An update on the pharmacological management of acute respiratory distress syndrome.

INTRODUCTION: Acute respiratory distress syndrome (ARDS) is characterized by acute inflammatory injury to the lungs, alterations in vascular permeability, loss of aerated tissue, bilateral infiltrates, and refractory hypoxemia. ARDS is considered a heterogeneous syndrome, which complicates the search for effective therapies. The goal of this review is to provide an update on the pharmacological management of ARDS. AREAS COVERED: The difficulties in finding effective pharmacological therapies are mainly due to the challenges in designing clinical trials for this unique, varied population of critically ill patients. Recently, some trials have been retrospectively analyzed by dividing patients into hyper-inflammatory and hypo-inflammatory sub-phenotypes. This approach has led to significant outcome improvements with some pharmacological treatments that previously failed to demonstrate efficacy, which suggests that a more precise selection of ARDS patients for clinical trials could be the key to identifying effective pharmacotherapies. This review is provided after searching the main studies on this topics on the PubMed and clinicaltrials.gov databases. EXPERT OPINION: The future of ARDS therapy lies in precision medicine, innovative approaches to drug delivery, immunomodulation, cell-based therapies, and robust clinical trial designs. These should lead to more effective and personalized treatments for patients with ARDS.

Differential Effect of Positive End-Expiratory Pressure Strategies in Patients With ARDS: A Bayesian Analysis of Clinical Subphenotypes.

BACKGROUND: ARDS is a heterogeneous condition with two subphenotypes identified by different methodologies. Our group similarly identified two ARDS subphenotypes using nine routinely available clinical variables. However, whether these are associated with differential response to treatment has yet to be explored. RESEARCH QUESTION: Are there differential responses to positive end-expiratory pressure (PEEP) strategies on 28-day mortality according to subphenotypes in adult patients with ARDS? STUDY DESIGN AND METHODS: We evaluated data from two prior ARDS trials (Higher vs Lower Positive End-Expiratory Pressures in Patients With the ARDS [ALVEOLI] and ARDS Trial [ART]) that compared different PEEP strategies. We classified patients into one of two subphenotypes as described previously. We assessed the differential effect of PEEP with a Bayesian hierarchical logistic model for the primary outcome of 28-day mortality. RESULTS: We analyzed data from 1,559 patients with ARDS. Compared with lower PEEP, a higher PEEP strategy resulted in higher 28-day mortality in patients with subphenotype A disease in the ALVEOLI study (OR, 1.61; 95% credible interval [CrI], 0.90-2.94) and ART (OR, 1.73; 95% CrI, 1.01-2.98), with a probability of harm resulting from higher PEEP in this subphenotype of 94.3% and 97.7% in the ALVEOLI and ART studies, respectively. Higher PEEP was not associated with mortality in patients with subphenotype B disease in each trial (OR, 0.95 [95% CrI, 0.51-1.73] and 1.00 [95% CrI, 0.63-1.55], respectively), with probability of benefit of 56.4% and 50.7% in the ALVEOLI and ART studies, respectively. These effects were not modified by Pao2 to Fio2 ratio, driving pressure, or the severity of illness for the cohorts. INTERPRETATION: We found evidence of differential response to PEEP strategies across two ARDS subphenotypes, suggesting possible harm with a higher PEEP strategy in one subphenotype. These observations may assist with predictive enrichment in future clinical trials.

Inflammatory subphenotypes previously identified in ARDS are associated with mortality at intensive care unit discharge: a secondary analysis of a prospective observational study.

BACKGROUND: Intensive care unit (ICU)-survivors have an increased risk of mortality after discharge compared to the general population. On ICU admission subphenotypes based on the plasma biomarker levels of interleukin-8, protein C and bicarbonate have been identified in patients admitted with acute respiratory distress syndrome (ARDS) that are prognostic of outcome and predictive of treatment response. We hypothesized that if these inflammatory subphenotypes previously identified among ARDS patients are assigned at ICU discharge in a more general critically ill population, they are associated with short- and long-term outcome. METHODS: A secondary analysis of a prospective observational cohort study conducted in two Dutch ICUs between 2011 and 2014 was performed. All patients discharged alive from the ICU were at ICU discharge adjudicated to the previously identified inflammatory subphenotypes applying a validated parsimonious model using variables measured median 10.6 h [IQR, 8.0-31.4] prior to ICU discharge. Subphenotype distribution at ICU discharge, clinical characteristics and outcomes were analyzed. As a sensitivity analysis, a latent class analysis (LCA) was executed for subphenotype identification based on plasma protein biomarkers at ICU discharge reflective of coagulation activation, endothelial cell activation and inflammation. Concordance between the subphenotyping strategies was studied. RESULTS: Of the 8332 patients included in the original cohort, 1483 ICU-survivors had plasma biomarkers available and could be assigned to the inflammatory subphenotypes. At ICU discharge 6% (n = 86) was assigned to the hyperinflammatory and 94% (n = 1397) to the hypoinflammatory subphenotype. Patients assigned to the hyperinflammatory subphenotype were discharged with signs of more severe organ dysfunction (SOFA scores 7 [IQR 5-9] vs. 4 [IQR 2-6], p < 0.001). Mortality was higher in patients assigned to the hyperinflammatory subphenotype (30-day mortality 21% vs. 11%, p = 0.005; one-year mortality 48% vs. 28%, p < 0.001). LCA deemed 2 subphenotypes most suitable. ICU-survivors from class 1 had significantly higher mortality compared to class 2. Patients belonging to the hyperinflammatory subphenotype were mainly in class 1. CONCLUSIONS: Patients assigned to the hyperinflammatory subphenotype at ICU discharge showed significantly stronger anomalies in coagulation activation, endothelial cell activation and inflammation pathways implicated in the pathogenesis of critical disease and increased mortality until one-year follow up.

Identifying novel acute pancreatitis sub-phenotypes using total serum calcium trajectories.

BACKGROUND: Acute pancreatitis (AP) has heterogeneous clinical features, and identifying clinically relevant sub-phenotypes is useful. We aimed to identify novel sub-phenotypes in hospitalized AP patients using longitudinal total serum calcium (TSC) trajectories. METHODS: AP patients had at least two TSC measurements during the first 24 h of hospitalization in the US-based critical care database (Medical Information Mart for Intensive Care-III (MIMIC-III) and MIMIC-IV were included. Group-based trajectory modeling was used to identify calcium trajectory phenotypes, and patient characteristics and treatment outcomes were compared between the phenotypes. RESULTS: A total of 4518 admissions were included in the analysis. Four TSC trajectory groups were identified: "Very low TSC, slow resolvers" (n = 65; 1.4% of the cohort); "Moderately low TSC" (n = 559; 12.4%); "Stable normal-calcium" (n = 3875; 85.8%); and "Fluctuating high TSC" (n = 19; 0.4%). The "Very low TSC, slow resolvers" had the lowest initial, maximum, minimum, and mean TSC, and highest SOFA score, creatinine and glucose level. In contrast, the "Stable normal-calcium" had the fewest ICU admission, antibiotic use, intubation and renal replace treatment. In adjusted analysis, significantly higher in-hospital mortality was noted among "Very low TSC, slow resolvers" (odds ratio [OR], 7.2; 95% CI, 3.7 to 14.0), "moderately low TSC" (OR, 5.0; 95% CI, 3.8 to 6.7), and "Fluctuating high TSC" (OR, 5.6; 95% CI, 1.5 to 20.6) compared with the "Stable normal-calcium" group. CONCLUSIONS: We identified four novel sub-phenotypes of patients with AP, with significant variability in clinical outcomes. Not only the absolute TSC levels but also their trajectories were significantly associated with in-hospital mortality.

Schizophrenia polygenic risk scores, clinical variables and genetic pathways as predictors of phenotypic traits of bipolar I disorder.

AIM: We investigated the predictive value of polygenic risk scores (PRS) derived from the schizophrenia GWAS (Trubetskoy et al., 2022) (SCZ3) for phenotypic traits of bipolar disorder type-I (BP-I) in 1878 BP-I cases and 2751 controls from Romania and UK. METHODS: We used PRSice-v2.3.3 and PRS-CS for computing SCZ3-PRS for testing the predictive power of SCZ3-PRS alone and in combination with clinical variables for several BP-I subphenotypes and for pathway analysis. Non-linear predictive models were also used. RESULTS: SCZ3-PRS significantly predicted psychosis, incongruent and congruent psychosis, general age-of-onset (AO) of BP-I, AO-depression, AO-Mania, rapid cycling in univariate regressions. A negative correlation between the number of depressive episodes and psychosis, mainly incongruent and an inverse relationship between increased SCZ3-SNP loading and BP-I-rapid cycling were observed. In random forest models comparing the predictive power of SCZ3-PRS alone and in combination with nine clinical variables, the best predictions were provided by combinations of SCZ3-PRS-CS and clinical variables closely followed by models containing only clinical variables. SCZ3-PRS performed worst. Twenty-two significant pathways underlying psychosis were identified. LIMITATIONS: The combined RO-UK sample had a certain degree of heterogeneity of the BP-I severity: only the RO sample and partially the UK sample included hospitalized BP-I cases. The hospitalization is an indicator of illness severity. Not all UK subjects had complete subphenotype information. CONCLUSION: Our study shows that the SCZ3-PRS have a modest clinical value for predicting phenotypic traits of BP-I. For clinical use their best performance is in combination with clinical variables.

[Recent developments in acute kidney injury : Definition, biomarkers, subphenotypes, and management]. / Neueste Entwicklungen bei der akuten Nierenschädigung : Definition, Biomarker, Subphänotypen und Management.

Acute kidney injury (AKI) is a common problem in critically ill patients and is associated with increased morbidity and mortality. Since 2012, AKI has been defined according to the KDIGO (Kidney Disease Improving Global Outcome) guidelines. As some biomarkers are now available that can provide useful clinical information, a new definition including a new stage 1S has been proposed by an expert group of the Acute Disease Quality Initiative (ADQI). At this stage, classic AKI criteria are not yet met, but biomarkers are already positive defining subclinical AKI. This stage 1S is associated with a worse patient outcome, regardless of the biomarker chosen. The PrevAKI and PrevAKI-Multicenter trial also showed that risk stratification with a biomarker and implementation of the KDIGO bundle (in the high-risk group) can reduce the rate of moderate and severe AKI. In the absence of a successful clinical trial, conservative management remains the primary focus of treatment. This mainly involves optimization of hemodynamics and an individualized (restrictive) fluid management. The STARRT-AKI trial has shown that there is no benefit from accelerated initiation of renal replacement therapy. However, delaying too long might be associated with potential harm, as shown in the AKIKI2 study. Prospective studies are needed to determine whether artificial intelligence will play a role in AKI in the future, helping to guide treatment decisions and improve outcomes.

The effect of androgens on the risk of endometriosis sub-phenotypes and ovarian neoplasms: A Mendelian randomization study.

Endometriosis is a complex gynecological pathology with a broad spectrum of symptoms, affecting around 10% of reproductive-aged women. Ovarian cancer (OC) is a heterogeneous disease for which we lack effective diagnostic and therapeutic strategies. The etiology and pathogenesis of both diseases remain ambiguous. Androgens in endometriosis could have a distinct role beyond serving as estrogen sources, whereas in the case of serous OC could be important in the formation of precursor lesions which ultimately lead to tumor formation. Here we performed two-sample Mendelian randomization (MR) analysis to examine the causal relationship between the androgen precursor - dehydroepiandrosterone sulphate (DHEAS), bioactive androgen - testosterone (T), androgen metabolite - androsterone sulphate, steroid hormone binding globulin (SHBG) and albumin and the risk of endometrioses of various sub-phenotypes and ovarian neoplasms across the benign-borderline-malignant spectrum. Stringent quality control procedures were followed to select eligible instrumental variables that were strongly associated with the selected exposures, sensitivity analyses were performed to assess the heterogeneities, horizontal pleiotropy, and stabilities of SNPs in endometriosis and ovarian neoplasms. We discovered an inverse association between genetically predicted levels of all androgens and risk of endometriosis, the same trend was most evident in the ovarian sub-phenotype. Total T levels were also inversely associated with peritoneal sub-phenotype of endometriosis. Likewise, T was causally associated with decreased risk of clear-cell OC, an endometriosis-associated OC subtype, and with malignant serous OC of both low- and high-grade, but with higher risk of their counterpart of low malignant potential. These findings support further investigation of androgen's action at a molecular level in ovary-associated endometriotic lesions, clear cell ovarian tumors and serous precursor lesions.

Author Response.

How to cite this article: Todur P, Chaudhuri S. Author Response. Indian J Crit Care Med 2024;28(2):179-180.

Persistent acute kidney injury and fluid accumulation with outcomes after the Norwood procedure: report from NEPHRON.

BACKGROUND: Cardiac surgery-associated acute kidney injury (CS-AKI) is common, but its impact on clinical outcomes is variable. Parsing AKI into sub-phenotype(s) and integrating pathologic positive cumulative fluid balance (CFB) may better inform prognosis. We sought to determine whether durational sub-phenotyping of CS-AKI with CFB strengthens association with outcomes among neonates undergoing the Norwood procedure. METHODS: Multicenter, retrospective cohort study from the Neonatal and Pediatric Heart and Renal Outcomes Network. Transient CS-AKI: present only on post-operative day (POD) 1 and/or 2; persistent CS-AKI: continued after POD 2. CFB was evaluated per day and peak CFB during the first 7 postoperative days. Primary and secondary outcomes were mortality, respiratory support-free and hospital-free days (at 28, 60 days, respectively). The primary predictor was persistent CS-AKI, defined by modified neonatal Kidney Disease: Improving Global Outcomes criteria. RESULTS: CS-AKI occurred in 59% (205/347) neonates: 36.6% (127/347) transient and 22.5% (78/347) persistent; CFB > 10% occurred in 18.7% (65/347). Patients with either persistent CS-AKI or peak CFB > 10% had higher mortality. Combined persistent CS-AKI with peak CFB > 10% (n = 21) associated with increased mortality (aOR: 7.8, 95% CI: 1.4, 45.5; p = 0.02), decreased respiratory support-free (predicted mean 12 vs. 19; p < 0.001) and hospital-free days (17 vs. 29; p = 0.048) compared to those with neither. CONCLUSIONS: The combination of persistent CS-AKI and peak CFB > 10% after the Norwood procedure is associated with mortality and hospital resource utilization. Prospective studies targeting intra- and postoperative CS-AKI risk factors and reducing CFB have the potential to improve outcomes.

Subphenotypes of severe early-onset pre-eclampsia at hospital admission. A Latin American single-center exploratory latent class analysis.

OBJECTIVES: To identify distinct subphenotypes of severe early-onset pre-eclampsia in Latin America and analyze biomarker and hemodynamic trends between subphenotypes after hospital admission. METHODS: A single-center prospective cohort study was conducted in Colombia. The latent class analysis identified subphenotypes using clinical variables, biomarkers, laboratory tests, and maternal hemodynamics. Class-defining variables were restricted to measurements at and 24 h after admission. Primary and secondary outcomes were severe maternal and perinatal complications. RESULTS: Among 49 patients, two subphenotypes were identified: Subphenotype 1 (34.7%) had a higher likelihood of an sFlt-1/PlGF ratio ≤ 38, maternal age > 35, and low probability of TPR > 1400, CO <8, and IUGR; Subphenotype 2 (65.3%) had a low likelihood of an sFlt-1/PlGF ratio < 38, maternal age > 35, and high probability of TPR > 1400, CO <8, and IUGR. At 24 h postadmission, 64.7% of subphenotype 1 patients changed to subphenotype 2, while 25% of subphenotype 2 patients were reclassified as subphenotype 1. Subphenotype 1 displayed significant changes in CO and TPR, while subphenotype 2 did not. Maternal complications were more prevalent in subphenotype 2, with an odds ratio of 5.3 (95% CI: 1.3-22.0; P = 0.02), but no significant differences in severe neonatal complications were observed. CONCLUSIONS: We identified two distinct subphenotypes in a Latin American cohort of patients with severe early-onset pre-eclampsia. Subphenotype 2, characterized by higher TPR, sFlt-1, and serum creatinine and lower CO and PlGF at admission, was associated with worse maternal outcomes and appeared less modifiable after in-hospital treatment.

Using Trajectories of Bedside Vital Signs to Identify COVID-19 Subphenotypes.

BACKGROUND: Trajectories of bedside vital signs have been used to identify sepsis subphenotypes with distinct outcomes and treatment responses. The objective of this study was to validate the vitals trajectory model in a multicenter cohort of patients hospitalized with COVID-19 and to evaluate the clinical characteristics and outcomes of the resulting subphenotypes. RESEARCH QUESTION: Can the trajectory of routine bedside vital signs identify COVID-19 subphenotypes with distinct clinical characteristics and outcomes? STUDY DESIGN AND METHODS: The study included adult patients admitted with COVID-19 to four academic hospitals in the Emory Healthcare system between March 1, 2020, and May 31, 2022. Using a validated group-based trajectory model, we classified patients into previously defined vital sign trajectories using oral temperature, heart rate, respiratory rate, and systolic and diastolic BP measured in the first 8 h of hospitalization. Clinical characteristics, biomarkers, and outcomes were compared between subphenotypes. Heterogeneity of treatment effect to tocilizumab was evaluated. RESULTS: The 7,065 patients with hospitalized COVID-19 were classified into four subphenotypes: group A (n = 1,429, 20%)-high temperature, heart rate, respiratory rate, and hypotensive; group B (1,454, 21%)-high temperature, heart rate, respiratory rate, and hypertensive; group C (2,996, 42%)-low temperature, heart rate, respiratory rate, and normotensive; and group D (1,186, 17%)-low temperature, heart rate, respiratory rate, and hypotensive. Groups A and D had higher ORs of mechanical ventilation, vasopressors, and 30-day inpatient mortality (P < .001). On comparing patients receiving tocilizumab (n = 55) with those who met criteria for tocilizumab but were admitted before its use (n = 461), there was significant heterogeneity of treatment effect across subphenotypes in the association of tocilizumab with 30-day mortality (P = .001). INTERPRETATION: By using bedside vital signs available in even low-resource settings, we found novel subphenotypes associated with distinct manifestations of COVID-19, which could lead to preemptive and targeted treatments.

Advanced hemodynamic and cluster analysis for identifying novel RV function subphenotypes in patients with pulmonary hypertension.

BACKGROUND: Quantifying right ventricular (RV) function is important to describe the pathophysiology of in pulmonary hypertension (PH). Current phenotyping strategies in PH rely on few invasive hemodynamic parameters to quantify RV dysfunction severity. The aim of this study was to identify novel RV phenotypes using unsupervised clustering methods on advanced hemodynamic features of RV function. METHODS: Participants were identified from the University of Arizona Pulmonary Hypertension Registry (n = 190). RV-pulmonary artery coupling (Ees/Ea), RV systolic (Ees), and diastolic function (Eed) were quantified from stored RV pressure waveforms. Consensus clustering analysis with bootstrapping was used to identify the optimal clustering method. Pearson correlation analysis was used to reduce collinearity between variables. RV cluster subphenotypes were characterized using clinical data and compared to pulmonary vascular resistance (PVR) quintiles. RESULTS: Five distinct RV clusters (C1-C5) with distinct RV subphenotypes were identified using k-medoids with a Pearson distance matrix. Clusters 1 and 2 both have low diastolic stiffness (Eed) and afterload (Ea) but RV-PA coupling (Ees/Ea) is decreased in C2. Intermediate cluster (C3) has a similar Ees/Ea as C2 but with higher PA pressure and afterload. Clusters C4 and C5 have increased Eed and Ea but C5 has a significant decrease in Ees/Ea. Cardiac output was high in C3 distinct from the other clusters. In the PVR quintiles, contractility increased and stroke volume decreased as a function of increased afterload. World Symposium PH classifications were distributed across clusters and PVR quintiles. CONCLUSIONS: RV-centric phenotyping offers an opportunity for a more precise-medicine-based management approach.

Joint Modeling of Social Determinants and Clinical Factors to Define Subphenotypes in Out-of-Hospital Cardiac Arrest Survival: Cluster Analysis.

Background: Machine learning clustering offers an unbiased approach to better understand the interactions of complex social and clinical variables via integrative subphenotypes, an approach not studied in out-of-hospital cardiac arrest (OHCA). Objective: We conducted a cluster analysis for a cohort of OHCA survivors to examine the association of clinical and social factors for mortality at 1 year. Methods: We used a retrospective observational OHCA cohort identified from Medicare claims data, including area-level social determinants of health (SDOH) features and hospital-level data sets. We applied k-means clustering algorithms to identify subphenotypes of beneficiaries who had survived an OHCA and examined associations of outcomes by subphenotype. Results: We identified 27,028 unique beneficiaries who survived to discharge after OHCA. We derived 4 distinct subphenotypes. Subphenotype 1 included a distribution of more urban, female, and Black beneficiaries with the least robust area-level SDOH measures and the highest 1-year mortality (2375/4417, 53.8%). Subphenotype 2 was characterized by a greater distribution of male, White beneficiaries and had the strongest zip code-level SDOH measures, with 1-year mortality at 49.9% (4577/9165). Subphenotype 3 had the highest rates of cardiac catheterization at 34.7% (1342/3866) and the greatest distribution with a driving distance to the index OHCA hospital from their primary residence >16.1 km at 85.4% (8179/9580); more were also discharged to a skilled nursing facility after index hospitalization. Subphenotype 4 had moderate median household income at US $51,659.50 (IQR US $41,295 to $67,081) and moderate to high median unemployment at 5.5% (IQR 4.2%-7.1%), with the lowest 1-year mortality (1207/3866, 31.2%). Joint modeling of these features demonstrated an increased hazard of death for subphenotypes 1 to 3 but not for subphenotype 4 when compared to reference. Conclusions: We identified 4 distinct subphenotypes with differences in outcomes by clinical and area-level SDOH features for OHCA. Further work is needed to determine if individual or other SDOH domains are specifically tied to long-term survival after OHCA.

Kidney-lung Crosstalk in Determining the Prognosis of Acute Kidney Injury Phenotypes in Acute Respiratory Distress Syndrome Patients.

How to cite this article: Banerjee T, Bose P. Kidney-lung Crosstalk in Determining the Prognosis of Acute Kidney Injury Phenotypes in Acute Respiratory Distress Syndrome Patients. Indian J Crit Care Med 2023;27(10):701-703.

Incidence, Outcomes, and Predictors of Subphenotypes of Acute Kidney Injury among Acute Respiratory Distress Syndrome Patients: A Prospective Observational Study.

Background: Acute kidney injury (AKI) is a heterogeneous syndrome with subphenotypes. Acute kidney injury is one of the most common complications in acute respiratory distress syndrome (ARDS) patients, which influences mortality. Material and methods: It was a single-center observational study on 266 ARDS patients on invasive mechanical ventilation (IMV) to determine the subphenotypes of AKI associated with ARDS. Subphenotyping was done based on the serum creatinine (SCr) trajectories from day 1 to day 5 of IMV into resolving (subphenotype 1) or non-resolving (subphenotype 2) AKI. Results: Out of 266 ARDS patients, 222 patients were included for data analysis. 141 patients (63.51%) had AKI. The incidence of subphenotype 2 AKI among the ARDS cohort was 78/222 (35.13%). Subphenotype 2 AKI was significantly more among the non-survivors (87.7% vs 36.2 %, p < 0.001). Subphenotype 2 AKI was an independent predictor of mortality among ARDS patients (p < 0.001, adjusted odds ratio 8.978, 95% CI [2.790-28.89]. AKI subphenotype 1 had higher median day 1 SCr than subphenotype 2 but lower levels by day 3 and day 5 of IMV. The median time of survival was 8 days in AKI subphenotype 2 vs 45 days in AKI with subphenotype 1 [Log-Rank (Mantel-Cox) p < 0.001]. The novel DRONE score (Driving pressure, Oxygenation, and Nutritional Evaluation) ≥ 4 predicted subphenotype 2 AKI. Conclusion: The incidence of subphenotype 2 (non-resolving) AKI among ARDS patients on IMV was about 35% (vs 20% subphenotype 1 AKI), and it was an independent predictor of mortality. The DRONE score ≥4 can predict the AKI subphenotype 2. Highlights: The serum creatinine trajectory-based subphenotype of AKI (resolving vs non-resolving) determines survival in ARDS patients. Non-resolving AKI subphenotype 2 is an independent predictor of mortality in ARDS. The novel DRONE score (driving pressure, oxygenation, and nutritional evaluation) ≥ 4 within 48 hours of IMV predicted the AKI subphenotype 2 among ventilated ARDS patients. How to cite this article: Todur P, Nileshwar A, Chaudhuri S, Srinivas T. Incidence, Outcomes, and Predictors of Subphenotypes of Acute Kidney Injury among Acute Respiratory Distress Syndrome Patients: A Prospective Observational Study. Indian J Crit Care Med 2023;27(10):724-731.

Subphenotypes in patients with acute respiratory distress syndrome treated with high-flow oxygen.

BACKGROUND: Acute respiratory distress syndrome (ARDS) subphenotypes differ in outcomes and treatment responses. Subphenotypes in high-flow nasal oxygen (HFNO)-treated ARDS patients have not been investigated. OBJECTIVES: To identify biological subphenotypes in HFNO-treated ARDS patients. METHODS: Secondary analysis of a prospective multicenter observational study including ARDS patients supported with HFNO. Plasma inflammation markers (interleukin [IL]-6, IL-8, and IL-33 and soluble suppression of tumorigenicity-2 [sST2]) and lung epithelial (receptor for advanced glycation end products [RAGE] and surfactant protein D [SP-D]) and endothelial (angiopoietin-2 [Ang-2]) injury were measured. These biomarkers and bicarbonate were used in K-means cluster analysis to identify subphenotypes. Logistic regression was performed on biomarker combinations to predict clustering. We chose the model with the best AUROC and the lowest number of variables. This model was used to describe the HAIS (High-flow ARDS Inflammatory Subphenotype) score. RESULTS: Among 41 HFNO patients, two subphenotypes were identified. Hyperinflammatory subphenotype (n = 17) showed higher biomarker levels than hypoinflammatory (n = 24). Despite similar baseline characteristics, the hyperinflammatory subphenotype had higher 60-day mortality (47 vs 8.3% p = 0.014) and longer ICU length of stay (22.0 days [18.0-30.0] vs 39.5 [25.5-60.0], p = 0.034). The HAIS score, based on IL-8 and sST2, accurately distinguished subphenotypes (AUROC 0.96 [95%CI: 0.90-1.00]). A HAIS score ≥ 7.45 was predictor of hyperinflammatory subphenotype. CONCLUSION: ARDS patients treated with HFNO exhibit two biological subphenotypes that have similar clinical characteristics, but hyperinflammatory patients have worse outcomes. The HAIS score may identify patients with hyperinflammatory subphenotype and might be used for enrichment strategies in future clinical trials.

Neonatal AKI: An update.

Neonatal acute kidney injury (AKI) is a common complication, especially in the neonatal intensive care unit, that is associated with long term consequences and poor outcomes. Early detection and treatment is critical. Currently, neonatal AKI is defined with urinary markers and serum creatinine, with limitations on early detection and individual treatment. There have been numerous biomarkers and risk factor scores that have been studied for their ability to predict neonatal AKI. To move towards personalized medicine, neonatal AKI must be categorized into phenotypes and subphenotypes that fully encapsulate the diverse causes and specific treatments. This review aims to advance our understanding of neonatal AKI detection through the use of biomarkers, subphenotypes, and phenotypes to move towards personalized treatment strategies.

Subphenotypes of Pediatric Acute Kidney Injury.

Acute kidney injury (AKI) is seen frequently in hospitalized patients and is associated with increased risk of mortality and adverse short- and long-term renal and systemic complications. Emerging data suggest that AKI is a heterogenous syndrome with a variety of underlying causes, predisposing illnesses, and range of clinical trajectories and outcomes. This mini-review aims to discuss emerging AKI subphenotype classifications as our understanding of the heterogeneity and underlying pathophysiology has improved.

Endothelial dysfunction triggers acute respiratory distress syndrome in patients with sepsis: a narrative review.

Acute respiratory distress syndrome (ARDS) is a severe organ failure occurring mainly in critically ill patients as a result of different types of insults such as sepsis, trauma or aspiration. Sepsis is the main cause of ARDS, and it contributes to a high mortality and resources consumption both in hospital setting and in the community. ARDS develops mainly an acute respiratory failure with severe and often refractory hypoxemia. ARDS also has long term implications and sequelae. Endothelial damage plays an important role in the pathogenesis of ARDS. Understanding the mechanisms of ARDS presents opportunities for novel diagnostic and therapeutic targets. Biochemical signals can be used in concert to identify and classify patients into ARDS phenotypes allowing earlier effective treatment with personalised therapies. This is a narrative review where we aimed to flesh out the pathogenetic mechanisms and heterogeneity of ARDS. We examine the links between endothelium damage and its contribution to organ failure. We have also investigated future strategies for treatment with a special emphasis in endothelial damage.

Cardiovascular Subphenotypes in ARDS: Diagnostic and Therapeutic Implications and Overlap with Other ARDS Subphenotypes.

Acute respiratory distress syndrome (ARDS) is a highly heterogeneous clinical condition. Shock is a poor prognostic sign in ARDS, and heterogeneity in its pathophysiology may be a barrier to its effective treatment. Although right ventricular dysfunction is commonly implicated, there is no consensus definition for its diagnosis, and left ventricular function is neglected. There is a need to identify the homogenous subgroups within ARDS, that have a similar pathobiology, which can then be treated with targeted therapies. Haemodynamic clustering analyses in patients with ARDS have identified two subphenotypes of increasingly severe right ventricular injury, and a further subphenotype of hyperdynamic left ventricular function. In this review, we discuss how phenotyping the cardiovascular system in ARDS may align with haemodynamic pathophysiology, can aid in optimally defining right ventricular dysfunction and can identify tailored therapeutic targets for shock in ARDS. Additionally, clustering analyses of inflammatory, clinical and radiographic data describe other subphenotypes in ARDS. We detail the potential overlap between these and the cardiovascular phenotypes.