Identification of Distinct Clinical Subphenotypes in Critically Ill Patients With COVID-19

Applicability of an unsupervised cluster model developed on first wave COVID-19 patients in second/third wave critically ill patients

OBJECTIVE

To validate the unsupervised cluster model (USCM) developed during the first pandemic wave in a cohort of critically ill patients from the second and third pandemic waves.

DESIGN

Observational, retrospective, multicentre study.

SETTING

Intensive Care Unit (ICU).

PATIENTS

Adult patients admitted with COVID-19 and respiratory failure during the second and third pandemic waves.

INTERVENTIONS

None.

MAIN VARIABLES OF INTEREST

Collected data included demographic and clinical characteristics, comorbidities, laboratory tests and ICU outcomes. To validate our original USCM, we assigned a phenotype to each patient of the validation cohort. The performance of the classification was determined by Silhouette coefficient (SC) and general linear modelling. In a post-hoc analysis we developed and validated a USCM specific to the validation set. The model's performance was measured using accuracy test and area under curve (AUC) ROC.

RESULTS

A total of 2330 patients (mean age 63 [53-82] years, 1643 (70.5%) male, median APACHE II score (12 [9-16]) and SOFA score (4 [3-6]) were included. The ICU mortality was 27.2%. The USCM classified patients into 3 clinical phenotypes: A (n = 1206 patients, 51.8%); B (n = 618 patients, 26.5%), and C (n = 506 patients, 21.7%). The characteristics of patients within each phenotype were significantly different from the original population. The SC was -0.007 and the inclusion of phenotype classification in a regression model did not improve the model performance (0.79 and 0.78 ROC for original and validation model). The post-hoc model performed better than the validation model (SC -0.08).

CONCLUSION

Models developed using machine learning techniques during the first pandemic wave cannot be applied with adequate performance to patients admitted in subsequent waves without prior validation.

COVID-19 subphenotypes at hospital admission are associated with mortality: a cross-sectional study

BACKGROUND

We have an incomplete understanding of COVID-19 characteristics at hospital presentation and whether underlying subphenotypes are associated with clinical outcomes and therapeutic responses.

METHODS

For this cross-sectional study, we extracted electronic health data from adults hospitalized between 1 March and 30 August 2020 with a PCR-confirmed diagnosis of COVID-19 at five New York City Hospitals. We obtained clinical and laboratory data from the first 24 h of the patient's hospitalization. Treatment with tocilizumab and convalescent plasma was assessed over hospitalization. The primary outcome was mortality; secondary outcomes included intubation, intensive care unit (ICU) admission and length of stay (LOS). First, we employed latent class analysis (LCA) to identify COVID-19 subphenotypes on admission without consideration of outcomes and assigned each patient to a subphenotype. We then performed robust Poisson regression to examine associations between COVID-19 subphenotype assignment and outcome. We explored whether the COVID-19 subphenotypes had a differential response to tocilizumab and convalescent plasma therapies.

RESULTS

A total of 4620 patients were included. LCA identified six subphenotypes, which were distinct by level of inflammation, clinical and laboratory derangements and ranged from a hypoinflammatory subphenotype with the fewest derangements to a hyperinflammatory with multiorgan dysfunction subphenotypes. Multivariable regression analyses found differences in risk for mortality, intubation, ICU admission and LOS, as compared to the hypoinflammatory subphenotype. For example, in multivariable analyses the moderate inflammation with fever subphenotype had 3.29 times the risk of mortality (95% CI 2.05, 5.28), while the hyperinflammatory with multiorgan failure subphenotype had 17.87 times the risk of mortality (95% CI 11.56, 27.63), as compared to the hypoinflammatory subphenotype. Exploratory analyses suggested that subphenotypes may differential respond to convalescent plasma or tocilizumab therapy.

CONCLUSION

COVID-19 subphenotype at hospital admission may predict risk for mortality, ICU admission and intubation and differential response to treatment.KEY MESSAGEThis cross-sectional study of COVID patients admitted to the Mount Sinai Health System, identified six distinct COVID subphenotypes on admission. Subphenotypes correlated with ICU admission, intubation, mortality and differential response to treatment.

Analysis of Protein Biomarkers From Hospitalized COVID-19 Patients Reveals Severity-Specific Signatures and Two Distinct Latent Profiles With Differential Responses to Corticosteroids

OBJECTIVES

To identify and validate novel COVID-19 subphenotypes with potential heterogenous treatment effects (HTEs) using electronic health record (EHR) data and 33 unique biomarkers.

DESIGN

Retrospective cohort study of adults presenting for acute care, with analysis of biomarkers from residual blood collected during routine clinical care. Latent profile analysis (LPA) of biomarker and EHR data identified subphenotypes of COVID-19 inpatients, which were validated using a separate cohort of patients. HTE for glucocorticoid use among subphenotypes was evaluated using both an adjusted logistic regression model and propensity matching analysis for in-hospital mortality.

SETTING

Emergency departments from four medical centers.

PATIENTS

Patients diagnosed with COVID-19 based on International Classification of Diseases , 10th Revision codes and laboratory test results.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Biomarker levels generally paralleled illness severity, with higher levels among more severely ill patients. LPA of 522 COVID-19 inpatients from three sites identified two profiles: profile 1 ( n = 332), with higher levels of albumin and bicarbonate, and profile 2 ( n = 190), with higher inflammatory markers. Profile 2 patients had higher median length of stay (7.4 vs 4.1 d; p < 0.001) and in-hospital mortality compared with profile 1 patients (25.8% vs 4.8%; p < 0.001). These were validated in a separate, single-site cohort ( n = 192), which demonstrated similar outcome differences. HTE was observed ( p = 0.03), with glucocorticoid treatment associated with increased mortality for profile 1 patients (odds ratio = 4.54).

CONCLUSIONS

In this multicenter study combining EHR data with research biomarker analysis of patients with COVID-19, we identified novel profiles with divergent clinical outcomes and differential treatment responses.

Integrating patient-reported physical, mental, and social impacts to classify long COVID experiences

Long COVID was originally identified through patient-reported experiences of prolonged symptoms. Many studies have begun to describe long COVID; however, this work typically focuses on medical records, instead of patient experiences, and lacks a comprehensive view of physical, mental, and social impacts. As part of our larger My COVID Diary (MCD) study, we captured patient experiences using a prospective and longitudinal patient-reported outcomes survey (PROMIS-10) and free-text narrative submissions. From this study population, we selected individuals who were still engaged in the MCD study and reporting poor health (PROMIS-10 scores < 3) at 6 months (n = 634). We used their PROMIS-10 and narrative data to describe and classify their long COVID experiences. Using Latent Class Analysis of the PROMIS-10 data, we identified four classifications of long COVID experiences: a few lingering issues (n = 107), significant physical symptoms (n = 113), ongoing mental and cognitive struggles (n = 235), and numerous compounding challenges (n = 179); each classification included a mix of physical, mental, and social health struggles with varying levels of impairment. The classifications were reinforced and further explained by patient narratives. These results provide a new understanding of the varying ways that long COVID presents to help identify and care for patients.

Interpreting biologically informed neural networks for enhanced proteomic biomarker discovery and pathway analysis

The incorporation of machine learning methods into proteomics workflows improves the identification of disease-relevant biomarkers and biological pathways. However, machine learning models, such as deep neural networks, typically suffer from lack of interpretability. Here, we present a deep learning approach to combine biological pathway analysis and biomarker identification to increase the interpretability of proteomics experiments. Our approach integrates a priori knowledge of the relationships between proteins and biological pathways and biological processes into sparse neural networks to create biologically informed neural networks. We employ these networks to differentiate between clinical subphenotypes of septic acute kidney injury and COVID-19, as well as acute respiratory distress syndrome of different aetiologies. To gain biological insight into the complex syndromes, we utilize feature attribution-methods to introspect the networks for the identification of proteins and pathways important for distinguishing between subtypes. The algorithms are implemented in a freely available open source Python-package ( https://github.com/InfectionMedicineProteomics/BINN ).

Clinical Sepsis Phenotypes in Critically Ill Patients

Sepsis, defined as the life-threatening dysregulated host response to an infection leading to organ dysfunction, is considered as one of the leading causes of mortality worldwide, especially in intensive care units (ICU). Moreover, sepsis remains an enigmatic clinical syndrome, with complex pathophysiology incompletely understood and a great heterogeneity both in terms of clinical expression, patient response to currently available therapeutic interventions and outcomes. This heterogeneity proves to be a major obstacle in our quest to deliver improved treatment in septic critical care patients; thus, identification of clinical phenotypes is absolutely necessary. Although this might be seen as an extremely difficult task, nowadays, artificial intelligence and machine learning techniques can be recruited to quantify similarities between individuals within sepsis population and differentiate them into distinct phenotypes regarding not only temperature, hemodynamics or type of organ dysfunction, but also fluid status/responsiveness, trajectories in ICU and outcome. Hopefully, we will eventually manage to determine both the subgroup of septic patients that will benefit from a therapeutic intervention and the correct timing of applying the intervention during the disease process.

ESICM guidelines on acute respiratory distress syndrome: definition, phenotyping and respiratory support strategies

The aim of these guidelines is to update the 2017 clinical practice guideline (CPG) of the European Society of Intensive Care Medicine (ESICM). The scope of this CPG is limited to adult patients and to non-pharmacological respiratory support strategies across different aspects of acute respiratory distress syndrome (ARDS), including ARDS due to coronavirus disease 2019 (COVID-19). These guidelines were formulated by an international panel of clinical experts, one methodologist and patients' representatives on behalf of the ESICM. The review was conducted in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement recommendations. We followed the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach to assess the certainty of evidence and grade recommendations and the quality of reporting of each study based on the EQUATOR (Enhancing the QUAlity and Transparency Of health Research) network guidelines. The CPG addressed 21 questions and formulates 21 recommendations on the following domains: (1) definition; (2) phenotyping, and respiratory support strategies including (3) high-flow nasal cannula oxygen (HFNO); (4) non-invasive ventilation (NIV); (5) tidal volume setting; (6) positive end-expiratory pressure (PEEP) and recruitment maneuvers (RM); (7) prone positioning; (8) neuromuscular blockade, and (9) extracorporeal life support (ECLS). In addition, the CPG includes expert opinion on clinical practice and identifies the areas of future research.

Functional enhancement strategies to potentiate the therapeutic properties of mesenchymal stromal cells for respiratory diseases

Respiratory diseases remain a major health concern worldwide because they subject patients to considerable financial and psychosocial burdens and result in a high rate of morbidity and mortality. Although significant progress has been made in understanding the underlying pathologic mechanisms of severe respiratory diseases, most therapies are supportive, aiming to mitigate symptoms and slow down their progressive course but cannot improve lung function or reverse tissue remodeling. Mesenchymal stromal cells (MSCs) are at the forefront of the regenerative medicine field due to their unique biomedical potential in promoting immunomodulation, anti-inflammatory, anti-apoptotic and antimicrobial activities, and tissue repair in various experimental models. However, despite several years of preclinical research on MSCs, therapeutic outcomes have fallen far short in early-stage clinical trials for respiratory diseases. This limited efficacy has been associated with several factors, such as reduced MSC homing, survival, and infusion in the late course of lung disease. Accordingly, genetic engineering and preconditioning methods have emerged as functional enhancement strategies to potentiate the therapeutic actions of MSCs and thus achieve better clinical outcomes. This narrative review describes various strategies that have been investigated in the experimental setting to functionally potentiate the therapeutic properties of MSCs for respiratory diseases. These include changes in culture conditions, exposure of MSCs to inflammatory environments, pharmacological agents or other substances, and genetic manipulation for enhanced and sustained expression of genes of interest. Future directions and challenges in efficiently translating MSC research into clinical practice are discussed.

Impact of Alcohol Misuse on Requirements for Critical Care Services and Development of Hospital Delirium in Patients With COVID-19 pneumonia

Alcohol misuse has been associated with increased morbidity in the setting of pulmonary infections, including the need for critical care resource utilization and development of delirium. How alcohol misuse impacts morbidity and outcomes among patients admitted with COVID-19 pneumonia is not well described. We sought to determine if alcohol misuse was associated with an increased need for critical care resources and development of delirium among patients hospitalized with COVID-19 pneumonia.

DESIGN

Retrospective cohort study.

SETTING

Twelve University of Colorado hospitals between March 2020 and April 2021.

PATIENTS

Adults with a COVID-19 diagnosis.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

The primary outcome was admission to the ICU. Secondary outcomes included need for mechanical ventilation, development of delirium, and in-hospital mortality. Alcohol misuse was defined by International Classification of Diseases, 10th Revision codes. Of 5,979 patients hospitalized with COVID-19, 26% required ICU admission and 15.4% required mechanical ventilation. Delirium developed in 4.5% and 10.5% died during hospitalization. Alcohol misuse was identified in 4%. In analyses adjusted for age, sex, body mass index, diabetes, and liver disease, alcohol misuse was associated with increased odds of ICU admission (adjusted odds ratio [aOR], 1.46; p < 0.01), mechanical ventilation (aOR, 1.43; p = 0.03), and delirium (aOR, 5.55; p < 0.01) compared with patients without misuse. Mortality rates were not associated with alcohol misuse alone, although the presence of both alcohol misuse and in-hospital delirium significantly increased odds of in-hospital death (aOR, 2.60; p = 0.04).

CONCLUSIONS

Among patients hospitalized with COVID-19, alcohol misuse was associated with increased utilization of critical care resources including ICU admission and mechanical ventilation. Delirium was an important modifiable risk factor associated with worse outcomes in hospitalized patients with alcohol misuse, including increased odds of death.

Relationship Between Preexisting Cardiovascular Disease and Death and Cardiovascular Outcomes in Critically Ill Patients With COVID-19

BACKGROUND

Preexisting cardiovascular disease (CVD) is perceived as a risk factor for poor outcomes in patients with COVID-19. We sought to determine whether CVD is associated with in-hospital death and cardiovascular events in critically ill patients with COVID-19.

METHODS

This study used data from a multicenter cohort of adults with laboratory-confirmed COVID-19 admitted to intensive care units at 68 centers across the United States from March 1 to July 1, 2020. The primary exposure was CVD, defined as preexisting coronary artery disease, congestive heart failure, or atrial fibrillation/flutter. Myocardial injury on intensive care unit admission defined as a troponin I or T level above the 99th percentile upper reference limit of normal was a secondary exposure. The primary outcome was 28-day in-hospital mortality. Secondary outcomes included cardiovascular events (cardiac arrest, new-onset arrhythmias, new-onset heart failure, myocarditis, pericarditis, or stroke) within 14 days.

RESULTS

Among 5133 patients (3231 male [62.9%]; mean age 61 years [SD, 15]), 1174 (22.9%) had preexisting CVD. A total of 1178 (34.6%) died, and 920 (17.9%) had a cardiovascular event. After adjusting for age, sex, race, body mass index, history of smoking, and comorbidities, preexisting CVD was associated with a 1.15 (95% CI, 0.98-1.34) higher odds of death. No independent association was observed between preexisting CVD and cardiovascular events. Myocardial injury on intensive care unit admission was associated with higher odds of death (adjusted odds ratio, 1.93 [95% CI, 1.61-2.31]) and cardiovascular events (adjusted odds ratio, 1.82 [95% CI, 1.47-2.24]), regardless of the presence of CVD.

CONCLUSIONS

CVD risk factors, rather than CVD itself, were the major contributors to outcomes in critically ill patients with COVID-19. The occurrence of myocardial injury, regardless of CVD, and its association with outcomes suggests it is likely due to multiorgan injury related to acute inflammation rather than exacerbation of preexisting CVD.

REGISTRATION

NCT04343898; https://clinicaltrials.gov/ct2/show/NCT04343898.

Clinical clustering with prognostic implications in Japanese COVID-19 patients: report from Japan COVID-19 Task Force, a nation-wide consortium to investigate COVID-19 host genetics

Background

The clinical course of coronavirus disease (COVID-19) is diverse, and the usefulness of phenotyping in predicting the severity or prognosis of the disease has been demonstrated overseas. This study aimed to investigate clinically meaningful phenotypes in Japanese COVID-19 patients using cluster analysis.

Methods

From April 2020 to May 2021, data from inpatients aged ≥ 18 years diagnosed with COVID-19 and who agreed to participate in the study were collected. A total of 1322 Japanese patients were included. Hierarchical cluster analysis was performed using variables reported to be associated with COVID-19 severity or prognosis, namely, age, sex, obesity, smoking history, hypertension, diabetes mellitus, malignancy, chronic obstructive pulmonary disease, hyperuricemia, cardiovascular disease, chronic liver disease, and chronic kidney disease.

Results

Participants were divided into four clusters: Cluster 1, young healthy (n = 266, 20.1%); Cluster 2, middle-aged (n = 245, 18.5%); Cluster 3, middle-aged obese (n = 435, 32.9%); and Cluster 4, elderly (n = 376, 28.4%). In Clusters 3 and 4, sore throat, dysosmia, and dysgeusia tended to be less frequent, while shortness of breath was more frequent. Serum lactate dehydrogenase, ferritin, KL-6, d-dimer, and C-reactive protein levels tended to be higher in Clusters 3 and 4. Although Cluster 3 had a similar age as Cluster 2, it tended to have poorer outcomes. Both Clusters 3 and 4 tended to exhibit higher rates of oxygen supplementation, intensive care unit admission, and mechanical ventilation, but the mortality rate tended to be lower in Cluster 3.

Conclusions

We have successfully performed the first phenotyping of COVID-19 patients in Japan, which is clinically useful in predicting important outcomes, despite the simplicity of the cluster analysis method that does not use complex variables.

Prognostic implications of comorbidity patterns in critically ill COVID-19 patients: A multicenter, observational study

Background

The clinical heterogeneity of COVID-19 suggests the existence of different phenotypes with prognostic implications. We aimed to analyze comorbidity patterns in critically ill COVID-19 patients and assess their impact on in-hospital outcomes, response to treatment and sequelae.

Methods

Multicenter prospective/retrospective observational study in intensive care units of 55 Spanish hospitals. 5866 PCR-confirmed COVID-19 patients had comorbidities recorded at hospital admission; clinical and biological parameters, in-hospital procedures and complications throughout the stay; and, clinical complications, persistent symptoms and sequelae at 3 and 6 months.

Findings

Latent class analysis identified 3 phenotypes using training and test subcohorts: low-morbidity (n=3385; 58%), younger and with few comorbidities; high-morbidity (n=2074; 35%), with high comorbid burden; and renal-morbidity (n=407; 7%), with chronic kidney disease (CKD), high comorbidity burden and the worst oxygenation profile. Renal-morbidity and high-morbidity had more in-hospital complications and higher mortality risk than low-morbidity (adjusted HR (95% CI): 1.57 (1.34-1.84) and 1.16 (1.05-1.28), respectively). Corticosteroids, but not tocilizumab, were associated with lower mortality risk (HR (95% CI) 0.76 (0.63-0.93)), especially in renal-morbidity and high-morbidity. Renal-morbidity and high-morbidity showed the worst lung function throughout the follow-up, with renal-morbidity having the highest risk of infectious complications (6%), emergency visits (29%) or hospital readmissions (14%) at 6 months (p<0.01).

Interpretation

Comorbidity-based phenotypes were identified and associated with different expression of in-hospital complications, mortality, treatment response, and sequelae, with CKD playing a major role. This could help clinicians in day-to-day decision making including the management of post-discharge COVID-19 sequelae.

Funding

ISCIII, UNESPA, CIBERES, FEDER, ESF.

Novel subgroups of functional ability in older adults and their associations with adverse outcomes

Background

There is no general agreement on a standard form of functional classification in older adults and is mainly assessed by Activities of Daily Living (ADL) and/or Instrument Activity of Daily Living. A refined classification based on evaluation the limitations of intrinsic capacity, environment and social interaction, could provide a basis to predict the future disability and identify individuals with increased risk of adverse outcomes.

Methods

A new functional classification among older adults aged 60 and over was conducted by latent class analysis and compared with the traditional classifications, based on the China Health and Retirement Longitudinal Study. To further investigate the scientific validity of this new classification, associations with 7-year mortality and ADLs impairments among categories were tested by using Survival curves and Cox proportional hazard models. This was followed by the confirmatory analysis related to the prospective data. Competing risk analysis was also performed to analysis the sensitivity to further support our conclusions.

Results

Five categories were identified among 5,992 older adults which gave the best fitting, yielding a significant Bootstrap Likelihood Ratio Test (p < 0.001) and Lo-Mendell-Rubin adjusted likelihood ratio test (p < 0.001), with an entropy over 0.80. The presence of five categories: “health” (34.0%), “sub-disorder status” (36.6%), “acute diseases” (10.3%), “somatic functional disorder” (7.7%), and “viability disorder” (11.4%), which matched well with the functional independence rates by the international classifications. Among them, those in “sub-disorder status” were considered as an intermediate status between disability and health. The findings also revealed that those who were in “acute disease”, “somatic functional disorders”, “health” and “sub-disorder status” had a significant lower risk of mortality and ADLs limitations than “viability disorder”. And the risks gradually increased towards the less functionally independent end of the classification. However, the distribution of characteristics among five categories were in a synchronous change, indicating a stable classification.

Conclusions

A new classification representing the functional heterogeneity of older adults could effectively stratify the risk of mortality and ADLs limitations. Identifying the clusters of functional decline might be useful in predicting subsequent ageing trends, designing personalized intervention, and delaying the progression of disability and preventing its occurrence.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-022-03081-9.

The Association of New-Onset Acute Kidney Injury and Mortality in Critically Ill Patients With COVID-19 With Less Severe Clinical Conditions at Admission: A Moderation Analysis

Acute kidney injury (AKI), electrolyte, and acid–base disorders complicate the clinical course of critically ill patients with coronavirus-associated disease (COVID-19) and are associated with poor outcomes. It is not known whether the severity of clinical conditions at admission in the intensive care unit (ICU) changes the clinical significance of AKI and/or electrolyte or acid–base disorders developing during ICU stay. We conducted a retrospective study in critically ill patients with COVID-19 to evaluate whether the severity of clinical conditions at admission in the ICU affects the impact of AKI and of serum electrolytes or acid–base status on mortality. We carried out a 28-day retrospective follow-up study on 115 critically ill patients consecutively admitted to ICU for severe COVID-19 at a tertiary care university hospital and surviving longer than 24 h. We collected baseline demographic and clinical characteristics, and longitudinal data on kidney function, kidney replacement therapy, serum electrolytes, and acid–base status. We used Cox proportional hazards multiple regression models to test the interaction between the time-varying variates new-onset AKI or electrolyte or acid–base disorders and Sequential Organ Failure Assessment (SOFA) or Acute Physiology and Chronic Health Evaluation II (APACHE II) score at admission. After adjusting for age, sex, Charlson’s comorbidity index, and AKI present at ICU admission, new-onset AKI was significantly associated with 28-day mortality only in the patients in the lowest and middle SOFA score tertiles [lowest SOFA tertile, hazard ratio (HR) 4.27 (95% CI: 1.27–14.44; P = 0.019), middle SOFA tertile, HR 3.17 (95% CI: 1.11–9.04, P = 0.031), highest SOFA tertile, HR 0.77 (95% CI: 0.24–2.50; P = 0.66); P = 0.026 for interaction with SOFA as a continuous variable]. After stratifying for APACHE II tertile, results were similar [adjusted HR (aHR) in the lowest tertile 6.24 (95% CI: 1.85–21.03, P = 0.003)]. SOFA or APACHE II at admission did not affect the relationship of serum electrolytes and acid–base status with mortality, except for new-onset acidosis which was associated with increased mortality, with the HR of death increasing with SOFA or APACHE II score (P < 0.001 and P = 0.013, respectively). Thus, unlike in the most severe critically ill patients admitted to the ICU for COVID-19, in patients with the less severe conditions at admission the development of AKI during the stay is a strong indicator of increased hazard of death.

Using sequence clustering to identify clinically relevant subphenotypes in patients with COVID-19 admitted to the intensive care unit

OBJECTIVE

The novel coronavirus disease 2019 (COVID-19) has heterogenous clinical courses, indicating that there might be distinct subphenotypes in critically ill patients. Although prior research has identified these subphenotypes, the temporal pattern of multiple clinical features has not been considered in cluster models. We aimed to identify temporal subphenotypes in critically ill patients with COVID-19 using a novel sequence cluster analysis and associate them with clinically relevant outcomes.

MATERIALS AND METHODS

We analyzed 1036 confirmed critically ill patients with laboratory-confirmed SARS-COV-2 infection admitted to the Mount Sinai Health System in New York city. The agglomerative hierarchical clustering method was used with Levenshtein distance and Ward's minimum variance linkage.

RESULTS

We identified four subphenotypes. Subphenotype I (N = 233 [22.5%]) included patients with rapid respirations and a rapid heartbeat but less need for invasive interventions within the first 24 hours, along with a relatively good prognosis. Subphenotype II (N = 418 [40.3%]) represented patients with the least degree of ailments, relatively low mortality, and the highest probability of discharge from the hospital. Subphenotype III (N = 259 [25.0%]) represented patients who experienced clinical deterioration during the first 24 hours of intensive care unit admission, leading to poor outcomes. Subphenotype IV (N = 126 [12.2%]) represented an acute respiratory distress syndrome trajectory with an almost universal need for mechanical ventilation.

CONCLUSION

We utilized the sequence cluster analysis to identify clinical subphenotypes in critically ill COVID-19 patients who had distinct temporal patterns and different clinical outcomes. This study points toward the utility of including temporal information in subphenotyping approaches.

Identification of Endotypes of Hospitalized COVID-19 Patients

Background: Characterization of coronavirus disease 2019 (COVID-19) endotypes may help explain variable clinical presentations and response to treatments. While risk factors for COVID-19 have been described, COVID-19 endotypes have not been elucidated.

Objectives: We sought to identify and describe COVID-19 endotypes of hospitalized patients.

Methods: Consensus clustering (using the ensemble method) of patient age and laboratory values during admission identified endotypes. We analyzed data from 528 patients with COVID-19 who were admitted to telemetry capable beds at Columbia University Irving Medical Center and discharged between March 12 to July 15, 2020.

Results: Four unique endotypes were identified and described by laboratory values, demographics, outcomes, and treatments. Endotypes 1 and 2 were comprised of low numbers of intubated patients (1 and 6%) and exhibited low mortality (1 and 6%), whereas endotypes 3 and 4 included high numbers of intubated patients (72 and 85%) with elevated mortality (21 and 43%). Endotypes 2 and 4 had the most comorbidities. Endotype 1 patients had low levels of inflammatory markers (ferritin, IL-6, CRP, LDH), low infectious markers (WBC, procalcitonin), and low degree of coagulopathy (PTT, PT), while endotype 4 had higher levels of those markers.

Conclusions: Four unique endotypes of hospitalized patients with COVID-19 were identified, which segregated patients based on inflammatory markers, infectious markers, evidence of end-organ dysfunction, comorbidities, and outcomes. High comorbidities did not associate with poor outcome endotypes. Further work is needed to validate these endotypes in other cohorts and to study endotype differences to treatment responses.

The Inflammatory Lung Microenvironment; a Key Mediator in MSC Licensing

Recent clinical trials of mesenchymal stromal cell (MSC) therapy for various inflammatory conditions have highlighted the significant benefit to patients who respond to MSC administration. Thus, there is strong interest in investigating MSC therapy in acute inflammatory lung conditions, such as acute respiratory distress syndrome (ARDS). Unfortunately, not all patients respond, and evidence now suggests that the differential disease microenvironment present across patients and sub-phenotypes of disease or across disease severities influences MSC licensing, function and therapeutic efficacy. Here, we discuss the importance of licensing MSCs and the need to better understand how the disease microenvironment influences MSC activation and therapeutic actions, in addition to the need for a patient-stratification approach.

Corticosteroid use in ARDS and its application to evolving therapeutics for coronavirus disease 2019 (COVID-19): A systematic review

Data regarding the use of corticosteroids for treatment of acute respiratory distress syndrome (ARDS) are conflicting. As the coronavirus disease 2019 (COVID‐19) pandemic progresses, more literature supporting the use of corticosteroids for COVID‐19 and non‐COVID‐19 ARDS have emerged. Glucocorticoids are proposed to attenuate the inflammatory response and prevent progression to the fibroproliferative phase of ARDS through their multiple mechanisms and anti‐inflammatory properties. The purpose of this systematic review was to comprehensively evaluate the literature surrounding corticosteroid use in ARDS (non‐COVID‐19 and COVID‐19) in addition to a narrative review of clinical considerations of corticosteroid use in these patient populations. OVID Medline and EMBASE were searched. Randomized controlled trials evaluating the use of corticosteroids for COVID‐19 and non‐COVID‐19 ARDS in adult patients on mortality outcomes were included. Risk of bias was assessed with the Risk of Bias 2.0 tool. There were 388 studies identified, 15 of which met the inclusion criteria that included a total of 8877 patients. The studies included in our review reported a mortality benefit in 6/15 (40%) studies with benefit being seen at varying time points of mortality follow‐up (ICU survival, hospital, and 28 and 60 days) in the COVID‐19 and non‐COVID‐19 ARDS studies. The two non‐COVID19 trials assessing lung injury score improvements found that corticosteroids led to significant improvements with corticosteroid use. The number of mechanical ventilation‐free days significantly were found to be increased with the use of corticosteroids in all four studies that assessed this outcome. Corticosteroids are associated with improvements in mortality and ventilator‐free days in critically ill patients with both COVID‐19 and non‐COVID‐19 ARDS, and evidence suggests their use should be encouraged in these settings. However, due to substantial differences in the corticosteroid regimens utilized in these trials, questions still remain regarding the optimal corticosteroid agent, dose, and duration in patients with ARDS.

Sepsis in patients hospitalized with coronavirus disease 2019: how often and how severe?

PURPOSE OF REVIEW

To discuss why severe COVID-19 should be considered sepsis and how co-infection and secondary infection can aggravate this condition and perpetuate organ dysfunction leading to high mortality rates.

RECENT FINDINGS

In severe COVID-19, there is both direct viral toxicity and dysregulated host response to infection. Although both coinfection and/or secondary infection are present, the latest is of greater concern mainly in resource-poor settings. Patients with severe COVID-19 present a phenotype of multiorgan dysfunction that leads to death in an unacceptable high percentage of the patients, with wide variability around the world. Similarly to endemic sepsis, the mortality of COVID-19 critically ill patients is higher in low-income and middle-income countries as compared with high-income countries. Disparities, including hospital strain, resources limitations, higher incidence of healthcare-associated infections (HAI), and staffing issues could in part explain this variability.

SUMMARY

The high mortality rates of critically ill patients with severe COVID-19 disease are not only related to the severity of patient disease but also to modifiable factors, such as the ICU strain, HAI incidence, and organizational aspects. Therefore, HAI prevention and the delivery of best evidence-based care for these patients to avoid additional damage is important. Quality improvement interventions might help in improving outcomes mainly in resource-limited settings.

Comprehensive investigation of RNA-sequencing dataset reveals the hub genes and molecular mechanisms of coronavirus disease 2019 acute respiratory distress syndrome

The goal of this study is to reveal the hub genes and molecular mechanisms of the coronavirus disease 2019 (COVID-19) acute respiratory distress syndrome (ARDS) based on the genome-wide RNA sequencing dataset. The RNA sequencing dataset of COVID-19 ARDS was obtained from GSE163426. A total of 270 differentially expressed genes (DEGs) were identified between COVID-19 ARDS and control group patients. Functional enrichment analysis of DEGs suggests that these DEGs may be involved in the following biological processes: response to cytokine, G-protein coupled receptor activity, ionotropic glutamate receptor signalling pathway and G-protein coupled receptor signalling pathway. By using the weighted correlation network analysis approach to analyse these DEGs, 10 hub DEGs that may play an important role in COVID-19 ARDS were identified. A total of 67 potential COVID-19 ARDS targetted drugs were identified by a complement map analysis. Immune cell infiltration analysis revealed that the levels of T cells CD4 naive, T cells follicular helper, macrophages M1 and eosinophils in COVID-19 ARDS patients were significantly different from those in control group patients. In conclusion, this study identified 10 COVID-19 ARDS-related hub DEGs and numerous potential molecular mechanisms through a comprehensive analysis of the RNA sequencing dataset and also revealed the difference in immune cell infiltration of COVID-19 ARDS.