In critical illness, such as sepsis or the acute respiratory distress syndrome, acute kidney injury (AKI) is common and associated with increased morbidity and mortality. Mechanical ventilation in critical illnesses is also a risk factor for AKI, but it is potentially modifiable. Injurious ventilation strategies may lead to the systemic release of inflammatory mediators from the lung due to ventilator induced lung injury (VILI). The systemic consequences of VILI are difficult to differentiate clinically from other systemic inflammatory syndromes, such as sepsis. The purpose of this study was to identify unique changes in the expression of inflammatory mediators in kidney tissue in response to VILI compared with systemic sepsis to gain insight into direct effects of VILI on the kidney. Four groups of mice were compared-mice with sepsis from cecal ligation and puncture (CLP), mice subjected to injurious mechanical ventilation with high tidal volumes (VILI), mice exposed to CLP followed by VILI (CLP+VILI), and sham controls. Protein expression of common inflammatory mediators in kidneys was analyzed using a proteome array and confirmed by Western blot analysis or ELISA. VEGF and VCAM-1 were found to be significantly elevated in kidneys from VILI mice compared with sham and CLP. Angiopoietin-2 was significantly increased in CLP+VILI compared with CLP alone and was also correlated with higher levels of AKI biomarker, neutrophil gelatinase-associated lipocalin. These results suggest that VILI alters the renal expression of VEGF, VCAM-1, and angiopoietin-2, and these proteins warrant further investigation as potential biomarkers and therapeutic targets.

Rationale & objective

Uric acid is excreted by the kidney and accumulates in acute kidney injury (AKI). Whether higher plasma uric acid level predisposes to AKI or its complications is not known.

Study design

Prospective observational cohort study.

Setting & participants

2 independent cohorts of critically ill patients: (1) 208 patients without AKI admitted to the intensive care unit (ICU) at Brigham & Women's Hospital between October 2008 and December 2016; and (2) 250 participants with AKI requiring renal replacement therapy (RRT) who had not yet initiated RRT enrolled in the Acute Renal Failure Trial Network (ATN) Study.

Exposure

Plasma uric acid level upon ICU admission and before RRT initiation in the ICU and ATN Study cohorts, respectively.

Outcomes

Incident AKI and 60-day mortality in the ICU and ATN Study cohorts, respectively.

Analytical approach

Logistic regression models were used to test the association of plasma uric acid level with incident AKI and 60-day mortality.

Results

In the ICU cohort, median plasma uric acid level was 4.7 (interquartile range [IQR], 3.6-6.4) mg/dL, and 40 patients (19.2%) developed AKI. Higher plasma uric acid levels associated with incident AKI, but this association was confounded by serum creatinine level and was not significant after multivariable adjustment (adjusted OR per doubling of uric acid, 1.50; 95% CI, 0.80-2.81). In the ATN Study cohort, median plasma uric acid level was 11.1 (IQR, 8.6-14.2) mg/dL, and 125 participants (50.0%) died within 60 days. There was no statistically significant association between plasma uric acid levels and 60-day mortality in either unadjusted models or after multivariable adjustment for demographic, severity-of-illness, and kidney-specific covariates (adjusted OR per doubling of uric acid, 1.15; 95% CI, 0.71-1.86).

Limitations

Heterogeneity of ICU patients.

Conclusions

Plasma uric acid levels upon ICU admission or before RRT initiation are not independently associated with adverse clinical outcomes in critically ill patients.

Acute respiratory distress syndrome (ARDS) is a heterogeneous clinical syndrome. Understanding of the complex pathways involved in lung injury pathogenesis, resolution, and repair has grown considerably in recent decades. Nevertheless, to date, only therapies targeting ventilation-induced lung injury have consistently proven beneficial, and despite these gains, ARDS morbidity and mortality remain high. Many candidate therapies with promise in preclinical studies have been ineffective in human trials, probably at least in part due to clinical and biological heterogeneity that modifies treatment responsiveness in human ARDS. A precision medicine approach to ARDS seeks to better account for this heterogeneity by matching therapies to subgroups of patients that are anticipated to be most likely to benefit, which initially might be identified in part by assessing for heterogeneity of treatment effect in clinical trials. In October 2019, the US National Heart, Lung, and Blood Institute convened a workshop of multidisciplinary experts to explore research opportunities and challenges for accelerating precision medicine in ARDS. Topics of discussion included the rationale and challenges for a precision medicine approach in ARDS, the roles of preclinical ARDS models in precision medicine, essential features of cohort studies to advance precision medicine, and novel approaches to clinical trials to support development and validation of a precision medicine strategy. In this Position Paper, we summarise workshop discussions, recommendations, and unresolved questions for advancing precision medicine in ARDS. Although the workshop took place before the COVID-19 pandemic began, the pandemic has highlighted the urgent need for precision therapies for ARDS as the global scientific community grapples with many of the key concepts, innovations, and challenges discussed at this workshop.

Objective

A high plasma level of inflammasome mediator interleukin-18 was associated with mortality in observational acute respiratory distress syndrome cohorts. Statin exposure increases both inflammasome activation and lung injury in mouse models. We tested whether randomization to statin therapy correlated with increased interleukin-18 in the ARDS Network Statins for Acutely Injured Lungs from Sepsis trial.

Design

Retrospective analysis of randomized controlled clinical trial.

Setting

Multicenter North American clinical trial, the ARDS Network Statins for Acutely Injured Lungs from Sepsis.

Patients

Six hundred eighty-three subjects with infection-related acute respiratory distress syndrome, representing 92% of the original trial population.

Interventions

Random assignment of rosuvastatin or placebo for up to 28 days or 3 days after ICU discharge.

Measurements and main results

We measured plasma interleukin-18 levels in all Statins for Acutely Injured Lungs from Sepsis patients with sample available at day 0 (baseline, n = 683) and day 3 (after randomization, n = 588). We tested the association among interleukin-18 level at baseline, rising interleukin-18, and the impact of statin therapy on 60-day mortality, adjusting for severity of illness. Baseline plasma interleukin-18 level greater than or equal to 800 pg/mL was highly associated with 60-day mortality, with a hazard of death of 2.3 (95% CI, 1.7-3.1). Rising plasma interleukin-18 was also associated with increased mortality. For each unit increase in log2 (interleukin-18) at day 3 compared with baseline, the hazard of death increased by 2.3 (95% CI, 1.5-3.5). Subjects randomized to statin were significantly more likely to experience a rise in plasma interleukin-18 levels. Subjects with acute kidney injury, shock, low baseline interleukin-18, and those not receiving systemic corticosteroids were more likely to experience rising interleukin-18. Randomization to statin therapy was associated with rising in interleukin-18 in all of those subsets, however.

Conclusions

Elevated baseline plasma interleukin-18 was associated with higher mortality in sepsis-induced acute respiratory distress syndrome. A rise in plasma interleukin-18 was also associated with increased mortality and was more common in subjects randomized to statin therapy in this clinical trial.

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.