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

Respiratory Management of Newborns

In this book, you'll learn multiple new aspects of respiratory management of the newborn. For example, ventilator management of infants with unusually severe bronchopulmonary dysplasia and infants with omphalocele is discussed, as well as positioning of endotracheal tube in extremely low birth weight infants, noninvasive respiratory support, utilization of a protocol-driven respiratory management, and more. This book includes a chapter on noninvasive respiratory function monitoring during chest compression, analyzing the efficacy and quality of chest compression and exhaled carbon dioxide. It also provides an overview on new trends in the management of fetal and transitioning lungs in infants delivered prematurely. Lastly, the book includes a chapter on neonatal encephalopathy treated with hypothermia along with mechanical ventilation. The interaction of cooling with respiration and the strategies to optimize oxygenation and ventilation in asphyxiated newborns are discussed

Mechanical Ventilation

Mechanical ventilation, ventilator management, and weaning from mechanical ventilation vary based on location within the hospital, type of lung injury, and medical condition of the patient. Understanding the types of lung injury and various methods of achieving ventilation expand the armamentarium of the practitioner and allow for the best management decisions. This book begins with the use of a high-flow nasal cannula (HFNC) and a detailed description of the advanced modes of ventilation. The information on the types of ventilation can then be applied to the ventilation approaches in different populations of patients: the trauma patients, the obese patients, and the patients under neurocritical care. The conclusion contains a discussion of the mechanisms on how to wean from mechanical ventilation and how certain medical conditions affect the weaning process

Identifying and understanding Risk factors for instability and adverse Events Associated with CHest physiotherapy in ventilated children

Background: Chest physiotherapy is a treatment option for ventilated children. Evidence supporting treatment effectiveness is limited and the safety profile is unknown. This study aimed to identify and understand risk factors for physiological instability and adverse events associated with chest physiotherapy in ventilated children. / Methods: This was a convergent mixed methods study. Work package 1: Explanatory sequential study to describe current physiotherapy practice and explore decision making, utilising an anonymous questionnaire, semi-structured interviews, focus groups, and document analysis. Work package 2: Retrospective single-centre study, using high-resolution data and electronic patient records. Mechanically ventilated children, aged 0-4 years, receiving chest physiotherapy were included. The primary outcome was oxygen saturation index (OSI). Incidence of adverse events (change ≥0.3) in the 60 minutes post-physiotherapy was investigated. / Results: The questionnaire was sent to 26/27 (96%) UK paediatric intensive care units, with a response rate of 61% (72/118). Sixteen physiotherapist interviews and two focus groups (n=7) were completed. Twenty-nine organisational documents were analysed. The most frequently used techniques were position changes, saline instillation, manual hyperinflations and chest wall vibrations. Variation in practice included the personnel involved in treatments. Clinical decision making was described as complex, iterative, and collaborative, with experience and expertise important factors. OSI data were available for 247 patients. OSI adverse event rates were between 7.4%-9.3%. The highest rate was recorded in the 5 minutes immediately post-physiotherapy. A higher proportion of patients with an adverse event were emergency admissions (p<0.001). There was no association between occurrence of an OSI adverse event post-physiotherapy and length of ventilation or mortality. / Conclusion: This is the first study to explore the safety of chest physiotherapy in ventilated children and the wider contextual factors. It has provided novel data regarding a popular treatment used in a vulnerable patient group. Further research is required to understand the risks and benefits of chest physiotherapy