Parameter selection for and implementation of a web-based decision-support tool to predict extubation outcome in premature infants

BACKGROUND: Approximately 30% of intubated preterm infants with respiratory distress syndrome (RDS) will fail attempted extubation, requiring reintubation and mechanical ventilation. Although ventilator technology and monitoring of premature infants have improved over time, optimal extubation remains challenging. Furthermore, extubation decisions for premature infants require complex informational processing, techniques implicitly learned through clinical practice. Computer-aided decision-support tools would benefit inexperienced clinicians, especially during peak neonatal intensive care unit (NICU) census. METHODS: A five-step procedure was developed to identify predictive variables. Clinical expert (CE) thought processes comprised one model. Variables from that model were used to develop two mathematical models for the decision-support tool: an artificial neural network (ANN) and a multivariate logistic regression model (MLR). The ranking of the variables in the three models was compared using the Wilcoxon Signed Rank Test. The best performing model was used in a web-based decision-support tool with a user interface implemented in Hypertext Markup Language (HTML) and the mathematical model employing the ANN. RESULTS: CEs identified 51 potentially predictive variables for extubation decisions for an infant on mechanical ventilation. Comparisons of the three models showed a significant difference between the ANN and the CE (p = 0.0006). Of the original 51 potentially predictive variables, the 13 most predictive variables were used to develop an ANN as a web-based decision-tool. The ANN processes user-provided data and returns the prediction 0–1 score and a novelty index. The user then selects the most appropriate threshold for categorizing the prediction as a success or failure. Furthermore, the novelty index, indicating the similarity of the test case to the training case, allows the user to assess the confidence level of the prediction with regard to how much the new data differ from the data originally used for the development of the prediction tool. CONCLUSION: State-of-the-art, machine-learning methods can be employed for the development of sophisticated tools to aid clinicians' decisions. We identified numerous variables considered relevant for extubation decisions for mechanically ventilated premature infants with RDS. We then developed a web-based decision-support tool for clinicians which can be made widely available and potentially improve patient care world wide

Pulmonary Atelectasis In Newborn Infants: Etiology And Radiological Aspects [atelectasia Pulmonar Em Recém-nascidos: Etiologia E Aspectos Radiológicos]

Aims: To describe the main risk factors and the radiological findings of pulmonary atelectasis in newborns. Source of data: A literature review was conducted for the period of 2010/2011, through books and articles published over the past 30 years, obtained from the databases PubMed, SciELO and BIREME, using the descriptors: pulmonary atelectasis, diagnosis, infant, neonate, newborn. Summary of findings: Radiological signs of pulmonary atelectasis are related to volume loss, with fissural, hilar and mediastinal displacement, and diaphragmatic elevation toward the affected lung lobe or segment. The ribs of the affected hemithorax may have diminished spaces. Compensatory hyperinflation of the lung is present and the collapsed portions show increased radiopacity, with a triangular shape in at least one of radiographic projections. In newborn infants, the main etiological factors are mechanical ventilation with positive pressure, pneumonia, meconium aspiration syndrome, gastroesophageal reflux, bronchopulmonary dysplasia, congenital diaphragmatic hernia, pleural effusion and pneumothorax. Conclusions: Presence of pulmonary atelectasis is an important complication in infants admitted to neonatal intensive care unit. 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