Genomic Dissection of Bipolar Disorder and Schizophrenia, Including 28 Subphenotypes.

Schizophrenia and bipolar disorder are two distinct diagnoses that share symptomology. Understanding the genetic factors contributing to the shared and disorder-specific symptoms will be crucial for improving diagnosis and treatment. In genetic data consisting of 53,555 cases (20,129 bipolar disorder [BD], 33,426 schizophrenia [SCZ]) and 54,065 controls, we identified 114 genome-wide significant loci implicating synaptic and neuronal pathways shared between disorders. Comparing SCZ to BD (23,585 SCZ, 15,270 BD) identified four genomic regions including one with disorder-independent causal variants and potassium ion response genes as contributing to differences in biology between the disorders. Polygenic risk score (PRS) analyses identified several significant correlations within case-only phenotypes including SCZ PRS with psychotic features and age of onset in BD. For the first time, we discover specific loci that distinguish between BD and SCZ and identify polygenic components underlying multiple symptom dimensions. These results point to the utility of genetics to inform symptomology and potential treatment.

Biological Phenotyping in Sepsis and Acute Respiratory Distress Syndrome.

Heterogeneity in sepsis and acute respiratory distress syndrome (ARDS) is increasingly being recognized as one of the principal barriers to finding efficacious targeted therapies. The advent of multiple high-throughput biological data ("omics"), coupled with the widespread access to increased computational power, has led to the emergence of phenotyping in critical care. Phenotyping aims to use a multitude of data to identify homogenous subgroups within an otherwise heterogenous population. Increasingly, phenotyping schemas are being applied to sepsis and ARDS to increase understanding of these clinical conditions and identify potential therapies. Here we present a selective review of the biological phenotyping schemas applied to sepsis and ARDS. Further, we outline some of the challenges involved in translating these conceptual findings to bedside clinical decision-making tools.

Ischemic cardiac stromal fibroblast-derived protein mediators in the infarcted myocardium and transcriptomic profiling at single cell resolution.

This article focuses on screening the major secreted proteins by the ischemia-challenged cardiac stromal fibroblasts (CF), the assessment of their expression status and functional role in the post-ischemic left ventricle (LV) and in the ischemia-challenged CF culture and to phenotype CF at single cell resolution based on the positivity of the identified mediators. The expression level of CRSP2, HSP27, IL-8, Cofilin-1, and HSP90 in the LV tissues following coronary artery bypass graft (CABG) and myocardial infarction (MI) and CF cells followed the screening profile derived from the MS/MS findings. The histology data unveiled ECM disorganization, inflammation and fibrosis reflecting the ischemic pathology. CRSP2, HSP27, and HSP90 were significantly upregulated in the LV-CABG tissues with a concomitant reduction ion LV-MI whereas Cofilin-1, IL8, Nrf2, and Troponin I were downregulated in LV-CABG and increased in LV-MI. Similar trends were exhibited by ischemic CF. Single cell transcriptomics revealed multiple sub-phenotypes of CF based on their respective upregulation of CRSP2, HSP27, IL-8, Cofilin-1, HSP90, Troponin I and Nrf2 unveiling pathological and pro-healing phenotypes. Further investigations regarding the underlying signaling mechanisms and validation of sub-populations would offer novel translational avenues for the management of cardiac diseases.

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.

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.

Phenotyping COVID-19 respiratory failure in spontaneously breathing patients with AI on lung CT-scan.

BACKGROUND: Automated analysis of lung computed tomography (CT) scans may help characterize subphenotypes of acute respiratory illness. We integrated lung CT features measured via deep learning with clinical and laboratory data in spontaneously breathing subjects to enhance the identification of COVID-19 subphenotypes. METHODS: This is a multicenter observational cohort study in spontaneously breathing patients with COVID-19 respiratory failure exposed to early lung CT within 7 days of admission. We explored lung CT images using deep learning approaches to quantitative and qualitative analyses; latent class analysis (LCA) by using clinical, laboratory and lung CT variables; regional differences between subphenotypes following 3D spatial trajectories. RESULTS: Complete datasets were available in 559 patients. LCA identified two subphenotypes (subphenotype 1 and 2). As compared with subphenotype 2 (n = 403), subphenotype 1 patients (n = 156) were older, had higher inflammatory biomarkers, and were more hypoxemic. Lungs in subphenotype 1 had a higher density gravitational gradient with a greater proportion of consolidated lungs as compared with subphenotype 2. In contrast, subphenotype 2 had a higher density submantellar-hilar gradient with a greater proportion of ground glass opacities as compared with subphenotype 1. Subphenotype 1 showed higher prevalence of comorbidities associated with endothelial dysfunction and higher 90-day mortality than subphenotype 2, even after adjustment for clinically meaningful variables. CONCLUSIONS: Integrating lung-CT data in a LCA allowed us to identify two subphenotypes of COVID-19, with different clinical trajectories. These exploratory findings suggest a role of automated imaging characterization guided by machine learning in subphenotyping patients with respiratory failure. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04395482. Registration date: 19/05/2020.

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.

The Gut Microbiome Modulates Body Temperature Both in Sepsis and Health.

Rationale: Among patients with sepsis, variation in temperature trajectories predicts clinical outcomes. In healthy individuals, normal body temperature is variable and has decreased consistently since the 1860s. The biologic underpinnings of this temperature variation in disease and health are unknown. Objectives: To establish and interrogate the role of the gut microbiome in calibrating body temperature. Methods: We performed a series of translational analyses and experiments to determine whether and how variation in gut microbiota explains variation in body temperature in sepsis and in health. We studied patient temperature trajectories using electronic medical record data. We characterized gut microbiota in hospitalized patients using 16S ribosomal RNA gene sequencing. We modeled sepsis using intraperitoneal LPS in mice and modulated the microbiome using antibiotics, germ-free, and gnotobiotic animals. Measurements and Main Results: Consistent with prior work, we identified four temperature trajectories in patients hospitalized with sepsis that predicted clinical outcomes. In a separate cohort of 116 hospitalized patients, we found that the composition of patients' gut microbiota at admission predicted their temperature trajectories. Compared with conventional mice, germ-free mice had reduced temperature loss during experimental sepsis. Among conventional mice, heterogeneity of temperature response in sepsis was strongly explained by variation in gut microbiota. Healthy germ-free and antibiotic-treated mice both had lower basal body temperatures compared with control animals. The Lachnospiraceae family was consistently associated with temperature trajectories in hospitalized patients, experimental sepsis, and antibiotic-treated mice. Conclusions: The gut microbiome is a key modulator of body temperature variation in both health and critical illness and is thus a major, understudied target for modulating physiologic heterogeneity in sepsis.

Author Response.

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

Integrated Analysis of Blood and Urine Biomarkers to Identify Acute Kidney Injury Subphenotypes and Associations With Long-term Outcomes.

RATIONALE & OBJECTIVE: Acute kidney injury (AKI) is a heterogeneous clinical syndrome with varying causes, pathophysiology, and outcomes. We incorporated plasma and urine biomarker measurements to identify AKI subgroups (subphenotypes) more tightly linked to underlying pathophysiology and long-term clinical outcomes. STUDY DESIGN: Multicenter cohort study. SETTING & PARTICIPANTS: 769 hospitalized adults with AKI matched with 769 without AKI, enrolled from December 2009 to February 2015 in the ASSESS-AKI Study. PREDICTORS: 29 clinical, plasma, and urinary biomarker parameters used to identify AKI subphenotypes. OUTCOME: Composite of major adverse kidney events (MAKE) with a median follow-up period of 4.7 years. ANALYTICAL APPROACH: Latent class analysis (LCA) and k-means clustering were applied to 29 clinical, plasma, and urinary biomarker parameters. Associations between AKI subphenotypes and MAKE were analyzed using Kaplan-Meier curves and Cox proportional hazard models. RESULTS: Among 769 AKI patients both LCA and k-means identified 2 distinct AKI subphenotypes (classes 1 and 2). The long-term risk for MAKE was higher with class 2 (adjusted HR, 1.41 [95% CI, 1.08-1.84]; P=0.01) compared with class 1, adjusting for demographics, hospital level factors, and KDIGO stage of AKI. The higher risk of MAKE among class 2 was explained by a higher risk of long-term chronic kidney disease progression and dialysis. The top variables that were different between classes 1 and 2 included plasma and urinary biomarkers of inflammation and epithelial cell injury; serum creatinine ranked 20th out of the 29 variables for differentiating classes. LIMITATIONS: A replication cohort with simultaneously collected blood and urine sampling in hospitalized adults with AKI and long-term outcomes was unavailable. CONCLUSIONS: We identify 2 molecularly distinct AKI subphenotypes with differing risk of long-term outcomes, independent of the current criteria to risk stratify AKI. Future identification of AKI subphenotypes may facilitate linking therapies to underlying pathophysiology to prevent long-term sequalae after AKI. PLAIN-LANGUAGE SUMMARY: Acute kidney injury (AKI) occurs commonly in hospitalized patients and is associated with high morbidity and mortality. The AKI definition lumps many different types of AKI together, but subgroups of AKI may be more tightly linked to the underlying biology and clinical outcomes. We used 29 different clinical, blood, and urinary biomarkers and applied 2 different statistical algorithms to identify AKI subtypes and their association with long-term outcomes. Both clustering algorithms identified 2 AKI subtypes with different risk of chronic kidney disease, independent of the serum creatinine concentrations (the current gold standard to determine severity of AKI). Identification of AKI subtypes may facilitate linking therapies to underlying biology to prevent long-term consequences after AKI.

Plasma proteomic profiles of pain subtypes in adolescents and young adults with endometriosis.

STUDY QUESTION: What are the similarities and differences in the systemic proteomic profiles by endometriosis-associated pain subtypes among adolescents and young adults with endometriosis? SUMMARY ANSWER: Endometriosis-associated pain subtypes exhibited distinct plasma proteomic profiles. WHAT IS KNOWN ALREADY: Endometriosis patients, especially those diagnosed in adolescents and young adults, are often plagued by various pain symptoms. However, it is not clear what biological processes underlie this heterogeneity. STUDY DESIGN, SIZE, DURATION: We conducted a cross-sectional analysis using data and plasma samples from 142 adolescent or young adult participants of the Women's Health Study: From Adolescence to Adulthood cohort with laparoscopically confirmed endometriosis. PARTICIPANTS/MATERIALS, SETTING, METHODS: We measured 1305 plasma protein levels by SomaScan. We classified self-reported endometriosis-associated pain into subtypes of dysmenorrhea, acyclic pelvic pain, life impacting pelvic pain, bladder pain, bowel pain, and widespread pain phenotype. We used logistic regression to calculate the odds ratios and 95% confidence intervals for differentially expressed proteins, adjusting for age, BMI, fasting status, and hormone use at blood draw. Ingenuity Pathway Analysis identified enriched biological pathways. MAIN RESULTS AND THE ROLE OF CHANCE: Our study population consisted mainly of adolescents and young adults (mean age at blood draw = 18 years), with nearly all (97%) scored as rASRM stage I/II at laparoscopic diagnosis of endometriosis, which is a common clinical presentation of endometriosis diagnosed at a younger age. Pain subtypes exhibited distinct plasma proteomic profiles. Multiple cell movement pathways were downregulated in cases with severe dysmenorrhea and life impacting pelvic pain compared to those without (P < 7.5×10-15). Endometriosis cases with acyclic pelvic pain had upregulation of immune cell adhesion pathways (P < 9.0×10-9), while those with bladder pain had upregulation of immune cell migration (P < 3.7×10-8) and those with bowel pain had downregulation (P < 6.5×10-7) of the immune cell migration pathways compared to those without. Having a wide-spread pain phenotype involved downregulation of multiple immune pathways (P < 8.0×10-10). LIMITATIONS, REASONS FOR CAUTION: Our study was limited by the lack of an independent validation cohort. We were also only able to explore any presence of a pain subtype and could not evaluate multiple combinations by pain subtypes. Further mechanistic studies are warranted to elucidate the differences in pathophysiology by endometriosis-pain subtype. WIDER IMPLICATIONS OF THE FINDINGS: The observed variation in plasma protein profiles by pain subtypes suggests different underlying molecular mechanisms, highlighting the need for potential consideration of pain subtypes for effectively treating endometriosis patients presenting with various pain symptoms. STUDY FUNDING/COMPETING INTEREST(S): This study was supported by the Department of Defense W81XWH1910318 and the 2017 Boston Center for Endometriosis Trainee Award. Financial support for establishment of and data collection within the A2A cohort were provided by the J. Willard and Alice S. Marriott Foundation. N.S., A.F.V., S.A.M., and K.L.T. have received funding from the Marriott Family Foundation. C.B.S. is funded by an R35 MIRA Award from NIGMS (5R35GM142676). S.A.M. and K.L.T. are supported by NICHD R01HD094842. S.A.M. reports serving as an advisory board member for AbbVie and Roche, Field Chief Editor for Frontiers in Reproductive Health, personal fees from Abbott for roundtable participation; none of these are related to this study. Other authors report no conflict of interest. TRIAL REGISTRATION NUMBER: N/A.

Serum metabolomic signatures of fatty acid oxidation defects differentiate host-response subphenotypes of acute respiratory distress syndrome.

BACKGROUND: Fatty acid oxidation (FAO) defects have been implicated in experimental models of acute lung injury and associated with poor outcomes in critical illness. In this study, we examined acylcarnitine profiles and 3-methylhistidine as markers of FAO defects and skeletal muscle catabolism, respectively, in patients with acute respiratory failure. We determined whether these metabolites were associated with host-response ARDS subphenotypes, inflammatory biomarkers, and clinical outcomes in acute respiratory failure. METHODS: In a nested case-control cohort study, we performed targeted analysis of serum metabolites of patients intubated for airway protection (airway controls), Class 1 (hypoinflammatory), and Class 2 (hyperinflammatory) ARDS patients (N = 50 per group) during early initiation of mechanical ventilation. Relative amounts were quantified by liquid chromatography high resolution mass spectrometry using isotope-labeled standards and analyzed with plasma biomarkers and clinical data. RESULTS: Of the acylcarnitines analyzed, octanoylcarnitine levels were twofold increased in Class 2 ARDS relative to Class 1 ARDS or airway controls (P = 0.0004 and < 0.0001, respectively) and was positively associated with Class 2 by quantile g-computation analysis (P = 0.004). In addition, acetylcarnitine and 3-methylhistidine were increased in Class 2 relative to Class 1 and positively correlated with inflammatory biomarkers. In all patients within the study with acute respiratory failure, increased 3-methylhistidine was observed in non-survivors at 30 days (P = 0.0018), while octanoylcarnitine was increased in patients requiring vasopressor support but not in non-survivors (P = 0.0001 and P = 0.28, respectively). CONCLUSIONS: This study demonstrates that increased levels of acetylcarnitine, octanoylcarnitine, and 3-methylhistidine distinguish Class 2 from Class 1 ARDS patients and airway controls. Octanoylcarnitine and 3-methylhistidine were associated with poor outcomes in patients with acute respiratory failure across the cohort independent of etiology or host-response subphenotype. These findings suggest a role for serum metabolites as biomarkers in ARDS and poor outcomes in critically ill patients early in the clinical course.

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.

Identifying acute kidney injury subphenotypes using an outcome-driven deep-learning approach.

OBJECTIVE: Acute kidney injury (AKI), a common condition on the intensive-care unit (ICU), is characterized by an abrupt decrease in kidney function within a few hours or days, leading to kidney failure or damage. Although AKI is associated with poor outcomes, current guidelines overlook the heterogeneity among patients with this condition. Identification of AKI subphenotypes could enable targeted interventions and a deeper understanding of the injury's pathophysiology. While previous approaches based on unsupervised representation learning have been used to identify AKI subphenotypes, these methods cannot assess time series or disease severity. METHODS: In this study, we developed a data- and outcome-driven deep-learning (DL) approach to identify and analyze AKI subphenotypes with prognostic and therapeutic implications. Specifically, we developed a supervised long short-term memory (LSTM) autoencoder (AE) with the aim of extracting representation from time-series EHR data that were intricately correlated with mortality. Then, subphenotypes were identified via application of K-means. RESULTS: In two publicly available datasets, three distinct clusters were identified, characterized by mortality rates of 11.3%, 17.3%, and 96.2% in one dataset and 4.6%, 12.1%, and 54.6% in the other. Further analysis demonstrated that AKI subphenotypes identified by our proposed approach were statistically significant on several clinical characteristics and outcomes. CONCLUSION: In this study, our proposed approach could successfully cluster the AKI population in ICU settings into 3 distinct subphenotypes. Thus, such approach could potentially improve outcomes of AKI patients in the ICU, with better risk assessment and potentially better personalized treatment.

Investigations of a combination of atopic status and age of asthma onset identify asthma subphenotypes.

OBJECTIVE: Subphenotypes of asthma may be determined by age onset and atopic status. We sought to characterize early or late onset atopic asthma with fungal or non-fungal sensitization (AAFS or AANFS) and non-atopic asthma (NAA) in children and adults in the Severe Asthma Research Program (SARP). SARP is an ongoing project involving well-phenotyped patients with mild to severe asthma. METHODS: Phenotypic comparisons were performed using Kruskal-Wallis or chi-square test. Genetic association analyses were performed using logistic or linear regression. RESULTS: Airway hyper-responsiveness, total serum IgE levels, and T2 biomarkers showed an increasing trend from NAA to AANFS and then to AAFS. Children and adults with early onset asthma had greater % of AAFS than adults with late onset asthma (46% and 40% vs. 32%; P < 0.00001). In children, AAFS and AANFS had lower % predicted FEV1 (86% and 91% vs. 97%) and greater % of patients with severe asthma than NAA (61% and 59% vs. 43%). In adults with early or late onset asthma, NAA had greater % of patients with severe asthma than AANFS and AAFS (61% vs. 40% and 37% or 56% vs. 44% and 49%). The G allele of rs2872507 in GSDMB had higher frequency in AAFS than AANFS and NAA (0.63 vs. 0.55 and 0.55), and associated with earlier age onset and asthma severity. CONCLUSIONS: Early or late onset AAFS, AANFS, and NAA have shared and distinct phenotypic characteristics in children and adults. AAFS is a complex disorder involving genetic susceptibility and environmental factors.

Evolution of Clinical Phenotypes of COVID-19 Patients During Intensive Care Treatment: An Unsupervised Machine Learning Analysis.

BACKGROUND: Identification of clinical phenotypes in critically ill COVID-19 patients could improve understanding of the disease heterogeneity and enable prognostic and predictive enrichment. However, previous attempts did not take into account temporal dynamics with high granularity. By including the dimension of time, we aim to gain further insights into the heterogeneity of COVID-19. METHODS: We used granular data from 3202 adult COVID patients in the Dutch Data Warehouse that were admitted to one of 25 Dutch ICUs between February 2020 and March 2021. Parameters including demographics, clinical observations, medications, laboratory values, vital signs, and data from life support devices were selected. Twenty-one datasets were created that each covered 24 h of ICU data for each day of ICU treatment. Clinical phenotypes in each dataset were identified by performing cluster analyses. Both evolution of the clinical phenotypes over time and patient allocation to these clusters over time were tracked. RESULTS: The final patient cohort consisted of 2438 COVID-19 patients with a ICU mortality outcome. Forty-one parameters were chosen for cluster analysis. On admission, both a mild and a severe clinical phenotype were found. After day 4, the severe phenotype split into an intermediate and a severe phenotype for 11 consecutive days. Heterogeneity between phenotypes appears to be driven by inflammation and dead space ventilation. During the 21-day period, only 8.2% and 4.6% of patients in the initial mild and severe clusters remained assigned to the same phenotype respectively. The clinical phenotype half-life was between 5 and 6 days for the mild and severe phenotypes, and about 3 days for the medium severe phenotype. CONCLUSIONS: Patients typically do not remain in the same cluster throughout intensive care treatment. This may have important implications for prognostic or predictive enrichment. Prominent dissimilarities between clinical phenotypes are predominantly driven by inflammation and dead space ventilation.

Identifying two distinct subphenotypes of patent ductus arteriosus in preterm infants using machine learning.

To use unsupervised machine learning to identify potential subphenotypes of preterm infants with patent ductus arteriosus (PDA). The study was conducted retrospectively at a neonatal intensive care unit in Brazil. Patients with a gestational age < 28 weeks who had undergone at least one echocardiogram within the first two weeks of life and had PDA size > 1.5 or LA/AO ratio > 1.5 were included. Agglomerative hierarchical clustering on principal components was used to divide the data into different clusters based on common characteristics. Two distinct subphenotypes of preterm infants with hemodynamically significant PDA were identified: "inflamed," characterized by high leukocyte, neutrophil, and neutrophil-to-lymphocyte ratio, and "respiratory acidosis," characterized by low pH and high pCO2 levels.    Conclusions: This study suggests that there may be two distinct subphenotypes of preterm infants with hemodynamically significant PDA: "inflamed" and "respiratory acidosis." By dividing the population into different subgroups based on common characteristics, it is possible to get a more nuanced understanding of the effectiveness of PDA interventions. What is Known: • Treatment of PDA in preterm infants has been controversial. • Stratification of preterm infants with PDA into subgroups is important in order to determine the best treatment. What is New: • Unsupervised machine learning was used to identify two subphenotypes of preterm infants with hemodynamically significant PDA. • The 'inflamed' cluster was characterized by higher values of leukocyte, neutrophil, and neutrophil-to-lymphocyte ratio. The 'respiratory acidosis' cluster was characterized by lower pH values and higher pCO2 values.

Physiological and clinical variables identify ARDS classes and therapeutic heterogeneity to glucocorticoids: a retrospective study.

OBJECTIVE: We aimed to identify new classes in acute respiratory distress syndrome (ARDS) using physiological and clinical variables and to explore heterogeneity in the effects of glucocorticoid therapy between classes. METHODS: Using the Medical Information Mart for Intensive Care-IV database, we identified patients with ARDS. Potential profile analysis was used to identify classes with physiological and clinical data as delineating variables. Baseline characteristics and clinical outcomes were compared between classes. The effect of glucocorticoid treatment was explored by stratifying by class and glucocorticoid treatment. RESULTS: From 2008 to 2019, 1104 patients with ARDS were enrolled in the study. The 2-class potential analysis model had the best fit (P < 0.0001), with 78% of patients falling into class 1 and 22% into class 2. Additional classes did not improve the model fit. Patients in class 2 had higher anion gap, lactate, creatinine, and glucose levels and lower residual base, blood pressure, and bicarbonate compared with class 1. In-hospital mortality and 28-day mortality were significantly higher among patients in class 2 than those in class 1 (P < 0.001). Heterogeneity of glucocorticoid treatment was observed, stratified by class and treatment, with no significant effect in class 1 (P = 0.496), increased mortality in class 2 (P = 0.001), and a significant interaction (P = 0.0381). In class 2, 28-day survival was significantly lower with glucocorticoid treatment compared with no hormone treatment (P = 0.001). CONCLUSION: We used clinical and physiological variables to identify two classes of non-COVID-19-associated ARDS with different baseline characteristics and clinical outcomes. The response to glucocorticoid therapy varied among different classes of patients.

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.