To estimate the minimum percent change in failed extubation to make a tool designed to reduce extubation failure (Extubation Advisor [EA]) economically viable.

We conducted an early return on investment (ROI) analysis using data from intubated intensive care unit (ICU) patients at a large Canadian tertiary care hospital. We obtained input parameters from the hospital database and published literature. We ran generalized linear models to estimate the attributable length of stay, total hospital cost, and time to subsequent extubation attempt following failure. We developed a Markov model to estimate the expected ROI and performed probabilistic sensitivity analyses to assess the robustness of findings. Costs were presented in 2020 Canadian dollars (C$).

The model estimated a 1 percent reduction in failed extubation could save the hospital C$289 per intubated patient (95 percent CI: 197, 459). A large center seeing 2,500 intubated ICU patients per year could save C$723,124/year/percent reduction in failed extubation. At the current annual price of C$164,221, the EA tool must reduce extubation failure by at least 0.24 percent (95 percent CI: .14, .41) to make the tool cost-effective at our site.

Clinical decision-support tools like the EA may play an important role in reducing healthcare costs by reducing the rate of extubation failure, a costly event in the ICU.

Cardiac intensivists frequently assess patient readiness to wean off mechanical ventilation with an extubation readiness trial despite it being no more effective than clinician judgement alone. We evaluated the utility of high-frequency physiologic data and machine learning for improving the prediction of extubation failure in children with cardiovascular disease.

This was a retrospective analysis of clinical registry data and streamed physiologic extubation readiness trial data from one paediatric cardiac ICU (12/2016-3/2018). We analysed patients’ final extubation readiness trial. Machine learning methods (classification and regression tree, Boosting, Random Forest) were performed using clinical/demographic data, physiologic data, and both datasets. Extubation failure was defined as reintubation within 48 hrs. Classifier performance was assessed on prediction accuracy and area under the receiver operating characteristic curve.

Of 178 episodes, 11.2% (N = 20) failed extubation. Using clinical/demographic data, our machine learning methods identified variables such as age, weight, height, and ventilation duration as being important in predicting extubation failure. Best classifier performance with this data was Boosting (prediction accuracy: 0.88; area under the receiver operating characteristic curve: 0.74). Using physiologic data, our machine learning methods found oxygen saturation extremes and descriptors of dynamic compliance, central venous pressure, and heart/respiratory rate to be of importance. The best classifier in this setting was Random Forest (prediction accuracy: 0.89; area under the receiver operating characteristic curve: 0.75). Combining both datasets produced classifiers highlighting the importance of physiologic variables in determining extubation failure, though predictive performance was not improved.

Physiologic variables not routinely scrutinised during extubation readiness trials were identified as potential extubation failure predictors. Larger analyses are necessary to investigate whether these markers can improve clinical decision-making.

To investigate the risk factors associated with prolonged ventilation after Fontan surgery.

Retrospective case series.

Tertiary childrens hospital.

We included 123 children who underwent Fontan surgery without delayed sternal closure or extracorporeal membrane oxygenation between 2011 and 2017.

Fontan surgery.

Prolonged ventilation was defined as intubation for more than 24 hours after surgery. Preoperative, intraoperative, and perioperative data were collected retrospectively from medical records. Multivariate logistic regression analysis was used to identify risk factors for prolonged ventilation. The median age and weight of patients were 2.2 years and 10.0 kg, respectively. Seventeen per cent of the patients (n = 21) received prolonged mechanical ventilation, and the median intubation period was 2.9 days. There were no 90-day or in-hospital deaths. The independent predictors of prolonged ventilation identified were fenestration (p < 0.01), low pulmonary artery index (p = 0.02), and advanced atrioventricular regurgitation (p < 0.01). The duration of ICU stay was significantly longer in the prolonged ventilation group than in the early extubation group (10 days versus 6 days, p < 0.01).

Fenestration, low pulmonary artery index, and significant atrioventricular regurgitation are risk factors for prolonged ventilation after Fontan surgery. Careful preoperative and perioperative management that considers the risk factors for prolonged ventilation in each individual is important.

Reliable predictors of extubation readiness are needed and may reduce morbidity related to extubation failure. We aimed to examine the relationship between changes in pre-extubation near-infrared spectroscopy measurements from baseline and extubation outcomes after neonatal cardiac surgery.

In this retrospective cross-sectional multi-centre study, a secondary analysis of prospectively collected data from neonates who underwent cardiac surgery at seven tertiary-care children’s hospitals in 2015 was performed. Extubation failure was defined as need for re-intubation within 72 hours of the first planned extubation attempt. Near-infrared spectroscopy measurements obtained before surgery and before extubation in patients who failed extubation were compared to those of patients who extubated successfully using t-tests.

Near-infrared spectroscopy measurements were available for 159 neonates, including 52 with single ventricle physiology. Median age at surgery was 6 days (range: 1–29 days). A total of 15 patients (9.4 %) failed extubation. Baseline cerebral and renal near-infrared spectroscopy measurements were not statistically different between those who were successfully extubated and those who failed, but pre-extubation cerebral and renal values were significantly higher in neonates who extubated successfully. An increase from baseline to time of extubation values in cerebral oximetry saturation by ≥ 5 % had a positive predictive value for extubation success of 98.6 % (95%CI: 91.1–99.8 %).

Pre-extubation cerebral near-infrared spectroscopy measurements, when compared to baseline, were significantly associated with extubation outcomes. These findings demonstrate the potential of this tool as a valuable adjunct in assessing extubation readiness after paediatric cardiac surgery and warrant further evaluation in a larger prospective study.