Extubation readiness is a complex, multifactorial topic with important clinical implications. In the premature neonatal population, there are no proven predictors of extubation readiness, success or failure; decisions surrounding extubation are left to clinician judgement resulting in practice variability and high rates of failure.1 Frequency of failed elective extubation in this patient population ranges from 23% to 42%.2-4 Extubation failure has been demonstrated to be associated with increased rates of death, bronchopulmonary dysplasia and retinopathy of prematurity as well as prolonged respiratory support requirement and hospitalisation.3, 4 In the current study, Gupta and colleagues created an extubation readiness estimator with available clinical and demographic data. The authors have made their calculator available to the public (http://elasticbeanstalk-us-east-2-676799334712.s3-website.us-east-2.amazonaws.com). After entering the gestational age, extubation day of life, pre-extubation per cent oxygen, highest RSS in first 6 hours, weight at extubation and pre-extubation pH, the clinician will receive the probability of successful extubation. Importantly, in this predictive model, the sensitivity and specificity of the prediction change with the results of the calculated probability of successful extubation. If the calculator predicts a 60% chance of success, the authors report 87% sensitivity and 53% specificity. This indicates that 87% of patients with similar values inserted into the calculator who succeed extubation will be predicted to succeed, and 53% of patients who fail extubation will be predicted to fail. If the calculator predicts an 80% chance of success, the sensitivity falls considerably to 54%; this implies that the ability to predict who will succeed is decreased at this level. Conversely, the sensitivity increases to 81%, suggesting that the ability to predict who will fail improves. Though difficult to conceptualise, this change in ‘test performance’ is intuitive and analogous to the way a clinician might make their own prediction. For example, a provider who feels an infant has a 95% chance of successful extubation likely has a high degree of certainty about the estimation. A clinical who feels an infant has only a 60% chance of extubation success may have uncertainty regarding the exact percentage. In this way, the predictive model performs similarly to clinical judgement, performing less well as uncertainty increases. A predictive model is most useful when it provides added certainty in the latter scenario; it is unlikely to be applied or improve outcomes if it only performs well when extubation success is either highly probable or very unlikely. It is unknown how the tool derived retrospectively will perform prospectively, and this uncertainty has potential to result in unanticipated harm. The authors comment that use of the estimator may help facilitate earlier extubation in infants estimated to have a high chance of success—if this hypothesis is true, rates of successful extubation should increase. Alternatively, a poor score could prevent an extubation attempt in some babies, with the unanticipated consequence of increasing ventilation days and lung injury. The balance of these potential harms and benefits should be prospectively assessed with broad implementation of this tool. None. URL TO THE FULL REVIEW ON THE EBNEO WEBSITE https://ebneo.org/2020/12/extubation-readiness/
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