Predictive model development for premature infant extubation outcomes: development and analysis.

Abstract

Given the knowledge of the damage caused by prolonged invasive mechanical ventilation in premature newborns, withdrawing this support as quickly as possible is important to minimize morbidity. The aim of this study was to analyze the variables associated with extubation outcomes and to develop a predictive model for successful extubation in premature newborns. Data were obtained from a multicenter study involving six public maternity hospitals. The variables with the highest correlation to the extubation outcome were used to construct the predictive model through data analysis and machine learning methods, followed by training and testing of algorithms. Data were collected from 405 premature newborns. The predictive model with the best metrics was trained and tested using the variables of gestational age, birth weight, weight at extubation, congenital infections, and time on invasive mechanical ventilation, based on 393 samples according to the extubation outcome (12 were discarded due to irretrievable missing data in important attributes). The model exhibited an accuracy of 77.78%, sensitivity of 79.41%, and specificity of 60%. These variables generated a predictive model capable of estimating the probability of successful extubation in premature newborns. Prolonged use of invasive mechanical ventilation in preterm newborns increases morbidity/mortality rates, emphasizing the importance of early withdrawal from invasive ventilatory support. However, the decision to extubate lacks tools with higher extubation outcome precision. The use of artificial intelligence through the construction of a predictive model can assist in the decision-making process for extubating preterm newborns based on real-world data. The implementation of this tool can optimize the decision to extubate preterm newborns, promoting successful extubation and reducing preterm newborns exposure to adverse events associated with extubation failure.