Identifying Clinical Phenotypes in Moderate to Severe Acute Respiratory Distress Syndrome Related to COVID-19: The COVADIS Study

Longitudinal dynamic clinical phenotypes of in-hospital COVID-19 patients across three dominant virus variants in New York

BACKGROUND

COVID-19 is a challenging disease to characterize given its wide-ranging heterogeneous symptomatology. Several studies have attempted to extract clinical phenotypes but often relied on data from small patient cohorts, usually limited to only one viral variant and utilizing a static snapshot of patient data.

OBJECTIVE

This study aimed to identify clinical phenotypes of hospitalized COVID-19 patients and investigate their longitudinal dynamics throughout the pandemic, with the goal to relate these phenotypes to clinical outcomes and treatment strategies.

METHODS

We utilized routinely collected demographic and clinical data throughout the hospitalization of 38,077 patients admitted between 3/2020 to 5/2022, in 12 New York hospitals. Uniform Manifold Approximation and Projection and agglomerative hierarchical clustering were used to derive the clusters, followed by exploratory data analysis to compare the prevalence of comorbidities and treatments per cluster.

RESULTS

4 distinct clinical phenotypes remained robust in multi-site validation and were associated with different mortality rates. The temporal progression of these phenotypes throughout the COVID-19 pandemic demonstrated increased variability across the waves of the three dominant viral variants (alpha, delta, omicron). Longitudinal analysis evaluating changes in clinical phenotypes of each patient throughout the course of a 4-week hospital stay exemplified the dynamic nature of the disease progression. Factors such as sex, race/ethnicity and specific treatment modalities revealed significant and clinically relevant differences between the observed phenotypes.

CONCLUSIONS

Our proposed methodology has the potential of enabling clinicians and policy makers to draw evidence-based conclusions for guiding treatment modalities in a dynamic fashion.

External validation of unsupervised COVID-19 clinical phenotypes and their prognostic impact

BACKGROUND

Hospitalized patients with coronavirus disease 2019 (COVID-19) can be classified into different clinical phenotypes based on their demographic, clinical, radiology, and laboratory features. We aimed to validate in an external cohort of hospitalized COVID-19 patients the prognostic value of a previously described phenotyping system (FEN-COVID-19) and to assess the reproducibility of phenotypes development as a secondary analysis.

METHODS

Patients were classified in phenotypes A, B or C according to the severity of oxygenation impairment, inflammatory response, hemodynamic and laboratory tests according to the FEN-COVID-19 method.

RESULTS

Overall, 992 patients were included in the study, and 181 (18%), 757 (76%) and 54 (6%) of them were assigned to the FEN-COVID-19 phenotypes A, B, and C, respectively. An association with mortality was observed for phenotype C vs. A (hazard ratio [HR] 3.10, 95% confidence interval [CI] 1.81-5.30, p < 0.001) and for phenotype C vs. B (HR 2.20, 95% CI 1.50-3.23, p < 0.001). A non-statistically significant trend towards higher mortality was also observed for phenotype B vs. A (HR 1.41; 95% CI 0.92-2.15, p = 0.115). By means of cluster analysis, three different phenotypes were also identified in our cohort, with an overall similar gradient in terms of prognostic impact to that observed when patients were assigned to FEN-COVID-19 phenotypes.

CONCLUSIONS

The prognostic impact of FEN-COVID-19 phenotypes was confirmed in our external cohort, although with less difference in mortality between phenotypes A and B than in the original study.

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.

Hypoxemia Trajectory of Non-COVID-19 Acute Respiratory Distress Syndrome Patients. An Observational Study Focusing on Hypoxemia Resolver Status

IMPORTANCE

Most studies on acute respiratory distress syndrome (ARDS) group patients by severity based on their initial degree of hypoxemia. However, this grouping has limitations, including inconsistent hypoxemia trajectories and outcomes.

OBJECTIVES

This study explores the benefits of grouping patients by resolver status based on their hypoxemia progression over the first 7 days.

DESIGN SETTING AND PARTICIPANTS

This is an observational study from a large single-center database. Medical Information Mart for Intensive Care (MIMIC)-IV and MIMIC Chest X-ray JPEG databases were used. Mechanically ventilated patients that met the Berlin ARDS criteria were included.

MAIN OUTCOMES AND MEASURES

The primary outcome was the proportion of hypoxemia resolvers vs. nonresolvers in non-COVID-19 ARDS patients. Nonresolvers were defined as those whose hypoxemia worsened or remained moderate or severe over the first 7 days. Secondary outcomes included baseline admission characteristics, initial blood gases and ventilation settings, length of invasive mechanical ventilation, length of ICU stay, and ICU survival rates across resolver groups.

RESULTS

A total of 894 ICU admissions were included in the study. Of these, 33.9% were hypoxemia nonresolvers. The resolver groups showed no significant difference in age, body mass index, comorbidities, or Charlson score. There was no significant difference in the percentage of those with initial severe hypoxemia between the two groups (8.1% vs. 9.2%; p = 0.126). The initial Pao2/Fio2 ratio did not significantly increase the odds ratio (OR) of being a nonresolver (OR, 0.84; 95% CI, 0.65-1.10). Nonresolver mortality was 61.4%, comparable to the survival rates seen in nonresolvers in a previous large COVID-19 ARDS study.

CONCLUSIONS AND RELEVANCE

Our study shows that resolver status is a valuable grouping in ARDS. It has significant advantages over grouping by initial degree of hypoxemia, including better mapping of trajectory and comparable outcomes across other studies. While it may offer insights into disease-specific associations, future studies should include resolver status analysis for more definitive conclusions.

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.

Association between social vulnerability profiles, prenatal care use and pregnancy outcomes

BACKGROUND

Evaluating social vulnerability is a challenging task. Indeed, former studies demonstrated an association between geographical social deprivation indicators, administrative indicators, and poor pregnancy outcomes.

OBJECTIVE

To evaluate the association between social vulnerability profiles, prenatal care use (PCU) and poor pregnancy outcomes (Preterm birth (PTB: <37 gestational weeks (GW)), small for gestational age (SGA), stillbirth, medical abortion, and late miscarriage).

METHODS

Retrospective single center study between January 2020 and December 2021. A total of 7643 women who delivered a singleton after 14 GW in a tertiary care maternity unit were included. Multiple component analysis (MCA) was used to assess the associations between the following social vulnerabilities: social isolation, poor or insecure housing conditions, not work-related household income, absence of standard health insurance, recent immigration, linguistic barrier, history of violence, severe dependency, psychologic vulnerability, addictions, and psychiatric disease. Hierarchical clustering on principal component (HCPC) from the MCA was used to classify patients into similar social vulnerability profiles. Associations between social vulnerability profiles and poor pregnancy outcomes were tested using multiple logistic regression or Poisson regression when appropriate.

RESULTS

The HCPC analysis revealed 5 different social vulnerability profiles. Profile 1 included the lowest rates of vulnerability and was used as a reference. After adjustment for maternal characteristics and medical factors, profiles 2 to 5 were independently associated with inadequate PCU (highest risk for profile 5, aOR = 3.14, 95%CI[2.33-4.18]), PTB (highest risk for profile 2, aOR = 4.64, 95%CI[3.80-5.66]) and SGA status (highest risk for profile 5, aOR = 1.60, 95%CI[1.20-2.10]). Profile 2 was the only profile associated with late miscarriage (adjusted incidence rate ratio (aIRR) = 7.39, 95%CI[4.17-13.19]). Profiles 2 and 4 were independently associated with stillbirth (highest association for profile 2 (aIRR = 10.9, 95%CI[6.11-19.99]) and medical abortion (highest association for profile 2 (aIRR = 12.65, 95%CI[5.96-28.49]).

CONCLUSIONS

This study unveiled 5 clinically relevant social vulnerability profiles with different risk levels of inadequate PCU and poor pregnancy outcomes. A personalized patient management according to their profile could offer better pregnancy management and reduce adverse outcomes.

Machine learning-based COVID-19 acute respiratory distress syndrome phenotyping and clinical outcomes: A systematic review

COVID-19-related acute respiratory distress syndrome (CARDS) has been suggested to differ from the typical ARDS. While distinct phenotypes of ARDS have been identified through latent class analysis (LCA), it is unclear whether such phenotypes exist for CARDS and how they affect clinical outcomes. To address this question, we conducted a systematic review of the current evidence.We searched several, including PubMed, EBSCO Host, and Web of Science, from inception to July 1, 2022. Our exposure and outcome of interest were different CARDS phenotypes identified and their associated outcomes, such as 28-day, 90-day, 180-day mortality, ventilator-free days, and other relevant outcomes.We identified four studies comprising a total of 1776 CARDS patients.Of the four studies, three used LCA to identify subphenotypes (SPs) of CARDS. One study based on longitudinal data identified two SPs, with SP2 associated with worse ventilation and mechanical parameters than SP1. The other two studies based on baseline data also identified two SPs, with SP2 and SP1 were associated with hyperinflammatory and hypoinflammatory CARDS, respectively. The fourth study identified three SPs primarily stratified by comorbidities using multifactorial analysis.All studies identified a subphenotype associated with poorer outcomes, including mortality, ventilator-free days, multiple-organ injury, and pulmonary embolism. Two studies reported differential responses to corticosteroids among the SPs, with improved mortality in the hyperinflammatory and worse in the hypoinflammatory SPs.Overall, our review highlights the importance of phenotyping in understanding CARDS and its impact on disease management and prognostication. However, a consensus approach to phenotyping is necessary to ensure consistency and comparability across studies. We recommend that randomized clinical trials stratified by phenotype should only be initiated after such consensus is reached.

Short title

COVID-19 ARDS subphenotypes and outcomes.

Barotrauma in COVID-19 acute respiratory distress syndrome: retrospective analysis of the COVADIS prospective multicenter observational database

BACKGROUND

Despite evidence suggesting a higher risk of barotrauma during COVID-19-related acute respiratory distress syndrome (ARDS) compared to ARDS due to other causes, data are limited about possible associations with patient characteristics, ventilation strategy, and survival.

METHODS

This prospective observational multicenter study included consecutive patients with moderate-to-severe COVID-19 ARDS requiring invasive mechanical ventilation and managed at any of 12 centers in France and Belgium between March and December 2020. The primary objective was to determine whether barotrauma was associated with ICU mortality (censored on day 90), and the secondary objective was to identify factors associated with barotrauma.

RESULTS

Of 586 patients, 48 (8.2%) experienced barotrauma, including 35 with pneumothorax, 23 with pneumomediastinum, 1 with pneumoperitoneum, and 6 with subcutaneous emphysema. Median time from mechanical ventilation initiation to barotrauma detection was 3 [0-17] days. All patients received protective ventilation and nearly half (23/48) were in volume-controlled mode. Barotrauma was associated with higher hospital mortality (P < 0.001) even after adjustment on age, sex, comorbidities, PaO2/FiO2 at intubation, plateau pressure at intubation, and center (P < 0.05). The group with barotrauma had a lower mean body mass index (28.6 ± 5.8 vs. 30.3 ± 5.9, P = 0.03) and a higher proportion of patients given corticosteroids (87.5% vs. 63.4%, P = 0.001).

CONCLUSION

Barotrauma during mechanical ventilation for COVID-19 ARDS was associated with higher hospital mortality.

Interpretable clinical phenotypes among patients hospitalized with COVID-19 using cluster analysis

Background

Multiple clinical phenotypes have been proposed for coronavirus disease (COVID-19), but few have used multimodal data. Using clinical and imaging data, we aimed to identify distinct clinical phenotypes in patients admitted with COVID-19 and to assess their clinical outcomes. Our secondary objective was to demonstrate the clinical applicability of this method by developing an interpretable model for phenotype assignment.

Methods

We analyzed data from 547 patients hospitalized with COVID-19 at a Canadian academic hospital. We processed the data by applying a factor analysis of mixed data (FAMD) and compared four clustering algorithms: k-means, partitioning around medoids (PAM), and divisive and agglomerative hierarchical clustering. We used imaging data and 34 clinical variables collected within the first 24 h of admission to train our algorithm. We conducted a survival analysis to compare the clinical outcomes across phenotypes. With the data split into training and validation sets (75/25 ratio), we developed a decision-tree-based model to facilitate the interpretation and assignment of the observed phenotypes.

Results

Agglomerative hierarchical clustering was the most robust algorithm. We identified three clinical phenotypes: 79 patients (14%) in Cluster 1, 275 patients (50%) in Cluster 2, and 203 (37%) in Cluster 3. Cluster 2 and Cluster 3 were both characterized by a low-risk respiratory and inflammatory profile but differed in terms of demographics. Compared with Cluster 3, Cluster 2 comprised older patients with more comorbidities. Cluster 1 represented the group with the most severe clinical presentation, as inferred by the highest rate of hypoxemia and the highest radiological burden. Intensive care unit (ICU) admission and mechanical ventilation risks were the highest in Cluster 1. Using only two to four decision rules, the classification and regression tree (CART) phenotype assignment model achieved an AUC of 84% (81.5-86.5%, 95 CI) on the validation set.

Conclusions

We conducted a multidimensional phenotypic analysis of adult inpatients with COVID-19 and identified three distinct phenotypes associated with different clinical outcomes. We also demonstrated the clinical usability of this approach, as phenotypes can be accurately assigned using a simple decision tree. Further research is still needed to properly incorporate these phenotypes in the management of patients with COVID-19.

Early steroids and ventilator-associated pneumonia in COVID-19-related ARDS

RATIONALE

Early corticosteroid treatment is used to treat COVID-19-related acute respiratory distress syndrome (ARDS). Infection is a well-documented adverse effect of corticosteroid therapy.

OBJECTIVES

To determine whether early corticosteroid therapy to treat COVID-19 ARDS was associated with ventilator-associated pneumonia (VAP).

METHODS

We retrospectively included adults with COVID-19-ARDS requiring invasive mechanical ventilation (MV) for ≥ 48 h at any of 15 intensive care units in 2020. We divided the patients into two groups based on whether they did or did not receive corticosteroids within 24 h. The primary outcome was VAP incidence, with death and extubation as competing events. Secondary outcomes were day 90-mortality, MV duration, other organ dysfunctions, and VAP characteristics.

MEASUREMENTS AND MAIN RESULTS

Of 670 patients (mean age, 65 years), 369 did and 301 did not receive early corticosteroids. The cumulative VAP incidence was higher with early corticosteroids (adjusted hazard ratio [aHR] 1.29; 95% confidence interval [95% CI] 1.05-1.58; P = 0.016). Antibiotic resistance of VAP bacteria was not different between the two groups (odds ratio 0.94, 95% CI 0.58-1.53; P = 0.81). 90-day mortality was 30.9% with and 24.3% without early corticosteroids, a nonsignificant difference after adjustment on age, SOFA score, and VAP occurrence (aHR 1.15; 95% CI 0.83-1.60; P = 0.411). VAP was associated with higher 90-day mortality (aHR 1.86; 95% CI 1.33-2.61; P = 0.0003).

CONCLUSIONS

Early corticosteroid treatment was associated with VAP in patients with COVID-19-ARDS. Although VAP was associated with higher 90-day mortality, early corticosteroid treatment was not. Longitudinal randomized controlled trials of early corticosteroids in COVID-19-ARDS requiring MV are warranted.

Severe Protracted Hypophosphatemia in a Patient with Persistent Vegetative State on Long-Term Assisted Respiratory Support

BACKGROUND Phosphorous is an essential component of cell structure and physiology, and is required for energy conservation and expenditure. Severe hypophosphatemia can lead to profound dysfunction and injury affecting most organs and can be life-threatening. It can also compromise weaning of mechanically ventilated patients. Long-term assisted ventilatory care in ambulatory or inpatient settings is an expanding medical service for patients with various forms of persistent or progressive incapacitating diseases. Hypophosphatemia, caused by respiratory alkalosis, has been reported in critical-care settings, but its occurrence in medically stable patients requiring long-term respiratory support has not been thoroughly investigated. CASE REPORT We report the case of a ventilated patient in a chronic vegetative state displaying progressive hypophosphatemia spanning over 3 months, with plasma levels gradually declining to 0.8 mg/dL. Evaluation did not reveal conditions leading to diminished phosphate absorption or enhanced urinary phosphate excretion, but it identified respiratory alkalosis related to a recent increase in target minute-volume ventilation in the adaptive support ventilation (ASV) mode as the cause of hypophosphatemia. Despite the very low plasma phosphate level, the patient was asymptomatic, probably because this type of hypophosphatemia may not represent physiologically significant intracellular phosphate depletion. The respiratory alkalosis resolved upon decreasing the target minute-volume ventilation settings, and serum phosphate was normalized. CONCLUSIONS Since blood gases are not routinely monitored in respiratory and hemodynamically stable patients on long-term respiratory support, hypophosphatemia may herald the development of significant respiratory alkalosis. Assessment of acid-base balance is thus warranted in patients receiving long-term ventilation, especially in those developing hypophosphatemia.

Subphenotyping of COVID-19 patients at pre-admission towards anticipated severity stratification: an analysis of 778 692 Mexican patients through an age-sex unbiased meta-clustering technique

Background

The COVID-19 pandemic has led to an unprecedented global health care challenge for both medical institutions and researchers. Recognizing different COVID-19 subphenotypes—the division of populations of patients into more meaningful subgroups driven by clinical features—and their severity characterization may assist clinicians during the clinical course, the vaccination process, research efforts, the surveillance system, and the allocation of limited resources.

Objective

We aimed to discover age-sex unbiased COVID-19 patient subphenotypes based on easily available phenotypical data before admission, such as pre-existing comorbidities, lifestyle habits, and demographic features, to study the potential early severity stratification capabilities of the discovered subgroups through characterizing their severity patterns, including prognostic, intensive care unit (ICU), and morbimortality outcomes.

Methods

We used the Mexican Government COVID-19 open data, including 778,692 SARS-CoV-2 population-based patient-level data as of September 2020. We applied a meta-clustering technique that consists of a 2-stage clustering approach combining dimensionality reduction (ie, principal components analysis and multiple correspondence analysis) and hierarchical clustering using the Ward minimum variance method with Euclidean squared distance.

Results

In the independent age-sex clustering analyses, 56 clusters supported 11 clinically distinguishable meta-clusters (MCs). MCs 1-3 showed high recovery rates (90.27%-95.22%), including healthy patients of all ages, children with comorbidities and priority in receiving medical resources (ie, higher rates of hospitalization, intubation, and ICU admission) compared with other adult subgroups that have similar conditions, and young obese smokers. MCs 4-5 showed moderate recovery rates (81.30%-82.81%), including patients with hypertension or diabetes of all ages and obese patients with pneumonia, hypertension, and diabetes. MCs 6-11 showed low recovery rates (53.96%-66.94%), including immunosuppressed patients with high comorbidity rates, patients with chronic kidney disease with a poor survival length and probability of recovery, older smokers with chronic obstructive pulmonary disease, older adults with severe diabetes and hypertension, and the oldest obese smokers with chronic obstructive pulmonary disease and mild cardiovascular disease. Group outcomes conformed to the recent literature on dedicated age-sex groups. Mexican states and several types of clinical institutions showed relevant heterogeneity regarding severity, potentially linked to socioeconomic or health inequalities.

Conclusions

The proposed 2-stage cluster analysis methodology produced a discriminative characterization of the sample and explainability over age and sex. These results can potentially help in understanding the clinical patient and their stratification for automated early triage before further tests and laboratory results are available and even in locations where additional tests are not available or to help decide resource allocation among vulnerable subgroups such as to prioritize vaccination or treatments.

Artificial intelligence for COVID-19: battling the pandemic with computational intelligence

The new coronavirus disease 2019 (COVID-19) has become a global pandemic leading to over 180 million confirmed cases and nearly 4 million deaths until June 2021, according to the World Health Organization. Since the initial report in December 2019 , COVID-19 has demonstrated a high transmission rate (with an R₀ > 2), a diverse set of clinical characteristics (e.g., high rate of hospital and intensive care unit admission rates, multi-organ dysfunction for critically ill patients due to hyperinflammation, thrombosis, etc.), and a tremendous burden on health care systems around the world. To understand the serious and complex diseases and develop effective control, treatment, and prevention strategies, researchers from different disciplines have been making significant efforts from different aspects including epidemiology and public health, biology and genomic medicine, as well as clinical care and patient management. In recent years, artificial intelligence (AI) has been introduced into the healthcare field to aid clinical decision-making for disease diagnosis and treatment such as detecting cancer based on medical images, and has achieved superior performance in multiple data-rich application scenarios. In the COVID-19 pandemic, AI techniques have also been used as a powerful tool to overcome the complex diseases. In this context, the goal of this study is to review existing studies on applications of AI techniques in combating the COVID-19 pandemic. Specifically, these efforts can be grouped into the fields of epidemiology, therapeutics, clinical research, social and behavioral studies and are summarized. Potential challenges, directions, and open questions are discussed accordingly, which may provide new insights into addressing the COVID-19 pandemic and would be helpful for researchers to explore more related topics in the post-pandemic era.

Longitudinal respiratory subphenotypes in patients with COVID-19-related acute respiratory distress syndrome: results from three observational cohorts

BACKGROUND

Patients with COVID-19-related acute respiratory distress syndrome (ARDS) have been postulated to present with distinct respiratory subphenotypes. However, most phenotyping schema have been limited by sample size, disregard for temporal dynamics, and insufficient validation. We aimed to identify respiratory subphenotypes of COVID-19-related ARDS using unbiased data-driven approaches.

METHODS

PRoVENT-COVID was an investigator-initiated, national, multicentre, prospective, observational cohort study at 22 intensive care units (ICUs) in the Netherlands. Consecutive patients who had received invasive mechanical ventilation for COVID-19 (aged 18 years or older) served as the derivation cohort, and similar patients from two ICUs in the USA served as the replication cohorts. COVID-19 was confirmed by positive RT-PCR. We used latent class analysis to identify subphenotypes using clinically available respiratory data cross-sectionally at baseline, and longitudinally using 8-hourly data from the first 4 days of invasive ventilation. We used group-based trajectory modelling to evaluate trajectories of individual variables and to facilitate potential clinical translation. The PRoVENT-COVID study is registered with ClinicalTrials.gov, NCT04346342.

FINDINGS

Between March 1, 2020, and May 15, 2020, 1007 patients were admitted to participating ICUs in the Netherlands, and included in the derivation cohort. Data for 288 patients were included in replication cohort 1 and 326 in replication cohort 2. Cross-sectional latent class analysis did not identify any underlying subphenotypes. Longitudinal latent class analysis identified two distinct subphenotypes. Subphenotype 2 was characterised by higher mechanical power, minute ventilation, and ventilatory ratio over the first 4 days of invasive mechanical ventilation than subphenotype 1, but PaO₂/FiO₂, pH, and compliance of the respiratory system did not differ between the two subphenotypes. 185 (28%) of 671 patients with subphenotype 1 and 109 (32%) of 336 patients with subphenotype 2 had died at day 28 (p=0·10). However, patients with subphenotype 2 had fewer ventilator-free days at day 28 (median 0, IQR 0-15 vs 5, 0-17; p=0·016) and more frequent venous thrombotic events (109 [32%] of 336 patients vs 176 [26%] of 671 patients; p=0·048) compared with subphenotype 1. Group-based trajectory modelling revealed trajectories of ventilatory ratio and mechanical power with similar dynamics to those observed in latent class analysis-derived trajectory subphenotypes. The two trajectories were: a stable value for ventilatory ratio or mechanical power over the first 4 days of invasive mechanical ventilation (trajectory A) or an upward trajectory (trajectory B). However, upward trajectories were better independent prognosticators for 28-day mortality (OR 1·64, 95% CI 1·17-2·29 for ventilatory ratio; 1·82, 1·24-2·66 for mechanical power). The association between upward ventilatory ratio trajectories (trajectory B) and 28-day mortality was confirmed in the replication cohorts (OR 4·65, 95% CI 1·87-11·6 for ventilatory ratio in replication cohort 1; 1·89, 1·05-3·37 for ventilatory ratio in replication cohort 2).

INTERPRETATION

At baseline, COVID-19-related ARDS has no consistent respiratory subphenotype. Patients diverged from a fairly homogenous to a more heterogeneous population, with trajectories of ventilatory ratio and mechanical power being the most discriminatory. Modelling these parameters alone provided prognostic value for duration of mechanical ventilation and mortality.

FUNDING

Amsterdam UMC.

SARS-CoV-2 in Urine May Predict a Severe Evolution of COVID-19

We hypothesized that the spread of SARS-CoV-2 in urine during a severe COVID-19 infection may be the expression of the worsening disease evolution. Therefore, the aim of this study was to verify if the COVID-19 disease severity is related to the viral presence in urine samples. We evaluated the clinical evolution in acute COVID-19 patients admitted in the sub-intensive care and intensive care units between 28 of December 2020 and 15th of February 2021 and being positive for SARS-CoV-2 RNA in the respiratory tract, including repeated endotracheal aspirates (ETA), sputum, nasopharyngeal swabs (NPS) and urine. We found that those subjects with SARS-COV-2 in the urine at admittance (8 out of 60 eligible patients) had a more severe disease than those with negative SARS-CoV-2 in urine. Further, they showed an increase in fibrinogen and (C-reactive Protein) CRP serum levels, requiring mechanic ventilation. Of those with positive SARS-CoV-2 in the urine, 50% died. According to our preliminary results, it seems that the presence of SARS-CoV-2 in the urine characterizes patients with a more severe disease and is also related to a higher death rate.