Development of a clinical decision support tool using machine learning to assess a patient’s individual risk of extubation failure in mechanically-ventilated surgical ICU patients

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Patients in the intensive care unit (ICU) require intubation and mechanical ventilation for various reasons. Prolonged mechanical ventilation can result in serious complications such as ventilator-associated pneumonia, and tracheostomy may become necessary. However, patients who are extubated prematurely may need to be reintubated under suboptimal conditions, which in turn may carry a higher risk of serious complications. Both scenarios may lead to extended ICU length of stay and increased mortality. Determining when an ICU patient is ready to be extubated is a critical decision that depends on many different factors discussed among all members of the patient's care team. Risk factors for extubation failure may vary greatly among different ICU populations and individual patients. The aim of the proposed project is to develop a clinical decision support tool using machine learning to predict the risk of extubation failure in mechanically-ventilated surgical ICU patients. This tool may guide the interdisciplinary discussion around a patient’s extubation readiness. By accurately identifying patients at high risk of extubation failure and providing information on the parameters that contribute to the patient’s individual high risk, this tool would support the critical care team in tailoring the care plan to the patient’s individual risk profile, thus helping to reduce extubation failure while avoiding prolonged mechanical ventilation. Focusing on surgical ICU patients allows for the integration of risk factors specific to this population that may not be considered in a mixed ICU population due to a large proportion of missing values in non-surgical patients.