ABSTRACT
Accumulating evidence indicates that coronavirus disease 2019 is a major cause of delirium. Given the global dimension of the current pandemic and the fact that delirium is a strong predictor of cognitive decline for critically ill patients, this raises concerns regarding the neurological cost of coronavirus disease 2019. Currently, there is a major knowledge gap related to the covert yet potentially incapacitating higher-order cognitive impairment underpinning coronavirus disease 2019 related delirium. The aim of the current study was to analyse the electrophysiological signatures of language processing in coronavirus disease 2019 patients with delirium by using a specifically designed multidimensional auditory event-related potential battery to probe hierarchical cognitive processes, including self-processing (P300) and semantic/lexical priming (N400). Clinical variables and electrophysiological data were prospectively collected in controls subjects (n = 14) and in critically ill coronavirus disease 2019 patients with (n = 19) and without (n = 22) delirium. The time from intensive care unit admission to first clinical sign of delirium was of 8 (3.5-20) days, and the delirium lasted for 7 (4.5-9.5) days. Overall, we have specifically identified in coronavirus disease 2019 patients with delirium, both a preservation of low-level central auditory processing (N100 and P200) and a coherent ensemble of covert higher-order cognitive dysfunctions encompassing self-related processing (P300) and sematic/lexical language priming (N400) (spatial-temporal clustering, P-cluster ≤ 0.05). We suggest that our results shed new light on the neuropsychological underpinnings of coronavirus disease 2019 related delirium, and may constitute a valuable method for patient's bedside diagnosis and monitoring in this clinically challenging setting.
ABSTRACT
BACKGROUND: The ongoing COVID-19 pandemic has highlighted the potential of digital health solutions to adapt the organization of care in a crisis context. OBJECTIVE: Our aim was to describe the relationship between the MyRISK score, derived from self-reported data collected by a chatbot before the preanesthetic consultation, and the occurrence of postoperative complications. METHODS: This was a single-center prospective observational study that included 401 patients. The 16 items composing the MyRISK score were selected using the Delphi method. An algorithm was used to stratify patients with low (green), intermediate (orange), and high (red) risk. The primary end point concerned postoperative complications occurring in the first 6 months after surgery (composite criterion), collected by telephone and by consulting the electronic medical database. A logistic regression analysis was carried out to identify the explanatory variables associated with the complications. A machine learning model was trained to predict the MyRISK score using a larger data set of 1823 patients classified as green or red to reclassify individuals classified as orange as either modified green or modified red. User satisfaction and usability were assessed. RESULTS: Of the 389 patients analyzed for the primary end point, 16 (4.1%) experienced a postoperative complication. A red score was independently associated with postoperative complications (odds ratio 5.9, 95% CI 1.5-22.3; P=.009). A modified red score was strongly correlated with postoperative complications (odds ratio 21.8, 95% CI 2.8-171.5; P=.003) and predicted postoperative complications with high sensitivity (94%) and high negative predictive value (99%) but with low specificity (49%) and very low positive predictive value (7%; area under the receiver operating characteristic curve=0.71). Patient satisfaction numeric rating scale and system usability scale median scores were 8.0 (IQR 7.0-9.0) out of 10 and 90.0 (IQR 82.5-95.0) out of 100, respectively. CONCLUSIONS: The MyRISK digital perioperative risk score established before the preanesthetic consultation was independently associated with the occurrence of postoperative complications. Its negative predictive strength was increased using a machine learning model to reclassify patients identified as being at intermediate risk. This reliable numerical categorization could be used to objectively refer patients with low risk to teleconsultation.
ABSTRACT
INTRODUCTION: Avoiding coronavirus disease 2019 (COVID-19) work-related infection in frontline healthcare workers is a major challenge. A massive training program was launched in our university hospital for anesthesia/intensive care unit and operating room staff, aiming at upskilling 2249 healthcare workers for COVID-19 patients' management. We hypothesized that such a massive training was feasible in a 2-week time frame and efficient in avoiding sick leaves. METHODS: We performed a retrospective observational study. Training focused on personal protective equipment donning/doffing and airway management in a COVID-19 simulated patient. The educational models used were in situ procedural and immersive simulation, peer-teaching, and rapid cycle deliberate practice. Self-learning organization principles were used for trainers' management. Ordinary disease quantity in full-time equivalent in March and April 2020 were compared with the same period in 2017, 2018, and 2019. RESULTS: A total of 1668 healthcare workers were trained (74.2% of the target population) in 99 training sessions over 11 days. The median number of learners per session was 16 (interquartile range = 9-25). In the first 5 days, the median number of people trained per weekday was 311 (interquartile range = 124-385). Sick leaves did not increase in March to April 2020 compared with the same period in the 3 preceding years. CONCLUSIONS: Massive training for COVID-19 patient management in frontline healthcare workers is feasible in a very short time and efficient in limiting the rate of sick leave. This experience could be used in the anticipation of new COVID-19 waves or for rapidly preparing hospital staff for an unexpected major health crisis.