Data-derived subtypes of delirium during critical illness.

BACKGROUND: To understand delirium heterogeneity, prior work relied on psychomotor symptoms or risk factors to identify subtypes. Data-driven approaches have used machine learning to identify biologically plausible, treatment-responsive subtypes of other acute illnesses but have not been used to examine delirium. METHODS: We conducted a secondary analysis of a large, multicenter prospective cohort study involving adults in medical or surgical ICUs with respiratory failure or shock who experienced delirium per the Confusion Assessment Method for the ICU. We used data collected before delirium diagnosis in an unsupervised latent class model to identify delirium subtypes and then compared demographics, clinical characteristics, and outcomes between subtypes in the final model. FINDINGS: The 731 patients who developed delirium during critical illness had a median age of 63 [IQR, 54-72] years, a median Sequential Organ Failure Assessment score of 8.0 [6.0-11.0] and 613 [83.4%] were mechanically ventilated at delirium identification. A four-class model best fit the data with 50% of patients in subtype (ST) 1, 18% in subtype 2, 17% in subtype 3, and 14% in subtype 4. Subtype 2-which had more shock and kidney impairment-had the highest mortality (33% [ST2] vs. 17% [ST1], 25% [ST3], and 17% [ST4], p = 0.003). Subtype 4-which received more benzodiazepines and opioids-had the longest duration of delirium (6 days [ST4] vs. 3 [ST1], 4 [ST2], and 3 days [ST3], p < 0.001) and coma (4 days [ST4] vs. 2 [ST1], 1 [ST2], and 2 days [ST3], p < 0.001). Each of the four data-derived delirium subtypes was observed within previously identified psychomotor and risk factor-based delirium subtypes. Clinically significant cognitive impairment affected all subtypes at follow-up, but its severity did not differ by subtype (3-month, p = 0.26; 12-month, p = 0.80). INTERPRETATION: The four data-derived delirium subtypes identified in this study should now be validated in independent cohorts, examined for differential treatment effects in trials, and inform mechanistic work evaluating treatment targets. FUNDING: National Institutes of Health (T32HL007820, R01AG027472).

Agitation is a Common Barrier to Recovery of ICU Patients.

Importance: Agitation is common in mechanically ventilated ICU patients, but little is known about physician attitudes regarding agitation in this setting. Objectives: To characterize physician attitudes regarding agitation in mechanically ventilated ICU patients. Design, Setting, and Participants: We surveyed critical care physicians within a multicenter health system in Western Pennsylvania, assessing attitudes regarding agitation during mechanical ventilation and use of and confidence in agitation management options. We used quantitative clinical vignettes to determine whether agitation influences confidence regarding readiness for extubation. We sent our survey to 332 critical care physicians, of whom 80 (24%) responded and 69 were eligible (had cared for a mechanically ventilated patient in the preceding three months). Main Outcomes and Measures: Respondent confidence in patient readiness for extubation (0-100%, continuous) and frequency of use and confidence in management options (1-5, Likert). Results: Of 69 eligible responders, 61 (88%) agreed agitation is common and 49 (71%) agreed agitation is a barrier to extubation, but only 27 (39%) agreed their approach to agitation is evidence-based. Attitudes regarding agitation did not differ much by practice setting or physician demographics, though respondents working in medical ICUs were more likely (P = .04) and respondents trained in surgery or emergency medicine were less likely (P = .03) than others to indicate that agitation is an extubation barrier. Fifty-three (77%) respondents reported they frequently use non-pharmacologic measures to treat agitation, and 42 (70%) of those who reported they used non-pharmacologic measures during the prior 3 months indicated confidence in their effectiveness. In responses to clinical vignettes, confidence in patient's readiness for extubation was significantly lower if the patient was agitated (P < .001) or tachypneic (P < .001), but the presence of both agitation and tachypnea did not reduce confidence compared with tachypnea alone (P = .24). Conclusions and Relevance: Most critical care physicians consider agitation during mechanical ventilation a common problem and agreed that agitation is a barrier to extubation. Treatment practice varies widely.

Trajectories of Host-Response Subphenotypes in Patients With COVID-19 Across the Spectrum of Respiratory Support.

BACKGROUND: Hospitalized patients with severe COVID-19 follow heterogeneous clinical trajectories, requiring different levels of respiratory support and experiencing diverse clinical outcomes. Differences in host immune responses to SARS-CoV-2 infection may account for the heterogeneous clinical course, but we have limited data on the dynamic evolution of systemic biomarkers and related subphenotypes. Improved understanding of the dynamic transitions of host subphenotypes in COVID-19 may allow for improved patient selection for targeted therapies. RESEARCH QUESTION: We examined the trajectories of host-response profiles in severe COVID-19 and evaluated their prognostic impact on clinical outcomes. STUDY DESIGN AND METHODS: In this prospective observational study, we enrolled 323 inpatients with COVID-19 receiving different levels of baseline respiratory support: (1) low-flow oxygen (37%), (2) noninvasive ventilation (NIV) or high-flow oxygen (HFO; 29%), (3) invasive mechanical ventilation (27%), and (4) extracorporeal membrane oxygenation (7%). We collected plasma samples on enrollment and at days 5 and 10 to measure host-response biomarkers. We classified patients by inflammatory subphenotypes using two validated predictive models. We examined clinical, biomarker, and subphenotype trajectories and outcomes during hospitalization. RESULTS: IL-6, procalcitonin, and angiopoietin 2 persistently were elevated in patients receiving higher levels of respiratory support, whereas soluble receptor of advanced glycation end products (sRAGE) levels displayed the inverse pattern. Patients receiving NIV or HFO at baseline showed the most dynamic clinical trajectory, with 24% eventually requiring intubation and exhibiting worse 60-day mortality than patients receiving invasive mechanical ventilation at baseline (67% vs 35%; P < .0001). sRAGE levels predicted NIV failure and worse 60-day mortality for patients receiving NIV or HFO, whereas IL-6 levels were predictive in all patients regardless of level of support (P < .01). Patients classified to a hyperinflammatory subphenotype at baseline (< 10%) showed worse 60-day survival (P < .0001) and 50% of them remained classified as hyperinflammatory at 5 days after enrollment. INTERPRETATION: Longitudinal study of the systemic host response in COVID-19 revealed substantial and predictive interindividual variability influenced by baseline levels of respiratory support.

Pharmacologic Management of Delirium in the Intensive Care Unit.

Delirium, often underdiagnosed in the intensive care unit, is a common complication of critical illness that contributes to significant morbidity and mortality. Clinicians should be aware of common risk factors and triggers and should work to mitigate these as much as possible to reduce the occurrence of delirium. This review first provides an overview of the epidemiology, pathophysiology, evaluation, and consequences of delirium in critically ill patients. Presented next is the current evidence for the pharmacologic management of delirium, focusing on prevention and treatment of delirium in the intensive care unit. It concludes by outlining some emerging treatments of delirium.

Treatment of Delirium During Critical Illness.

Delirium, an acute disturbance in mental status due to another medical condition, is common and morbid in the intensive care unit. Despite its clear association with multiple common risk factors and important outcomes, including mortality and long-term cognitive impairment, both the ultimate causes of and ideal treatments for delirium remain unclear. Studies suggest that neuroinflammation, hypoxia, alterations in energy metabolism, and imbalances in multiple neurotransmitter pathways contribute to delirium, but commonly used treatments (e.g., antipsychotic medications) target only one or a few of these potential mechanisms and are not supported by evidence of efficacy. At this time, the optimal treatment for delirium during critical illness remains avoidance of risk factors, though ongoing trials may expand on the promise shown by agents such as melatonin and dexmedetomidine.