Delirium is a condition that, when left unmanaged, is associated with increased mortality and longer hospitalization of patients in intensive care; therefore, its detection should be an integral part of care. It is characterized by confusion, anxiety and reduced alertness. It is estimated that 75% of delirium cases are not detected on admission to hospital. Detecting such an acute condition requires frequent monitoring of participants, which is labor intensive and requires expertise. However, the participants' vital signs, which can be collected continuously throughout their stay in intensive care, could contain information indicative of the present state of consciousness, and possibly predictive of the future state.
Our goal is to build an automatic machine learning classifier based on vital sign data to (a) identify times when the patient was delirious, and (b) predict delirium incipience. As a primary measure, we will use a clinically validated tool, the Confusion Assessment Method for Intensive Care Unit (CAM-ICU). This assessment was performed twice a day, once in the morning and once in the afternoon, in our study population at the CISSS de Chaudière-Appalache (Hotel Dieu de Lévis). The learning algorithm will be trained on the participants' vital signs before, after, and during the delirium episodes in order to (a) extract the vital sign characteristics related to a delirium state; (b) the probability that the patient is delirious or not, based on these characteristics; and (c) the probability that the patient will develop a delirium state within a reasonable time window (e.g. 1 hour).
Even if the machine learning model does not reach the accuracy and precision of a validated questionnaire, its use in healthcare facilities would optimize care, mainly by drawing attention to any suspicious drift (high sensitivity). Considering that patients who remain with untreated delirium are associated with higher mortality rates and longer ICU stays, a clinical indicator such as this model can help the care team manage this otherwise unnoticed symptom.