A Single Center Observational Study of Spirometry Assessments in Children with Congenital Heart Disease after Surgery.
Chien-Heng LinTsai-Chun HsiaoChieh-Ho ChenJia-Wen ChenTzu-Yao ChuangJeng-Shang ChangSyuan-Yu HongPublished in: Medicina (Kaunas, Lithuania) (2023)
Background : Children with congenital heart disease (CHD) have impaired pulmonary function both before and after surgery; therefore, pulmonary function assessments are important and should be performed both before and after open-heart surgery. This study aimed to compare pulmonary function between variant pediatric CHD types after open-heart surgery via spirometry. Methods : In this retrospective study, the data for forced vital capacity (FVC), forced expiratory volume in the first second (FEV1), and the ratio between FEV1 and FVC (FEV1/FVC) were collected from patients with CHD who underwent conventional spirometry between 2015 and 2017. Results : A total of 86 patients (55 males and 31 females, with a mean age of 13.24 ± 3.32 years) were enrolled in our study. The diagnosis of CHD included 27.9% with atrial septal defects, 19.8% with ventricular septal defects, 26.7% with tetralogy of Fallot, 7.0% with transposition of the great arteries, and 46.5% with other diagnoses. Abnormal lung function was identified by spirometry assessments after surgery. Spirometry was abnormal in 54.70% of patients: obstructive type in 29.06% of patients, restrictive type in 19.76% of patients, and mixed type in 5.81% of patients. More abnormal findings were found in patients who received the Fontan procedure (80.00% vs. 35.80%, p = 0.048). Conclusions : Developing novel therapies to optimize pulmonary function will be critical for improving clinical outcomes.
Keyphrases
- end stage renal disease
- lung function
- newly diagnosed
- chronic kidney disease
- ejection fraction
- minimally invasive
- prognostic factors
- heart failure
- chronic obstructive pulmonary disease
- cystic fibrosis
- young adults
- coronary artery disease
- patient reported outcomes
- air pollution
- atrial fibrillation
- intensive care unit
- machine learning
- deep learning
- extracorporeal membrane oxygenation
- acute respiratory distress syndrome