BACKGROUND : Heart disease in children is frequently diagnosed late in life. This study aimed to establish and validate a scoring system to screen for heart disease in children, based on clinical examinations to accelerate referral. METHODS : This 2-phase cross-sectional study was conducted in pediatric cardiology outpatients and emergency room Ngoerah Hospital, Bali, Indonesia, from January to May 2024 and July to October 2024. Children aged 0–18 who were suspected of having heart disease and willing to be research participants were included in this study. Patients who had already been diagnosed with heart disease and acute lower respiratory tract infection were excluded from the study. The chi-square, multivariate logistic regression, and ROC analysis were performed to identify and validate variables eligible for the scoring system. Phase 1 for generating the score, continued to phase 2 to validate the developed score. RESULTS : Of 310 patients enrolled, 15 were excluded from the study. Congenital heart disease was the dominant condition in this study. The first model of the scoring system, named VEKARD score, consisted of a significant murmur, exercise intolerance, difficulty in breathing, and SpO2 <95%. The second model replaced SpO2 <95% with cyanosis, which can be used if pulse oximetry is not available. Using a sensitivity-first threshold suitable for screening purposes, a cutoff ≥1 for model 1 and cutoff ≥2 for model 2 yielded sensitivities of 85% and 81.8%, respectively. Both models underwent validation in a sample of 241 patients. The results demonstrated good discriminative performance, with AUC values of 0.859 for model 1 and 0.853 for model 2. CONCLUSIONS The VEKARD score can be used to enhance pediatric heart disease screening and accelerate referral to tertiary healthcare facilities, thereby preventing delays in treatment.
children ; heart disease ; heart murmur ; prediction ; score

To address the current issues of data imbalance and scarcity in photoplethysmography (PPG) data for type 2 diabetes mellitus (T2DM) prediction, this study proposes an improved conditional Wasserstein generative adversarial network with gradient penalty (CWGAN-GP). The algorithm integrated gated recurrent unit (GRU) networks and self-attention mechanisms to construct a generator, aiming to produce high-quality PPG signals. Various data augmentation methods, including the improved CWGAN-GP, were employed to expand the PPG dataset, and multiple classifiers were applied for T2DM prediction analysis. Experimental results showed that the model trained on data generated by the improved CWGAN-GP achieved the optimal prediction performance. The highest accuracy reached 0.895 0, and compared with other data enhancement methods, this approach exhibited significant advantages in terms of precision and F1-score. The generated data notably enhances the accuracy and generalization ability of T2DM prediction models, providing a more reliable technical basis for non-invasive early T2DM screening based on PPG signals.
Photoplethysmography/methods* ; Diabetes Mellitus, Type 2/diagnosis* ; Humans ; Algorithms ; Neural Networks, Computer ; Signal Processing, Computer-Assisted ; Prediction Algorithms

The shake test, the tap test and the turbidity test were evaluated to determine their accuracy in predicting lung function maturity, ie their ability to predict respiratory distress syndrome (RDS). The turbidity test was the most efficient with a sensitivity of 60%, a specificity of 97%, a positive predictive value of 82% and a negative predictive value of 92%. The shake test had a sensitivity of 40%, a specificity of 95%, a positive predictive value of 63% and a negative predictive value of 88%. The tap test at 2 minutes had a sensitivity of 57%, a specificity of 78%, a positive predictive value of 35% and a negative predictive value of 89%. It was fortuitous that the simplest and cheapest test was found to be the most efficient test of the three. We recommend that the turbidity test or at least one of these tests should be used to determine the maturity of lung function when non-urgent elective deliveries are contemplated, to help reduce the incidence of RDS in this group of patients.
predictive ; Respiratory distress ; Syndrome ; Restrained Drinking Scale ; prediction