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To evaluate the feasibility of continuous determination of the optimal mean arterial blood pressure (opt-MAP) according to cerebral autoregulation and to describe the opt-MAP, the autoregulation limits, and the time spent outside these limits in children within 48 h of cardiac surgery. Cerebral autoregulation was assessed using the correlation coefficient (COx) between cerebral oxygenation and MAP in children following cardiac surgery. Plots depicting the COx according to the MAP were used to determine the opt-MAP using weighted multiple time windows. For each patient, we estimated (1) the time spent with MAP outside the autoregulation limits and (2) the burden of deviation, defined as the area between the MAP curve and the autoregulation limits when the MAP was outside these limits. Fifty-one patients with a median age of 7.1 (IQR 0.7–52.0) months old were included. The opt-MAP was calculated for 94% (IQR 90–96) of the monitored time. The opt-MAP was significantly lower in neonates < 1 month old. The patients spent 24% (18–31) of the time outside of the autoregulation limits, with no significant differences between age groups. Continuous determination of the opt-MAP is feasible in children within the first 48 h following cardiac surgery. [ABSTRACT FROM AUTHOR]

Background: In an intensive care units, experts in mechanical ventilation are not continuously at patient’s bedside to adjust ventilation settings and to analyze the impact of these adjustments on gas exchange. The development of clinical decision support systems analyzing patients’ data in real time offers an opportunity to fill this gap. Objective: The objective of this study was to determine whether a machine learning predictive model could be trained on a set of clinical data and used to predict transcutaneous hemoglobin oxygen saturation 5 min (5min SpO2) after a ventilator setting change. Data sources: Data of mechanically ventilated children admitted between May 2015 and April 2017 were included and extracted from a high-resolution research database. More than 776,727 data rows were obtained from 610 patients, discretized into 3 class labels (< 84%, 85% to 91% and c92% to 100%). Performance metrics of predictive models: Due to data imbalance, four different data balancing processes were applied. Then, two machine learning models (artificial neural network and Bootstrap aggregation of complex decision trees) were trained and tested on these four different balanced datasets. The best model predicted SpO2 with area under the curves < 0.75. Conclusion: This single center pilot study using machine learning predictive model resulted in an algorithm with poor accuracy. The comparison of machine learning models showed that bagged complex trees was a promising approach. However, there is a need to improve these models before incorporating them into a clinical decision support systems. One potentially solution for improving predictive model, would be to increase the amount of data available to limit over-fitting that is potentially one of the cause for poor classification performances for 2 of the three class labels. [ABSTRACT FROM AUTHOR]