Clinical characteristics and outcomes of patients who underwent neonatal cardiac surgery: ten years of experience in a tertiary surgery center.

OBJECTIVE: To evaluate the outcomes after neonatal cardiac surgery at our institute, and identify factors associated with operative mortality. METHODS: We examined 224 neonates who underwent cardiac surgery at a single institution from 2013 to 2022. Relevant data, such as demographic information, operative details, and postoperative records, were gathered from medical and surgical records. Our primary focus was on the operative mortality. RESULTS: Median age and weight at surgery were 12 (7-20) days and 3.4 (3.0-3.8) kg, respectively. Overall mortality was 14.3% (32/224). Mortality rates showed improvement over time (2013-2017 vs. 2018-2022), with rates decreasing from 21.9% to 10.6% (p = 0.023). ECMO use, extubation failure, lactate > 4.8 mmol/l and VIS > 15.5 on 24 h after operation were significantly associated with operative mortality, according to multivariate logistic regression analysis. Patients admitted to the cardiac intensive care unit (CICU) before surgery and those with prenatal diagnosis showed lower operative mortality. Median follow-up time of 192 hospital survivors was 28.0 (11.0-62.3) months. 10 patients experienced late deaths, and 7 patients required reinterventions after neonatal cardiac surgery. Risk factors for composite end-point of death and reintervention on multivariable analysis were: surgical period (HR = 0.230, 95% CI 0.081-0.654; p = 0.006), prolonged ventilation (HR = 4.792, 95% CI 1.296-16.177; p = 0.018) and STAT categories 3-5 (HR = 5.936, 95% CI 1.672-21.069; p = 0.006). CONCLUSIONS: Our institution has observed improved surgical outcomes in neonatal cardiac surgery over the past five years with low mortality, but late death and reintervention remain necessary in some patients. The location and prenatal diagnosis prior to surgery may affect the outcomes of neonates undergoing congenital heart disease operations.

Research on Application of Data Mining Algorithm in Cardiac Medical Diagnosis System.

Heart disease is a very common high-incidence disease. Due to the wide variety of pathology of heart disease, how to improve the medical diagnosis of heart disease and carry out earlier intervention and treatment is a problem that needs to be solved urgently. The paper adds the decision tree algorithm and its comparison and proposes an optimized classification algorithm Co-SVM. Based on the establishment of a heart disease diagnosis classifier based on data mining algorithms, it is aimed at exploring which of these four algorithms is more suitable for heart disease diagnosis problems and optimizing them. A brief description of the cause, influencing factors, and acquired data of heart disease can be seen from the accuracy and scientificity of the data, which further enhances the authenticity and reliability of the clinical diagnosis model of heart disease. At the same time, the ultrasound diagnosis technology of heart disease is introduced, and the important role of ultrasound diagnosis technology in the medical diagnosis of heart disease is discussed. This thesis uses the heart disease clinical data set to establish a heart disease diagnosis classifier based on the decision tree algorithm, neural network algorithm, support vector machine algorithm, and Co-SVM algorithm. Through experimental comparison and analysis, the optimal classification is selected according to the obtained results. The algorithm is Co-SVM algorithm. The experimental results show that the proposed Co-SVM algorithm has a higher accuracy rate than the other three classic algorithms, and the effectiveness of the Co-SVM algorithm is verified by the evaluation results of multiple algorithms. By applying the Co-SVM algorithm in the medical diagnosis system, it is helpful to assist doctors in making more accurate and precise diagnosis of the condition.