Platelet Bioenergetics and Associations With Delirium and Coma in Patients With Sepsis: A Prospective Cohort Study.

BACKGROUND: Acute brain dysfunction during sepsis, which manifests as delirium or coma, is common and is associated with multiple adverse outcomes, including longer periods of mechanical ventilation, prolonged hospital stays, and increased mortality. Delirium and coma during sepsis may be manifestations of alteration in systemic metabolism. Because access to brain mitochondria is a limiting factor, measurement of peripheral platelet bioenergetics offers a potential opportunity to understand metabolic changes associated with acute brain dysfunction during sepsis. RESEARCH QUESTION: Are altered platelet mitochondrial bioenergetics associated with acute brain dysfunction during sepsis? STUDY DESIGN AND METHODS: We assessed participants with critical illness in the ICU for the presence of delirium or coma via validated assessment measures. Blood samples were collected and processed to isolate and measure platelet mitochondrial oxygen consumption. We used Seahorse extracellular flux to measure directly baseline, proton leak, maximal oxygen consumption rate, and extracellular acidification rate. We calculated adenosine triphosphate-linked, spare respiratory capacity, and nonmitochondrial oxygen consumption rate from the measured values. RESULTS: Maximum oxygen consumption was highest in patients with coma, as was spare respiratory capacity and extracellular acidification rate in unadjusted analysis. After adjusting for age, sedation, modified Sequential Organ Failure Assessment score without the neurologic component, and preexisting cognitive function, increased spare respiratory capacity remained associated with coma. Delirium was not associated with any platelet mitochondrial bioenergetics. INTERPRETATION: In this single-center exploratory prospective cohort study, we found that increased platelet mitochondrial spare respiratory capacity was associated with coma in patients with sepsis. Future studies powered to determine any relationship between delirium and mitochondrial respiration bioenergetics are needed.

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).

Advancing specificity in delirium: The delirium subtyping initiative.

BACKGROUND: Delirium, a common syndrome with heterogeneous etiologies and clinical presentations, is associated with poor long-term outcomes. Recording and analyzing all delirium equally could be hindering the field's understanding of pathophysiology and identification of targeted treatments. Current delirium subtyping methods reflect clinically evident features but likely do not account for underlying biology. METHODS: The Delirium Subtyping Initiative (DSI) held three sessions with an international panel of 25 experts. RESULTS: Meeting participants suggest further characterization of delirium features to complement the existing Diagnostic and Statistical Manual of Mental Disorders Fifth Edition Text Revision diagnostic criteria. These should span the range of delirium-spectrum syndromes and be measured consistently across studies. Clinical features should be recorded in conjunction with biospecimen collection, where feasible, in a standardized way, to determine temporal associations of biology coincident with clinical fluctuations. DISCUSSION: The DSI made recommendations spanning the breadth of delirium research including clinical features, study planning, data collection, and data analysis for characterization of candidate delirium subtypes. HIGHLIGHTS: Delirium features must be clearly defined, standardized, and operationalized. Large datasets incorporating both clinical and biomarker variables should be analyzed together. Delirium screening should incorporate communication and reasoning.

Understanding and Managing Anxiety Sensitivity During Critical Illness and Long-Term Recovery.

Anxiety sensitivity is a fear of symptoms associated with anxiety (eg, rapid respiration and heart rate, perspiration), also known as "fear of fear." This fear is a misinterpretation of nonthreatening symptoms as threatening across 3 domains: physical ("When my heart rate increases, I'm afraid I may have a heart attack"), social ("If people see me perspire, I fear they will negatively evaluate me"), and cognitive ("When I feel these symptoms, I fear it means I'm going crazy or will lose control and do something dangerous like disconnect my IV"). These thoughts stimulate the sympathetic nervous system, resulting in stronger sensations and further catastrophic misinterpretations, which may spiral into a panic attack. Strategies to address anxiety sensitivity include pharmacologic and nonpharmacologic interventions. In intensive care unit settings, anxiety sensitivity may be related to common monitoring and interventional procedures (eg, oxygen therapy, repositioning, use of urine collection systems). Anxiety sensitivity can be a barrier to weaning from mechanical ventilation when patients are uncomfortable following instructions to perform awakening or breathing trials. Fortunately, anxiety sensitivity is a malleable trait with evidence-based intervention options. However, few health care providers are aware of this psychological construct and available treatment. This article describes the nature of anxiety sensitivity, its potential impact on intensive care, how to assess and interpret scores from validated instruments such as the Anxiety Sensitivity Index, and treatment approaches across the critical care trajectory, including long-term recovery. Implications for critical care practice and future directions are also addressed.

A voice-based digital assistant for intelligent prompting of evidence-based practices during ICU rounds.

OBJECTIVE: To evaluate the technical feasibility and potential value of a digital assistant that prompts intensive care unit (ICU) rounding teams to use evidence-based practices based on analysis of their real-time discussions. METHODS: We evaluated a novel voice-based digital assistant which audio records and processes the ICU care team's rounding discussions to determine which evidence-based practices are applicable to the patient but have yet to be addressed by the team. The system would then prompt the team to consider indicated but not yet delivered practices, thereby reducing cognitive burden compared to traditional rigid rounding checklists. In a retrospective analysis, we applied automatic transcription, natural language processing, and a rule-based expert system to generate personalized prompts for each patient in 106 audio-recorded ICU rounding discussions. To assess technical feasibility, we compared the system's prompts to those created by experienced critical care nurses who directly observed rounds. To assess potential value, we also compared the system's prompts to a hypothetical paper checklist containing all evidence-based practices. RESULTS: The positive predictive value, negative predictive value, true positive rate, and true negative rate of the system's prompts were 0.45 ± 0.06, 0.83 ± 0.04, 0.68 ± 0.07, and 0.66 ± 0.04, respectively. If implemented in lieu of a paper checklist, the system would generate 56% fewer prompts per patient, with 50%±17% greater precision. CONCLUSION: A voice-based digital assistant can reduce prompts per patient compared to traditional approaches for improving evidence uptake on ICU rounds. Additional work is needed to evaluate field performance and team acceptance.

Identifying Comorbid Subtypes of Patients With Acute Respiratory Failure.

BACKGROUND: Patients with acute respiratory failure have multiple risk factors for disability following their intensive care unit stay. Interventions to facilitate independence at hospital discharge may be more effective if personalized for patient subtypes. OBJECTIVES: To identify subtypes of patients with acute respiratory failure requiring mechanical ventilation and compare post-intensive care functional disability and intensive care unit mobility level among subtypes. METHODS: Latent class analysis was conducted in a cohort of adult medical intensive care unit patients with acute respiratory failure receiving mechanical ventilation who survived to hospital discharge. Demographic and clinical medical record data were collected early in the stay. Clinical characteristics and outcomes were compared among subtypes by using Kruskal-Wallis tests and χ2 tests of independence. RESULTS: In a cohort of 934 patients, the 6-class model provided the optimal fit. Patients in class 4 (obesity and kidney impairment) had worse functional impairment at hospital discharge than patients in classes 1 through 3. Patients in class 3 (alert patients) had the lowest magnitude of functional impairment (P < .001) and achieved the earliest out-of-bed mobility and highest mobility level of all subtypes (P < .001). CONCLUSIONS: Acute respiratory failure survivor subtypes identified from clinical data available early in the intensive care unit stay differ in post-intensive care functional disability. Future research should target high-risk patients in early rehabilitation trials in the intensive care unit. Additional investigation of contextual factors and mechanisms of disability is critical to improving quality of life in acute respiratory failure survivors.

Return to Driving After Critical Illness.

This cross-sectional study examines the postintensive care syndrome in patients who had vs patients who had not resumed driving 1 month after hospitalization for a critical illness.

Measuring Performance on the ABCDEF Bundle During Interprofessional Rounds via a Nurse-Based Assessment Tool.

BACKGROUND: Nurse-led rounding checklists are a common strategy for facilitating evidence-based practice in the intensive care unit (ICU). To streamline checklist workflow, some ICUs have the nurse or another individual listen to the conversation and customize the checklist for each patient. Such customizations assume that individuals can reliably assess whether checklist items have been addressed. OBJECTIVE: To evaluate whether 1 critical care nurse can reliably assess checklist items on rounds. METHODS: Two nurses performed in-person observation of multidisciplinary ICU rounds. Using a standardized paper-based assessment tool, each nurse indicated whether 17 items related to the ABCDEF bundle were discussed during rounds. For each item, generalizability coefficients were used as a measure of reliability, with a single-rater value of 0.70 or greater considered sufficient to support its assessment by 1 nurse. RESULTS: The nurse observers assessed 118 patient discussions across 15 observation days. For 11 of 17 items (65%), the generalizability coefficient for a single rater met or exceeded the 0.70 threshold. The generalizability coefficients (95% CIs) of a single rater for key items were as follows: pain, 0.86 (0.74-0.97); delirium score, 0.74 (0.64-0.83); agitation score, 0.72 (0.33-1.00); spontaneous awakening trial, 0.67 (0.49-0.83); spontaneous breathing trial, 0.80 (0.70-0.89); mobility, 0.79 (0.69-0.87); and family (future/past) engagement, 0.82 (0.73-0.90). CONCLUSION: Using a paper-based assessment tool, a single trained critical care nurse can reliably assess the discussion of elements of the ABCDEF bundle during multidisciplinary rounds.

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.