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1. The impact of environmental risk factors on delirium and benefits of noise and light modifications: a scoping review

   https://doi.org/10.12688/f1000research.25901.1  

   Hashemighouchani, Haleh; Cupka, Julie; Lipori, Jessica; Ruppert, Matthew M.; Ingersent, Elizabeth; Ozrazgat-Baslanti, Tezcan; Rashidi, Parisa; Bihorac, Azra (January 2020, F1000Research)

   null (Ed.)

   Background: To explore existing literature on the association between environmental risk factors and delirium, and to investigate the effectiveness of environmental modifications on prevention or management of delirium. Methods: This is a scoping review of peer-reviewed studies in PubMed and the reference lists of reviewed articles. Observational studies reporting the effect of noise, light, and circadian rhythm on delirium and interventional studies assessing delirium in modified environments were reviewed. Results: 37 studies were included, 21 of which evaluated the impact of environment on delirium and 16 studied possible solutions to mitigate those impacts. Mixed findings of the reviewed studies yielded inconclusive results; a clearly delineated association between high noise levels, abnormal amounts of light exposure, and sleep disruption with delirium could not be established. The environmental interventions targeted reducing noise exposure, improving daytime and mitigating night-time light exposure to follow circadian rhythm, and promoting sleep. The overall evidence supporting effectiveness of environmental interventions was also of a low confidence; however, quiet-time protocols, earplugs, and bright light therapy showed a benefit for prevention or management of delirium. Conclusions: Environmental modifications are non-invasive, risk-free, and low-cost strategies that may be beneficial in preventing and managing delirium, especially when used as part of a multi-component plan. However, given the limited evidence-based conclusions, further high-quality and larger studies focusing on environmental modifications and delirium outcomes are strongly recommended. 

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2. Outcomes and risk factors for delayed-onset postoperative respiratory failure: a multi-center case-control study by the University of California Critical Care Research Collaborative (UC3RC)

   https://doi.org/10.1186/s12871-022-01681-x  

   Stocking, Jacqueline C.; Drake, Christiana; Aldrich, J. Matthew; Ong, Michael K.; Amin, Alpesh; Marmor, Rebecca A.; Godat, Laura; Cannesson, Maxime; Gropper, Michael A.; Romano, Patrick S.; et al (December 2022, BMC Anesthesiology)

   Abstract Background Few interventions are known to reduce the incidence of respiratory failure that occurs following elective surgery (postoperative respiratory failure; PRF). We previously reported risk factors associated with PRF that occurs within the first 5 days after elective surgery (early PRF; E-PRF); however, PRF that occurs six or more days after elective surgery (late PRF; L-PRF) likely represents a different entity. We hypothesized that L-PRF would be associated with worse outcomes and different risk factors than E-PRF. Methods This was a retrospective matched case-control study of 59,073 consecutive adult patients admitted for elective non-cardiac and non-pulmonary surgical procedures at one of five University of California academic medical centers between October 2012 and September 2015. We identified patients with L-PRF, confirmed by surgeon and intensivist subject matter expert review, and matched them 1:1 to patients who did not develop PRF (No-PRF) based on hospital, age, and surgical procedure. We then analyzed risk factors and outcomes associated with L-PRF compared to E-PRF and No-PRF. Results Among 95 patients with L-PRF, 50.5% were female, 71.6% white, 27.4% Hispanic, and 53.7% Medicare recipients; the median age was 63 years (IQR 56, 70). Compared to 95 matched patients with No-PRF and 319 patients who developed E-PRF, L-PRF was associated with higher morbidity and mortality, longer hospital and intensive care unit length of stay, and increased costs. Compared to No-PRF, factors associated with L-PRF included: preexisiting neurologic disease (OR 4.36, 95% CI 1.81–10.46), anesthesia duration per hour (OR 1.22, 95% CI 1.04–1.44), and maximum intraoperative peak inspiratory pressure per cm H 2 0 (OR 1.14, 95% CI 1.06–1.22). Conclusions We identified that pre-existing neurologic disease, longer duration of anesthesia, and greater maximum intraoperative peak inspiratory pressures were associated with respiratory failure that developed six or more days after elective surgery in adult patients (L-PRF). Interventions targeting these factors may be worthy of future evaluation. 

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3. Developing a Patient-Centered Virtual Reality Healthcare System To Prevent the Onset of Delirium in ICU Patients

   https://doi.org/10.1109/SeGAH.2019.8882442  

   Suvajdzic, Marko; Bihorac, Azra; Rashidi, Parisa; Ruppert, Matthew; Williams, Seth; Ozrazgat-Baslanti, Tezcan; Ong, Triton; Appelbaum, Joel (August 2019, Developing a Patient-Centered Virtual Reality Healthcare System To Prevent the Onset of Delirium in ICU Patients)

   The purpose of the DREAMS project (DREAMS = Digital Rehabilitation Environment-Augmenting Medical System) is to research the feasibility and clinical potential of a virtual reality (VR) system for reducing the occurrence of delirium among patients in the intensive care unit (ICU). Preliminary results of this ongoing study show VR produces minimal clinical effects but are strongly enjoyed by patients and easy to administer. We discuss important lessons learned from applying VR in the ICU. 

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4. Optimized Risk Score to Predict Mortality in Patients With Cardiogenic Shock in the Cardiac Intensive Care Unit

   https://doi.org/10.1161/JAHA.122.029232  

   Yamga, Eric; Mantena, Sreekar; Rosen, Darin; Bucholz, Emily M; Yeh, Robert W; Celi, Leo A; Ustun, Berk; Butala, Neel M (July 2023, Journal of the American Heart Association)

   BackgroundMortality prediction in critically ill patients with cardiogenic shock can guide triage and selection of potentially high‐risk treatment options. Methods and ResultsWe developed and externally validated a checklist risk score to predict in‐hospital mortality among adults admitted to the cardiac intensive care unit with Society for Cardiovascular Angiography & Interventions Shock Stage C or greater cardiogenic shock using 2 real‐world data sets and Risk‐Calibrated Super‐sparse Linear Integer Modeling (RiskSLIM). We compared this model to those developed using conventional penalized logistic regression and published cardiogenic shock and intensive care unit mortality prediction models. There were 8815 patients in our training cohort (in‐hospital mortality 13.4%) and 2237 patients in our validation cohort (in‐hospital mortality 22.8%), and there were 39 candidate predictor variables. The final risk score (termed BOS,MA₂) included maximum blood urea nitrogen ≥25 mg/dL, minimum oxygen saturation <88%, minimum systolic blood pressure <80 mm Hg, use of mechanical ventilation, age ≥60 years, and maximum anion gap ≥14 mmol/L, based on values recorded during the first 24 hours of intensive care unit stay. Predicted in‐hospital mortality ranged from 0.5% for a score of 0 to 70.2% for a score of 6. The area under the receiver operating curve was 0.83 (0.82–0.84) in training and 0.76 (0.73–0.78) in validation, and the expected calibration error was 0.9% in training and 2.6% in validation. ConclusionsDeveloped using a novel machine learning method and the largest cardiogenic shock cohorts among published models, BOS,MA₂is a simple, clinically interpretable risk score that has improved performance compared with existing cardiogenic‐shock risk scores and better calibration than general intensive care unit risk scores. 

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5. Models for Assessing Strategies for Improving Hospital Capacity for Handling Patients during a Pandemic

   https://doi.org/10.1017/dmp.2022.12  

   Shahverdi, Bahar; Miller-Hooks, Elise; Tariverdi, Mersedeh; Ghayoomi, Hadi; Prentiss, David; Kirsch, Thomas D. (January 2022, Disaster Medicine and Public Health Preparedness)

   Abstract Objective: The aim of this study was to investigate the performance of key hospital units associated with emergency care of both routine emergency and pandemic (COVID-19) patients under capacity enhancing strategies. Methods: This investigation was conducted using whole-hospital, resource-constrained, patient-based, stochastic, discrete-event, simulation models of a generic 200-bed urban U.S. tertiary hospital serving routine emergency and COVID-19 patients. Systematically designed numerical experiments were conducted to provide generalizable insights into how hospital functionality may be affected by the care of COVID-19 pandemic patients along specially designated care paths, under changing pandemic situations, from getting ready to turning all of its resources to pandemic care. Results: Several insights are presented. For example, each day of reduction in average ICU length of stay increases intensive care unit patient throughput by up to 24% for high COVID-19 daily patient arrival levels. The potential of 5 specific interventions and 2 critical shifts in care strategies to significantly increase hospital capacity is also described. Conclusions: These estimates enable hospitals to repurpose space, modify operations, implement crisis standards of care, collaborate with other health care facilities, or request external support, thereby increasing the likelihood that arriving patients will find an open staffed bed when 1 is needed. 

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