Causes and attributable fraction of death from ARDS in inflammatory phenotypes of sepsis

BACKGROUND

Hypoinflammatory and hyperinflammatory phenotypes have been identified in both Acute Respiratory Distress Syndrome (ARDS) and sepsis. Attributable mortality of ARDS in each phenotype of sepsis is yet to be determined. We aimed to estimate the population attributable fraction of death from ARDS (PAFARDS) in hypoinflammatory and hyperinflammatory sepsis, and to determine the primary cause of death within each phenotype.

METHODS

We studied 1737 patients with sepsis from two prospective cohorts. Patients were previously assigned to the hyperinflammatory or hypoinflammatory phenotype using latent class analysis. The PAFARDS in patients with sepsis was estimated separately in the hypo and hyperinflammatory phenotypes. Organ dysfunction, severe comorbidities, and withdrawal of life support were abstracted from the medical record in a subset of patients from the EARLI cohort who died (n = 130/179). Primary cause of death was defined as the organ system that most directly contributed to death or withdrawal of life support.

RESULTS

The PAFARDS was 19% (95%CI 10,28%) in hypoinflammatory sepsis and, 14% (95%CI 6,20%) in hyperinflammatory sepsis. Cause of death differed between the two phenotypes (p < 0.001). Respiratory failure was the most common cause of death in hypoinflammatory sepsis, whereas circulatory shock was the most common cause in hyperinflammatory sepsis. Death with severe underlying comorbidities was more frequent in hypoinflammatory sepsis (81% vs. 67%, p = 0.004).

CONCLUSIONS

The PAFARDS is modest in both phenotypes whereas primary cause of death among patients with sepsis differed substantially by phenotype. This study identifies challenges in powering future clinical trials to detect changes in mortality outcomes among patients with sepsis and ARDS.

Associations Between Volume of Early Intravenous Fluid and Hospital Outcomes in Septic Patients With and Without Heart Failure: A Retrospective Cohort Study

OBJECTIVES

To evaluate the relationship between early IV fluid volume and hospital outcomes, including death in-hospital or discharge to hospice, in septic patients with and without heart failure (HF).

DESIGN

A retrospective cohort study using logistic regression with restricted cubic splines to assess for nonlinear relationships between fluid volume and outcomes, stratified by HF status and adjusted for propensity to receive a given fluid volume in the first 6 hours. An ICU subgroup analysis was performed. Secondary outcomes of vasopressor use, mechanical ventilation, and length of stay in survivors were assessed.

SETTING

An urban university-based hospital.

PATIENTS

A total of 9613 adult patients were admitted from the emergency department from 2012 to 2021 that met electronic health record-based Sepsis-3 criteria. Preexisting HF diagnosis was identified by the International Classification of Diseases codes.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

There were 1449 admissions from patients with HF. The relationship between fluid volume and death or discharge to hospice was nonlinear in patients without HF, and approximately linear in patients with HF. Receiving 0-15 mL/kg in the first 6 hours was associated with lower likelihood of death or discharge to hospice compared with 30-45 mL/kg (odds ratio = 0.61; 95% CI, 0.41-0.90; p = 0.01) in HF patients, but no significant difference for non-HF patients. A similar pattern was identified in ICU admissions and some secondary outcomes. Volumes larger than 15-30 mL/kg for non-HF patients and 30-45 mL/kg for ICU-admitted non-HF patients were not associated with improved outcomes.

CONCLUSIONS

Early fluid resuscitation showed distinct patterns of potential harm and benefit between patients with and without HF who met Sepsis-3 criteria. Restricted cubic splines analysis highlighted the importance of considering nonlinear fluid outcomes relationships and identified potential points of diminishing returns (15-30 mL/kg across all patients without HF and 30-45 mL/kg when admitted to the ICU). Receiving less than 15 mL/kg was associated with better outcomes in HF patients, suggesting small volumes may be appropriate in select patients. Future studies may benefit from investigating nonlinear fluid-outcome associations and a focus on other conditions like HF.

Rapidly improving ARDS differs clinically and biologically from persistent ARDS

BACKGROUND

Rapidly improving acute respiratory distress syndrome (RIARDS) is an increasingly appreciated subgroup of ARDS in which hypoxemia improves within 24 h after initiation of mechanical ventilation. Detailed clinical and biological features of RIARDS have not been clearly defined, and it is unknown whether RIARDS is associated with the hypoinflammatory or hyperinflammatory phenotype of ARDS. The purpose of this study was to define the clinical and biological features of RIARDS and its association with inflammatory subphenotypes.

METHODS

We analyzed data from 215 patients who met Berlin criteria for ARDS (endotracheally intubated) and were enrolled in a prospective observational cohort conducted at two sites, one tertiary care center and one urban safety net hospital. RIARDS was defined according to previous studies as improvement of hypoxemia defined as (i) PaO2:FiO2 > 300 or (ii) SpO2: FiO2 > 315 on the day following diagnosis of ARDS (day 2) or (iii) unassisted breathing by day 2 and for the next 48 h (defined as absence of endotracheal intubation on day 2 through day 4). Plasma biomarkers were measured on samples collected on the day of study enrollment, and ARDS phenotypes were allocated as previously described.

RESULTS

RIARDS accounted for 21% of all ARDS participants. Patients with RIARDS had better clinical outcomes compared to those with persistent ARDS, with lower hospital mortality (13% vs. 57%; p value < 0.001) and more ICU-free days (median 24 vs. 0; p value < 0.001). Plasma levels of interleukin-6, interleukin-8, and plasminogen activator inhibitor-1 were significantly lower among patients with RIARDS. The hypoinflammatory phenotype of ARDS was more common among patients with RIARDS (78% vs. 51% in persistent ARDS; p value = 0.001).

CONCLUSIONS

This study identifies a high prevalence of RIARDS in a multicenter observational cohort and confirms the more benign clinical course of these patients. We report the novel finding that RIARDS is characterized by lower concentrations of plasma biomarkers of inflammation compared to persistent ARDS, and that hypoinflammatory ARDS is more prevalent among patients with RIARDS. Identification and exclusion of RIARDS could potentially improve prognostic and predictive enrichment in clinical trials.

Host and Microbe Blood Metagenomics Reveals Key Pathways Characterizing Critical Illness Phenotypes

Rationale: Two molecular phenotypes of sepsis and acute respiratory distress syndrome, termed hyperinflammatory and hypoinflammatory, have been consistently identified by latent class analysis in numerous cohorts, with widely divergent clinical outcomes and differential responses to some treatments; however, the key biological differences between these phenotypes remain poorly understood.Objectives: We used host and microbe metagenomic sequencing data from blood to deepen our understanding of biological differences between latent class analysis-derived phenotypes and to assess concordance between the latent class analysis-derived phenotypes and phenotypes reported by other investigative groups (e.g., Sepsis Response Signature [SRS1-2], molecular diagnosis and risk stratification of sepsis [MARS1-4], reactive and uninflamed).Methods: We analyzed data from 113 patients with hypoinflammatory sepsis and 76 patients with hyperinflammatory sepsis enrolled in a two-hospital prospective cohort study. Molecular phenotypes had been previously assigned using latent class analysis.Measurements and Main Results: The hyperinflammatory and hypoinflammatory phenotypes of sepsis had distinct gene expression signatures, with 5,755 genes (31%) differentially expressed. The hyperinflammatory phenotype was associated with elevated expression of innate immune response genes, whereas the hypoinflammatory phenotype was associated with elevated expression of adaptive immune response genes and, notably, T cell response genes. Plasma metagenomic analysis identified differences in prevalence of bacteremia, bacterial DNA abundance, and composition between the phenotypes, with an increased presence and abundance of Enterobacteriaceae in the hyperinflammatory phenotype. Significant overlap was observed between these phenotypes and previously identified transcriptional subtypes of acute respiratory distress syndrome (reactive and uninflamed) and sepsis (SRS1-2). Analysis of data from the VANISH trial indicated that corticosteroids might have a detrimental effect in patients with the hypoinflammatory phenotype.Conclusions: The hyperinflammatory and hypoinflammatory phenotypes have distinct transcriptional and metagenomic features that could be leveraged for precision treatment strategies.

Timing of antibiotic treatment identifies distinct clinical presentations among patients presenting with suspected septic shock

Objective

Recent clinical guidelines for sepsis management emphasize immediate antibiotic initiation for suspected septic shock. Though hypotension is a high-risk marker of sepsis severity, prior studies have not considered the precise timing of hypotension in relation to antibiotic initiation and how clinical characteristics and outcomes may differ. Our objective was to evaluate antibiotic initiation in relation to hypotension to characterize differences in sepsis presentation and outcomes in patients with suspected septic shock.

Methods

Adults presenting to the emergency department (ED) June 2012-December 2018 diagnosed with sepsis (Sepsis-III electronic health record [EHR] criteria) and hypotension (non-resolving for ≥30 min, systolic blood pressure <90 mmHg) within 24 h. We categorized patients who received antibiotics before hypotension ("early"), 0-60 min after ("immediate"), and >60 min after ("late") treatment.

Results

Among 2219 patients, 55% received early treatment, 13% immediate, and 32% late. The late subgroup often presented to the ED with hypotension (median 0 min) but received antibiotics a median of 191 min post-ED presentation. Clinical characteristics notable for this subgroup included higher prevalence of heart failure and liver disease (p < 0.05) and later onset of systemic inflammatory response syndrome (SIRS) criteria compared to early/immediate treatment subgroups (median 87 vs. 35 vs. 20 min, p < 0.0001). After adjustment, there was no difference in clinical outcomes among treatment subgroups.

Conclusions

There was significant heterogeneity in presentation and timing of antibiotic initiation for suspected septic shock. Patients with later treatment commonly had hypotension on presentation, had more hypotension-associated comorbidities, and developed overt markers of infection (eg, SIRS) later. While these factors likely contribute to delays in clinician recognition of suspected septic shock, it may not impact sepsis outcomes.

Exploring the Impact of COVID-19 on Women Hospitalists: A Mixed-Gender Qualitative Analysis

BACKGROUND

Women physicians have faced persistent challenges, including gender bias, salary inequities, a disproportionate share of caregiving and domestic responsibilities, and limited representation in leadership. Data indicate the COVID-19 pandemic further highlighted and exacerbated these inequities.

OBJECTIVE

To understand the pandemic's impact on women physicians and to brainstorm solutions to better support women physicians.

DESIGN

Mixed-gender semi-structured focus groups.

PARTICIPANTS

Hospitalists in the Hospital Medicine Reengineering Network (HOMERuN).

APPROACH

Six semi-structured virtual focus groups were held with 22 individuals from 13 institutions comprised primarily of academic hospitalist physicians. Rapid qualitative methods including templated summaries and matrix analysis were applied to identify major themes and subthemes.

KEY RESULTS

Four key themes emerged: (1) the pandemic exacerbated perceived gender inequities, (2) women's academic productivity and career development were negatively impacted, (3) women held disproportionate roles as caregivers and household managers, and (4) institutional pandemic responses were often misaligned with workforce needs, especially those of women hospitalists. Multiple interventions were proposed including: creating targeted workforce solutions and benefits to address the disproportionate caregiving burden placed on women, addressing hospitalist scheduling and leave practices, ensuring promotion pathways value clinical and COVID-19 contributions, creating transparency around salary and non-clinical time allocation, and ensuring women are better represented in leadership roles.

CONCLUSIONS

Hospitalists perceived and experienced that women physicians faced negative impacts from the pandemic in multiple domains including leadership opportunities and scholarship, while also shouldering larger caregiving duties than men. There are many opportunities to improve workplace conditions for women; however, current institutional efforts were perceived as misaligned to actual needs. Thus, policy and programmatic changes, such as those proposed by this cohort of hospitalists, are needed to advance equity in the workplace.

Identifying molecular phenotypes in sepsis: an analysis of two prospective observational cohorts and secondary analysis of two randomised controlled trials

BACKGROUND

In sepsis and acute respiratory distress syndrome (ARDS), heterogeneity has contributed to difficulty identifying effective pharmacotherapies. In ARDS, two molecular phenotypes (hypoinflammatory and hyperinflammatory) have consistently been identified, with divergent outcomes and treatment responses. In this study, we sought to derive molecular phenotypes in critically ill adults with sepsis, determine their overlap with previous ARDS phenotypes, and evaluate whether they respond differently to treatment in completed sepsis trials.

METHODS

We used clinical data and plasma biomarkers from two prospective sepsis cohorts, the Validating Acute Lung Injury biomarkers for Diagnosis (VALID) study (N=1140) and the Early Assessment of Renal and Lung Injury (EARLI) study (N=818), in latent class analysis (LCA) to identify the optimal number of classes in each cohort independently. We used validated models trained to classify ARDS phenotypes to evaluate concordance of sepsis and ARDS phenotypes. We applied these models retrospectively to the previously published Prospective Recombinant Human Activated Protein C Worldwide Evaluation in Severe Sepsis and Septic Shock (PROWESS-SHOCK) trial and Vasopressin and Septic Shock Trial (VASST) to assign phenotypes and evaluate heterogeneity of treatment effect.

FINDINGS

A two-class model best fit both VALID and EARLI (p<0·0001). In VALID, 804 (70·5%) of the 1140 patients were classified as hypoinflammatory and 336 (29·5%) as hyperinflammatory; in EARLI, 530 (64·8%) of 818 were hypoinflammatory and 288 (35·2%) hyperinflammatory. We observed higher plasma pro-inflammatory cytokines, more vasopressor use, more bacteraemia, lower protein C, and higher mortality in the hyperinflammatory than in the hypoinflammatory phenotype (p<0·0001 for all). Classifier models indicated strong concordance between sepsis phenotypes and previously identified ARDS phenotypes (area under the curve 0·87-0·96, depending on the model). Findings were similar excluding participants with both sepsis and ARDS. In PROWESS-SHOCK, 1142 (68·0%) of 1680 patients had the hypoinflammatory phenotype and 538 (32·0%) had the hyperinflammatory phenotype, and response to activated protein C differed by phenotype (p=0·0043). In VASST, phenotype proportions were similar to other cohorts; however, no treatment interaction with the type of vasopressor was observed (p=0·72).

INTERPRETATION

Molecular phenotypes previously identified in ARDS are also identifiable in multiple sepsis cohorts and respond differently to activated protein C. Molecular phenotypes could represent a treatable trait in critical illness beyond the patient's syndromic diagnosis.

FUNDING

US National Institutes of Health.

Distinct pulmonary and systemic effects of dexamethasone in severe COVID-19

Dexamethasone is the standard of care for critically ill patients with COVID-19, but the mechanisms by which it decreases mortality and its immunological effects in this setting are not understood. We performed bulk and single-cell RNA sequencing of the lower respiratory tract and blood, and plasma cytokine profiling to study the effect of dexamethasone on systemic and pulmonary immune cells. We find decreased signatures of antigen presentation, T cell recruitment, and viral injury in patients treated with dexamethasone. We identify compartment- and cell- specific differences in the effect of dexamethasone in patients with severe COVID-19 that are reproducible in publicly available datasets. Our results highlight the importance of studying compartmentalized inflammation in critically ill patients.

Early plasma angiopoietin-2 is prognostic for ARDS and mortality among critically ill patients with sepsis

Angiopoietin-2 (Ang-2) is associated with vascular endothelial injury and permeability in the acute respiratory distress syndrome (ARDS) and sepsis. Elevated circulating Ang-2 levels may identify critically ill patients with distinct pathobiology amenable to targeted therapy. We hypothesized that plasma Ang-2 measured shortly after hospitalization among patients with sepsis would be associated with the development of ARDS and poor clinical outcomes. To test this hypothesis, we measured plasma Ang-2 in a cohort of 757 patients with sepsis, including 267 with ARDS, enrolled in the emergency department or early in their ICU course before the COVID-19 pandemic. Multivariable models were used to test the association of Ang-2 with the development of ARDS and 30-day morality. We found that early plasma Ang-2 in sepsis was associated with higher baseline severity of illness, the development of ARDS, and mortality risk. The association between Ang-2 and mortality was strongest among patients with ARDS and sepsis as compared to those with sepsis alone (OR 1.81 vs. 1.52 per log Ang-2 increase). These findings might inform models testing patient risk prediction and strengthen the evidence for Ang-2 as an appealing biomarker for patient selection for novel therapeutic agents to target vascular injury in sepsis and ARDS.

Building a thriving academic hospitalist workforce: A rapid qualitative analysis identifying key areas of focus in the field

BACKGROUND

The hospitalist workforce has been at the forefront of the pandemic and has been stretched in both clinical and nonclinical domains. We aimed to understand current and future workforce concerns, as well as strategies to cultivate a thriving hospital medicine workforce.

DESIGN, SETTING, AND PARTICIPANTS

We conducted qualitative, semistructured focus groups with practicing hospitalists via video conferencing (Zoom). Utilizing components from the Brainwriting Premortem Approach, attendees were split into small focus groups and listed their thoughts about workforce issues that hospitalists may encounter in the next 3 years, identifying the highest priority workforce issues for the hospital medicine community. Each small group discussed the most pressing workforce issues. These ideas were then shared across the entire group and ranked. We used rapid qualitative analysis to guide a structured exploration of themes and subthemes.

RESULTS

Five focus groups were held with 18 participants from 13 academic institutions. We identified five key areas: (1) support for workforce wellness; (2) staffing and pipeline development to maintain an adequate workforce to match clinical growth; (3) scope of work, including how hospitalist work is defined and whether the clinical skillset should be expanded; (4) commitment to the academic mission in the setting of rapid and unpredictable clinical growth; and (5) alignment between the duties of hospitalists and resources of hospitals. Hospitalists voiced numerous concerns about the future of our workforce. Several domains were identified as high-priority areas of focus to address current and future challenges.

Plasma metabolic profiling implicates dysregulated lipid metabolism and glycolytic shift in hyperinflammatory ARDS

Using latent class analysis (LCA) of clinical and protein biomarkers, researchers have identified two phenotypes of the acute respiratory distress syndrome (ARDS) with divergent clinical trajectories and treatment responses. We investigated whether plasma metabolites differed among patients with LCA-derived hyperinflammatory and hypoinflammatory ARDS, and we tested the prognostic utility of adding metabolic clusters to LCA phenotypes. We analyzed data from 93 patients with ARDS and sepsis enrolled in a multicenter prospective cohort of critically ill patients, comparing 970 metabolites between the two LCA-derived phenotypes. In all, 188 metabolites were differentially abundant between the two LCA-derived phenotypes. After adjusting for age, sex, confounding medications, and comorbid liver and kidney disease, 82 metabolites remained significantly different. Patients with hyperinflammatory ARDS had reduced circulating lipids but high levels of pyruvate, lactate, and malate. Metabolic cluster and LCA-derived phenotypes were each significantly and independently associated with survival. Patients with hyperinflammatory ARDS may be experiencing a glycolytic shift leading to dysregulated lipid metabolism. Metabolic profiling offers prognostic information beyond what is captured by LCA phenotypes alone. Deeper biological profiling may identify key differences in pathogenesis among patients with ARDS and may lead to novel targeted therapies.

Adaptability on Shifting Ground: a Rapid Qualitative Assessment of Multi-institutional Inpatient Surge Planning and Workforce Deployment During the COVID-19 Pandemic

BACKGROUND

During the initial wave of COVID-19 hospitalizations, care delivery and workforce adaptations were rapidly implemented. In response to subsequent surges of patients, institutions have deployed, modified, and/or discontinued their workforce plans.

OBJECTIVE

Using rapid qualitative methods, we sought to explore hospitalists' experiences with workforce deployment, types of clinicians deployed, and challenges encountered with subsequent iterations of surge planning during the COVID-19 pandemic across a collaborative of hospital medicine groups.

APPROACH

Using rapid qualitative methods, focus groups were conducted in partnership with the Hospital Medicine Reengineering Network (HOMERuN). We interviewed physicians, advanced practice providers (APP), and physician researchers about (1) ongoing adaptations to the workforce as a result of the COVID-19 pandemic, (2) current struggles with workforce planning, and (3) evolution of workforce planning.

KEY RESULTS

We conducted five focus groups with 33 individuals from 24 institutions, representing 52% of HOMERuN sites. A variety of adaptations was described by participants, some common across institutions and others specific to the institution's location and context. Adaptations implemented shifted from the first waves of COVID patients to subsequent waves. Three global themes also emerged: (1) adaptability and comfort with dynamic change, (2) the importance of the unique hospitalist skillset for effective surge planning and redeployment, and (3) the lack of universal solutions.

CONCLUSIONS

Hospital workforce adaptations to the COVID pandemic continued to evolve. While few approaches were universally effective in managing surges of patients, and successful adaptations were highly context dependent, the ability to navigate a complex system, adaptability, and comfort in a chaotic, dynamic environment were themes considered most critical to successful surge management. However, resource constraints and sustained high workload levels raised issues of burnout.

Integrated host-microbe plasma metagenomics for sepsis diagnosis in a prospective cohort of critically ill adults

We carried out integrated host and pathogen metagenomic RNA and DNA next generation sequencing (mNGS) of whole blood (n = 221) and plasma (n = 138) from critically ill patients following hospital admission. We assigned patients into sepsis groups on the basis of clinical and microbiological criteria. From whole-blood gene expression data, we distinguished patients with sepsis from patients with non-infectious systemic inflammatory conditions using a trained bagged support vector machine (bSVM) classifier (area under the receiver operating characteristic curve (AUC) = 0.81 in the training set; AUC = 0.82 in a held-out validation set). Plasma RNA also yielded a transcriptional signature of sepsis with several genes previously reported as sepsis biomarkers, and a bSVM sepsis diagnostic classifier (AUC = 0.97 training set; AUC = 0.77 validation set). Pathogen detection performance of plasma mNGS varied on the basis of pathogen and site of infection. To improve detection of virus, we developed a secondary transcriptomic classifier (AUC = 0.94 training set; AUC = 0.96 validation set). We combined host and microbial features to develop an integrated sepsis diagnostic model that identified 99% of microbiologically confirmed sepsis cases, and predicted sepsis in 74% of suspected and 89% of indeterminate sepsis cases. In summary, we suggest that integrating host transcriptional profiling and broad-range metagenomic pathogen detection from nucleic acid is a promising tool for sepsis diagnosis.

Increased rates of secondary bacterial infections, including Enterococcus bacteremia, in patients hospitalized with coronavirus disease 2019 (COVID-19)

OBJECTIVE

We compared the rates of hospital-onset secondary bacterial infections in patients with coronavirus disease 2019 (COVID-19) with rates in patients with influenza and controls, and we investigated reports of increased incidence of Enterococcus infections in patients with COVID-19.

DESIGN

Retrospective cohort study.

SETTING

An academic quaternary-care hospital in San Francisco, California.

PATIENTS

Patients admitted between October 1, 2019, and October 1, 2020, with a positive SARS-CoV-2 PCR (N = 314) or influenza PCR (N = 82) within 2 weeks of admission were compared with inpatients without positive SARS-CoV-2 or influenza tests during the study period (N = 14,332).

METHODS

National Healthcare Safety Network definitions were used to identify infection-related ventilator-associated complications (IVACs), probable ventilator-associated pneumonia (PVAP), bloodstream infections (BSIs), and catheter-associated urinary tract infections (CAUTIs). A multiple logistic regression model was used to control for likely confounders.

RESULTS

COVID-19 patients had significantly higher rates of IVAC and PVAP compared to controls, with adjusted odds ratios of 4.7 (95% confidence interval [CI], 1.7-13.9) and 10.4 (95 % CI, 2.1-52.1), respectively. COVID-19 patients had higher incidence of BSI due to Enterococcus but not BSI generally, and whole-genome sequencing of Enterococcus isolates demonstrated that nosocomial transmission did not explain the increased rate. Subanalyses of patients admitted to the intensive care unit and patients who required mechanical ventilation revealed similar findings.

CONCLUSIONS

COVID-19 is associated with an increased risk of IVAC, PVAP, and Enterococcus BSI compared with hospitalized controls, which is not fully explained by factors such as immunosuppressive treatments and duration of mechanical ventilation. The mechanism underlying increased rates of Enterococcus BSI in COVID-19 patients requires further investigation.

Plasma SARS-CoV-2 nucleocapsid antigen levels are associated with progression to severe disease in hospitalized COVID-19

BACKGROUND

Studies quantifying SARS-CoV-2 have focused on upper respiratory tract or plasma viral RNA with inconsistent association with clinical outcomes. The association between plasma viral antigen levels and clinical outcomes has not been previously studied. Our aim was to investigate the relationship between plasma SARS-CoV-2 nucleocapsid antigen (N-antigen) concentration and both markers of host response and clinical outcomes.

METHODS

SARS-CoV-2 N-antigen concentrations were measured in the first study plasma sample (D0), collected within 72 h of hospital admission, from 256 subjects admitted between March 2020 and August 2021 in a prospective observational cohort of hospitalized patients with COVID-19. The rank correlations between plasma N-antigen and plasma biomarkers of tissue damage, coagulation, and inflammation were assessed. Multiple ordinal regression was used to test the association between enrollment N-antigen plasma concentration and the primary outcome of clinical deterioration at one week as measured by a modified World Health Organization (WHO) ordinal scale. Multiple logistic regression was used to test the association between enrollment plasma N-antigen concentration and the secondary outcomes of ICU admission, mechanical ventilation at 28 days, and death at 28 days. The prognostic discrimination of an externally derived "high antigen" cutoff of N-antigen ≥ 1000 pg/mL was also tested.

RESULTS

N-antigen on D0 was detectable in 84% of study participants. Plasma N-antigen levels significantly correlated with RAGE (r = 0.61), IL-10 (r = 0.59), and IP-10 (r = 0.59, adjusted p = 0.01 for all correlations). For the primary outcome of clinical status at one week, each 500 pg/mL increase in plasma N-antigen level was associated with an adjusted OR of 1.05 (95% CI 1.03-1.08) for worse WHO ordinal status. D0 plasma N-antigen ≥ 1000 pg/mL was 77% sensitive and 59% specific (AUROC 0.68) with a positive predictive value of 23% and a negative predictive value of 93% for a worse WHO ordinal scale at day 7 compared to baseline. D0 N-antigen concentration was independently associated with ICU admission and 28-day mechanical ventilation, but not with death at 28 days.

CONCLUSIONS

Plasma N-antigen levels are readily measured and provide important insight into the pathogenesis and prognosis of COVID-19. The measurement of N-antigen levels early in-hospital course may improve risk stratification, especially for identifying patients who are unlikely to progress to severe disease.

Phenotypes of disease severity in a cohort of hospitalized COVID-19 patients: Results from the IMPACC study

BACKGROUND

Better understanding of the association between characteristics of patients hospitalized with coronavirus disease 2019 (COVID-19) and outcome is needed to further improve upon patient management.

METHODS

Immunophenotyping Assessment in a COVID-19 Cohort (IMPACC) is a prospective, observational study of 1164 patients from 20 hospitals across the United States. Disease severity was assessed using a 7-point ordinal scale based on degree of respiratory illness. Patients were prospectively surveyed for 1 year after discharge for post-acute sequalae of COVID-19 (PASC) through quarterly surveys. Demographics, comorbidities, radiographic findings, clinical laboratory values, SARS-CoV-2 PCR and serology were captured over a 28-day period. Multivariable logistic regression was performed.

FINDINGS

The median age was 59 years (interquartile range [IQR] 20); 711 (61%) were men; overall mortality was 14%, and 228 (20%) required invasive mechanical ventilation. Unsupervised clustering of ordinal score over time revealed distinct disease course trajectories. Risk factors associated with prolonged hospitalization or death by day 28 included age ≥ 65 years (odds ratio [OR], 2.01; 95% CI 1.28-3.17), Hispanic ethnicity (OR, 1.71; 95% CI 1.13-2.57), elevated baseline creatinine (OR 2.80; 95% CI 1.63- 4.80) or troponin (OR 1.89; 95% 1.03-3.47), baseline lymphopenia (OR 2.19; 95% CI 1.61-2.97), presence of infiltrate by chest imaging (OR 3.16; 95% CI 1.96-5.10), and high SARS-CoV2 viral load (OR 1.53; 95% CI 1.17-2.00). Fatal cases had the lowest ratio of SARS-CoV-2 antibody to viral load levels compared to other trajectories over time (p=0.001). 589 survivors (51%) completed at least one survey at follow-up with 305 (52%) having at least one symptom consistent with PASC, most commonly dyspnea (56% among symptomatic patients). Female sex was the only associated risk factor for PASC.

INTERPRETATION

Integration of PCR cycle threshold, and antibody values with demographics, comorbidities, and laboratory/radiographic findings identified risk factors for 28-day outcome severity, though only female sex was associated with PASC. Longitudinal clinical phenotyping offers important insights, and provides a framework for immunophenotyping for acute and long COVID-19.

FUNDING

NIH.

Mass cytometry reveals a conserved immune trajectory of recovery in hospitalized COVID-19 patients

While studies have elucidated many pathophysiological elements of COVID-19, little is known about immunological changes during COVID-19 resolution. We analyzed immune cells and phosphorylated signaling states at single-cell resolution from longitudinal blood samples of patients hospitalized with COVID-19, pneumonia and/or sepsis, and healthy individuals by mass cytometry. COVID-19 patients showed distinct immune compositions and an early, coordinated, and elevated immune cell signaling profile associated with early hospital discharge. Intra-patient longitudinal analysis revealed changes in myeloid and T cell frequencies and a reduction in immune cell signaling across cell types that accompanied disease resolution and discharge. These changes, together with increases in regulatory T cells and reduced signaling in basophils, also accompanied recovery from respiratory failure and were associated with better outcomes at time of admission. Therefore, although patients have heterogeneous immunological baselines and highly variable disease courses, a core immunological trajectory exists that defines recovery from severe SARS-CoV-2 infection.

Validation and utility of ARDS subphenotypes identified by machine-learning models using clinical data: an observational, multicohort, retrospective analysis

BACKGROUND

Two acute respiratory distress syndrome (ARDS) subphenotypes (hyperinflammatory and hypoinflammatory) with distinct clinical and biological features and differential treatment responses have been identified using latent class analysis (LCA) in seven individual cohorts. To facilitate bedside identification of subphenotypes, clinical classifier models using readily available clinical variables have been described in four randomised controlled trials. We aimed to assess the performance of these models in observational cohorts of ARDS.

METHODS

In this observational, multicohort, retrospective study, we validated two machine-learning clinical classifier models for assigning ARDS subphenotypes in two observational cohorts of patients with ARDS: Early Assessment of Renal and Lung Injury (EARLI; n=335) and Validating Acute Lung Injury Markers for Diagnosis (VALID; n=452), with LCA-derived subphenotypes as the gold standard. The primary model comprised only vital signs and laboratory variables, and the secondary model comprised all predictors in the primary model, with the addition of ventilatory variables and demographics. Model performance was assessed by calculating the area under the receiver operating characteristic curve (AUC) and calibration plots, and assigning subphenotypes using a probability cutoff value of 0·5 to determine sensitivity, specificity, and accuracy of the assignments. We also assessed the performance of the primary model in EARLI using data automatically extracted from an electronic health record (EHR; EHR-derived EARLI cohort). In Large Observational Study to Understand the Global Impact of Severe Acute Respiratory Failure (LUNG SAFE; n=2813), a multinational, observational ARDS cohort, we applied a custom classifier model (with fewer variables than the primary model) to determine the prognostic value of the subphenotypes and tested their interaction with the positive end-expiratory pressure (PEEP) strategy, with 90-day mortality as the dependent variable.

FINDINGS

The primary clinical classifier model had an area under receiver operating characteristic curve (AUC) of 0·92 (95% CI 0·90-0·95) in EARLI and 0·88 (0·84-0·91) in VALID. Performance of the primary model was similar when using exclusively EHR-derived predictors compared with manually curated predictors (AUC=0·88 [95% CI 0·81-0·94] vs 0·92 [0·88-0·97]). In LUNG SAFE, 90-day mortality was higher in patients assigned the hyperinflammatory subphenotype than in those with the hypoinflammatory phenotype (414 [57%] of 725 vs 694 [33%] of 2088; p<0·0001). There was a significant treatment interaction with PEEP strategy and ARDS subphenotype (p=0·041), with lower 90-day mortality in the high PEEP group of patients with the hyperinflammatory subphenotype (hyperinflammatory subphenotype: 169 [54%] of 313 patients in the high PEEP group vs 127 [62%] of 205 patients in the low PEEP group; hypoinflammatory subphenotype: 231 [34%] of 675 patients in the high PEEP group vs 233 [32%] of 734 patients in the low PEEP group).

INTERPRETATION

Classifier models using clinical variables alone can accurately assign ARDS subphenotypes in observational cohorts. Application of these models can provide valuable prognostic information and could inform management strategies for personalised treatment, including application of PEEP, once prospectively validated.

FUNDING

US National Institutes of Health and European Society of Intensive Care Medicine.

A conserved immune trajectory of recovery in hospitalized COVID-19 patients

Many studies have provided insights into the immune response to COVID-19; however, little is known about the immunological changes and immune signaling occurring during COVID-19 resolution. Individual heterogeneity and variable disease resolution timelines obscure unifying immune characteristics. Here, we collected and profiled >200 longitudinal peripheral blood samples from patients hospitalized with COVID-19, with other respiratory infections, and healthy individuals, using mass cytometry to measure immune cells and signaling states at single cell resolution. COVID-19 patients showed a unique immune composition and an early, coordinated and elevated immune cell signaling profile, which correlated with early hospital discharge. Intra-patient time course analysis tied to clinically relevant events of recovery revealed a conserved set of immunological processes that accompany, and are unique to, disease resolution and discharge. This immunological process, together with additional changes in CD4 regulatory T cells and basophils, accompanies recovery from respiratory failure and is associated with better clinical outcomes at the time of admission. Our work elucidates the biological timeline of immune recovery from COVID-19 and provides insights into the fundamental processes of COVID-19 resolution in hospitalized patients.

Latent class analysis-derived subphenotypes are generalisable to observational cohorts of acute respiratory distress syndrome: a prospective study

RATIONALE

Using latent class analysis (LCA), two subphenotypes of acute respiratory distress syndrome (ARDS) have consistently been identified in five randomised controlled trials (RCTs), with distinct biological characteristics, divergent outcomes and differential treatment responses to randomised interventions. Their existence in unselected populations of ARDS remains unknown. We sought to identify subphenotypes in observational cohorts of ARDS using LCA.

METHODS

LCA was independently applied to patients with ARDS from two prospective observational cohorts of patients admitted to the intensive care unit, derived from the Validating Acute Lung Injury markers for Diagnosis (VALID) (n=624) and Early Assessment of Renal and Lung Injury (EARLI) (n=335) studies. Clinical and biological data were used as class-defining variables. To test for concordance with prior ARDS subphenotypes, the performance metrics of parsimonious classifier models (interleukin 8, bicarbonate, protein C and vasopressor-use), previously developed in RCTs, were evaluated in EARLI and VALID with LCA-derived subphenotypes as the gold-standard.

RESULTS

A 2-class model best fit the population in VALID (p=0.0010) and in EARLI (p<0.0001). Class 2 comprised 27% and 37% of the populations in VALID and EARLI, respectively. Consistent with the previously described 'hyperinflammatory' subphenotype, Class 2 was characterised by higher proinflammatory biomarkers, acidosis and increased shock and worse clinical outcomes. The similarities between these and prior RCT-derived subphenotypes were further substantiated by the performance of the parsimonious classifier models in both cohorts (area under the curves 0.92-0.94). The hyperinflammatory subphenotype was associated with increased prevalence of chronic liver disease and neutropenia and reduced incidence of chronic obstructive pulmonary disease. Measurement of novel biomarkers showed significantly higher levels of matrix metalloproteinase-8 and markers of endothelial injury in the hyperinflammatory subphenotype, whereas, matrix metalloproteinase-9 was significantly lower.

CONCLUSION

Previously described subphenotypes are generalisable to unselected populations of non-trauma ARDS.

Impaired immune signaling and changes in the lung microbiome precede secondary bacterial pneumonia in COVID-19

Secondary bacterial infections, including ventilator-associated pneumonia (VAP), lead to worse clinical outcomes and increased mortality following viral respiratory infections including in patients with coronavirus disease 2019 (COVID-19). Using a combination of tracheal aspirate bulk and single-cell RNA sequencing we assessed lower respiratory tract immune responses and microbiome dynamics in 23 COVID-19 patients, 10 of whom developed VAP, and eight critically ill uninfected controls. At a median of three days (range: 2-4 days) before VAP onset we observed a transcriptional signature of bacterial infection. At a median of 15 days prior to VAP onset (range: 8-38 days), we observed a striking impairment in immune signaling in COVID-19 patients who developed VAP. Longitudinal metatranscriptomic analysis revealed disruption of lung microbiome community composition in patients with VAP, providing a connection between dysregulated immune signaling and outgrowth of opportunistic pathogens. These findings suggest that COVID-19 patients who develop VAP have impaired antibacterial immune defense detectable weeks before secondary infection onset.

Increased risk of severe clinical course of COVID-19 in carriers of HLA-C*04:01

BACKGROUND

Since the beginning of the coronavirus disease 2019 (COVID-19) pandemic, there has been increasing urgency to identify pathophysiological characteristics leading to severe clinical course in patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Human leukocyte antigen alleles (HLA) have been suggested as potential genetic host factors that affect individual immune response to SARS-CoV-2. We sought to evaluate this hypothesis by conducting a multicenter study using HLA sequencing.

METHODS

We analyzed the association between COVID-19 severity and HLAs in 435 individuals from Germany (n = 135), Spain (n = 133), Switzerland (n = 20) and the United States (n = 147), who had been enrolled from March 2020 to August 2020. This study included patients older than 18 years, diagnosed with COVID-19 and representing the full spectrum of the disease. Finally, we tested our results by meta-analysing data from prior genome-wide association studies (GWAS).

FINDINGS

We describe a potential association of HLA-C*04:01 with severe clinical course of COVID-19. Carriers of HLA-C*04:01 had twice the risk of intubation when infected with SARS-CoV-2 (risk ratio 1.5 [95% CI 1.1-2.1], odds ratio 3.5 [95% CI 1.9-6.6], adjusted p-value = 0.0074). These findings are based on data from four countries and corroborated by independent results from GWAS. Our findings are biologically plausible, as HLA-C*04:01 has fewer predicted bindings sites for relevant SARS-CoV-2 peptides compared to other HLA alleles.

INTERPRETATION

HLA-C*04:01 carrier state is associated with severe clinical course in SARS-CoV-2. Our findings suggest that HLA class I alleles have a relevant role in immune defense against SARS-CoV-2.

FUNDING

Funded by Roche Sequencing Solutions, Inc.

Tracheal aspirate RNA sequencing identifies distinct immunological features of COVID-19 ARDS

The immunological features that distinguish COVID-19-associated acute respiratory distress syndrome (ARDS) from other causes of ARDS are incompletely understood. Here, we report the results of comparative lower respiratory tract transcriptional profiling of tracheal aspirate from 52 critically ill patients with ARDS from COVID-19 or from other etiologies, as well as controls without ARDS. In contrast to a "cytokine storm," we observe reduced proinflammatory gene expression in COVID-19 ARDS when compared to ARDS due to other causes. COVID-19 ARDS is characterized by a dysregulated host response with increased PTEN signaling and elevated expression of genes with non-canonical roles in inflammation and immunity. In silico analysis of gene expression identifies several candidate drugs that may modulate gene expression in COVID-19 ARDS, including dexamethasone and granulocyte colony stimulating factor. Compared to ARDS due to other types of viral pneumonia, COVID-19 is characterized by impaired interferon-stimulated gene (ISG) expression. The relationship between SARS-CoV-2 viral load and expression of ISGs is decoupled in patients with COVID-19 ARDS when compared to patients with mild COVID-19. In summary, assessment of host gene expression in the lower airways of patients reveals distinct immunological features of COVID-19 ARDS.

Identifying the Sickest During Triage: Using Point-of-Care Severity Scores to Predict Prognosis in Emergency Department Patients With Suspected Sepsis

BACKGROUND

Sepsis progresses rapidly and is associated with considerable morbidity and mortality. Bedside risk stratification scores can quickly identify patients at greatest risk of poor outcomes; however, there is lack of consensus on the best scale to use.

OBJECTIVE

To compare the ability of quick Sequential Organ Failure Assessment (qSOFA), the National Early Warning System (NEWS2), and the Shock Index-which does not require mental status assessment-to predict poor outcomes among patients with suspected sepsis during triage.

DESIGN, SETTING, AND PARTICIPANTS

Retrospective cohort study of adults presenting to an academic emergency department (ED) from June 2012 to December 2018 who had blood cultures and intravenous antibiotics within 24 hours.

MAIN OUTCOMES AND MEASURES

Clinical data were collected from the electronic health record. Patients were considered positive at qSOFA ≥2, Shock Index >0.7, or NEWS2 ≥5 scores. We calculated test characteristics and area under the receiver operating characteristics curves (AUROCs) to predict in-hospital mortality and ED-to-intensive care unit (ICU) admission.

RESULTS

We included 23,837 ED patients; 1,921(8.1%) were qSOFA-positive, 4,273 (17.9%) Shock Index-positive, and 11,832 (49.6%) NEWS2-positive. There were 1,427 (6.0%) deaths and 3,149 (13.2%) ED-to-ICU admissions in the sample. NEWS2 had the highest sensitivity for in-hospital mortality (76.0%) and ED-to-ICU admission (78.9%). qSOFA had the highest specificity for in-hospital mortality (93.4%) and ED-to-ICU admission (95.2%). Shock Index exhibited the highest AUROC for in-hospital mortality (0.648; 95 CI, 0.635-0.662) and ED-to-ICU admission (0.680; 95% CI, 0.617-0.689). Test characteristics were similar among those with sepsis.

CONCLUSIONS

Institution priorities should drive score selection, balancing sensitivity and specificity. In our study, qSOFA was highly specific and NEWS2 was the most sensitive for ruling out patients at high risk. Performance of the Shock Index fell between qSOFA and NEWS2 and could be considered because it is easy to implement.

Impaired immune signaling and changes in the lung microbiome precede secondary bacterial pneumonia in COVID-19

Secondary bacterial infections, including ventilator-associated pneumonia (VAP), lead to worse clinical outcomes and increased mortality following viral respiratory infections including in patients with coronavirus disease 2019 (COVID-19). Using a combination of tracheal aspirate bulk and single-cell RNA sequencing (scRNA-seq) we assessed lower respiratory tract immune responses and microbiome dynamics in 28 COVID-19 patients, 15 of whom developed VAP, and eight critically ill uninfected controls. Two days before VAP onset we observed a transcriptional signature of bacterial infection. Two weeks prior to VAP onset, following intubation, we observed a striking impairment in immune signaling in COVID-19 patients who developed VAP. Longitudinal metatranscriptomic analysis revealed disruption of lung microbiome community composition in patients with VAP, providing a connection between dysregulated immune signaling and outgrowth of opportunistic pathogens. These findings suggest that COVID-19 patients who develop VAP have impaired antibacterial immune defense detectable weeks before secondary infection onset.

COVID-19 ARDS is characterized by a dysregulated host response that differs from cytokine storm and is modified by dexamethasone

We performed comparative lower respiratory tract transcriptional profiling of 52 critically ill patients with the acute respiratory distress syndrome (ARDS) from COVID-19 or from other etiologies, as well as controls without ARDS. In contrast to a cytokine storm, we observed reduced proinflammatory gene expression in COVID-19 ARDS when compared to ARDS due to other causes. COVID-19 ARDS was characterized by a dysregulated host response with increased PTEN signaling and elevated expression of genes with non-canonical roles in inflammation and immunity that were predicted to be modulated by dexamethasone and granulocyte colony stimulating factor. Compared to ARDS due to other types of viral pneumonia, COVID-19 was characterized by impaired interferon-stimulated gene expression (ISG). We found that the relationship between SARS-CoV-2 viral load and expression of ISGs was decoupled in patients with COVID-19 ARDS when compared to patients with mild COVID-19. In summary, assessment of host gene expression in the lower airways of patients with COVID-19 ARDS did not demonstrate cytokine storm but instead revealed a unique and dysregulated host response predicted to be modified by dexamethasone.

Evaluation of a novel metric for personalized opioid prescribing after hospitalization

Background

The duration of an opioid prescribed at hospital discharge does not intrinsically account for opioid needs during a hospitalization. This discrepancy may lead to patients receiving much larger supplies of opioids on discharge than they truly require.

Objective

Assess a novel discharge opioid supply metric that adjusts for opioid use during hospitalization, compared to the conventional discharge prescription signature.

Design, setting, & participants

Retrospective study using electronic health record data from June 2012 to November 2018 of adults who received opioids while hospitalized and after discharge from a single academic medical center.

Measures & analysis

We ascertained inpatient opioids received and milligrams of opioids supplied after discharge, then determined days of opioids supplied after discharge by the conventional prescription signature opioid-days (“conventional days”) and novel hospital-adjusted opioid-days (“adjusted days”) metrics. We calculated descriptive statistics, within-subject difference between measurements, and fold difference between measures. We used multiple linear regression to determine patient-level predictors associated with high difference in days prescribed between measures.

Results

The adjusted days metric demonstrates a 2.4 day median increase in prescription duration as compared to the conventional days metric (9.4 vs. 7.0 days; P<0.001). 95% of all adjusted days measurements fall within a 0.19 to 6.90-fold difference as compared to conventional days measurements, with a maximum absolute difference of 640 days. Receiving a liquid opioid prescription accounted for an increased prescription duration of 135.6% by the adjusted days metric (95% CI 39.1–299.0%; P = 0.001). Of patients who were not on opioids prior to admission and required opioids during hospitalization but not in the last 24 hours, 325 (8.6%) were discharged with an opioid prescription.

Conclusions

The adjusted days metric, based on inpatient opioid use, demonstrates that patients are often prescribed a supply lasting longer than the prescription signature suggests, though with marked variability for some patients that suggests potential under-prescribing as well. Adjusted days is more patient-centered, reflecting the reality of how patients will take their prescription rather than providers’ intended prescription duration.

A neutrophil subset defined by intracellular olfactomedin 4 is associated with mortality in sepsis

Sepsis is a heterogeneous syndrome clinically and biologically, but biomarkers of distinct host response pathways for early prognostic information and testing targeted treatments are lacking. Olfactomedin 4 (OLFM4), a matrix glycoprotein of neutrophil-specific granules, defines a distinct neutrophil subset that may be an independent risk factor for poor outcomes in sepsis. We hypothesized that increased percentage of OLFM4⁺ neutrophils on sepsis presentation would be associated with mortality. In a single-center, prospective cohort study, we enrolled adults admitted to an academic medical center from the emergency department (ED) with suspected sepsis [identified by 2 or greater systemic inflammatory response syndrome (SIRS) criteria and antibiotic receipt] from March 2016 through December 2017, followed by sepsis adjudication according to Sepsis-3. We collected 200 µL of whole blood within 24 h of admission and stained for the neutrophil surface marker CD66b followed by intracellular staining for OLFM4 quantitated by flow cytometry. The predictors for 60-day mortality were 1) percentage of OLFM4⁺ neutrophils and 2) OLFM4⁺ neutrophils at a cut point of ≥37.6% determined by the Youden Index. Of 120 enrolled patients with suspected sepsis, 97 had sepsis and 23 had nonsepsis SIRS. The mean percentage of OLFM4⁺ neutrophils was significantly increased in both sepsis and nonsepsis SIRS patients who died (P ≤ 0.01). Among sepsis patients with elevated OLFM4⁺ (≥37.6%), 56% died, compared with 18% with OLFM4⁺ <37.6% (P = 0.001). The association between OLFM4⁺ and mortality withstood adjustment for age, sex, absolute neutrophil count, comorbidities, and standard measures of severity of illness (SOFA score, APACHE III) (P < 0.03). In summary, OLFM4⁺ neutrophil percentage is independently associated with 60-day mortality in sepsis and may represent a novel measure of the heterogeneity of host response to sepsis.

Using best subset regression to identify clinical characteristics and biomarkers associated with sepsis-associated acute kidney injury

Sepsis-associated acute kidney injury (AKI) is a complex clinical disorder associated with inflammation, endothelial dysfunction, and dysregulated coagulation. With standard regression methods, collinearity among biomarkers may lead to the exclusion of important biological pathways in a single final model. Best subset regression is an analytic technique that identifies statistically equivalent models, allowing for more robust evaluation of correlated variables. Our objective was to identify common clinical characteristics and biomarkers associated with sepsis-associated AKI. We enrolled 453 septic adults within 24 h of intensive care unit admission. Using best subset regression, we evaluated for associations using a range of models consisting of 1-38 predictors (composed of clinical risk factors and plasma and urine biomarkers) with AKI as the outcome [defined as a serum creatinine (SCr) increase of ≥0.3 mg/dL within 48 h or ≥1.5× baseline SCr within 7 days]. Two hundred ninety-seven patients had AKI. Five-variable models were found to be of optimal complexity, as the best subset of five- and six-variable models were statistically equivalent. Within the subset of five-variable models, 46 permutations of predictors were noted to be statistically equivalent. The most common predictors in this subset included diabetes, baseline SCr, angiopoetin-2, IL-8, soluble tumor necrosis factor receptor-1, and urine neutrophil gelatinase-associated lipocalin. The models had a c-statistic of ∼0.70 (95% confidence interval: 0.65-0.75). In conclusion, using best subset regression, we identified common clinical characteristics and biomarkers associated with sepsis-associated AKI. These variables may be especially relevant in the pathogenesis of sepsis-associated AKI.

Upper airway gene expression reveals suppressed immune responses to SARS-CoV-2 compared with other respiratory viruses

SARS-CoV-2 infection is characterized by peak viral load in the upper airway prior to or at the time of symptom onset, an unusual feature that has enabled widespread transmission of the virus and precipitated a global pandemic. How SARS-CoV-2 is able to achieve high titer in the absence of symptoms remains unclear. Here, we examine the upper airway host transcriptional response in patients with COVID-19 (n = 93), other viral (n = 41) or non-viral (n = 100) acute respiratory illnesses (ARIs). Compared with other viral ARIs, COVID-19 is characterized by a pronounced interferon response but attenuated activation of other innate immune pathways, including toll-like receptor, interleukin and chemokine signaling. The IL-1 and NLRP3 inflammasome pathways are markedly less responsive to SARS-CoV-2, commensurate with a signature of diminished neutrophil and macrophage recruitment. This pattern resembles previously described distinctions between symptomatic and asymptomatic viral infections and may partly explain the propensity for pre-symptomatic transmission in COVID-19. We further use machine learning to build 27-, 10- and 3-gene classifiers that differentiate COVID-19 from other ARIs with AUROCs of 0.981, 0.954 and 0.885, respectively. Classifier performance is stable across a wide range of viral load, suggesting utility in mitigating false positive or false negative results of direct SARS-CoV-2 tests.

Acute respiratory distress syndrome-attributable mortality in critically ill patients with sepsis

PURPOSE

Previous studies assessing impact of acute respiratory distress syndrome (ARDS) on mortality have shown conflicting results. We sought to assess the independent association of ARDS with in-hospital mortality among intensive care unit (ICU) patients with sepsis.

METHODS

We studied two prospective sepsis cohorts drawn from the Early Assessment of Renal and Lung Injury (EARLI; n = 474) and Validating Acute Lung Injury markers for Diagnosis (VALID; n = 337) cohorts. ARDS was defined by Berlin criteria. We used logistic regression to compare in-hospital mortality in patients with and without ARDS, controlling for baseline severity of illness. We also estimated attributable mortality, adjusted for illness severity by stratification.

RESULTS

ARDS occurred in 195 EARLI patients (41%) and 99 VALID patients (29%). ARDS was independently associated with risk of hospital death in multivariate analysis, even after controlling for severity of illness, as measured by APACHE II (odds ratio [OR] 1.65 (95% confidence interval [CI] 1.02, 2.67), p = 0.04 in EARLI; OR 2.12 (CI 1.16, 3.92), p = 0.02 in VALID). Patients with severe ARDS (P/F < 100) primarily drove this relationship. The attributable mortality of ARDS was 27% (CI 14%, 37%) in EARLI and 37% (CI 10%, 51%) in VALID. ARDS was independently associated with ICU mortality, hospital length of stay (LOS), ICU LOS, and ventilator-free days.

CONCLUSIONS

Development of ARDS among ICU patients with sepsis confers increased risk of ICU and in-hospital mortality in addition to other important outcomes. Clinical trials targeting patients with severe ARDS will be best poised to detect measurable differences in these outcomes.

Upper airway gene expression differentiates COVID-19 from other acute respiratory illnesses and reveals suppression of innate immune responses by SARS-CoV-2

We studied the host transcriptional response to SARS-CoV-2 by performing metagenomic sequencing of upper airway samples in 238 patients with COVID-19, other viral or non-viral acute respiratory illnesses (ARIs). Compared to other viral ARIs, COVID-19 was characterized by a diminished innate immune response, with reduced expression of genes involved in toll-like receptor and interleukin signaling, chemokine binding, neutrophil degranulation and interactions with lymphoid cells. Patients with COVID-19 also exhibited significantly reduced proportions of neutrophils and macrophages, and increased proportions of goblet, dendritic and B-cells, compared to other viral ARIs. Using machine learning, we built 26-, 10- and 3-gene classifiers that differentiated COVID-19 from other acute respiratory illnesses with AUCs of 0.980, 0.950 and 0.871, respectively. Classifier performance was stable at low viral loads, suggesting utility in settings where direct detection of viral nucleic acid may be unsuccessful. Taken together, our results illuminate unique aspects of the host transcriptional response to SARS-CoV-2 in comparison to other respiratory viruses and demonstrate the feasibility of COVID-19 diagnostics based on patient gene expression.

The Association between Limited English Proficiency and Sepsis Mortality

BACKGROUND

Limited English proficiency (LEP) has been implicated in poor health outcomes. Sepsis is a frequently fatal syndrome that is commonly encountered in hospital medicine. The impact of LEP on sepsis mortality is not currently known.

OBJECTIVE

To determine the association between LEP and sepsis mortality.

DESIGN

Retrospective cohort study.

SETTING

800-bed, tertiary care, academic medical center.

PATIENTS

Electronic health record data were obtained for adults admitted to the hospital with sepsis between June 1, 2012 and December 31, 2016.

MEASUREMENTS

The primary predictor was LEP. Patients were defined as having LEP if their self-reported primary language was anything other than English and interpreter services were required during hospitalization. The primary outcome was inpatient mortality. Mortality was compared across races stratified by LEP using chi-squared tests of significance. Bivariable and multivariable logistic regressions were performed to investigate the association between mortality, race, and LEP, adjusting for baseline characteristics, comorbidities, and illness severity.

RESULTS

Among 8,974 patients with sepsis, we found that 1 in 5 had LEP, 62% of whom were Asian. LEP was highly associated with death across all races except those identifying as Black and Latino. LEP was associated with a 31% increased odds of mortality after adjusting for illness severity, comorbidities, and other baseline characteristics, including race (OR 1.31, 95% CI 1.06-1.63, P = .02).

CONCLUSIONS

In a single-center study of patients hospitalized with sepsis, LEP was associated with mortality across nearly all races. This is a novel finding that will require further exploration into the causal nature of this association.

Time to Recognition of Sepsis in the Emergency Department Using Electronic Health Record Data: A Comparative Analysis of Systemic Inflammatory Response Syndrome, Sequential Organ Failure Assessment, and Quick Sequential Organ Failure Assessment

OBJECTIVES

Early identification of sepsis is critical to improving patient outcomes. Impact of the new sepsis definition (Sepsis-3) on timing of recognition in the emergency department has not been evaluated. Our study objective was to compare time to meeting systemic inflammatory response syndrome (Sepsis-2) criteria, Sequential Organ Failure Assessment (Sepsis-3) criteria, and quick Sequential Organ Failure Assessment criteria using electronic health record data.

DESIGN

Retrospective, observational study.

SETTING

The emergency department at the University of California, San Francisco.

PATIENTS

Emergency department encounters between June 2012 and December 2016 for patients greater than or equal to 18 years old with blood cultures ordered, IV antibiotic receipt, and identification with sepsis via systemic inflammatory response syndrome or Sequential Organ Failure Assessment within 72 hours of emergency department presentation.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We analyzed timestamped electronic health record data from 16,612 encounters identified as sepsis by greater than or equal to 2 systemic inflammatory response syndrome criteria or a Sequential Organ Failure Assessment score greater than or equal to 2. The primary outcome was time from emergency department presentation to meeting greater than or equal to 2 systemic inflammatory response syndrome criteria, Sequential Organ Failure Assessment greater than or equal to 2, and/or greater than or equal to 2 quick Sequential Organ Failure Assessment criteria. There were 9,087 patients (54.7%) that met systemic inflammatory response syndrome-first a median of 26 minutes post-emergency department presentation (interquartile range, 0-109 min), with 83.1% meeting Sequential Organ Failure Assessment criteria a median of 118 minutes later (interquartile range, 44-401 min). There were 7,037 patients (42.3%) that met Sequential Organ Failure Assessment-first, a median of 113 minutes post-emergency department presentation (interquartile range, 60-251 min). Quick Sequential Organ Failure Assessment was met in 46.4% of patients a median of 351 minutes post-emergency department presentation (interquartile range, 67-1,165 min). Adjusted odds of in-hospital mortality were 39% greater in patients who met systemic inflammatory response syndrome-first compared with those who met Sequential Organ Failure Assessment-first (odds ratio, 1.39; 95% CI, 1.20-1.61).

CONCLUSIONS

Systemic inflammatory response syndrome and Sequential Organ Failure Assessment initially identified distinct populations. Using systemic inflammatory response syndrome resulted in earlier electronic health record sepsis identification in greater than 50% of patients. Using Sequential Organ Failure Assessment alone may delay identification. Using systemic inflammatory response syndrome alone may lead to missed sepsis presenting as acute organ dysfunction. Thus, a combination of inflammatory (systemic inflammatory response syndrome) and organ dysfunction (Sequential Organ Failure Assessment) criteria may enhance timely electronic health record-based sepsis identification.

Peripheral blood leukocyte telomere length is associated with survival of sepsis patients

Shorter peripheral blood leukocyte (PBL) telomere length (TL) has been associated with poor outcomes in various chronic lung diseases. Whether PBL-TL is associated with survival from critical illness was tested in this study.We analysed data from a prospective observational cohort study of 937 critically ill patients at Vanderbilt University Medical Center (VUMC). PBL-TL was measured using quantitative PCR of DNA isolated from PBLs. Findings were validated in an independent cohort of 394 critically ill patients with sepsis admitted to the University of California San Francisco (UCSF).In the VUMC cohort, shorter PBL-TL was associated with worse 90-day survival (adjusted hazard ratio (aHR) 1.3, 95% CI 1.1-1.6 per 1 kb TL decrease; p=0.004); in subgroup analyses, shorter PBL-TL was associated with worse 90-day survival for patients with sepsis (aHR 1.5, 95% CI 1.2-2.0 per 1 kb TL decrease; p=0.001), but not trauma. Although not associated with development of acute respiratory distress syndrome (ARDS), among ARDS subjects, shorter PBL-TL was associated with more severe ARDS (OR 1.7, 95% CI 1.2-2.5 per 1 kb TL decrease; p=0.006). The associations of PBL-TL with survival (adjusted HR 1.6, 95% CI 1.2-2.1 per 1 kb TL decrease; p=0.003) and risk for developing severe ARDS (OR 2.5, 95% CI 1.1-6.3 per 1 kb TL decrease; p=0.044) were validated in the UCSF cohort.Short PBL-TL is strongly associated with worse survival and more severe ARDS in critically ill patients, especially patients with sepsis. These findings suggest that telomere dysfunction may contribute to outcomes from critical illness.

Plasma sTNFR1 and IL8 for prognostic enrichment in sepsis trials: a prospective cohort study

Background

Enrichment strategies improve therapeutic targeting and trial efficiency, but enrichment factors for sepsis trials are lacking. We determined whether concentrations of soluble tumor necrosis factor receptor-1 (sTNFR1), interleukin-8 (IL8), and angiopoietin-2 (Ang2) could identify sepsis patients at higher mortality risk and serve as prognostic enrichment factors.

Methods

In a multicenter prospective cohort study of 400 critically ill septic patients, we derived and validated thresholds for each marker and expressed prognostic enrichment using risk differences (RD) of 30-day mortality as predictive values. We then used decision curve analysis to simulate the prognostic enrichment of each marker and compare different prognostic enrichment strategies.

Measurements and main results

An admission sTNFR1 concentration > 8861 pg/ml identified patients with increased mortality in both the derivation (RD 21.6%) and validation (RD 17.8%) populations. Among immunocompetent patients, an IL8 concentration > 94 pg/ml identified patients with increased mortality in both the derivation (RD 17.7%) and validation (RD 27.0%) populations. An Ang2 level > 9761 pg/ml identified patients at 21.3% and 12.3% increased risk of mortality in the derivation and validation populations, respectively. Using sTNFR1 or IL8 to select high-risk patients improved clinical trial power and efficiency compared to selecting patients with septic shock. Ang2 did not outperform septic shock as an enrichment factor.

Conclusions

Thresholds for sTNFR1 and IL8 consistently identified sepsis patients with higher mortality risk and may have utility for prognostic enrichment in sepsis trials.

External validation of a biomarker and clinical prediction model for hospital mortality in acute respiratory distress syndrome

PURPOSE

Mortality prediction in ARDS is important for prognostication and risk stratification. However, no prediction models have been independently validated. A combination of two biomarkers with age and APACHE III was superior in predicting mortality in the NHLBI ARDSNet ALVEOLI trial. We validated this prediction tool in two clinical trials and an observational cohort.

METHODS

The validation cohorts included 849 patients from the NHLBI ARDSNet Fluid and Catheter Treatment Trial (FACTT), 144 patients from a clinical trial of sivelestat for ARDS (STRIVE), and 545 ARDS patients from the VALID observational cohort study. To evaluate the performance of the prediction model, the area under the receiver operating characteristic curve (AUC), model discrimination, and calibration were assessed, and recalibration methods were applied.

RESULTS

The biomarker/clinical prediction model performed well in all cohorts. Performance was better in the clinical trials with an AUC of 0.74 (95% CI 0.70-0.79) in FACTT, compared to 0.72 (95% CI 0.67-0.77) in VALID, a more heterogeneous observational cohort. The AUC was 0.73 (95% CI 0.70-0.76) when FACTT and VALID were combined.

CONCLUSION

We validated a mortality prediction model for ARDS that includes age, APACHE III, surfactant protein D, and interleukin-8 in a variety of clinical settings. Although the model performance as measured by AUC was lower than in the original model derivation cohort, the biomarker/clinical model still performed well and may be useful for risk assessment for clinical trial enrollment, an issue of increasing importance as ARDS mortality declines, and better methods are needed for selection of the most severely ill patients for inclusion.

Acute Respiratory Distress Syndrome Subphenotypes Respond Differently to Randomized Fluid Management Strategy

RATIONALE

We previously identified two acute respiratory distress syndrome (ARDS) subphenotypes in two separate randomized controlled trials with differential response to positive end-expiratory pressure.

OBJECTIVES

To identify these subphenotypes in a third ARDS cohort, to test whether subphenotypes respond differently to fluid management strategy, and to develop a practical model for subphenotype identification.

METHODS

We used latent class analysis of baseline clinical and plasma biomarker data to identify subphenotypes in FACTT (Fluid and Catheter Treatment Trial; n = 1,000). Logistic regression was used to test for an interaction between subphenotype and treatment for mortality. We used stepwise modeling to generate a model for subphenotype identification in FACTT and validated its accuracy in the two cohorts in which we previously identified ARDS subphenotypes.

MEASUREMENTS AND MAIN RESULTS

We confirmed that a two-class (two-subphenotype) model best described the study population. Subphenotype 2 was again characterized by higher inflammatory biomarkers and hypotension. Fluid management strategy had significantly different effects on 90-day mortality in the two subphenotypes (P = 0.0039 for interaction); mortality in subphenotype 1 was 26% with fluid-liberal strategy versus 18% with fluid-conservative, whereas mortality in subphenotype 2 was 40% with fluid-liberal strategy versus 50% in fluid-conservative. A three-variable model of IL-8, bicarbonate, and tumor necrosis factor receptor-1 accurately classified the subphenotypes.

CONCLUSIONS

This analysis confirms the presence of two ARDS subphenotypes that can be accurately identified with a limited number of variables and that responded differently to randomly assigned fluid management. These findings support the presence of ARDS subtypes that may require different treatment approaches.

Distinct molecular phenotypes of direct vs indirect ARDS in single-center and multicenter studies

BACKGROUND

ARDS is a heterogeneous syndrome that encompasses lung injury from both direct and indirect sources. Direct ARDS (pneumonia, aspiration) has been hypothesized to cause more severe lung epithelial injury than indirect ARDS (eg, nonpulmonary sepsis); however, this hypothesis has not been well studied in humans.

METHODS

We measured plasma biomarkers of lung epithelial and endothelial injury and inflammation in a single-center study of 100 patients with ARDS and severe sepsis and in a secondary analysis of 853 patients with ARDS drawn from a multicenter randomized controlled trial. Biomarker levels in patients with direct vs indirect ARDS were compared in both cohorts.

RESULTS

In both studies, patients with direct ARDS had significantly higher levels of a biomarker of lung epithelial injury (surfactant protein D) and significantly lower levels of a biomarker of endothelial injury (angiopoietin-2) than those with indirect ARDS. These associations were robust to adjustment for severity of illness and ARDS severity. In the multicenter study, patients with direct ARDS also had lower levels of von Willebrand factor antigen and IL-6 and IL-8, markers of endothelial injury and inflammation, respectively. The prognostic value of the biomarkers was similar in direct and indirect ARDS.

CONCLUSIONS

Direct lung injury in humans is characterized by a molecular phenotype consistent with more severe lung epithelial injury and less severe endothelial injury. The opposite pattern was identified in indirect lung injury. Clinical trials of novel therapies targeted specifically at the lung epithelium or endothelium may benefit from preferentially enrolling patients with direct and indirect ARDS, respectively.

Design and implementation of the START (STem cells for ARDS Treatment) trial, a phase 1/2 trial of human mesenchymal stem/stromal cells for the treatment of moderate-severe acute respiratory distress syndrome

BACKGROUND

Despite advances in supportive care, moderate-severe acute respiratory distress syndrome (ARDS) is associated with high mortality rates, and novel therapies to treat this condition are needed. Compelling pre-clinical data from mouse, rat, sheep and ex vivo perfused human lung models support the use of human mesenchymal stem (stromal) cells (MSCs) as a novel intravenous therapy for the early treatment of ARDS.

METHODS

This article describes the study design and challenges encountered during the implementation and phase 1 component of the START (STem cells for ARDS Treatment) trial, a phase 1/2 trial of bone marrow-derived human MSCs for moderate-severe ARDS. A trial enrolling 69 subjects is planned (9 subjects in phase 1, 60 subjects in phase 2 treated with MSCs or placebo in a 2:1 ratio).

RESULTS

This report describes study design features that are unique to a phase 1 trial in critically ill subjects and the specific challenges of implementation of a cell-based therapy trial in the ICU.

CONCLUSIONS

Experience gained during the design and implementation of the START study will be useful to investigators planning future phase 1 clinical trials based in the ICU, as well as trials of cell-based therapy for other acute illnesses.

TRIAL REGISTRATION

CLINICAL TRIALS REGISTRATION

NCT01775774 and NCT02097641.

Intensive care unit scoring systems outperform emergency department scoring systems for mortality prediction in critically ill patients: a prospective cohort study

BACKGROUND

Multiple scoring systems have been developed for both the intensive care unit (ICU) and the emergency department (ED) to risk stratify patients and predict mortality. However, it remains unclear whether the additional data needed to compute ICU scores improves mortality prediction for critically ill patients compared to the simpler ED scores.

METHODS

We studied a prospective observational cohort of 227 critically ill patients admitted to the ICU directly from the ED at an academic, tertiary care medical center. We compared Acute Physiology and Chronic Health Evaluation (APACHE) II, APACHE III, Simplified Acute Physiology Score (SAPS) II, Modified Early Warning Score (MEWS), Rapid Emergency Medicine Score (REMS), Prince of Wales Emergency Department Score (PEDS), and a pre-hospital critical illness prediction score developed by Seymour et al. (JAMA 2010, 304(7):747-754). The primary endpoint was 60-day mortality. We compared the receiver operating characteristic (ROC) curves of the different scores and their calibration using the Hosmer-Lemeshow goodness-of-fit test and visual assessment.

RESULTS

The ICU scores outperformed the ED scores with higher area under the curve (AUC) values (p = 0.01). There were no differences in discrimination among the ED-based scoring systems (AUC 0.698 to 0.742; p = 0.45) or among the ICU-based scoring systems (AUC 0.779 to 0.799; p = 0.60). With the exception of the Seymour score, the ED-based scoring systems did not discriminate as well as the best-performing ICU-based scoring system, APACHE III (p = 0.005 to 0.01 for comparison of ED scores to APACHE III). The Seymour score had a superior AUC to other ED scores and, despite a lower AUC than all the ICU scores, was not significantly different than APACHE III (p = 0.09). When data from the first 24 h in the ICU was used to calculate the ED scores, the AUC for the ED scores improved numerically, but this improvement was not statistically significant. All scores had acceptable calibration.

CONCLUSIONS

In contrast to prior studies of patients based in the emergency department, ICU scores outperformed ED scores in critically ill patients admitted from the emergency department. This difference in performance seemed to be primarily due to the complexity of the scores rather than the time window from which the data was derived.

Performance of interleukin-27 as a sepsis diagnostic biomarker in critically ill adults

PURPOSE

We recently identified interleukin-27 (IL-27) as a sepsis diagnostic biomarker in children. Here we assess IL-27 as a sepsis diagnostic biomarker in critically ill adults with systemic inflammatory response syndrome and sepsis.

METHODS

IL-27 and procalcitonin (PCT) were measured from plasma samples in three groups: no sepsis (n = 78), pulmonary source of sepsis (n = 66), and non-pulmonary source of sepsis (n = 43). Receiver operating characteristic curves and classification and regression tree methodology were used to evaluate biomarker performance.

RESULTS

IL-27 did not discriminate effectively between sepsis and sterile systemic inflammatory response syndrome in unselected patients. The highest area under the curve (AUC) was 0.70 (95% C.I. 0.60 - 0.80) for IL-27 in subjects with a non-pulmonary source of sepsis. A decision tree incorporating IL-27, PCT, and age had an AUC of 0.79 (0.71-0.87) in subjects with a non-pulmonary source of sepsis. Compared to children with sepsis, adults with sepsis express less IL-27.

CONCLUSIONS

IL-27 performed overall poorly in this cohort as a sepsis diagnostic biomarker. Combining IL-27, PCT, and age reasonably estimated the risk of sepsis in subjects with a non-pulmonary source of sepsis. IL-27 may be a more reliable sepsis diagnostic biomarker in children than in adults.

Plasma angiopoietin-2 predicts the onset of acute lung injury in critically ill patients

RATIONALE

Current clinical prediction scores for acute lung injury (ALI) have limited positive predictive value. No studies have evaluated predictive plasma biomarkers in a broad population of critically ill patients or as an adjunct to clinical prediction scores.

OBJECTIVES

To determine whether plasma angiopoietin-2 (Ang-2), von Willebrand factor (vWF), interleukin-8 (IL-8), and/or receptor for advanced glycation end products (sRAGE) predict ALI in critically ill patients.

METHODS

Plasma samples were drawn from critically ill patients (n = 230) identified in the emergency department. Patients who had ALI at baseline or in the subsequent 6 hours were excluded, and the remaining patients were followed for development of ALI.

MEASUREMENTS AND MAIN RESULTS

Nineteen patients developed ALI at least 6 hours after the sample draw. Higher levels of Ang-2 and IL-8 were significantly associated with increased development of ALI (P = 0.0008, 0.004, respectively). The association between Ang-2 and subsequent development of ALI was robust to adjustment for sepsis and vasopressor use. Ang-2 and the Lung Injury Prediction Score each independently discriminated well between those who developed ALI and those who did not (area under the receiver operating characteristic curve, 0.74 for each), and using the two together improved the area under the curve to 0.84 (vs. 0.74, P = 0.05). In contrast, plasma levels of sRAGE and vWF were not predictive of ALI.

CONCLUSIONS

Plasma biomarkers such as Ang-2 can improve clinical prediction scores and identify patients at high risk for ALI. In addition, the early rise of Ang-2 emphasizes the importance of endothelial injury in the early pathogenesis of ALI.

FGF-23 and PTH levels in patients with acute kidney injury: A cross-sectional case series study

Background

Fibroblast growth factor-23 (FGF-23), a novel regulator of mineral metabolism, is markedly elevated in chronic kidney disease and has been associated with poor long-term outcomes. However, whether FGF-23 has an analogous role in acute kidney injury is unknown. The goal of this study was to measure FGF-23 levels in critically ill patients with acute kidney injury to determine whether FGF-23 levels were elevated, as in chronic kidney disease.

Methods

Plasma FGF-23 and intact parathyroid hormone (PTH) levels were measured in 12 patients with acute kidney injury and 8 control subjects.

Results

FGF-23 levels were significantly higher in acute kidney injury cases than in critically ill subjects without acute kidney injury, with a median FGF-23 level of 1948 RU/mL (interquartile range (IQR), 437-4369) in cases compared with 252 RU/mL (IQR, 65-533) in controls (p = 0.01). No correlations were observed between FGF-23 and severity of acute kidney injury (defined by the Acute Kidney Injury Network criteria); among patients with acute kidney injury, FGF-23 levels were higher in nonsurvivors than survivors (median levels of 4446 RU/mL (IQR, 3455-5443) versus 544 RU/mL (IQR, 390-1948; p = 0.02). Severe hyperparathyroidism (defined as intact PTH >250 mg/dL) was present in 3 of 12 (25%) of the acute kidney injury subjects versus none of the subjects without acute kidney injury, although this result did not meet statistical significance.

Conclusions

We provide novel data that demonstrate that FGF-23 levels are elevated in acute kidney injury, suggesting that FGF-23 dysregulation occurs in acute kidney injury as well as chronic kidney disease. Further studies are needed to define the short- and long-term clinical effects of dysregulated mineral metabolism in acute kidney injury patients.