Background Chronic lung diseases (CLD) in children such as bronchiectasis and interstitial lung disease represent a major public health problem with limited therapeutic options. These patients develop pulmonary hypertension (and core-pulmonale in severe cases) because of the recurrent hypoxia and chronic inflammation; which results in right heart enlargement and ventricular hypertrophy. The early identification and convenient treatment of diastolic dysfunction can prevent further complications of the disease including diastolic heart failure and death. We aim to demonstrate the usefulness of tissue Doppler imaging echocardiography (TDI) in the detection of subtle myocardial affection in interstitial lung disease and bronchiectasis as subgroups of (CLD) in children. We studied echocardiographic parameters of 40 pediatric patients with chronic lung disease using conventional M mode and tissue Doppler imaging and compared them with 40 healthy controls of matching age and sex distribution. Results Myocardial performance index (MPI) showed that 28 subjects had abnormal right ventricular (RV) MPI (10 with severe affection ≥ 0.6) and 21 subjects had abnormal LV MPI (11 severe affections ≥ 0.6). Thirty percent (30%) of the cases had affected lateral E/E' and 47.5% had affected septal E/E' when compared to controls. Grades of diastolic dysfunction were: 0, 1, 2, 3 in 18, 15, 6, and 1 patients, respectively. MPI LV and MPI RV showed statistically higher values in patients compared to controls (P < 0.001). Conclusion This study proved that TDI can accurately detect subtle myocardial dysfunction in pediatric CLD patients.
Abstract-Diagnosis of congenital cardiac defects is challenging, with some being diagnosed during pregnancy while others are diagnosed after birth or later on during childhood. Prompt diagnosis allows early intervention and best prognosis. Contemporary diagnosis relies upon the history, clinical examination, pulse oximetery, chest X-ray, electrocardiogram (ECG), echocardiography (ECHO), computed tomography (CT) and cardiac catheterization. These diagnostic modalities reliable upon recording electrical activity or sound waves or upon radiation. Yet, congenital heart diseases are still liable to misdiagnosis because of level of operator expertise and other multiple factors. In an attempt to minimize effect of operator expertise this paper built a classification model for heart murmur recognition using Hidden Markov Model (HMM). This paper used Mel Frequency Cepestral coefficient (MFCC) as a feature and 13 MFCC coefficients. The machine learning model built by studying 1069 different heart sounds covering normal heart sounds, ventricular septal defect (VSD), mitral regurgitation (MR), aortic stenosis (AS), aortic regurgitation (AR), patent ductus arteriosus (PDA), pulmonary regurgitation (PR), and pulmonary stenosis (PS). MFCC feature used to extract feature matrix for each type of heart sounds after separation according to amplitude threshold. The frequency of normal heart sound (range= 1Hz to 139Hz) was specific without overlap with any of the studied defects (ranged= 156-556Hz). The frequency ranges for each of these defects was typical without overlap according to examined heart area (aortic, pulmonary, tricuspid and mitral area). The overall correct classification rate (CCR) using this model was 96% and sensitivity 98%. This model has great potential for prompt screening and specific defect detection. Effect of cardiac contractility, cardiomegaly or cardiac electrical activity on this novel detection system needs to be verified in future works.
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