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Asynchrony in the intubated patient occurs when there is a poor interaction with the ventilator. According to the results of a study by Better Care, a biotechnology company that develops software solutions based on artificial intelligence for continuous patient monitoring, when this asynchrony occurs, it is associated with worse clinical outcomes. Therefore, its early detection for prompt intervention by the clinician is a factor influencing a better patient's prognosis. These were the main conclusions of the cohort study published in the journal Critical Care Medicine, in which the Intensive Care Medicine Units of the Parc Taulí Hospital in Sabadell and the Althaia Foundation in Manresa participated, and whose objective was to identify clusters of patient-ventilator asynchrony, double-cycling clusters and ineffective inspiratory efforts in critically ill patients during mechanical ventilation and investigate their association with mortality, length of stay in the ICU and mechanical ventilation through continuous monitoring with Better Care software. To identify asynchronies and determine their potency and duration, researchers processed and analyzed continuously captured respirator signals and determined that, if they occur grouped together over time and frequently, it is associated with worse clinical outcomes. Along the same lines, they also determined that there is an association between duration and potency with the length of the patient's stay in the ICU, the duration of mechanical ventilation and mortality. “These findings represent a further step towards precision mechanical ventilation and predictive medicine,” explains Rudys Magrans, principal investigator in the study, doctor in biomedical engineering and director of research and development at Better Care, since “to date, the influence of asynchronous clusters on patient prognosis was unknown.” To more accurately anticipate the prognosis of patients in the ICU, “Better Care is delving into predictive models that, in addition to mechanical ventilation, take into account other variables, such as pathology or critical criteria for patient admission to the ICU,” adds Magrans. AI, a support tool for clinician decision-making. “In order to be able to identify these patient-ventilator asynchronies early, intervene on time and adapt mechanical ventilation to their needs, continuous and exhaustive monitoring is essential throughout the course of mechanical ventilation,” explains Dr. Rafael Fernández, intensive care physician and co-author of the study. However, the clinician does not have the capacity to understand and real-time interpretation of the multiple signals and parameters generated by medical devices connected to a patient. For these reasons, Dr. Fernández adds, “it is essential to have appropriate technologies designed for this purpose. Therefore, this study supports the important role played by artificial intelligence applied to the hospital environment as a tool for optimizing and processing information that facilitates agile clinical decision-making and, ultimately, helps to improve critical patient care.” Article reference in Critical Care Medicine Magazine Magrans R, Ferreira F, Sarlabous L, López-Aguilar J, Gomà G, Fernandez-Gonzalo S, Navarra-Ventura G, Fernández R, Montanyà J, Kacmarek R, Rué M, Forné C, Blanch L, de Haro C, Aquino-Esperanza J, for the ASYNICU group. The Effect of Clusters of Double Triggering and Ineffective Efforts in Critically Ill Patients. Crit Care Med. 2022 Feb 7. doi: 10.1097/CCM.0000000000005471. Online ahead of print.
