Deploying unsupervised clustering analysis to derive clinical phenotypes and risk factors associated with mortality risk in 2022 critically ill patients with COVID-19 in Spain

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.

Are routine chest radiographs still indicated after central line insertion? A scoping review

Introduction

Central venous catheters are increasingly inserted using point-of-care ultrasound (POCUS) guidance. Following insertion, it is still common to request a confirmatory chest radiograph for subclavian and internal jugular lines, at least outside of the operating theater. This scoping review addresses: (i) the justification for routine post-insertion radiographs, (ii) whether it would better to use post-insertion POCUS instead, and (iii) the perceived barriers to change.

Methods

We searched the electronic databases, Ovid MEDLINE (1946-) and Ovid EMBASE (1974-), using the MESH terms ("Echography" OR "Ultrasonography" OR "Ultrasound") AND "Central Venous Catheter" up until February 2023. We also searched clinical practice guidelines, and targeted literature, including cited and citing articles. We included adults (⩾18 years) and English and French language publications. We included randomized control trials, prospective and retrospective cohort studies, systematic reviews, and surveys.

Results

Four thousand seventy-one articles were screened, 117 full-text articles accessed, and 41 retained. Thirteen examined cardiac/vascular methods; 5 examined isolated contrast-enhanced ultrasonography; 7 examined isolated rapid atrial swirl sign; and 13 examined combined/integrated methods. In addition, three systematic reviews/meta-analyses and one survey addressed barriers to POCUS adoption.

Discussion

We believe that the literature supports retiring the routine post-central line chest radiograph. This is not only because POCUS has made line insertion safer, but because POCUS performs at least as well, and is associated with less radiation, lower cost, time savings, and greater accuracy. There has been less written about perceived barriers to change, but the literature shows that these concerns- which include upfront costs, time-to-train, medicolegal concerns and habit- can be challenged and hence overcome.

A scoping review finds a growing trend in studies validating multimorbidity patterns and identifies five broad types of validation methods

OBJECTIVES

Multimorbidity, the presence of two or more long-term conditions, is a growing public health concern. Many studies use analytical methods to discover multimorbidity patterns from data. We aimed to review approaches used in published literature to validate these patterns.

STUDY DESIGN AND SETTING

We systematically searched PubMed and Web of Science for studies published between July 2017 and July 2023 that used analytical methods to discover multimorbidity patterns.

RESULTS

Out of 31,617 studies returned by the searches, 172 were included. Of these, 111 studies (64%) conducted validation, the number of studies with validation increased from 53.13% (17 out of 32 studies) to 71.25% (57 out of 80 studies) in 2017-2019 to 2022-2023, respectively. Five types of validation were identified: assessing the association of multimorbidity patterns with clinical outcomes (n = 79), stability across subsamples (n = 26), clinical plausibility (n = 22), stability across methods (n = 7) and exploring common determinants (n = 2). Some studies used multiple types of validation.

CONCLUSION

The number of studies conducting a validation of multimorbidity patterns is clearly increasing. The most popular validation approach is assessing the association of multimorbidity patterns with clinical outcomes. Methodological guidance on the validation of multimorbidity patterns is needed.

Major adverse cardiovascular events (MACE) in patients with severe COVID-19 registered in the ISARIC WHO clinical characterization protocol: A prospective, multinational, observational study

PURPOSE

To determine its cumulative incidence, identify the risk factors associated with Major Adverse Cardiovascular Events (MACE) development, and its impact clinical outcomes.

MATERIALS AND METHODS

This multinational, multicentre, prospective cohort study from the ISARIC database. We used bivariate and multivariate logistic regressions to explore the risk factors related to MACE development and determine its impact on 28-day and 90-day mortality.

RESULTS

49,479 patients were included. Most were male 63.5% (31,441/49,479) and from high-income countries (84.4% [42,774/49,479]); however, >6000 patients were registered in low-and-middle-income countries. MACE cumulative incidence during their hospital stay was 17.8% (8829/49,479). The main risk factors independently associated with the development of MACE were older age, chronic kidney disease or cardiovascular disease, smoking history, and requirement of vasopressors or invasive mechanical ventilation at admission. The overall 28-day and 90-day mortality were higher among patients who developed MACE than those who did not (63.1% [5573/8829] vs. 35.6% [14,487/40,650] p < 0.001; 69.9% [6169/8829] vs. 37.8% [15,372/40,650] p < 0.001, respectively). After adjusting for confounders, MACE remained independently associated with higher 28-day and 90-day mortality (Odds Ratio [95% CI], 1.36 [1.33-1.39];1.47 [1.43-1.50], respectively).

CONCLUSIONS

Patients with severe COVID-19 frequently develop MACE, which is independently associated with worse clinical outcomes.

Immunomodulatory approaches in managing lung inflammation in COVID-19: A double-edge sword

INTRODUCTION

The novel coronavirus infectious disease 2019 (COVID-19) which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has emerged as a gigantic problem. The lung is the major target organ of SARS-CoV-2 and some of its variants like Delta and Omicron variant adapted in such a way that these variants can significantly damage this vital organ of the body. These variants raised a few eyebrows as the outbreaks have been seen in the vaccinated population. Patients develop severe respiratory illnesses which eventually prove fatal unless treated early.

MAIN BODY

Studies have shown that SARS-CoV-2 causes the release of pro-inflammatory cytokines such as interleukin (IL)-6, IL-1β and tumor necrosis factor (TNF)-α which are mediators of lung inflammation, lung damage, fever, and fibrosis. Additionally, various chemokines have been found to play an important role in the disease progression. A plethora of pro-inflammatory cytokines "cytokine storm" has been observed in severe cases of SARS-CoV-2 infection leading to acute respiratory distress syndrome (ARDS) and pneumonia that may prove fatal. To counteract cytokine storm-inducing lung inflammation, several promising immunomodulatory approaches are being investigated in numerous clinical trials. However, the benefits of using these strategies should outweigh the risks involved as the use of certain immunosuppressive approaches might lead the host susceptible to secondary bacterial infections.

CONCLUSION

The present review discusses promising immunomodulatory approaches to manage lung inflammation in COVID-19 cases which may serve as potential therapeutic options in the future and may prove lifesaving.

A scoping review of the clinical application of machine learning in data-driven population segmentation analysis

OBJECTIVE

Data-driven population segmentation is commonly used in clinical settings to separate the heterogeneous population into multiple relatively homogenous groups with similar healthcare features. In recent years, machine learning (ML) based segmentation algorithms have garnered interest for their potential to speed up and improve algorithm development across many phenotypes and healthcare situations. This study evaluates ML-based segmentation with respect to (1) the populations applied, (2) the segmentation details, and (3) the outcome evaluations.

MATERIALS AND METHODS

MEDLINE, Embase, Web of Science, and Scopus were used following the PRISMA-ScR criteria. Peer-reviewed studies in the English language that used data-driven population segmentation analysis on structured data from January 2000 to October 2022 were included.

RESULTS

We identified 6077 articles and included 79 for the final analysis. Data-driven population segmentation analysis was employed in various clinical settings. K-means clustering is the most prevalent unsupervised ML paradigm. The most common settings were healthcare institutions. The most common targeted population was the general population.

DISCUSSION

Although all the studies did internal validation, only 11 papers (13.9%) did external validation, and 23 papers (29.1%) conducted methods comparison. The existing papers discussed little validating the robustness of ML modeling.

CONCLUSION

Existing ML applications on population segmentation need more evaluations regarding giving tailored, efficient integrated healthcare solutions compared to traditional segmentation analysis. Future ML applications in the field should emphasize methods' comparisons and external validation and investigate approaches to evaluate individual consistency using different methods.

Characterizing subgroup performance of probabilistic phenotype algorithms within older adults: a case study for dementia, mild cognitive impairment, and Alzheimer's and Parkinson's diseases

Objective

Biases within probabilistic electronic phenotyping algorithms are largely unexplored. In this work, we characterize differences in subgroup performance of phenotyping algorithms for Alzheimer's disease and related dementias (ADRD) in older adults.

Materials and methods

We created an experimental framework to characterize the performance of probabilistic phenotyping algorithms under different racial distributions allowing us to identify which algorithms may have differential performance, by how much, and under what conditions. We relied on rule-based phenotype definitions as reference to evaluate probabilistic phenotype algorithms created using the Automated PHenotype Routine for Observational Definition, Identification, Training and Evaluation framework.

Results

We demonstrate that some algorithms have performance variations anywhere from 3% to 30% for different populations, even when not using race as an input variable. We show that while performance differences in subgroups are not present for all phenotypes, they do affect some phenotypes and groups more disproportionately than others.

Discussion

Our analysis establishes the need for a robust evaluation framework for subgroup differences. The underlying patient populations for the algorithms showing subgroup performance differences have great variance between model features when compared with the phenotypes with little to no differences.

Conclusion

We have created a framework to identify systematic differences in the performance of probabilistic phenotyping algorithms specifically in the context of ADRD as a use case. Differences in subgroup performance of probabilistic phenotyping algorithms are not widespread nor do they occur consistently. This highlights the great need for careful ongoing monitoring to evaluate, measure, and try to mitigate such differences.

Risk factors for developing ventilator-associated lower respiratory tract infection in patients with severe COVID-19: a multinational, multicentre study, prospective, observational study

Around one-third of patients diagnosed with COVID-19 develop a severe illness that requires admission to the Intensive Care Unit (ICU). In clinical practice, clinicians have learned that patients admitted to the ICU due to severe COVID-19 frequently develop ventilator-associated lower respiratory tract infections (VA-LRTI). This study aims to describe the clinical characteristics, the factors associated with VA-LRTI, and its impact on clinical outcomes in patients with severe COVID-19. This was a multicentre, observational cohort study conducted in ten countries in Latin America and Europe. We included patients with confirmed rtPCR for SARS-CoV-2 requiring ICU admission and endotracheal intubation. Only patients with a microbiological and clinical diagnosis of VA-LRTI were included. Multivariate Logistic regression analyses and Random Forest were conducted to determine the risk factors for VA-LRTI and its clinical impact in patients with severe COVID-19. In our study cohort of 3287 patients, VA-LRTI was diagnosed in 28.8% [948/3287]. The cumulative incidence of ventilator-associated pneumonia (VAP) was 18.6% [610/3287], followed by ventilator-associated tracheobronchitis (VAT) 10.3% [338/3287]. A total of 1252 bacteria species were isolated. The most frequently isolated pathogens were Pseudomonas aeruginosa (21.2% [266/1252]), followed by Klebsiella pneumoniae (19.1% [239/1252]) and Staphylococcus aureus (15.5% [194/1,252]). The factors independently associated with the development of VA-LRTI were prolonged stay under invasive mechanical ventilation, AKI during ICU stay, and the number of comorbidities. Regarding the clinical impact of VA-LRTI, patients with VAP had an increased risk of hospital mortality (OR [95% CI] of 1.81 [1.40-2.34]), while VAT was not associated with increased hospital mortality (OR [95% CI] of 1.34 [0.98-1.83]). VA-LRTI, often with difficult-to-treat bacteria, is frequent in patients admitted to the ICU due to severe COVID-19 and is associated with worse clinical outcomes, including higher mortality. Identifying risk factors for VA-LRTI might allow the early patient diagnosis to improve clinical outcomes.Trial registration: This is a prospective observational study; therefore, no health care interventions were applied to participants, and trial registration is not applicable.

Identification of Distinct Clinical Phenotypes of Critically Ill COVID-19 Patients: Results from a Cohort Observational Study

Purpose: COVID-19 presents complex pathophysiology, and evidence collected points towards an intricate interaction between viral-dependent and individual immunological mechanisms. Identifying phenotypes through clinical and biological markers may provide a better understanding of the subjacent mechanisms and an early patient-tailored characterization of illness severity. Methods: A multicenter prospective cohort study was performed in 5 hospitals in Portugal and Brazil for one year between 2020-2021. All adult patients with an Intensive Care Unit admission with SARS-CoV-2 pneumonia were eligible. COVID-19 was diagnosed using clinical and radiologic criteria with a SARS-CoV-2 positive RT-PCR test. A two-step hierarchical cluster analysis was made using several class-defining variables. Results: 814 patients were included. The cluster analysis revealed a three-class model, allowing for the definition of three distinct COVID-19 phenotypes: 407 patients in phenotype A, 244 patients in phenotype B, and 163 patients in phenotype C. Patients included in phenotype A were significantly older, with higher baseline inflammatory biomarkers profile, and a significantly higher requirement of organ support and mortality rate. Phenotypes B and C demonstrated some overlapping clinical characteristics but different outcomes. Phenotype C patients presented a lower mortality rate, with consistently lower C-reactive protein, but higher procalcitonin and interleukin-6 serum levels, describing an immunological profile significantly different from phenotype B. Conclusions: Severe COVID-19 patients exhibit three different clinical phenotypes with distinct profiles and outcomes. Their identification could have an impact on patients' care, justifying different therapy responses and inconsistencies identified across different randomized control trial results.

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.

Novel architecture for gated recurrent unit autoencoder trained on time series from electronic health records enables detection of ICU patient subgroups

Electronic health records (EHRs) are used in hospitals to store diagnoses, clinician notes, examinations, lab results, and interventions for each patient. Grouping patients into distinct subsets, for example, via clustering, may enable the discovery of unknown disease patterns or comorbidities, which could eventually lead to better treatment through personalized medicine. Patient data derived from EHRs is heterogeneous and temporally irregular. Therefore, traditional machine learning methods like PCA are ill-suited for analysis of EHR-derived patient data. We propose to address these issues with a new methodology based on training a gated recurrent unit (GRU) autoencoder directly on health record data. Our method learns a low-dimensional feature space by training on patient data time series, where the time of each data point is expressed explicitly. We use positional encodings for time, allowing our model to better handle the temporal irregularity of the data. We apply our method to data from the Medical Information Mart for Intensive Care (MIMIC-III). Using our data-derived feature space, we can cluster patients into groups representing major classes of disease patterns. Additionally, we show that our feature space exhibits a rich substructure at multiple scales.

Intensive Care and Organ Support Related Mortality in Patients With COVID-19: A Systematic Review and Meta-Analysis

To perform a systematic review and meta-analysis to generate estimates of mortality in patients with COVID-19 that required hospitalization, ICU admission, and organ support.

DATA SOURCES

A systematic search of PubMed, Embase, and the Cochrane databases was conducted up to December 31, 2021.

STUDY SELECTION

Previously peer-reviewed observational studies that reported ICU, mechanical ventilation (MV), renal replacement therapy (RRT) or extracorporeal membrane oxygenation (ECMO)-related mortality among greater than or equal to 100 individual patients.

DATA EXTRACTION

Random-effects meta-analysis was used to generate pooled estimates of case fatality rates (CFRs) for in-hospital, ICU, MV, RRT, and ECMO-related mortality. ICU-related mortality was additionally analyzed by the study country of origin. Sensitivity analyses of CFR were assessed based on completeness of follow-up data, by year, and when only studies judged to be of high quality were included.

DATA SYNTHESIS

One hundred fifty-seven studies evaluating 948,309 patients were included. The CFR for in-hospital mortality, ICU mortality, MV, RRT, and ECMO were 25.9% (95% CI: 24.0-27.8%), 37.3% (95% CI: 34.6-40.1%), 51.6% (95% CI: 46.1-57.0%), 66.1% (95% CI: 59.7-72.2%), and 58.0% (95% CI: 46.9-68.9%), respectively. MV (52.7%, 95% CI: 47.5-58.0% vs 31.3%, 95% CI: 16.1-48.9%; p = 0.023) and RRT-related mortality (66.7%, 95% CI: 60.1-73.0% vs 50.3%, 95% CI: 42.4-58.2%; p = 0.003) decreased from 2020 to 2021.

CONCLUSIONS

We present updated estimates of CFR for patients hospitalized and requiring intensive care for the management of COVID-19. Although mortality remain high and varies considerably worldwide, we found the CFR in patients supported with MV significantly improved since 2020.

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.

Clustering using unsupervised machine learning to stratify the risk of immune-related liver injury

BACKGROUND AND AIM

Immune-related liver injury (liver-irAE) is a clinical problem with a potentially poor prognosis.

METHODS

We retrospectively collected clinical data from patients treated with immune checkpoint inhibitors between September 2014 and December 2021 at the Nagoya University Hospital. Using an unsupervised machine learning method, the Gaussian mixture model, to divide the cohort into clusters based on inflammatory markers, we investigated the cumulative incidence of liver-irAEs in these clusters.

RESULTS

This study included a total of 702 patients. Among them, 492 (70.1%) patients were male, and the mean age was 66.6 years. During the mean follow-up period of 423 days, severe liver-irAEs (Common Terminology Criteria for Adverse Events grade ≥ 3) occurred in 43 patients. Patients were divided into five clusters (a, b, c, d, and e). The cumulative incidence of liver-irAE was higher in cluster c than in cluster a (hazard ratio [HR]: 13.59, 95% confidence interval [CI]: 1.70-108.76, P = 0.014), and overall survival was worse in clusters c and d than in cluster a (HR: 2.83, 95% CI: 1.77-4.50, P < 0.001; HR: 2.87, 95% CI: 1.47-5.60, P = 0.002, respectively). Clusters c and d were characterized by high temperature, C-reactive protein, platelets, and low albumin. However, there were differences in the prevalence of neutrophil count, neutrophil-to-lymphocyte ratio, and liver metastases between both clusters.

CONCLUSIONS

The combined assessment of multiple markers and body temperature may help stratify high-risk groups for developing liver-irAE.

A differential therapeutic consideration for use of corticosteroids according to established COVID-19 clinical phenotypes in critically ill patients

OBJECTIVE

To determine if the use of corticosteroids was associated with Intensive Care Unit (ICU) mortality among whole population and pre-specified clinical phenotypes.

DESIGN

A secondary analysis derived from multicenter, observational study.

SETTING

Critical Care Units.

PATIENTS

Adult critically ill patients with confirmed COVID-19 disease admitted to 63 ICUs in Spain.

INTERVENTIONS

Corticosteroids vs. no corticosteroids.

MAIN VARIABLES OF INTEREST

Three phenotypes were derived by non-supervised clustering analysis from whole population and classified as (A: severe, B: critical and C: life-threatening). We performed a multivariate analysis after propensity optimal full matching (PS) for whole population and weighted Cox regression (HR) and Fine-Gray analysis (sHR) to assess the impact of corticosteroids on ICU mortality according to the whole population and distinctive patient clinical phenotypes.

RESULTS

A total of 2017 patients were analyzed, 1171 (58%) with corticosteroids. After PS, corticosteroids were shown not to be associated with ICU mortality (OR: 1.0; 95% CI: 0.98-1.15). Corticosteroids were administered in 298/537 (55.5%) patients of "A" phenotype and their use was not associated with ICU mortality (HR=0.85 [0.55-1.33]). A total of 338/623 (54.2%) patients in "B" phenotype received corticosteroids. No effect of corticosteroids on ICU mortality was observed when HR was performed (0.72 [0.49-1.05]). Finally, 535/857 (62.4%) patients in "C" phenotype received corticosteroids. In this phenotype HR (0.75 [0.58-0.98]) and sHR (0.79 [0.63-0.98]) suggest a protective effect of corticosteroids on ICU mortality.

CONCLUSION

Our finding warns against the widespread use of corticosteroids in all critically ill patients with COVID-19 at moderate dose. Only patients with the highest inflammatory levels could benefit from steroid treatment.

A differential therapeutic consideration for use of corticosteroids according to established COVID-19 clinical phenotypes in critically ill patients

Objective

To determine if the use of corticosteroids was associated with Intensive Care Unit (ICU) mortality among whole population and pre-specified clinical phenotypes.

Design

A secondary analysis derived from multicenter, observational study.

Setting

Critical Care Units.

Patients

Adult critically ill patients with confirmed COVID-19 disease admitted to 63 ICUs in Spain.

Interventions

Corticosteroids vs. no corticosteroids.

Main variables of interest

Three phenotypes were derived by non-supervised clustering analysis from whole population and classified as (A: severe, B: critical and C: life-threatening). We performed a multivariate analysis after propensity optimal full matching (PS) for whole population and weighted Cox regression (HR) and Fine-Gray analysis (sHR) to assess the impact of corticosteroids on ICU mortality according to the whole population and distinctive patient clinical phenotypes.

Results

A total of 2017 patients were analyzed, 1171 (58%) with corticosteroids. After PS, corticosteroids were shown not to be associated with ICU mortality (OR: 1.0; 95% CI: 0.98-1.15). Corticosteroids were administered in 298/537 (55.5%) patients of "A" phenotype and their use was not associated with ICU mortality (HR = 0.85 [0.55-1.33]). A total of 338/623 (54.2%) patients in "B" phenotype received corticosteroids. No effect of corticosteroids on ICU mortality was observed when HR was performed (0.72 [0.49-1.05]). Finally, 535/857 (62.4%) patients in "C" phenotype received corticosteroids. In this phenotype HR (0.75 [0.58-0.98]) and sHR (0.79 [0.63-0.98]) suggest a protective effect of corticosteroids on ICU mortality.

Conclusion

Our finding warns against the widespread use of corticosteroids in all critically ill patients with COVID-19 at moderate dose. Only patients with the highest inflammatory levels could benefit from steroid treatment.

Comparison of systemic inflammatory profiles in COVID-19 and community-acquired pneumonia patients: a prospective cohort study

BACKGROUND

Inflammatory responses contribute to tissue damage in COVID-19 and community-acquired pneumonia (CAP). Although predictive values of different inflammatory biomarkers have been reported in both, similarities and differences of inflammatory profiles between these conditions remain uncertain. Therefore, we aimed to determine the similarities and differences of the inflammatory profiles between COVID-19 and CAP, and their association with clinical outcomes.

METHODS

We report a prospective observational cohort study; conducted in a reference hospital in Latin America. Patients with confirmed COVID-19 pneumonia and CAP were included. Multiplex (Luminex) cytokine assays were used to measure the plasma concentration of 14 cytokines at hospital admission. After comparing similarities and differences in the inflammatory profile between COVID-19 and CAP patients, an unsupervised classification method (i.e., hierarchical clustering) was used to identify subpopulations within COVID-19 and CAP patients.

RESULTS

A total of 160 patients were included, 62.5% were diagnosed with COVID-19 (100/160), and 37.5% with CAP (60/160). Using the hierarchical clustering, COVID-19 and CAP patients were divided based on its inflammatory profile: pauci, moderate, and hyper-inflammatory immune response. COVID-19 hyper-inflammatory subpopulation had the highest mortality. COVID-19 hyper-inflammatory subpopulation, compared to pauci-inflammatory, had higher levels of IL-10 (median [IQR] 61.4 [42.0-109.4] vs 13.0 [5.0-24.9], P: < 0.001), IL-6 (48.1 [22.3-82.6] vs 9.1 [0.1-30.4], P: < 0.001), among others. Hyper-inflammatory vs pauci-inflammatory CAP patients were characterized by elevation of IFN2 (48.8 [29.7-110.5] vs 3.0 [1.7-10.3], P: < 0.001), TNFα (36.3 [24.8-53.4] vs 13.1 [11.3-16.9], P: < 0.001), among others. Hyper-inflammatory subpopulations in COVID-19 and CAP compared to the corresponding pauci-inflammatory subpopulations had higher MCP-1.

CONCLUSIONS

Our data highlights three distinct subpopulations in COVID-19 and CAP, with differences in inflammatory marker profiles and risks of adverse clinical outcomes.

TRIAL REGISTRATION

This is a prospective study, therefore no health care intervention were applied to participants and trial registration is not applicable.

Clustering analysis of geriatric and acute characteristics in a cohort of very old patients on admission to ICU

PURPOSE

The biological and functional heterogeneity in very old patients constitutes a major challenge to prognostication and patient management in intensive care units (ICUs). In addition to the characteristics of acute diseases, geriatric conditions such as frailty, multimorbidity, cognitive impairment and functional disabilities were shown to influence outcome in that population. The goal of this study was to identify new and robust phenotypes based on the combination of these features to facilitate early outcome prediction.

METHODS

Patients aged 80 years old or older with and without limitations of life-sustaining treatment and with complete data were recruited from the VIP2 study for phenotyping and from the COVIP study for external validation. The sequential organ failure assessment (SOFA) score and its sub-scores taken on admission to ICU as well as demographic and geriatric patient characteristics were subjected to clustering analysis. Phenotypes were identified after repeated bootstrapping and clustering runs.

RESULTS

In patients from the VIP2 study without limitations of life-sustaining treatment (n = 1977), ICU mortality was 12% and 30-day mortality 19%. Seven phenotypes with distinct profiles of acute and geriatric characteristics were identified in that cohort. Phenotype-specific mortality within 30 days ranged from 3 to 57%. Among the patients assigned to a phenotype with pronounced geriatric features and high SOFA scores, 50% died in ICU and 57% within 30 days. Mortality differences between phenotypes were confirmed in the COVIP study cohort (n = 280).

CONCLUSIONS

Phenotyping of very old patients on admission to ICU revealed new phenotypes with different mortality and potential need for anticipatory intervention.

The COVID HOME study research protocol: Prospective cohort study of non-hospitalised COVID-19 patients

BACKGROUND

Guidelines on COVID-19 management are developed as we learn from this pandemic. However, most research has been done on hospitalised patients and the impact of the disease on non-hospitalised and their role in transmission are not yet well understood. The COVID HOME study conducts research among COVID-19 patients and their family members who were not hospitalised during acute disease, to guide patient care and inform public health guidelines for infection prevention and control in the community and household.

METHODS

An ongoing prospective longitudinal observational study of COVID-19 outpatients was established in March 2020 at the beginning of the COVID-19 pandemic in the Netherlands. Laboratory confirmed SARS-CoV-2 infected individuals of all ages that did not merit hospitalisation, and their household (HH) members, were enrolled after written informed consent. Enrolled participants were visited at home within 48 hours after initial diagnosis, and then weekly on days 7, 14 and 21 to obtain clinical data, a blood sample for biochemical parameters/cytokines and serological determination; and a nasopharyngeal/throat swab plus urine, stool and sperm or vaginal secretion (if consenting) to test for SARS-CoV-2 by RT-PCR (viral shedding) and for viral culturing. Weekly nasopharyngeal/throat swabs and stool samples, plus a blood sample on days 0 and 21 were also taken from HH members to determine whether and when they became infected. All participants were invited to continue follow-up at 3-, 6-, 12- and 18-months post-infection to assess long-term sequelae and immunological status.

Respiratory support in patients with severe COVID-19 in the International Severe Acute Respiratory and Emerging Infection (ISARIC) COVID-19 study: a prospective, multinational, observational study

BACKGROUND

Up to 30% of hospitalised patients with COVID-19 require advanced respiratory support, including high-flow nasal cannulas (HFNC), non-invasive mechanical ventilation (NIV), or invasive mechanical ventilation (IMV). We aimed to describe the clinical characteristics, outcomes and risk factors for failing non-invasive respiratory support in patients treated with severe COVID-19 during the first two years of the pandemic in high-income countries (HICs) and low middle-income countries (LMICs).

METHODS

This is a multinational, multicentre, prospective cohort study embedded in the ISARIC-WHO COVID-19 Clinical Characterisation Protocol. Patients with laboratory-confirmed SARS-CoV-2 infection who required hospital admission were recruited prospectively. Patients treated with HFNC, NIV, or IMV within the first 24 h of hospital admission were included in this study. Descriptive statistics, random forest, and logistic regression analyses were used to describe clinical characteristics and compare clinical outcomes among patients treated with the different types of advanced respiratory support.

RESULTS

A total of 66,565 patients were included in this study. Overall, 82.6% of patients were treated in HIC, and 40.6% were admitted to the hospital during the first pandemic wave. During the first 24 h after hospital admission, patients in HICs were more frequently treated with HFNC (48.0%), followed by NIV (38.6%) and IMV (13.4%). In contrast, patients admitted in lower- and middle-income countries (LMICs) were less frequently treated with HFNC (16.1%) and the majority received IMV (59.1%). The failure rate of non-invasive respiratory support (i.e. HFNC or NIV) was 15.5%, of which 71.2% were from HIC and 28.8% from LMIC. The variables most strongly associated with non-invasive ventilation failure, defined as progression to IMV, were high leukocyte counts at hospital admission (OR [95%CI]; 5.86 [4.83-7.10]), treatment in an LMIC (OR [95%CI]; 2.04 [1.97-2.11]), and tachypnoea at hospital admission (OR [95%CI]; 1.16 [1.14-1.18]). Patients who failed HFNC/NIV had a higher 28-day fatality ratio (OR [95%CI]; 1.27 [1.25-1.30]).

CONCLUSIONS

In the present international cohort, the most frequently used advanced respiratory support was the HFNC. However, IMV was used more often in LMIC. Higher leucocyte count, tachypnoea, and treatment in LMIC were risk factors for HFNC/NIV failure. HFNC/NIV failure was related to worse clinical outcomes, such as 28-day mortality. Trial registration This is a prospective observational study; therefore, no health care interventions were applied to participants, and trial registration is not applicable.

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.

Machine learning of COVID-19 clinical data identifies population structures with therapeutic potential

Clinical outcomes for patients with COVID-19 are heterogeneous and there is interest in defining subgroups for prognostic modeling and development of treatment algorithms. We obtained 28 demographic and laboratory variables in patients admitted to hospital with COVID-19. These comprised a training cohort (n = 6099) and two validation cohorts during the first and second waves of the pandemic (n = 996; n = 1011). Uniform manifold approximation and projection (UMAP) dimension reduction and Gaussian mixture model (GMM) analysis was used to define patient clusters. 29 clusters were defined in the training cohort and associated with markedly different mortality rates, which were predictive within confirmation datasets. Deconvolution of clinical features within clusters identified unexpected relationships between variables. Integration of large datasets using UMAP-assisted clustering can therefore identify patient subgroups with prognostic information and uncovers unexpected interactions between clinical variables. This application of machine learning represents a powerful approach for delineating disease pathogenesis and potential therapeutic interventions.

Immunomodulatory therapy for the management of critically ill patients with COVID-19: A narrative review

BACKGROUND

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the ongoing coronavirus disease 2019 (COVID-19) pandemic. Understanding the physiological and immunological processes underlying the clinical manifestations of COVID-19 is vital for the identification and rational design of effective therapies.

AIM

To describe the interaction of SARS-CoV-2 with the immune system and the subsequent contribution of hyperinflammation and abnormal immune responses to disease progression together with a complete narrative review of the different immunoadjuvant treatments used so far in COVID-19 and their indication in severe and life-threatening subsets.

METHODS

A comprehensive literature search was developed. Authors reviewed the selected manuscripts following the PRISMA recommendations for systematic review and meta-analysis documents and selected the most appropriate. Finally, a recommendation of the use of each treatment was established based on the level of evidence of the articles and documents reviewed. This recommendation was made based on the consensus of all the authors.

RESULTS

A brief rationale on the SARS-CoV-2 pathogenesis, immune response, and inflammation was developed. The usefulness of 10 different families of treatments related to inflammation and immunopathogenesis of COVID-19 was reviewed and discussed. Finally, based on the level of scientific evidence, a recommendation was established for each of them.

CONCLUSION

Although several promising therapies exist, only the use of corticosteroids and tocilizumab (or sarilumab in absence of this) have demonstrated evidence enough to recommend its use in critically ill patients with COVID-19. Endotypes including both, clinical and biological characteristics can constitute specific targets for better select certain therapies based on an individualized approach to treatment.

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.

A novel model to predict severe COVID-19 and mortality using an artificial intelligence algorithm to interpret chest X-Rays and clinical variables

Background

Patients with coronavirus disease 2019 (COVID-19) could develop severe disease requiring admission to the intensive care unit (ICU). This article presents a novel method that predicts whether a patient will need admission to the ICU and assesses the risk of in-hospital mortality by training a deep-learning model that combines a set of clinical variables and features in chest radiographs.

Methods

This was a prospective diagnostic test study. Patients with confirmed severe acute respiratory syndrome coronavirus 2 infection between March 2020 and January 2021 were included. This study was designed to build predictive models obtained by training convolutional neural networks for chest radiograph images using an artificial intelligence (AI) tool and a random forest analysis to identify critical clinical variables. Then, both architectures were connected and fine-tuned to provide combined models.

Results

2552 patients were included in the clinical cohort. The variables independently associated with ICU admission were age, fraction of inspired oxygen (F_iO2) on admission, dyspnoea on admission and obesity. Moreover, the variables associated with hospital mortality were age, F_iO2 on admission and dyspnoea. When implementing the AI model to interpret the chest radiographs and the clinical variables identified by random forest, we developed a model that accurately predicts ICU admission (area under the curve (AUC) 0.92±0.04) and hospital mortality (AUC 0.81±0.06) in patients with confirmed COVID-19.

Conclusions

This automated chest radiograph interpretation algorithm, along with clinical variables, is a reliable alternative to identify patients at risk of developing severe COVID-19 who might require admission to the ICU.

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A time-incorporated SOFA score-based machine learning model for predicting mortality in critically ill patients: A multicenter, real-world study

BACKGROUND

Organ dysfunction (OD) assessment is essential in intensive care units (ICUs). However, current OD assessment scores merely describe the number and the severity of each OD, without evaluating the duration of organ injury. The objective of this study is to develop and validate a machine learning model based on the Sequential Organ Failure Assessment (SOFA) score for the prediction of mortality in critically ill patients.

MATERIAL AND METHODS

Data from the eICU Collaborative Research Database and Medical Information Mart for Intensive Care (MIMIC) -III were mixed for model development. The MIMIC-IV and Nanjing Jinling Hospital Surgical ICU database were used as external test set A and set B, respectively. The outcome of interest was in-ICU mortality. A modified SOFA model incorporating time-dimension (T-SOFA) was stepwise developed to predict ICU mortality using extreme gradient boosting (XGBoost), support vector machine, random forest and logistic regression algorithms. Time-dimensional features were calculated based on six consecutive SOFA scores collected every 12 h within the first three days of admission. The predictive performance was assessed with the area under the receiver operating characteristic curves (AUROC) and calibration plot.

RESULTS

A total of 82,132 patients from the real-world datasets were included in this study, and 7,494 patients (9.12%) died during their ICU stay. The T-SOFA M3 that incorporated the time-dimension features and age, using the XGBoost algorithm, significantly outperformed the original SOFA score in the validation set (AUROC 0.800 95% CI [0.787-0.813] vs. 0.693 95% CI [0.678-0.709], p < 0.01). Good discrimination and calibration were maintained in the test set A and B, with AUROC of 0.803, 95% CI [0.791-0.815] and 0.830, 95% CI [0.789-0.870], respectively.

CONCLUSIONS

The time-incorporated T-SOFA model could significantly improve the prediction performance of the original SOFA score and is of potential for identifying high-risk patients in future clinical application.

Clustering phosphate and iron-related markers and prognosis in dialysis patients

Background

Hyperphosphatemia in patients undergoing dialysis is common and is associated with mortality. Recently, the link between phosphate metabolism and iron dynamics has received increasing attention. However, the association between this relationship and prognosis remains largely unexplored.

Methods

We conducted an observational study of patients who initiated dialysis in the 17 centers participating in the Aichi Cohort Study of the Prognosis in Patients Newly Initiated into Dialysis. Data were available on sex, age, use of phosphate binder, drug history, medical history and laboratory data. After excluding patients with missing values of phosphate, hemoglobin, ferritin and transferrin saturation, we used the Gaussian mixture model to divide the cohort into clusters based on phosphate, hemoglobin, logarithmic ferritin and transferrin saturation. We investigated the prognosis of patients in these clusters. The primary outcome was all-cause death. In each cluster, the prognostic impact of phosphate binder was also studied.

Results

The study included 1175 patients with chronic kidney disease who initiated dialysis between October 2011 and September 2013. Among them, 785 were men and 390 were women, with a mean ± SD age of 67.9 ± 13.0 years. The patients were divided into three clusters, and mortality was higher in cluster c than in cluster a (P = 0.005). Moreover, the use of phosphate binders was associated with a lower risk of all-cause death in two clusters (a and c) that were characterized by older age and higher prevalence of diabetes mellitus, among other things.

Conclusions

We used an unsupervised machine learning method to cluster patients, using phosphate, hemoglobin and iron-related markers. In two of the clusters, the oral use of a phosphate binder might improve prognosis.

Coronavirus Disease 2019 Temperature Trajectories Correlate With Hyperinflammatory and Hypercoagulable Subphenotypes

OBJECTIVES

Body temperature trajectories of infected patients are associated with specific immune profiles and survival. We determined the association between temperature trajectories and distinct manifestations of coronavirus disease 2019.

DESIGN

Retrospective observational study.

SETTING

Four hospitals within an academic healthcare system from March 2020 to February 2021.

PATIENTS

All adult patients hospitalized with coronavirus disease 2019.

INTERVENTIONS

Using a validated group-based trajectory model, we classified patients into four previously defined temperature trajectory subphenotypes using oral temperature measurements from the first 72 hours of hospitalization. Clinical characteristics, biomarkers, and outcomes were compared between subphenotypes.

MEASUREMENTS AND MAIN RESULTS

The 5,903 hospitalized coronavirus disease 2019 patients were classified into four subphenotypes: hyperthermic slow resolvers (n = 1,452, 25%), hyperthermic fast resolvers (1,469, 25%), normothermics (2,126, 36%), and hypothermics (856, 15%). Hypothermics had abnormal coagulation markers, with the highest d-dimer and fibrin monomers (p < 0.001) and the highest prevalence of cerebrovascular accidents (10%, p = 0.001). The prevalence of venous thromboembolism was significantly different between subphenotypes (p = 0.005), with the highest rate in hypothermics (8.5%) and lowest in hyperthermic slow resolvers (5.1%). Hyperthermic slow resolvers had abnormal inflammatory markers, with the highest C-reactive protein, ferritin, and interleukin-6 (p < 0.001). Hyperthermic slow resolvers had increased odds of mechanical ventilation, vasopressors, and 30-day inpatient mortality (odds ratio, 1.58; 95% CI, 1.13-2.19) compared with hyperthermic fast resolvers. Over the course of the pandemic, we observed a drastic decrease in the prevalence of hyperthermic slow resolvers, from representing 53% of admissions in March 2020 to less than 15% by 2021. We found that dexamethasone use was associated with significant reduction in probability of hyperthermic slow resolvers membership (27% reduction; 95% CI, 23-31%; p < 0.001).

CONCLUSIONS

Hypothermics had abnormal coagulation markers, suggesting a hypercoagulable subphenotype. Hyperthermic slow resolvers had elevated inflammatory markers and the highest odds of mortality, suggesting a hyperinflammatory subphenotype. Future work should investigate whether temperature subphenotypes benefit from targeted antithrombotic and anti-inflammatory strategies.

Cluster analysis integrating age and body temperature for mortality in patients with sepsis: a multicenter retrospective study

It is not clear whether mortality is associated with body temperature (BT) in older sepsis patients. This study aimed to evaluate the mortality rates in sepsis patients according to age and BT and identify the risk factors for mortality. We investigated the clusters using a machine learning method based on a combination of age and BT, and identified the mortality rates according to these clusters. This retrospective multicenter study was conducted at five hospitals in Korea. Data of sepsis patients aged ≥ 18 years who were admitted to the intensive care unit between January 1, 2011 and April 30, 2021 were collected. BT was divided into three groups (hypothermia < 36 °C, normothermia 36‒38 °C, and hyperthermia > 38 °C), and age groups were divided using a 75-year age threshold. Kaplan‒Meier analysis was performed to assess the cumulative mortality over 90 days. A K-means clustering algorithm using age and BT was used to characterize phenotypes. During the study period, 15,574 sepsis patients were enrolled. Overall, 90-day mortality was 20.5%. Kaplan‒Meier survival analyses demonstrated that 90-day mortality rates were 27.4%, 19.6%, and 11.9% in the hypothermia, normothermia, and hyperthermia groups, respectively, in those ≥ 75 years old (Log-rank p < 0.001). Cluster analysis demonstrated three groups: Cluster A (relatively older age and lower BT), Cluster B (relatively younger age and wide range of BT), and Cluster C (relatively higher BT than Cluster A). Kaplan‒Meier curve analysis showed that the 90-day mortality rates of Cluster A was significantly higher than those of Clusters B and C (24.2%, 17.1%, and 17.0%, respectively; Log-rank p < 0.001). The 90-day mortality rate correlated inversely with BT groups among sepsis patients in either age group (< 75 and ≥ 75 years). Clustering analysis revealed that the mortality rate was higher in the cluster of patients with relatively older age and lower BT.

Clinical and laboratory predictors at ICU admission affecting course of illness and mortality rates in a tertiary COVID-19 center

Background

Survival rates of critically ill COVID-19 patients are affected by various clinical features and laboratory parameters at ICU admission. Some of these predictors are universal but others may be population specific.

Objective

To determine utility of baseline clinical and laboratory parameters in a multivariate regression model to predict outcomes in critically ill COVID-19 patients in a tertiary hospital in Croatia.

Methods

692 critically ill COVID-19 patients treated during a 10-month period were included in this retrospective observational trial to assess the risk factors determining mortality rates. Various anthropometric features, comorbidities, laboratory parameters, clinical features and therapeutic interventions were included in the analysis. ICU mortality rates and length of ICU stay were primary endpoints analyzed in this study.

Results

After multivariate adjustment, only the SOFA score, PaO₂/FiO₂ and history of arterial hypertension had an effect on ICU mortality, as well as the need to initiate invasive mechanical ventilation. Increase in PaO₂/FiO₂ over the first 7 days was present in survivors, while reverse applied to SOFA. Length of ICU stay was 9 (4–14) days. Factors affecting survival times were admission from wards, congestive heart failure, invasive mechanical ventilation, bacterial superinfections, age > 75 years, SOFA score, and serum ferritin, CRP and IL-6 values at ICU admission.

Conclusion

Elevated inflammatory biomarkers and SOFA score at ICU admission were detected as significant predictors of ICU mortality in this cohort, while initiation of invasive mechanical ventilation is the most relevant interventional mortality risk factor in critically ill COVID-19 patients.

Clinical characteristics, risk factors and outcomes in patients with severe COVID-19 registered in the International Severe Acute Respiratory and Emerging Infection Consortium WHO clinical characterisation protocol: a prospective, multinational, multicentre, observational study

Due to the large number of patients with severe coronavirus disease 2019 (COVID-19), many were treated outside the traditional walls of the intensive care unit (ICU), and in many cases, by personnel who were not trained in critical care. The clinical characteristics and the relative impact of caring for severe COVID-19 patients outside the ICU is unknown. This was a multinational, multicentre, prospective cohort study embedded in the International Severe Acute Respiratory and Emerging Infection Consortium World Health Organization COVID-19 platform. Severe COVID-19 patients were identified as those admitted to an ICU and/or those treated with one of the following treatments: invasive or noninvasive mechanical ventilation, high-flow nasal cannula, inotropes or vasopressors. A logistic generalised additive model was used to compare clinical outcomes among patients admitted or not to the ICU. A total of 40 440 patients from 43 countries and six continents were included in this analysis. Severe COVID-19 patients were frequently male (62.9%), older adults (median (interquartile range (IQR), 67 (55-78) years), and with at least one comorbidity (63.2%). The overall median (IQR) length of hospital stay was 10 (5-19) days and was longer in patients admitted to an ICU than in those who were cared for outside the ICU (12 (6-23) days versus 8 (4-15) days, p<0.0001). The 28-day fatality ratio was lower in ICU-admitted patients (30.7% (5797 out of 18 831) versus 39.0% (7532 out of 19 295), p<0.0001). Patients admitted to an ICU had a significantly lower probability of death than those who were not (adjusted OR 0.70, 95% CI 0.65-0.75; p<0.0001). Patients with severe COVID-19 admitted to an ICU had significantly lower 28-day fatality ratio than those cared for outside an ICU.

Recommendations for the management of critically ill patients with COVID-19 in Intensive Care Units

The COVID-19 pandemic has led to the admission of a high number of patients to the ICU, generally due to severe respiratory failure. Since the appearance of the first cases of SARS-CoV-2 infection, at the end of 2019, in China, a huge number of treatment recommendations for this entity have been published, not always supported by sufficient scientific evidence or with methodological rigor necessary. Thanks to the efforts of different groups of researchers, we currently have the results of clinical trials, and other types of studies, of higher quality. We consider it necessary to create a document that includes recommendations that collect this evidence regarding the diagnosis and treatment of COVID-19, but also aspects that other guidelines have not considered and that we consider essential in the management of critical patients with COVID-19. For this, a drafting committee has been created, made up of members of the SEMICYUC Working Groups more directly related to different specific aspects of the management of these patients.

Extracorporeal Cytokine Removal in Critically Ill COVID-19 Patients: A Case Series

Introduction: Extracorporeal hemoadsorption (HA) is a potential adjunctive therapy in severe cases of COVID-19 associated pneumonia. In this retrospective study we report data from critically ill patients treated with HA during the first and second wave of the pandemic. Patients and Methods: All patients, who received HA therapy with CytoSorb within the first 96 h of intensive care unit (ICU) admission without hospital-acquired bacterial superinfection, were included. Clinical and laboratory data were collected: on admission, before (T_B) and after (T_A) HA therapy. Results: Out of the 367 COVID-19 cases, 13 patients were treated with CytoSorb, also requiring mechanical ventilation and renal replacement therapy. All patients were alive at the end of HA, but only 3 survived hospital stay. From T_B-T_A there was a tendency of decreasing norepinephrine requirement: 193.7 [IQR: 34.8-270.4] to 50.2 [6.5-243.5] ug/kg/day and increasing PaO2/FiO2 ratio 127.8 (95% CI: 96.0-159.6) to 155.0 (115.3-194.6) mmHg but they did not reach statistical significance (p = 0.14 and 0.58, respectively). Treatment related adverse events were not reported. Conclusion: The treatment was well-tolerated, and there was a tendency toward an improvement in vasopressor need and oxygenation during the course of HA. These observations render the need for prospective randomized trials.

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.

Corticosteroid treatment and mortality in mechanically ventilated COVID-19-associated acute respiratory distress syndrome (ARDS) patients: a multicentre cohort study

Background

Some unanswered questions persist regarding the effectiveness of corticosteroids for severe coronavirus disease 2019 (COVID-19) patients. We aimed to assess the clinical effect of corticosteroids on intensive care unit (ICU) mortality among mechanically ventilated COVID-19-associated acute respiratory distress syndrome (ARDS) patients.

Methods

This was a retrospective study of prospectively collected data conducted in 70 ICUs (68 Spanish, one Andorran, one Irish), including mechanically ventilated COVID-19-associated ARDS patients admitted between February 6 and September 20, 2020. Individuals who received corticosteroids for refractory shock were excluded. Patients exposed to corticosteroids at admission were matched with patients without corticosteroids through propensity score matching. Primary outcome was all-cause ICU mortality. Secondary outcomes were to compare in-hospital mortality, ventilator-free days at 28 days, respiratory superinfection and length of stay between patients with corticosteroids and those without corticosteroids. We performed survival analysis accounting for competing risks and subgroup sensitivity analysis.

Results

We included 1835 mechanically ventilated COVID-19-associated ARDS, of whom 1117 (60.9%) received corticosteroids. After propensity score matching, ICU mortality did not differ between patients treated with corticosteroids and untreated patients (33.8% vs. 30.9%; p = 0.28). In survival analysis, corticosteroid treatment at ICU admission was associated with short-term survival benefit (HR 0.53; 95% CI 0.39–0.72), although beyond the 17th day of admission, this effect switched and there was an increased ICU mortality (long-term HR 1.68; 95% CI 1.16–2.45). The sensitivity analysis reinforced the results. Subgroups of age < 60 years, severe ARDS and corticosteroids plus tocilizumab could have greatest benefit from corticosteroids as short-term decreased ICU mortality without long-term negative effects were observed. Larger length of stay was observed with corticosteroids among non-survivors both in the ICU and in hospital. There were no significant differences for the remaining secondary outcomes.

Conclusions

Our results suggest that corticosteroid treatment for mechanically ventilated COVID-19-associated ARDS had a biphasic time-dependent effect on ICU mortality. Specific subgroups showed clear effect on improving survival with corticosteroid use. Therefore, further research is required to identify treatment-responsive subgroups among the mechanically ventilated COVID-19-associated ARDS patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13613-021-00951-0.

Association Between COVID-19 Diagnosis and In-Hospital Mortality in Patients Hospitalized With ST-Segment Elevation Myocardial Infarction

IMPORTANCE

There has been limited research on patients with ST-segment elevation myocardial infarction (STEMI) and COVID-19.

OBJECTIVE

To compare characteristics, treatment, and outcomes of patients with STEMI with vs without COVID-19 infection.

DESIGN, SETTING, AND PARTICIPANTS

Retrospective cohort study of consecutive adult patients admitted between January 2019 and December 2020 (end of follow-up in January 2021) with out-of-hospital or in-hospital STEMI at 509 US centers in the Vizient Clinical Database (N = 80 449).

EXPOSURES

Active COVID-19 infection present during the same encounter.

MAIN OUTCOMES AND MEASURES

The primary outcome was in-hospital mortality. Patients were propensity matched on the likelihood of COVID-19 diagnosis. In the main analysis, patients with COVID-19 were compared with those without COVID-19 during the previous calendar year.

RESULTS

The out-of-hospital STEMI group included 76 434 patients (551 with COVID-19 vs 2755 without COVID-19 after matching) from 370 centers (64.1% aged 51-74 years; 70.3% men). The in-hospital STEMI group included 4015 patients (252 with COVID-19 vs 756 without COVID-19 after matching) from 353 centers (58.3% aged 51-74 years; 60.7% men). In patients with out-of-hospital STEMI, there was no significant difference in the likelihood of undergoing primary percutaneous coronary intervention by COVID-19 status; patients with in-hospital STEMI and COVID-19 were significantly less likely to undergo invasive diagnostic or therapeutic coronary procedures than those without COVID-19. Among patients with out-of-hospital STEMI and COVID-19 vs out-of-hospital STEMI without COVID-19, the rates of in-hospital mortality were 15.2% vs 11.2% (absolute difference, 4.1% [95% CI, 1.1%-7.0%]; P = .007). Among patients with in-hospital STEMI and COVID-19 vs in-hospital STEMI without COVID-19, the rates of in-hospital mortality were 78.5% vs 46.1% (absolute difference, 32.4% [95% CI, 29.0%-35.9%]; P < .001).

CONCLUSIONS AND RELEVANCE

Among patients with out-of-hospital or in-hospital STEMI, a concomitant diagnosis of COVID-19 was significantly associated with higher rates of in-hospital mortality compared with patients without a diagnosis of COVID-19 from the past year. Further research is required to understand the potential mechanisms underlying this association.

Mortality and bleeding complications of COVID-19 critically ill patients with venous thromboembolism

BACKGROUND

VTE disease in COVID-19 patients is a remarkable issue, especially its relationship with bleeding events and mortality. The objective of this study was to describe the outcomes of critically ill patients with COVID-19 hospitalized in ICU in relationship with VTE during their stay.

METHODS

Prospective cohort study of critically ill COVID-19 patients in two hospitals that underwent a venous ultrasound at the beginning of follow-up of both lower limbs in April 2020. When clinical suspicion of new VTE during the 30-day follow-up, additional ultrasound or thoracic CT were performed. Global VTE frequency, major bleeding events and survival were collected, and their predictors were studied.

RESULTS

We included 230 patients. After 30 days of follow-up, there were 95 VTE events in 86 patients (37,4%). 13 patients (5,7%) developed major bleeding complications and 42 patients (18,3%) died. None of the comorbidities or previous treatments were related with bleeding events. D-dimer at admission was significantly related with VTE development and mortality. Independent predictors of mortality in the regression model were an older age (>66 years), D-dimer at admission (>1 500ng/mL) and low lymphocyte count (<0,45x109/L) with an AUC in the ROC curve of 0,81 (95%CI: 0,73-0,89). Patients presenting these three conditions presented a mortality of a 100% in the predictive model.

CONCLUSIONS

VTE frequency in ICU COVID-19 patients is high and risk of major bleeding is low. Comorbidities and laboratory parameters of admission in these patients can be a useful tool to predict mortality.

[Management of infectious complications associated with coronavirus infection in severe patients admitted to ICU]

Infections have become one of the main complications of patients with severe SARS-CoV-2 pneumonia admitted in ICU. Poor immune status, frequent development of organic failure requiring invasive supportive treatments, and prolonged ICU length of stay in saturated structural areas of patients are risk factors for infection development. The Working Group on Infectious Diseases and Sepsis GTEIS of the Spanish Society of Intensive Medicine and Coronary Units SEMICYUC emphasizes the importance of infection prevention measures related to health care, the detection and early treatment of major infections in the patient with SARS-CoV-2 infections. Bacterial co-infection, respiratory infections related to mechanical ventilation, catheter-related bacteremia, device-associated urinary tract infection and opportunistic infections are review in the document.

A machine-learning parsimonious multivariable predictive model of mortality risk in patients with Covid-19

The COVID-19 pandemic is impressively challenging the healthcare system. Several prognostic models have been validated but few of them are implemented in daily practice. The objective of the study was to validate a machine-learning risk prediction model using easy-to-obtain parameters to help to identify patients with COVID-19 who are at higher risk of death. The training cohort included all patients admitted to Fondazione Policlinico Gemelli with COVID-19 from March 5, 2020, to November 5, 2020. Afterward, the model was tested on all patients admitted to the same hospital with COVID-19 from November 6, 2020, to February 5, 2021. The primary outcome was in-hospital case-fatality risk. The out-of-sample performance of the model was estimated from the training set in terms of Area under the Receiving Operator Curve (AUROC) and classification matrix statistics by averaging the results of fivefold cross validation repeated 3-times and comparing the results with those obtained on the test set. An explanation analysis of the model, based on the SHapley Additive exPlanations (SHAP), is also presented. To assess the subsequent time evolution, the change in paO2/FiO2 (P/F) at 48 h after the baseline measurement was plotted against its baseline value. Among the 921 patients included in the training cohort, 120 died (13%). Variables selected for the model were age, platelet count, SpO2, blood urea nitrogen (BUN), hemoglobin, C-reactive protein, neutrophil count, and sodium. The results of the fivefold cross-validation repeated 3-times gave AUROC of 0.87, and statistics of the classification matrix to the Youden index as follows: sensitivity 0.840, specificity 0.774, negative predictive value 0.971. Then, the model was tested on a new population (n = 1463) in which the case-fatality rate was 22.6%. The test model showed AUROC 0.818, sensitivity 0.813, specificity 0.650, negative predictive value 0.922. Considering the first quartile of the predicted risk score (low-risk score group), the case-fatality rate was 1.6%, 17.8% in the second and third quartile (high-risk score group) and 53.5% in the fourth quartile (very high-risk score group). The three risk score groups showed good discrimination for the P/F value at admission, and a positive correlation was found for the low-risk class to P/F at 48 h after admission (adjusted R-squared = 0.48). We developed a predictive model of death for people with SARS-CoV-2 infection by including only easy-to-obtain variables (abnormal blood count, BUN, C-reactive protein, sodium and lower SpO2). It demonstrated good accuracy and high power of discrimination. The simplicity of the model makes the risk prediction applicable for patients in the Emergency Department, or during hospitalization. Although it is reasonable to assume that the model is also applicable in not-hospitalized persons, only appropriate studies can assess the accuracy of the model also for persons at home.

Secondary pneumonias in critically ill patients with COVID-19: risk factors and outcomes

PURPOSE OF REVIEW

The aim of this review is to provide an overview of the current evidence of secondary pneumonias in COVID-19 patients, its incidence, risk factors and impact outcomes.

RECENT FINDINGS

Early studies reported low incidence of hospital-acquired infections in COVID-19 patients. More recent large studies clearly showed that the incidence of secondary pneumonias was markedly high in patients under mechanical ventilation. Duration of mechanical ventilation, acute respiratory distress syndrome, prone position and male sex were identified as risk factors. The adjunctive therapy with steroids and immunomodulators were associated with a higher risk of pneumonia and invasive pulmonary Aspergillosis. Although secondary pneumonias seemed to be associated with poor outcomes, namely mortality, in comparison with influenza, no difference was found in heterogeneity of outcomes. Immunosuppressive therapy has been studied in several observational and randomized trials with conflicting results and the true impact on superinfections, namely secondary pneumonias, has not been properly assessed.

SUMMARY

According to the current evidence, COVID-19 patients are at an increased risk of secondary pneumonias. The impact of immunosuppressive therapies on superinfections is yet to be determined. Further studies are needed to assess the true risk of secondary infections associated with immunosuppressive therapies and to identify preventive strategies.

[Recommendations for the management of critically ill patients with COVID-19 in Intensive Care Units]

La pandemia por COVID-19 ha provocado el ingreso de un elevado número de pacientes en UCI, generalmente por insuficiencia respiratoria severa. Desde la aparición de los primeros casos de infección por SARS-CoV-2, a finales de 2019, en China, se ha publicado una cantidad ingente de recomendaciones de tratamiento de esta entidad, no siempre respaldadas por evidencia científica suficiente ni con el rigor metodológico necesario. Gracias al esfuerzo de distintos grupos de investigadores, actualmente disponemos de resultados de ensayos clínicos, y otro tipo de estudios, de mayor calidad. Consideramos necesario realizar un documento que incluya recomendaciones que recojan estas evidencias en cuanto al diagnóstico y tratamiento de la COVID-19, pero también aspectos que otras guías no han contemplado y que consideramos fundamentales en el manejo del paciente crítico con COVID-19. Para ello se ha creado un comité redactor, conformado por miembros de los Grupos de Trabajo de SEMICYUC más directamente relacionados con diferentes aspectos específicos del manejo de estos pacientes.

Management of infectious complications associated with coronavirus infection in severe patients admitted to ICU

Infections have become one of the main complications of patients with severe SARS-CoV-2 pneumonia admitted in ICU. Poor immune status, frequent development of organic failure requiring invasive supportive treatments, and prolonged ICU length of stay in saturated structural areas of patients are risk factors for infection development. The Working Group on Infectious Diseases and Sepsis GTEIS of the Spanish Society of Intensive Medicine and Coronary Units SEMICYUC emphasizes the importance of infection prevention measures related to health care, the detection and early treatment of major infections in the patient with SARS-CoV-2 infections. Bacterial co-infection, respiratory infections related to mechanical ventilation, catheter-related bacteremia, device-associated urinary tract infection and opportunistic infections are review in the document.

Immune System Disequilibrium-Neutrophils, Their Extracellular Traps, and COVID-19-Induced Sepsis

Equilibrium within the immune system can often determine the fate of its host. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the pathogen responsible for the coronavirus disease 2019 (COVID-19) pandemic. Immune dysregulation remains one of the main pathophysiological components of SARS-CoV-2-associated organ injury, with over-activation of the innate immune system, and induced apoptosis of adaptive immune cells. Here, we provide an overview of the innate immune system, both in general and relating to COVID-19. We specifically discuss "NETosis," the process of neutrophil release of their extracellular traps, which may be a more recently described form of cell death that is different from apoptosis, and how this may propagate organ dysfunction in COVID-19. We complete this review by discussing Stem Cell Therapies in COVID-19 and emerging COVID-19 phenotypes, which may allow for more targeted therapy in the future. Finally, we consider the array of potential therapeutic targets in COVID-19, and associated therapeutics.

Mortality and Clinical Interventions in Critically ill Patient With Coronavirus Disease 2019: A Systematic Review and Meta-Analysis

Objective: The aims of this systematic review and meta-analysis were to summarize the current existing evidence on the outcome of critically ill patients with COVID-19 as well as to evaluate the effectiveness of clinical interventions.

Data Sources: We searched MEDLINE, the Cochrane library, Web of Science, the China Biology Medicine disc, China National Knowledge Infrastructure, and Wanfang Data from their inception to May 15, 2021. The search strings consisted of various search terms related to the concepts of mortality of critically ill patients and clinical interventions.

Study Selection: After eliminating duplicates, two reviewers independently screened all titles and abstracts first, and then the full texts of potentially relevant articles were reviewed to identify cohort studies and case series that focus on the mortality of critically ill patients and clinical interventions.

Main Outcomes and Measures: The primary outcome was the mortality of critically ill patients with COVID-19. The secondary outcomes included all sorts of supportive care.

Results: There were 27 cohort studies and six case series involving 42,219 participants that met our inclusion criteria. All-cause mortality in the intensive care unit (ICU) was 35% and mortality in hospital was 32% in critically ill patients with COVID-19 for the year 2020, with very high between-study heterogeneity (I² = 97%; p < 0.01). In a subgroup analysis, the mortality during ICU hospitalization in China was 39%, in Asia—except for China—it was 48%, in Europe it was 34%, in America it was 15%, and in the Middle East it was 39%. Non-surviving patients who had an older age [−8.10, 95% CI (−9.31 to −6.90)], a higher APACHE II score [−4.90, 95% CI (−6.54 to −3.27)], a higher SOFA score [−2.27, 95% CI (−2.95 to −1.59)], and a lower PaO₂/FiO₂ ratio [34.77, 95% CI (14.68 to 54.85)] than those who survived. Among clinical interventions, invasive mechanical ventilation [risk ratio (RR) 0.49, 95% CI (0.39–0.61)], kidney replacement therapy [RR 0.34, 95% CI (0.26–0.43)], and vasopressor [RR 0.54, 95% CI (0.34–0.88)] were used more in surviving patients.

Conclusions: Mortality was high in critically ill patients with COVID-19 based on low-quality evidence and regional difference that existed. The early identification of critical characteristics and the use of support care help to indicate the outcome of critically ill patients.

The Use of CytoSorb Therapy in Critically Ill COVID-19 Patients: Review of the Rationale and Current Clinical Experiences

The COVID-19 pandemic has led to the biggest global health crisis of our lifetime. There is accumulating evidence that a substantial number of critically ill COVID-19 patients exhibit a dysregulated host response manifesting as cytokine storm or cytokine release syndrome, which in turn contributes to the high observed rates of mortality. Just as in other hyperinflammatory conditions, extracorporeal cytokine removal may have potential beneficial effects in this subgroup of COVID-19 patients. The CytoSorb blood purification device is the most extensively investigated cytokine removal platform with considerable evidence suggesting that early intervention can provide rapid hemodynamic stabilization and improvement in vital organ functions. The purpose of this review is to provide an overview of the pathophysiological background of hyperinflammation in COVID-19 and to summarize the currently available evidence on the effects of hemoadsorption in these patients.

Clinical subphenotypes in COVID-19: derivation, validation, prediction, temporal patterns, and interaction with social determinants of health

The coronavirus disease 2019 (COVID-19) is heterogeneous and our understanding of the biological mechanisms of host response to the viral infection remains limited. Identification of meaningful clinical subphenotypes may benefit pathophysiological study, clinical practice, and clinical trials. Here, our aim was to derive and validate COVID-19 subphenotypes using machine learning and routinely collected clinical data, assess temporal patterns of these subphenotypes during the pandemic course, and examine their interaction with social determinants of health (SDoH). We retrospectively analyzed 14418 COVID-19 patients in five major medical centers in New York City (NYC), between March 1 and June 12, 2020. Using clustering analysis, 4 biologically distinct subphenotypes were derived in the development cohort (N = 8199). Importantly, the identified subphenotypes were highly predictive of clinical outcomes (especially 60-day mortality). Sensitivity analyses in the development cohort, and rederivation and prediction in the internal (N = 3519) and external (N = 3519) validation cohorts confirmed the reproducibility and usability of the subphenotypes. Further analyses showed varying subphenotype prevalence across the peak of the outbreak in NYC. We also found that SDoH specifically influenced mortality outcome in Subphenotype IV, which is associated with older age, worse clinical manifestation, and high comorbidity burden. Our findings may lead to a better understanding of how COVID-19 causes disease in different populations and potentially benefit clinical trial development. The temporal patterns and SDoH implications of the subphenotypes may add insights to health policy to reduce social disparity in the pandemic.