Intestinal flow after anastomotic operations in neonates.

Stagnation of contents at the anastomotic site for intestinal flows after anastomotic operation is a critical issue in neonates. Although various anastomosis methods have been developed, in the clinical field, poor passage at the anastomotic site in cases of jejunal atresia is still observed. A CFD study was carried out to clarify the reasons for the stagnation and to find favorable anastomosis methods from a fluid dynamical point of view. Direct numerical simulations were performed using OpenFOAM. The boundaries of the computational domain were peristaltically moved to reproduce flow. The results reveal that the peristaltic motion on the distal side dominates the flow and that on the proximal side has a negligible influence. In particular, the contents do not pass the anastomotic site when the peristaltic motion on the distal side is not active. The flow rate as a measure of the driving force of the flow on the proximal side is large when the amplitude of the peristaltic motion is large and the diameter is small. It was also found that anastomosis methods do not affect flow resistance.

A deep learning model for real-time mortality prediction in critically ill children.

BACKGROUND: The rapid development in big data analytics and the data-rich environment of intensive care units together provide unprecedented opportunities for medical breakthroughs in the field of critical care. We developed and validated a machine learning-based model, the Pediatric Risk of Mortality Prediction Tool (PROMPT), for real-time prediction of all-cause mortality in pediatric intensive care units. METHODS: Utilizing two separate retrospective observational cohorts, we conducted model development and validation using a machine learning algorithm with a convolutional neural network. The development cohort comprised 1445 pediatric patients with 1977 medical encounters admitted to intensive care units from January 2011 to December 2017 at Severance Hospital (Seoul, Korea). The validation cohort included 278 patients with 364 medical encounters admitted to the pediatric intensive care unit from January 2016 to November 2017 at Samsung Medical Center. RESULTS: Using seven vital signs, along with patient age and body weight on intensive care unit admission, PROMPT achieved an area under the receiver operating characteristic curve in the range of 0.89-0.97 for mortality prediction 6 to 60 h prior to death. Our results demonstrated that PROMPT provided high sensitivity with specificity and outperformed the conventional severity scoring system, the Pediatric Index of Mortality, in predictive ability. Model performance was indistinguishable between the development and validation cohorts. CONCLUSIONS: PROMPT is a deep model-based, data-driven early warning score tool that can predict mortality in critically ill children and may be useful for the timely identification of deteriorating patients.