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.

From Traditional Typing to Intelligent Insights: A Narrative Review of Directions Toward Targeted Therapies in Delirium.

Delirium is a heterogeneous syndrome characterized by an acute change in level of consciousness that is associated with inattention and disorganized thinking. Delirium affects most critically ill patients and is associated with poor patient-oriented outcomes such as increased mortality, longer ICU and hospital length of stay, and worse long-term cognitive outcomes. The concept of delirium and its subtypes has existed since nearly the beginning of recorded medical literature, yet robust therapies have yet to be identified. Analogous to other critical illness syndromes, we suspect the lack of identified therapies stems from patient heterogeneity and prior subtyping efforts that do not capture the underlying etiology of delirium. The time has come to leverage machine learning approaches, such as supervised and unsupervised clustering, to identify clinical and pathophysiological distinct clusters of delirium that will likely respond differently to various interventions. We use sedation in the ICU as an example of how precision therapies can be applied to critically ill patients, highlighting the fact that while for some patients a sedative drug may cause delirium, in another cohort sedation is the specific treatment. Finally, we conclude with a proposition to move away from the term delirium, and rather focus on the treatable traits that may allow precision therapies to be tested.

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.

Inference and validation of predictive gene networks from biomedical literature and gene expression data.

Although many methods have been developed for inference of biological networks, the validation of the resulting models has largely remained an unsolved problem. Here we present a framework for quantitative assessment of inferred gene interaction networks using knock-down data from cell line experiments. Using this framework we are able to show that network inference based on integration of prior knowledge derived from the biomedical literature with genomic data significantly improves the quality of inferred networks relative to other approaches. Our results also suggest that cell line experiments can be used to quantitatively assess the quality of networks inferred from tumor samples.

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).

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.

Inter-rater reliability and prognostic value of baseline Radiographic Assessment of Lung Edema (RALE) scores in observational cohort studies of inpatients with COVID-19.

OBJECTIVES: To reliably quantify the radiographic severity of COVID-19 pneumonia with the Radiographic Assessment of Lung Edema (RALE) score on clinical chest X-rays among inpatients and examine the prognostic value of baseline RALE scores on COVID-19 clinical outcomes. SETTING: Hospitalised patients with COVID-19 in dedicated wards and intensive care units from two different hospital systems. PARTICIPANTS: 425 patients with COVID-19 in a discovery data set and 415 patients in a validation data set. PRIMARY AND SECONDARY OUTCOMES: We measured inter-rater reliability for RALE score annotations by different reviewers and examined for associations of consensus RALE scores with the level of respiratory support, demographics, physiologic variables, applied therapies, plasma host-response biomarkers, SARS-CoV-2 RNA load and clinical outcomes. RESULTS: Inter-rater agreement for RALE scores improved from fair to excellent following reviewer training and feedback (intraclass correlation coefficient of 0.85 vs 0.93, respectively). In the discovery cohort, the required level of respiratory support at the time of CXR acquisition (supplemental oxygen or non-invasive ventilation (n=178); invasive-mechanical ventilation (n=234), extracorporeal membrane oxygenation (n=13)) was significantly associated with RALE scores (median (IQR): 20.0 (14.1-26.7), 26.0 (20.5-34.0) and 44.5 (34.5-48.0), respectively, p<0.0001). Among invasively ventilated patients, RALE scores were significantly associated with worse respiratory mechanics (plateau and driving pressure) and gas exchange metrics (PaO2/FiO2 and ventilatory ratio), as well as higher plasma levels of IL-6, soluble receptor of advanced glycation end-products and soluble tumour necrosis factor receptor 1 (p<0.05). RALE scores were independently associated with 90-day survival in a multivariate Cox proportional hazards model (adjusted HR 1.04 (1.02-1.07), p=0.002). We replicated the significant associations of RALE scores with baseline disease severity and mortality in the independent validation data set. CONCLUSIONS: With a reproducible method to measure radiographic severity in COVID-19, we found significant associations with clinical and physiologic severity, host inflammation and clinical outcomes. The incorporation of radiographic severity assessments in clinical decision-making may provide important guidance for prognostication and treatment allocation in COVID-19.

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.

Trajectories of host-response biomarkers and inflammatory subphenotypes in COVID-19 patients across the spectrum of respiratory support.

Purpose: Enhanced understanding of the dynamic changes in the dysregulated inflammatory response in COVID-19 may help improve patient selection and timing for immunomodulatory therapies. Methods: We enrolled 323 COVID-19 inpatients on different levels of baseline respiratory support: i) Low Flow Oxygen (37%), ii) Non-Invasive Ventilation or High Flow Oxygen (NIV_HFO, 29%), iii) Invasive Mechanical Ventilation (IMV, 27%), and iv) Extracorporeal Membrane Oxygenation (ECMO, 7%). We collected plasma samples upon enrollment and days 5 and 10 to measure host-response biomarkers. We classified subjects into 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 were persistently elevated in patients at higher levels of respiratory support, whereas sRAGE displayed the inverse pattern. Patients on NIV_HFO at baseline had the most dynamic clinical trajectory, with 26% eventually requiring intubation and exhibiting worse 60-day mortality than IMV patients at baseline (67% vs. 35%, p<0.0001). sRAGE levels predicted NIV failure and worse 60-day mortality for NIV_HFO patients, whereas IL-6 levels were predictive in IMV or ECMO patients. Hyper-inflammatory subjects at baseline (<10% by both models) had worse 60-day survival (p<0.0001) and 50% of them remained classified as hyper-inflammatory on follow-up sampling at 5 days post-enrollment. Receipt of combined immunomodulatory therapies (steroids and anti-IL6 agents) was associated with markedly increased IL-6 and lower Angiopoietin-2 levels (p<0.05). Conclusions: Longitudinal study of systemic host responses in COVID-19 revealed substantial and predictive inter-individual variability, influenced by baseline levels of respiratory support and concurrent immunomodulatory therapies.

Radiographic Assessment of Lung Edema (RALE) Scores are Highly Reproducible and Prognostic of Clinical Outcomes for Inpatients with COVID-19.

INTRODUCTION: Chest imaging is necessary for diagnosis of COVID-19 pneumonia, but current risk stratification tools do not consider radiographic severity. We quantified radiographic heterogeneity among inpatients with COVID-19 with the Radiographic Assessment of Lung Edema (RALE) score on Chest X-rays (CXRs). METHODS: We performed independent RALE scoring by ≥2 reviewers on baseline CXRs from 425 inpatients with COVID-19 (discovery dataset), we recorded clinical variables and outcomes, and measured plasma host-response biomarkers and SARS-CoV-2 RNA load from subjects with available biospecimens. RESULTS: We found excellent inter-rater agreement for RALE scores (intraclass correlation co-efficient=0.93). The required level of respiratory support at the time of baseline CXRs (supplemental oxygen or non-invasive ventilation [n=178]; invasive-mechanical ventilation [n=234], extracorporeal membrane oxygenation [n=13]) was significantly associated with RALE scores (median [interquartile range]: 20.0[14.1-26.7], 26.0[20.5-34.0] and 44.5[34.5-48.0], respectively, p<0.0001). Among invasively-ventilated patients, RALE scores were significantly associated with worse respiratory mechanics (plateau and driving pressure) and gas exchange metrics (PaO2/FiO2 and ventilatory ratio), as well as higher plasma levels of IL-6, sRAGE and TNFR1 levels (p<0.05). RALE scores were independently associated with 90-day survival in a multivariate Cox proportional hazards model (adjusted hazard ratio 1.04[1.02-1.07], p=0.002). We validated significant associations of RALE scores with baseline severity and mortality in an independent dataset of 415 COVID-19 inpatients. CONCLUSION: Reproducible assessment of radiographic severity revealed significant associations with clinical and physiologic severity, host-response biomarkers and clinical outcome in COVID-19 pneumonia. Incorporation of radiographic severity assessments may provide prognostic and treatment allocation guidance in patients hospitalized with COVID-19.

Using shRNA experiments to validate gene regulatory networks.

Quantitative validation of gene regulatory networks (GRNs) inferred from observational expression data is a difficult task usually involving time intensive and costly laboratory experiments. We were able to show that gene knock-down experiments can be used to quantitatively assess the quality of large-scale GRNs via a purely data-driven approach (Olsen et al. 2014). Our new validation framework also enables the statistical comparison of multiple network inference techniques, which was a long-standing challenge in the field. In this Data in Brief we detail the contents and quality controls for the gene expression data (available from NCBI Gene Expression Omnibus repository with accession number GSE53091) associated with our study published in Genomics (Olsen et al. 2014). We also provide R code to access the data and reproduce the analysis presented in this article.

Therapeutic implications of GIPC1 silencing in cancer.

GIPC1 is a cytoplasmic scaffold protein that interacts with numerous receptor signaling complexes, and emerging evidence suggests that it plays a role in tumorigenesis. GIPC1 is highly expressed in a number of human malignancies, including breast, ovarian, gastric, and pancreatic cancers. Suppression of GIPC1 in human pancreatic cancer cells inhibits in vivo tumor growth in immunodeficient mice. To better understand GIPC1 function, we suppressed its expression in human breast and colorectal cancer cell lines and human mammary epithelial cells (HMECs) and assayed both gene expression and cellular phenotype. Suppression of GIPC1 promotes apoptosis in MCF-7, MDA-MD231, SKBR-3, SW480, and SW620 cells and impairs anchorage-independent colony formation of HMECs. These observations indicate GIPC1 plays an essential role in oncogenic transformation, and its expression is necessary for the survival of human breast and colorectal cancer cells. Additionally, a GIPC1 knock-down gene signature was used to interrogate publically available breast and ovarian cancer microarray datasets. This GIPC1 signature statistically correlates with a number of breast and ovarian cancer phenotypes and clinical outcomes, including patient survival. Taken together, these data indicate that GIPC1 inhibition may represent a new target for therapeutic development for the treatment of human cancers.