。
区分变量为从第一心音（S(1)）结束到收缩期杂音开始的间隔、第二心音分裂的呼吸变化、收缩期杂音强度以及从S(1)结束到杂音最大强度的间隔标准差。AUC为0.95，表明模型具有出色的分类性能。在概率截断点为0.45时，敏感性达到95%，特异性达到72%。重大心脏缺陷被正确分类。
间隔测量和频谱分析可用于确诊重大非青紫型先天性心脏病。为了检测出不显著的心脏缺陷，该方法需要进一步改进。
