RATIONALE: Increased family engagement has been associated with decreased delirium prevalence in the ICU and is a key pillar of the ABCDEF bundle. Digital tools are needed to address barriers to in-person family presence and enhance family engagement. VoiceLove is a HIPAA-compliant mobile phone app that enables patients and their families to exchange voice messages securely. For patients who are intubated or unable to communicate, VoiceLove allows family members to deliver real-time voice messages when they are away from the bedside without relying on the clinical team to coordinate calls. We sought to conduct iterative usability testing of VoiceLove with diverse, interdisciplinary stakeholders to refine and optimize this platform for patients, families, and their clinical teams in the adult ICU. METHODS: During one of two testing sessions, healthcare worker and family member participants independently completed a set of standardized tasks focused on basic app functionality with a live prototype and provided feedback on screenshots of new app design concepts under consideration. After task completion, participants completed the System Usability Scale (SUS), a validated 10-item questionnaire used to measure the perceived usability of applications. A standardized SUS score was then calculated with scores ranging from 0 (worst) to 100 (best) with a score of >70 reflecting the industry-accepted range for a usable product. Open-ended feedback on the prototype was elicited from participants and used to revise the VoiceLove app extensively for future investigations. RESULTS: The first session (n=12) included 6 nurses, 1 care partner, 1 chaplain, 1 geriatrician, and 3 ICU fellows. The mean SUS score was 77.5 (SD 11.1). The second session (n=8), conducted 7 months later with a refined prototype, included 6 family members and 2 nurses. The mean SUS score was 75.9 (SD 26.8). Accessibility, navigation, and simplicity were identified as design priorities. Participants identified multiple strengths including the ability to customize messaging by unique family groups and to archive and replay messages. Implementation concerns included the workload burden for nurses and challenges navigating complex family dynamics. CONCLUSIONS: A diverse group of ICU stakeholders found the VoiceLove app had great potential to improve communication with critically ill patients inherently disadvantaged in their ability to communicate. Usability testing of the VoiceLove app was conducted to help prepare for an upcoming randomized controlled trial to evaluate the impact of VoiceLove, as compared to standard care, on family engagement, communication, and the incidence of delirium in ICU patients.

Rationale: Delirium during critical illness, a heterogeneous syndrome of acute brain dysfunction with differences in presentation and outcomes, is typically considered a homogeneous entity in research and clinical practice. Data-derived subtyping of delirium using clinical data uncovered previously unrecognized latent heterogeneity, but it is unknown whether subtyping strategies that use biological data add value. Therefore, we sought to compare data-derived subtypes of delirium during critical illness classified using clinical versus biomarker data. Materials and Methods: We analyzed data from the multicenter BRAIN-ICU prospective cohort study, which included adults admitted to medical or surgical ICUs with respiratory failure and/or shock who experienced delirium during the study period, as assessed by the Confusion Assessment Method for the ICU. We classified patients into delirium subtypes using latent class analysis of: (1) clinical data from demographics, vital signs, laboratory results, and treatments (previously reported) and (2) plasma biomarkers of inflammation (e.g., IL-6, IL-8, IL-10), oxidative stress (e.g., isofuran, F2-isoprostane), and mitochondrial injury (e.g., mitochondrial DNA), We compared group characteristics between the subtypes using alluvial plots, chi-square tests, and Kruskal Wallis H tests. Results: Among the 731 patients in BRAIN-ICU who developed delirium, we identified a two-class biomarker subtype model using the elbow of the Bayesian Information Criterion plot. One subtype was characterized by lower levels of plasma inflammatory mediators, markers of oxidative stress, and mitochondrial DNA (Biomarker Subtype 1), while the other was characterized by higher levels (Biomarker Subtype 2) (p<0.001). A greater proportion (51%) of the previously reported Clinical Subtype 4 classified into the hyperinflammatory Biomarker Subtype 2 than did Clinical Subtypes 1-3 (25% of Class 1, 29% of Class 2, and 28% of Class 3; p<0.001; Figure). The median [IQR] duration of coma was longer in Biomarker Subtype 2 (3 [1-7] days) than in Biomarker Subtype 1 (1 [0-4] day), but—unlike the clinical subtypes—the biomarker subtypes did not differ in duration of delirium (p=0.09) or 30-day mortality (p=0.28). Conclusions: Subtypes of delirium during critical illness differ when derived using clinical versus biomarker data. The use of biomarker data when subtyping may advance our understanding of delirium heterogeneity and the pathophysiological processes implicated in its development, but it is not yet known whether clinical data- or biomarker-derived subtypes identify treatable traits responsive to personalized interventions. In addition to the biomarkers used in this study, future research should use a broader array of biomarker data in clustering analyses.

Background Mortality prediction is difficult in resource-constrained settings. Severity of illness scores have not been tested in hypoxemic adults in Africa. Research Question How well do 3 severity of illness scores (Modified Early Warning Score [MEWS], quick Sequential Organ Failure Assessment [qSOFA], and Universal Vital Assessment [UVA]) and the ability to walk predict mortality in hypoxemic hospitalized adults in Africa? Study Design and Methods All adults with hypoxemia on admission in 5 hospitals in Kenya, Malawi, and Rwanda between November 2022 and April 2023 were prospectively enrolled in the study. The capability of MEWS, qSOFA, UVA, and the ability to walk was evaluated to predict hospital mortality. In exploratory analyses, differences in disease severity and mortality were compared between sites. Results A total of 24,724 admissions were screened; 1,732 of these were hypoxemic and had complete outcomes data. Median age was 52 years (interquartile range, 36-70 years), and hospital mortality was 35% (n = 615). Sites varied in the completeness of score variables (44%-99%). Increased odds of mortality were found using predefined thresholds for each score; UVA predicted best, with an OR of 3.40 (95% CI, 2.54-4.56). The area under the receiver-operating curves for MEWS, qSOFA, and UVA were 0.66 (95% CI, 0.62-0.69), 0.66 (95% CI, 0.63-0.69), and 0.69 (95% CI, 0.65-0.72), respectively, using complete case analysis; they were 0.61 (95% CI, 0.58-0.64), 0.65 (95% CI, 0.62-0.67), and 0.66 (95% CI, 0.64-0.69) with missing data imputed as normal. Inability to walk independently was also predictive of mortality (OR, 2.26; 95% CI, 1.62-3.15). UVA pairwise comparisons showed different mortality between 4 of 6 sites; these differences remained significant in 2 comparisons when adjusting for illness severity. Interpretation In the largest prospective cohort of hypoxemic adults in Africa to date, MEWS, qSOFA, UVA, and ability to walk on admission had modest capability to predict hospital death. Missing data were common. Imputation of missing variables only slightly altered performance, and thus it is possible that scores could be simplified. UVA had the best predictive performance and may be cautiously used to aid clinical decision-making, quality improvement, research comparisons, and risk adjustment.

1 Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville TN. 2 Critical Illness, Brain Dysfunction and Survivorship Center, Nashville, TN. 3 Department of Veterans Affairs Medical Center, Geriatric Research, Education and Clinical Center Service, Nashville TN. Dr. Ely received funding from the National Institute of Aging (R01 AG058639). Dr. Rolfsen has disclosed that he does not have any potential conflicts of interest.

RATIONALE/OBJECTIVES:Despite plausible pathophysiological mechanisms, research is needed to confirm the relationship between sleep, circadian rhythm and delirium in patients admitted to the intensive care unit (ICU). The objective of this review is to summarise existing studies promoting, in whole or in part, the normalisation of sleep and circadian biology and their impact on the incidence, prevalence, duration and/or severity of delirium in ICU. METHODS:A sensitive search of electronic databases and conference proceedings was completed in March 2023. Inclusion criteria were English-language studies of any design that evaluated in-ICU non-pharmacological, pharmacological or mixed intervention strategies for promoting sleep or circadian biology and their association with delirium, as assessed at least daily. Data were extracted and independently verified. RESULTS:Of 7886 citations, we included 50 articles. Commonly evaluated interventions include care bundles (n=20), regulation or administration of light therapy (n=5), eye masks and/or earplugs (n=5), one nursing care-focused intervention and pharmacological intervention (eg, melatonin and ramelteon; n=19). The association between these interventions and incident delirium or severity of delirium was mixed. As multiple interventions were incorporated in included studies of care bundles and given that there was variable reporting of compliance with individual elements, identifying which components might have an impact on delirium is challenging. CONCLUSIONS:This scoping review summarises the existing literature as it relates to ICU sleep and circadian disruption (SCD) and delirium in ICU. Further studies are needed to better understand the role of ICU SCD promotion interventions in delirium mitigation.

Catatonia is a clinical syndrome characterized by psychomotor, neurological and behavioral changes. The clinical picture of catatonia ranges from akinetic stupor to severe motoric excitement. Catatonia can occur in the setting of a primary psychiatric condition such as bipolar disorder or secondary to a general medical illness like autoimmune encephalitis. Importantly, it can co-occur with delirium or coma. Malignant catatonia describes catatonia that presents with clinically significant autonomic abnormalities including change in temperature, blood pressure, heart rate, and respiratory rate. It is a life-threatening form of acute brain dysfunction that has several motoric manifestations and occurs secondary to a primary psychiatric condition or a medical cause. Many of the established predisposing and precipitating factors for catatonia such as exposure to neuroleptic medications or withdrawal states are common in the setting of critical illness. Catatonia typically improves with benzodiazepines and treatment of its underlying psychiatric or medical conditions, with electroconvulsive therapy reserved for catatonia refractory to benzodiazepines or for malignant catatonia. However, some forms of catatonia, such as catatonia secondary to a general medical condition or catatonia comorbid with delirium, may be less responsive to traditional treatments. Prompt recognition and treatment of catatonia are crucial because malignant catatonia may be fatal without treatment. Given the high morbidity and mortality associated with malignant catatonia, intensivists should familiarize themselves with this important and under-recognized condition.

Introduction: Catatonia, characterized by motor, behavioral and affective abnormalities, frequently co-occurs with delirium during critical illness. Advanced age is a known risk factor for development of delirium. However, the association between age and catatonia has not been described. We aim to describe the occurrence of catatonia, delirium, and coma by age group in a critically ill, adult population. Design: Convenience cohort, nested within two clinical trials and two observational cohort studies. Setting: Intensive care units in an academic medical center in Nashville, TN. Patients: 378 critically ill adult patients on mechanical ventilation and/or vasopressors. Measurements and Main Results: Patients were assessed for catatonia, delirium, and coma by independent and blinded personnel, the Bush Francis Catatonia Rating Scale, the Confusion Assessment Method for the Intensive Care Unit (ICU) and the Richmond Agitation and Sedation Scale. Of 378 patients, 23% met diagnostic criteria for catatonia, 66% experienced delirium, and 52% experienced coma during the period of observation. There was no relationship found between age and catatonia severity or age and presence of specific catatonia items. The prevalence of catatonia was strongly associated with age in the setting of critical illness (p < 0.05). Delirium and comas' association with age was limited to the setting of catatonia. Conclusion: Given the significant relationship between age and catatonia independent of coma and delirium status, these data demonstrate catatonia's association with advanced age in the setting of critical illness. Future studies can explore the causative factors for this association and further elucidate the risk factors for acute brain dysfunction across the age spectrum.

Hughes, Christopher1; Hayhurst, Christina1; Pandharipande, Pratik1; Feng, Xiaoke1; Shotwell, Matthew2; Chandrasekhar, Rameela3; Wes Ely, E4; Patel, Mayur4 Author Information

OBJECTIVES/GOALS: To explore the severity of posttraumatic stress disorder (PTSD) symptoms in association with hippocampal and amygdala volumes in ICU survivors. We hypothesize that the severity of posttraumatic stress symptoms in ICU survivors is associated with lower volumes of both the hippocampus and amygdala. METHODS/STUDY POPULATION: Secondary analysis of the VISIONS study, a prospective sub-study of the BRAIN-ICU cohort, which included survivors of critical illness. Patients were screened for preexisting PTSD before discharge. The PTSD Checklist Specific (PCL-S) was used at 3 and 12 months to evaluate the ICU as a traumatic experience. A score of >30, indicated significant symptoms of PTSD. A Philips Achieva 3T MRI scanner was used to scan patients at both discharge and 3-month follow-up. To compare median brain volumes at discharge and 3 months for those with and without significant PTSD symptomatology (PCL-S ≥30) at 3 and 12 months, we used a Kruskal-Wallis (KW) equality-of-populations rank test. RESULTS/ANTICIPATED RESULTS: The median age for our sample was 58.5 (52.6, 63.7). One-third of the sample was female, and 90% were Caucasian. Fifty-seven percent of individuals (N = 12) had at least one prior mental health diagnosis, with two having a prior history of PTSD. One third of individuals experienced delirium during their critical illness. At 3-month follow up, there were three patients with PTSD symptomatology and one at 12-month follow up. Median brain volumes (hippocampus or amygdala) did not differ between individuals with or without PTSD symptomatology at either 3 or 12 months (p-values for all tests >0.05). DISCUSSION/SIGNIFICANCE OF IMPACT: Although our study did not reveal significant differences in brain volumes between PTSD patients and non-PTSD patients, sample size is a major limitation and larger scale studies should be undertaken to elucidate possible neurobiological markers of PTSD in ICU survivors. CONFLICT OF INTEREST DESCRIPTION: Dr. Wilson would like to acknowledge salary support from the Vanderbilt Faculty Research Scholars Program (1KL2TR002245), HL111111 and GM120484. Drs. Ely and Jackson as well as Mrs. Kiehl all receive funding for their time working on this investigation from AG035117 and HL111111. Dr. Ely would additionally like to acknowledge salary support from the Tennessee Valley Healthcare System Geriatric Research Education and Clinical Center (GRECC). Dr. Ely will also disclose additional funding for his time from AG027472 and having received honoraria from Orion and Hospira for CME activity; he does not hold stock or consultant relationships with those companies. The authors would like to acknowledge the following: this work was conducted in part using the resources of the Center for Computational Imaging at Vanderbilt University Institute of Imaging Science and the Advanced Computing Center for Research and Education at Vanderbilt University, Nashville, TN, and study data were collected and managed using REDCap electronic data capture tools hosted at Vanderbilt University.

Objectives: To determine national readmission rates among sepsis survivors, variations in rates between hospitals, and determine whether measures of quality correlate with performance on sepsis readmissions.Design: Cross-sectional study of sepsis readmissions between 2008 and 2011 in the Medicare fee-for-service database.Setting: Acute care, Medicare participating hospitals from 2008 to 2011.Patients: Septic patients as identified by International Classification of Diseases, Ninth Revision codes using the Angus method.Interventions: None.Measurements and Main Results: We generated hospital-level, risk-standardized, 30-day readmission rates among survivors of sepsis and compared rates across region, ownership, teaching status, sepsis volume, hospital size, and proportion of underserved patients. We examined the relationship between risk-standardized readmission rates and hospital-level composite measures of quality and mortality. From 633,407 hospitalizations among 3,315 hospitals from 2008 to 2011, median risk-standardized readmission rates was 28.7% (inter-quartile range, 26.1-31.9). There were differences in risk-standardized readmission rates by region (Northeast, 30.4%; South, 29.6%; Midwest, 28.8%; and West, 27.7%; p < 0.001), teaching versus non-teaching status (31.1% vs 29.0%; p < 0.001), and hospitals serving the highest proportion of underserved patients (30.6% vs 28.7%; p < 0.001). The best performing hospitals on a composite quality measure had highest risk-standardized readmission rates compared with the lowest (32.0% vs 27.5%; p < 0.001). Risk-standardized readmission rates was lower in the highest mortality hospitals compared with those in the lowest (28.7% vs 30.7%; p < 0.001).Conclusions: One third of sepsis survivors were readmitted and wide variation exists between hospitals. Several demographic and structural factors are associated with this variation. Measures of higher quality in-hospital care were correlated with higher readmission rates. Several potential explanations are possible including poor risk standardization, more research is needed.

BACKGROUND:Collaborative and toolkit approaches have gained traction for improving quality in health care.OBJECTIVE:To determine if a quality improvement virtual collaborative intervention would perform better than a toolkit-only approach at preventing central line-associated bloodstream infections (CLABSIs) and ventilator-associated pneumonias (VAPs).DESIGN AND SETTING:Cluster randomized trial with the Intensive Care Units (ICUs) of 60 hospitals assigned to the Toolkit (n=29) or Virtual Collaborative (n=31) group from January 2006 through September 2007.MEASUREMENT:CLABSI and VAP rates. Follow-up survey on improvement interventions, toolkit utilization, and strategies for implementing improvement.RESULTS:A total of 83% of the Collaborative ICUs implemented all CLABSI interventions compared to 64% of those in the Toolkit group (P = 0.13), implemented daily catheter reviews more often (P = 0.04), and began this intervention sooner (P < 0.01). Eighty-six percent of the Collaborative group implemented the VAP bundle compared to 64% of the Toolkit group (P = 0.06). The CLABSI rate was 2.42 infections per 1000 catheter days at baseline and 2.73 at 18 months (P = 0.59). The VAP rate was 3.97 per 1000 ventilator days at baseline and 4.61 at 18 months (P = 0.50). Neither group improved outcomes over time; there was no differential performance between the 2 groups for either CLABSI rates (P = 0.71) or VAP rates (P = 0.80).CONCLUSION:The intensive collaborative approach outpaced the simpler toolkit approach in changing processes of care, but neither approach improved outcomes. Incorporating quality improvement methods, such as ICU checklists, into routine care processes is complex, highly context-dependent, and may take longer than 18 months to achieve.

The number of critically ill elderly continues to rise, causing health care workers to be faced with decisions regarding aggressiveness of care, rationing of resources, and optimizing outcome. Although survival rates in the critically ill elderly may be lower than those in the younger critically ill, health care workers must focus on customizing treatment to optimize physiologic recovery, quality of life, and functional status. We advocate better research designs incorporating long-term outcomes and genetic predisposition as a means of improving care in the elderly critically ill.