Post‐intensive care syndrome comprises physical, cognitive, and mental impairments in patients treated in an intensive care unit (ICU). It occurs either during the ICU stay or following ICU discharge and is related to the patients' long‐term prognosis. The same concept also applies to pediatric patients, and it can greatly affect the mental status of family members. In the 10 years since post‐intensive care syndrome was first proposed, research has greatly expanded. Here, we summarize the recent evidence on post‐intensive care syndrome regarding its pathophysiology, epidemiology, assessment, risk factors, prevention, and treatments. We highlight new topics, future directions, and strategies to overcome post‐intensive care syndrome among people treated in an ICU. Clinical and basic research are still needed to elucidate the mechanistic insights and to discover therapeutic targets and new interventions for post‐intensive care syndrome.

Background: Despite the increasing availability of clinical decision support systems (CDSSs) and rising expectation for CDSSs based on artificial intelligence (AI), little is known about the acceptance of AI-based CDSS by physicians and its barriers and facilitators in emergency care settings. Objective: We aimed to evaluate the acceptance, barriers, and facilitators to implementing AI-based CDSSs in the emergency care setting through the opinions of physicians on our newly developed, real-time AI-based CDSS, which alerts ED physicians by predicting aortic dissection based on numeric and text information from medical charts, by using the Unified Theory of Acceptance and Use of Technology (UTAUT; for quantitative evaluation) and the Consolidated Framework for Implementation Research (CFIR; for qualitative evaluation) frameworks. Methods: This mixed methods study was performed from March to April 2021. Transitional year residents (n=6), emergency medicine residents (n=5), and emergency physicians (n=3) from two community, tertiary care hospitals in Japan were included. We first developed a real-time CDSS for predicting aortic dissection based on numeric and text information from medical charts (eg, chief complaints, medical history, vital signs) with natural language processing. This system was deployed on the internet, and the participants used the system with clinical vignettes of model cases. Participants were then involved in a mixed methods evaluation consisting of a UTAUT-based questionnaire with a 5-point Likert scale (quantitative) and a CFIR-based semistructured interview (qualitative). Cronbach α was calculated as a reliability estimate for UTAUT subconstructs. Interviews were sampled, transcribed, and analyzed using the MaxQDA software. The framework analysis approach was used during the study to determine the relevance of the CFIR constructs. Results: All 14 participants completed the questionnaires and interviews. Quantitative analysis revealed generally positive responses for user acceptance with all scores above the neutral score of 3.0. In addition, the mixed methods analysis identified two significant barriers (System Performance, Compatibility) and two major facilitators (Evidence Strength, Design Quality) for implementation of AI-based CDSSs in emergency care settings. Conclusions: Our mixed methods evaluation based on theoretically grounded frameworks revealed the acceptance, barriers, and facilitators of implementation of AI-based CDSS. Although the concern of system failure and overtrusting of the system could be barriers to implementation, the locality of the system and designing an intuitive user interface could likely facilitate the use of optimal AI-based CDSS. Alleviating and resolving these factors should be key to achieving good user acceptance of AI-based CDSS.

Intensive care unit survivors experience prolonged physical impairments, cognitive impairments, and mental health problems, commonly referred to as post-intensive care syndrome (PICS). Previous studies reported the prevalence, assessment, and prevention of PICS, including the ABCDEF bundle approach. Although the management of PICS has been advanced, the outbreak of coronavirus disease 2019 (COVID-19) posed an additional challenge to PICS. The prevalence of PICS after COVID-19 extensively varied with 28–87% of cases pertaining to physical impairments, 20–57% pertaining to cognitive impairments, and 6–60% pertaining to mental health problems after 1–6 months after discharge. Each component of the ABCDEF bundle is not sufficiently provided from 16% to 52% owing to the highly transmissible nature of the virus. However, new data are emerging about analgesia, sedation, delirium care, nursing care, early mobilization, nutrition, and family support. In this review, we summarize the recent data on PICS and its new challenge in PICS after COVID-19 infection.

Objectives: To investigate implementation of evidence-based and supportive cares in ICUs, such as the ABCDEF, nutrition therapy, and ICU diary, for patients with coronavirus disease 2019 infection in ICUs and their association with ICU clinical practice and setting. Design: A worldwide, 2-day point prevalence study. Setting: The study was carried out on June 3, 2020, and July 1, 2020. A total of 212 ICUs in 38 countries participated. Clinicians in each participating ICU completed web-based online surveys. Patients: The ICU patients with coronavirus disease 2019. Interventions: None. Measurements and Main results: The implementation rate for the elements of the ABCDEF bundle, other supportive ICU care measures, and implementation-associated structures were investigated. Data were collected for 262 patients, of whom 47.3% underwent mechanical ventilation and 4.6% were treated with extracorporeal membrane oxygenation. Each element was implemented for the following percentages of patients: elements A (regular pain assessment), 45%; B (both spontaneous awakening and breathing trials), 28%; C (regular sedation assessment), 52%; D (regular delirium assessment), 35%; E (early mobility and exercise), 47%; and F (family engagement and empowerment), 16%. The implementation of element E was 4% for patients on mechanical ventilation and 8% for patients on extracorporeal membrane oxygenation. Supportive care, such as protein provision throughout the ICU stay (under 1.2 g/kg for more than 50% of the patients) and introduction of ICU diary (25%), was infrequent. Implementation rates of elements A and D were higher in ICUs with specific protocols and fewer ICU beds exclusively for patients with coronavirus disease 2019 infection. Element E was implemented at a higher rate in ICUs that had more ICU beds assigned for them. Conclusions: This point prevalence study showed low implementation of the ABCDEF bundle. Specific protocols and the number of ICU beds reserved for patients with coronavirus disease 2019 infection might be key factors for delivering appropriate supportive care.

The aim of this study was to develop a scoring system for identifying the post-cardiac arrest syndrome (PCAS) patients with a good potential for recovery prior to the initiation of induced therapeutic hypothermia. A multi-center, retrospective, observational study was performed. Data of a total of 151 consecutive adults who underwent induced hypothermia after cardiac arrest (77 learning cases from two hospitals and 74 validation cases from two other hospitals) were analyzed. In the learning set, 8 factors (initial rhythm, witnessed status and time until return of spontaneous circulation, pH, serum lactate, motor score according to the Glasgow Coma Scale (GCS), gray matter attenuation to white matter attenuation ratio (GWR), serum albumin, and hemoglobin) were found to be strongly correlated with the neurological outcomes. A tentative scoring system was created from the learning data using these factors, and the predictive accuracy (sensitivity and specificity) was evaluated in terms of both internal validation (0.85 and 0.84) and external validation (cutoff 50%: 0.95 and 0.90, 30%: 0.87 and 0.98, 15%: 0.67 and 1.00). Finally, using all the data, we established a post-Cardiac Arrest Syndrome for induced Therapeutic hypothermia (CAST) score to predict the neurologic prognosis prior to initiation of induced hypothermia. The CAST score was developed to predict the neurological outcomes of PCAS patients treated by induced hypothermia. The likelihood of good recovery at 30 days was extremely low in PCAS patients with a CAST score of ≤15%. Prospective validation of the score is needed in the future.

Introduction: Post-intensive care syndrome (PICS) is an emerging problem in critically ill patients and the prevalence and risk factors are unclear in patients with severe coronavirus disease 2019 (COVID-19). This multicenter prospective observational study aimed to investigate the prevalence and risk factors of PICS in ventilated patients with COVID-19 after ICU discharge. Methods: Questionnaires were administered twice in surviving patients with COVID-19 who had required mechanical ventilation, concerning Barthel Index, Short-Memory Questionnaire, and Hospital Anxiety and Depression Scale scores. The risk factors for PICS were examined using a multivariate logistic regression analysis. Results: The first and second PICS surveys were obtained at 5.5 and 13.5 months (mean) after ICU discharge, with 251 and 209 patients completing the questionnaires and with a prevalence of PICS of 58.6% and 60.8%, respectively, along with the highest percentages of cognitive impairment. Delirium (with an odds ratio of (OR) 2.34, 95% CI 1.1–4.9, and p = 0.03) and the duration of mechanical ventilation (with an OR of 1.29, 95% CI 1.05–1.58, and p = 0.02) were independently identified as the risk factors for PICS in the first PICS survey. Conclusion: Approximately 60% of the ventilated patients with COVID-19 experienced persistent PICS, especially delirium, and required longer mechanical ventilation.