Association of social determinants with the severity of congenital heart disease.

BACKGROUND: Congenital heart diseases are the most prevalent congenital malformations and cause greater morbi-mortality in newborns and infants. The aim of this study was to analyze the social determinants in families with children with the severity of congenital heart disease. METHODS: Analytical cross-sectional study in 140 families of children with congenital heart disease to whom a structured survey was applied addressing topics related to family structure, health, economic conditions, exposure factors, and other social conditions relevant to the study, during 1 year. RESULTS: In all, 53.7% of the studied population belonged to low socioeconomic levels. No association was found between the severity of the heart disease and the presence of pathological antecedents in the parents. The families resided in urban areas. Also, 28.3% of the mothers had four or fewer prenatal controls during pregnancy. Only 22% of heart diseases were diagnosed during pregnancy. It was found that exposure to cigarette and wood smoke during pregnancy, in addition to low socioeconomic status, was associated with greater severity of heart disease (RACHS-1 and STS-Score), when evaluated by pathophysiological groups (cyanotic/non-cyanotic/single ventricle). CONCLUSIONS: Exposure to cigarette smoke, wood smoke during pregnancy, and low socioeconomic status turned out to be social determinants associated with the severity of heart disease analyzed by pathophysiological groups. IMPACT: The social component has not been well characterized as a cause of congenital heart disease, especially in countries like ours, where the existence of gaps and social inequities have a high impact. The findings of this study could have an impact on public health to the extent that policies are implemented to reduce exposure to cigarettes, especially during pregnancy. Knowledge of these changes and their measurement in this type of pathology could open the door to the creation of policies aimed at their prevention, focusing on the local risk factors found, which can impact the disease.

Use of assessment scales, turnover and job strain in nursing staff: A study in a Colombian hospital.

AIM: To evaluate the association between job strain and socio-demographic characteristics, social support, job insecurity, use of patient assessment scales, and turnover of nursing staff in a Colombian hospital. BACKGROUND: Nursing is an occupation with high probability of job strain. Use of patient assessment scales and turnover of nursing staff could increase exposure to psychosocial risk. METHOD: A cross-sectional study of 222 nurses was conducted. A survey and the Job Content Questionnaire were used to obtain data at the individual level and free lists and institutional records were used at the hospital unit level. The associations of interest were evaluated with a logistic regression model with robust variance estimator. RESULTS: Many nurses (50.9%) nurses reported job strain, which was positively associated with high use of patient assessment scales (OR = 2.73; 95% CI = 1.35-5.51) but negatively associated with social support (OR = 0.89; 95% CI = 0.80-0.98). Turnover was not statistically associated with job strain. CONCLUSION: Job strain among nurses was associated with a high use of patient assessment scales, but not with turnover of nursing staff. IMPLICATIONS FOR NURSING MANAGEMENT: The findings of this study suggest possible opportunities for managers to improve nursing processes, the work conditions of nursing staff, and the quality of institutions.

Aid decision algorithms to estimate the risk in congenital heart surgery.

BACKGROUND AND OBJECTIVE: In this paper, we have tested the suitability of using different artificial intelligence-based algorithms for decision support when classifying the risk of congenital heart surgery. In this sense, classification of those surgical risks provides enormous benefits as the a priori estimation of surgical outcomes depending on either the type of disease or the type of repair, and other elements that influence the final result. This preventive estimation may help to avoid future complications, or even death. METHODS: We have evaluated four machine learning algorithms to achieve our objective: multilayer perceptron, self-organizing map, radial basis function networks and decision trees. The architectures implemented have the aim of classifying among three types of surgical risk: low complexity, medium complexity and high complexity. RESULTS: Accuracy outcomes achieved range between 80% and 99%, being the multilayer perceptron method the one that offered a higher hit ratio. CONCLUSIONS: According to the results, it is feasible to develop a clinical decision support system using the evaluated algorithms. Such system would help cardiology specialists, paediatricians and surgeons to forecast the level of risk related to a congenital heart disease surgery.