Impact of inhaled sedation on delirium incidence and neurological outcome after cardiac arrest - a propensity-matched control study (Isocare).

RATIONALE: poor neurological outcome is common following a cardiac arrest. The use of volatile anesthetic agents has been proposed during post-resuscitation to improve outcome. OBJECTIVES: to determine the effects of inhaled isoflurane on neurological outcome, delirium incidence, ICU length-of-stay, ventilation duration, mortality during post-resuscitation care of ICU patients. PATIENTS: 510 patients were admitted within our medical ICU following a cardiac arrest during the study period, 401 of them being sedated using intravenous sedation prior to 2017 and 109 of them using inhaled isoflurane according to a standardized protocol following 2017. RESULTS: matched-pair analysis depicted a delirium incidence decrease, without improved neurologic outcome on ICU discharge (CPC ≤ 2) for isoflurane patients (16.1% vs 32.2%, p 0.03 and 29% vs 23%, p 0.47, respectively). Ventilation duration and ICU length of stay were shorter for isoflurane patients (78 vs 167 hrs, p 0.01 and 7.9 vs 8.5 days, p 0.01 respectively). Isoflurane had no impact on mortality. CONCLUSION: in this propensity-matched control study, isoflurane sedation during the post-resuscitation care of ICU patients was associated with a lower incidence of delirium, a shorter duration of mechanical ventilation and a reduced ICU length of stay. Prospective data are needed before its widespread use.

Defining predictors for successful mechanical ventilation weaning, using a data-mining process and artificial intelligence.

Mechanical ventilation weaning within intensive care units (ICU) is a difficult process, while crucial when considering its impact on morbidity and mortality. Failed extubation and prolonged mechanical ventilation both carry a significant risk of adverse events. We aimed to determine predictive factors of extubation success using data-mining and artificial intelligence. A prospective physiological and biomedical signal data warehousing project. A 21-beds medical Intensive Care Unit of a University Hospital. Adult patients undergoing weaning from mechanical ventilation. Hemodynamic and respiratory parameters of mechanically ventilated patients were prospectively collected and combined with clinical outcome data. One hundred and eight patients were included, for 135 spontaneous breathing trials (SBT) allowing to identify physiological parameters either measured before or during the trial and considered as predictive for extubation success. The Early-Warning Score Oxygen (EWSO2) enables to discriminate patients deemed to succeed extubation, at 72-h and 7-days. Cut-off values for EWSO2 (AUC = 0.80; Se = 0.75; Sp = 0.76), mean arterial pressure and heart-rate variability parameters were determined. A predictive model for extubation success was established including body-mass index (BMI) on inclusion, occlusion pressure at 0,1 s. (P0.1) and heart-rate analysis parameters (LF/HF) both measured before SBT, and heart rate during SBT (global performance 62%; 83%). The data-mining process enabled to detect independent predictive factors for extubation success and to develop a dynamic predictive model using artificial intelligence. Such predictive tools may help clinicians to better discriminate patients deemed to succeed extubation and thus improve clinical performance.