From ICU Syndromes to ICU Subphenotypes: Consensus Report and Recommendations For Developing Precision Medicine in ICU

Critical care uses syndromic definitions to describe patient groups for clinical practice and research. There is growing recognition that a "precision medicine" approach is required and that integrated biologic and physiologic data identify reproducible subpopulations that may respond differently to treatment. This article reviews the current state of the field and considers how to successfully transition to a precision medicine approach. In order to impact clinical care, identified subpopulations must do more than differentiate prognosis. They must differentiate response to treatment, ideally by defining subgroups with distinct functional or pathobiological mechanisms (endotypes). There are now multiple examples of reproducible subpopulations of sepsis, acute respiratory distress syndrome, and acute kidney or brain injury described using clinical, physiological, and/or biological data. Many of these subpopulations have demonstrated the potential to define differential treatment response, largely in retrospective studies, and that the same treatment-responsive subpopulations may cross multiple clinical syndromes (treatable traits). To bring about a change in clinical practice, a precision medicine approach must be evaluated in prospective clinical studies requiring novel adaptive trial designs. Several such studies are underway but there are multiple challenges to be tackled. Such subpopulations must be readily identifiable and be applicable to all critically ill populations around the world. Subdividing clinical syndromes into subpopulations will require large patient numbers. Global collaboration of investigators, clinicians, industry and patients over many years will therefore be required to transition to a precision medicine approach and ultimately realize treatment advances seen in other medical fields. This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Social Networks as a Key Health Determinant in Acute Illness Recovery: A Lesson from the COVID-19 Pandemic

BACKGROUND

The COVID-19 pandemic highlighted the importance of considering social determinants of health in health outcomes. Within this spectrum of determinants, social networks garnered attention as the pandemic highlighted the negative effects of social isolation in the context of social distancing measures. Post-pandemic, examining the role social networks play in COVID-19 recovery can help guide patient care and shape future health policies. This study aimed to investigate the relationship between social networks and self-rated health change, as well as physical function, in patients recovering from COVID-19 pneumonia.

METHODS

This was a retrospective cohort study utilizing clinical data from two New York City hospitals and a 9-month follow-up survey of COVID-19 pneumonia survivors. We evaluated a composite Social Network Score from the 6-item Lubben Social Network Scale and its association with two outcomes: 1) self-rated health change and 2) physical function.

RESULTS

A total of 208 patients were included in this study. A one-point increase in the Social Network Score was associated with greater odds of both improved or similar self-rated health change (odds ratio [OR] 1.07, 95% CI 1.02 - 1.12, p = 0.01), as well as unimpaired physical function (OR 1.08, 95% CI 1.03 - 1.14, p < 0.01).

CONCLUSION

This study emphasized the importance of social networks as a social determinant of health among patients recovering from COVID-19 hospitalization. Targeted interventions to enhance social networks may benefit not only COVID-19 patients but also individuals recovering from other acute illnesses.

Corticosteroids for infectious critical illness: A multicenter target trial emulation stratified by predicted organ dysfunction trajectory

Corticosteroids decrease the duration of organ dysfunction in a range of infectious critical illnesses, but their risk and benefit are not fully defined using this construct. This retrospective multicenter study aimed to evaluate the association between usage of corticosteroids and mortality of patients with infectious critical illness by emulating a target trial framework. The study employed a novel stratification method with predictive machine learning (ML) subphenotyping based on organ dysfunction trajectory. Our analysis revealed that corticosteroids' effectiveness varied depending on the stratification method. The ML-based approach identified four distinct subphenotypes, two of which had a large enough sample size in our patient cohorts for further evaluation: "Rapidly Improving" (RI) and "Rapidly Worsening," (RW) which showed divergent responses to corticosteroid treatment. Specifically, the RW group either benefited or were not harmed from corticosteroids, whereas the RI group appeared to derive harm. In the development cohort, which comprised of a combination of patients from the eICU and MIMIC-IV datasets, hazard ratio estimates for the primary outcome, 28-day mortality, in the RW group was 1.05 (95% CI: 0.96 - 1.04) whereas for the RW group, it was 1.40 (95% CI: 1.28 - 1.54). For the validation cohort, which comprised of patients from the Critical carE Database for Advanced Research, estimates for 28-day mortality for the RW and RI groups were 1.24 (95% CI: 1.05 - 1.46) and 1.34 (95% CI: 1.14 - 1.59), respectively. For secondary outcomes, the RW group had a shorter time to ICU discharge and time to cessation of mechanical ventilation with corticosteroid treatment, where the RI group again demonstrated harm. The findings support matching treatment strategies to empirically observed pathobiology and offer a more nuanced understanding of corticosteroid utility. Our results have implications for the design and interpretation of both observational studies and randomized controlled trials (RCTs), suggesting the need for stratification methods that account for the differential response to standard of care.

Sensitivity analysis for causality in observational studies for regulatory science

Objective

The United States Congress passed the 21st Century Cures Act mandating the development of Food and Drug Administration guidance on regulatory use of real-world evidence. The Forum on the Integration of Observational and Randomized Data conducted a meeting with various stakeholder groups to build consensus around best practices for the use of real-world data (RWD) to support regulatory science. Our companion paper describes in detail the context and discussion of the meeting, which includes a recommendation to use a causal roadmap for study designs using RWD. This article discusses one step of the roadmap: the specification of a sensitivity analysis for testing robustness to violations of causal model assumptions.

Methods

We present an example of a sensitivity analysis from a RWD study on the effectiveness of Nifurtimox in treating Chagas disease, and an overview of various methods, emphasizing practical considerations on their use for regulatory purposes.

Results

Sensitivity analyses must be accompanied by careful design of other aspects of the causal roadmap. Their prespecification is crucial to avoid wrong conclusions due to researcher degrees of freedom. Sensitivity analysis methods require auxiliary information to produce meaningful conclusions; it is important that they have at least two properties: the validity of the conclusions does not rely on unverifiable assumptions, and the auxiliary information required by the method is learnable from the corpus of current scientific knowledge.

Conclusions

Prespecified and assumption-lean sensitivity analyses are a crucial tool that can strengthen the validity and trustworthiness of effectiveness conclusions for regulatory science.

Faithful AI in Medicine: A Systematic Review with Large Language Models and Beyond

Objective

While artificial intelligence (AI), particularly large language models (LLMs), offers significant potential for medicine, it raises critical concerns due to the possibility of generating factually incorrect information, leading to potential long-term risks and ethical issues. This review aims to provide a comprehensive overview of the faithfulness problem in existing research on AI in healthcare and medicine, with a focus on the analysis of the causes of unfaithful results, evaluation metrics, and mitigation methods.

Materials and Methods

Using PRISMA methodology, we sourced 5,061 records from five databases (PubMed, Scopus, IEEE Xplore, ACM Digital Library, Google Scholar) published between January 2018 to March 2023. We removed duplicates and screened records based on exclusion criteria.

Results

With 40 leaving articles, we conducted a systematic review of recent developments aimed at optimizing and evaluating factuality across a variety of generative medical AI approaches. These include knowledge-grounded LLMs, text-to-text generation, multimodality-to-text generation, and automatic medical fact-checking tasks.

Discussion

Current research investigating the factuality problem in medical AI is in its early stages. There are significant challenges related to data resources, backbone models, mitigation methods, and evaluation metrics. Promising opportunities exist for novel faithful medical AI research involving the adaptation of LLMs and prompt engineering.

Conclusion

This comprehensive review highlights the need for further research to address the issues of reliability and factuality in medical AI, serving as both a reference and inspiration for future research into the safe, ethical use of AI in medicine and healthcare.

Epigenetic memory of coronavirus infection in innate immune cells and their progenitors

Inflammation can trigger lasting phenotypes in immune and non-immune cells. Whether and how human infections and associated inflammation can form innate immune memory in hematopoietic stem and progenitor cells (HSPC) has remained unclear. We found that circulating HSPC, enriched from peripheral blood, captured the diversity of bone marrow HSPC, enabling investigation of their epigenomic reprogramming following coronavirus disease 2019 (COVID-19). Alterations in innate immune phenotypes and epigenetic programs of HSPC persisted for months to 1 year following severe COVID-19 and were associated with distinct transcription factor (TF) activities, altered regulation of inflammatory programs, and durable increases in myelopoiesis. HSPC epigenomic alterations were conveyed, through differentiation, to progeny innate immune cells. Early activity of IL-6 contributed to these persistent phenotypes in human COVID-19 and a mouse coronavirus infection model. Epigenetic reprogramming of HSPC may underlie altered immune function following infection and be broadly relevant, especially for millions of COVID-19 survivors.

Auto-immunoproteomics analysis of COVID-19 ICU patients revealed increased levels of autoantibodies related to the male reproductive system

Background: Coronavirus disease (COVID-19) manifests many clinical symptoms, including an exacerbated immune response and cytokine storm. Autoantibodies in COVID-19 may have severe prodromal effects that are poorly understood. The interaction between these autoantibodies and self-antigens can result in systemic inflammation and organ dysfunction. However, the role of autoantibodies in COVID-19 complications has yet to be fully understood. Methods: The current investigation screened two independent cohorts of 97 COVID-19 patients [discovery (Disc) cohort from Qatar (case = 49 vs. control = 48) and replication (Rep) cohort from New York (case = 48 vs. control = 28)] utilizing high-throughput KoRectly Expressed (KREX) Immunome protein-array technology. Total IgG autoantibody responses were evaluated against 1,318 correctly folded and full-length human proteins. Samples were randomly applied on the precoated microarray slides for 2 h. Cy3-labeled secondary antibodies were used to detect IgG autoantibody response. Slides were scanned at a fixed gain setting using the Agilent fluorescence microarray scanner, generating a 16-bit TIFF file. Group comparisons were performed using a linear model and Fisher's exact test. Differentially expressed proteins were used for KEGG and WIKIpathway annotation to determine pathways in which the proteins of interest were significantly over-represented. Results and conclusion: Autoantibody responses to 57 proteins were significantly altered in the COVID-19 Disc cohort compared to healthy controls (p ≤ 0.05). The Rep cohort had altered autoantibody responses against 26 proteins compared to non-COVID-19 ICU patients who served as controls. Both cohorts showed substantial similarities (r 2 = 0.73) and exhibited higher autoantibody responses to numerous transcription factors, immunomodulatory proteins, and human disease markers. Analysis of the combined cohorts revealed elevated autoantibody responses against SPANXN4, STK25, ATF4, PRKD2, and CHMP3 proteins in COVID-19 patients. The sequences for SPANXN4 and STK25 were cross-validated using sequence alignment tools. ELISA and Western blot further verified the autoantigen-autoantibody response of SPANXN4. SPANXN4 is essential for spermiogenesis and male fertility, which may predict a potential role for this protein in COVID-19-associated male reproductive tract complications, and warrants further research.

Faithful AI in Medicine: A Systematic Review with Large Language Models and Beyond

Artificial intelligence (AI), especially the most recent large language models (LLMs), holds great promise in healthcare and medicine, with applications spanning from biological scientific discovery and clinical patient care to public health policymaking. However, AI methods have the critical concern for generating factually incorrect or unfaithful information, posing potential long-term risks, ethical issues, and other serious consequences. This review aims to provide a comprehensive overview of the faithfulness problem in existing research on AI in healthcare and medicine, with a focus on the analysis of the causes of unfaithful results, evaluation metrics, and mitigation methods. We systematically reviewed the recent progress in optimizing the factuality across various generative medical AI methods, including knowledge-grounded LLMs, text-to-text generation, multimodality-to-text generation, and automatic medical fact-checking tasks. We further discussed the challenges and opportunities of ensuring the faithfulness of AI-generated information in these applications. We expect that this review will assist researchers and practitioners in understanding the faithfulness problem in AI-generated information in healthcare and medicine, as well as the recent progress and challenges in related research. Our review can also serve as a guide for researchers and practitioners who are interested in applying AI in medicine and healthcare.

Data-driven analysis to understand long COVID using electronic health records from the RECOVER initiative

Recent studies have investigated post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) using real-world patient data such as electronic health records (EHR). Prior studies have typically been conducted on patient cohorts with specific patient populations which makes their generalizability unclear. This study aims to characterize PASC using the EHR data warehouses from two large Patient-Centered Clinical Research Networks (PCORnet), INSIGHT and OneFlorida+, which include 11 million patients in New York City (NYC) area and 16.8 million patients in Florida respectively. With a high-throughput screening pipeline based on propensity score and inverse probability of treatment weighting, we identified a broad list of diagnoses and medications which exhibited significantly higher incidence risk for patients 30-180 days after the laboratory-confirmed SARS-CoV-2 infection compared to non-infected patients. We identified more PASC diagnoses in NYC than in Florida regarding our screening criteria, and conditions including dementia, hair loss, pressure ulcers, pulmonary fibrosis, dyspnea, pulmonary embolism, chest pain, abnormal heartbeat, malaise, and fatigue, were replicated across both cohorts. Our analyses highlight potentially heterogeneous risks of PASC in different populations.

Effect of Sepsis on Death as Modified by Solid Organ Transplantation

Background

Patients with solid organ transplants (SOT) have an increased risk for sepsis compared to the general population. Paradoxically, studies suggest that SOT patients with sepsis may experience better outcomes compared to those without a SOT. However, these analyses used previous definitions of sepsis. It remains unknown whether the more recent definitions of sepsis and modern analytic approaches demonstrate a similar relationship.

Methods

Using the Weill Cornell-Critical Care Database for Advanced Research (WC-CEDAR) we analyzed granular physiologic, microbiologic, comorbidity, and therapeutic data in patients with and without SOT admitted to intensive care units (ICU’s). We used a survival analysis with a targeted minimum loss-based estimation, adjusting for within group (SOT and non-SOT) potential confounders to ascertain whether the effect of sepsis, defined by sepsis-3, on 28-day mortality was modified by SOT status. We performed additional analyses on restricted populations.

Results

We analyzed 28,431 patients: 439 with SOT and sepsis, 281 with SOT without sepsis, 6793 with sepsis and without SOT, and 20918 with neither. The most common SOT types were kidney (475) and liver (163). Despite a higher severity of illness in both sepsis groups, the adjusted sepsis-attributable effect on 28-day mortality for non-SOT patients was 4.1% (3.8, 4.5) and -14.4% (-16.8, -12) for SOT patients. The adjusted SOT effect modification was -18.5% (-21.2, -15.9). The adjusted sepsis-attributable effect for immunocompromised controls was -3.5% (-4.5, -2.6).

Conclusions

Across a large database of patients admitted to ICU’s, the sepsis associated 28-day mortality effect was significantly lower in SOT patients compared to controls.

Excess burden of respiratory and abdominal conditions following COVID-19 infections during the ancestral and Delta variant periods in the United States: An EHR-based cohort study from the RECOVER Program

Importance

The frequency and characteristics of post-acute sequelae of SARS-CoV-2 infection (PASC) may vary by SARS-CoV-2 variant.

Objective

To characterize PASC-related conditions among individuals likely infected by the ancestral strain in 2020 and individuals likely infected by the Delta variant in 2021.

Design

Retrospective cohort study of electronic medical record data for approximately 27 million patients from March 1, 2020-November 30, 2021.

Setting

Healthcare facilities in New York and Florida.

Participants

Patients who were at least 20 years old and had diagnosis codes that included at least one SARS-CoV-2 viral test during the study period.

Exposure

Laboratory-confirmed COVID-19 infection, classified by the most common variant prevalent in those regions at the time.

Main Outcomes and Measures

Relative risk (estimated by adjusted hazard ratio [aHR]) and absolute risk difference (estimated by adjusted excess burden) of new conditions, defined as new documentation of symptoms or diagnoses, in persons between 31-180 days after a positive COVID-19 test compared to persons with only negative tests during the 31-180 days after the last negative test.

Results

We analyzed data from 560,752 patients. The median age was 57 years; 60.3% were female, 20.0% non-Hispanic Black, and 19.6% Hispanic. During the study period, 57,616 patients had a positive SARS-CoV-2 test; 503,136 did not. For infections during the ancestral strain period, pulmonary fibrosis, edema (excess fluid), and inflammation had the largest aHR, comparing those with a positive test to those with a negative test, (aHR 2.32 [95% CI 2.09 2.57]), and dyspnea (shortness of breath) carried the largest excess burden (47.6 more cases per 1,000 persons). For infections during the Delta period, pulmonary embolism had the largest aHR comparing those with a positive test to a negative test (aHR 2.18 [95% CI 1.57, 3.01]), and abdominal pain carried the largest excess burden (85.3 more cases per 1,000 persons).

Conclusions and Relevance

We documented a substantial relative risk of pulmonary embolism and large absolute risk difference of abdomen-related symptoms after SARS-CoV-2 infection during the Delta variant period. As new SARS-CoV-2 variants emerge, researchers and clinicians should monitor patients for changing symptoms and conditions that develop after infection.

Data-driven identification of post-acute SARS-CoV-2 infection subphenotypes

The post-acute sequelae of SARS-CoV-2 infection (PASC) refers to a broad spectrum of symptoms and signs that are persistent, exacerbated or newly incident in the period after acute SARS-CoV-2 infection. Most studies have examined these conditions individually without providing evidence on co-occurring conditions. In this study, we leveraged the electronic health record data of two large cohorts, INSIGHT and OneFlorida+, from the national Patient-Centered Clinical Research Network. We created a development cohort from INSIGHT and a validation cohort from OneFlorida+ including 20,881 and 13,724 patients, respectively, who were SARS-CoV-2 infected, and we investigated their newly incident diagnoses 30-180 days after a documented SARS-CoV-2 infection. Through machine learning analysis of over 137 symptoms and conditions, we identified four reproducible PASC subphenotypes, dominated by cardiac and renal (including 33.75% and 25.43% of the patients in the development and validation cohorts); respiratory, sleep and anxiety (32.75% and 38.48%); musculoskeletal and nervous system (23.37% and 23.35%); and digestive and respiratory system (10.14% and 12.74%) sequelae. These subphenotypes were associated with distinct patient demographics, underlying conditions before SARS-CoV-2 infection and acute infection phase severity. Our study provides insights into the heterogeneity of PASC and may inform stratified decision-making in the management of PASC conditions.

Comparison of a Target Trial Emulation Framework vs Cox Regression to Estimate the Association of Corticosteroids With COVID-19 Mortality

IMPORTANCE

Communication and adoption of modern study design and analytical techniques is of high importance for the improvement of clinical research from observational data.

OBJECTIVE

To compare a modern method for statistical inference, including a target trial emulation framework and doubly robust estimation, with approaches common in the clinical literature, such as Cox proportional hazards models.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective cohort study used longitudinal electronic health record data for outcomes at 28-days from time of hospitalization within a multicenter New York, New York, hospital system. Participants included adult patients hospitalized between March 1 and May 15, 2020, with COVID-19 and not receiving corticosteroids for chronic use. Data were analyzed from October 2021 to March 2022.

EXPOSURES

Corticosteroid exposure was defined as more than 0.5 mg/kg methylprednisolone equivalent in a 24-hour period. For target trial emulation, exposures were corticosteroids for 6 days if and when a patient met criteria for severe hypoxia vs no corticosteroids. For approaches common in clinical literature, treatment definitions used for variables in Cox regression models varied by study design (no time frame, 1 day, and 5 days from time of severe hypoxia).

MAIN OUTCOMES AND MEASURES

The main outcome was 28-day mortality from time of hospitalization. The association of corticosteroids with mortality for patients with moderate to severe COVID-19 was assessed using the World Health Organization (WHO) meta-analysis of corticosteroid randomized clinical trials as a benchmark.

RESULTS

A total of 3298 patients (median [IQR] age, 65 [53-77] years; 1970 [60%] men) were assessed, including 423 patients who received corticosteroids at any point during hospitalization and 699 patients who died within 28 days of hospitalization. Target trial emulation analysis found corticosteroids were associated with a reduced 28-day mortality rate, from 32.2%; (95% CI, 30.9%-33.5%) to 25.7% (95% CI, 24.5%-26.9%). This estimate is qualitatively identical to the WHO meta-analysis odds ratio of 0.66 (95% CI, 0.53-0.82). Hazard ratios using methods comparable with current corticosteroid research range in size and direction, from 0.50 (95% CI, 0.41-0.62) to 1.08 (95% CI, 0.80-1.47).

CONCLUSIONS AND RELEVANCE

These findings suggest that clinical research based on observational data can be used to estimate findings similar to those from randomized clinical trials; however, the correctness of these estimates requires designing the study and analyzing the data based on principles that are different from the current standard in clinical research.

Comparison of a Target Trial Emulation Framework to Cox Regression to Estimate the Effect of Corticosteroids on COVID-19 Mortality

Importance

Communication and adoption of modern study design and analytical techniques is of high importance for the improvement of clinical research from observational data.

Objective

To compare (1) a modern method for causal inference including a target trial emulation framework and doubly robust estimation to (2) approaches common in the clinical literature such as Cox proportional hazards models. To do this, we estimate the effect of corticosteroids on mortality for moderate-to-severe coronavirus disease 2019 (COVID-19) patients. We use the World Health Organization's (WHO) meta-analysis of corticosteroid randomized controlled trials (RCTs) as a benchmark.

Design

Retrospective cohort study using longitudinal electronic health record data for 28 days from time of hospitalization.

Settings

Multi-center New York City hospital system.

Participants

Adult patients hospitalized between March 1-May 15, 2020 with COVID-19 and not on corticosteroids for chronic use.

Intervention

Corticosteroid exposure defined as >0.5mg/kg methylprednisolone equivalent in a 24-hour period. For target trial emulation, interventions are (1) corticosteroids for six days if and when patient meets criteria for severe hypoxia and (2) no corticosteroids. For approaches common in clinical literature, treatment definitions used for variables in Cox regression models vary by study design (no time frame, one-, and five-days from time of severe hypoxia).

Main outcome

28-day mortality from time of hospitalization.

Results

3,298 patients (median age 65 (IQR 53-77), 60% male). 423 receive corticosteroids at any point during hospitalization, 699 die within 28 days of hospitalization. Target trial emulation estimates corticosteroids to reduce 28-day mortality from 32.2% (95% CI 30.9-33.5) to 25.7% (24.5-26.9). This estimate is qualitatively identical to the WHO's RCT meta-analysis odds ratio of 0.66 (0.53-0.82)). Hazard ratios using methods comparable to current corticosteroid research range in size and direction from 0.50 (0.41-0.62) to 1.08 (0.80-1.47).

Conclusion and Relevance

Clinical research based on observational data can unveil true causal relationships; however, the correctness of these effect estimates requires designing the study and analyzing the data based on principles which are different from the current standard in clinical research.

Assessing mortality differences across acute respiratory failure management strategies in Covid-19

PURPOSE

Prolonged observation could avoid invasive mechanical ventilation (IMV) and related risks in patients with Covid-19 acute respiratory failure (ARF) compared to initiating early IMV. We aimed to determine the association between ARF management strategy and in-hospital mortality.

MATERIALS AND METHODS

Patients in the Weill Cornell Covid-19 registry who developed ARF between March 5 - March 25, 2020 were exposed to an early IMV strategy; between March 26 - April 1, 2020 to an intermediate strategy; and after April 2 to prolonged observation. Cox proportional hazards regression was used to model in-hospital mortality and test an interaction between ARF management strategy and modified sequential organ failure assessment (mSOFA).

RESULTS

Among 632 patients with ARF, 24% of patients in the early IMV strategy died versus 28% in prolonged observation. At lower mSOFA, prolonged observation was associated with lower mortality compared to early IMV (at mSOFA = 0, HR 0.16 [95% CI 0.04-0.57]). Mortality risk increased in the prolonged observation strategy group with each point increase in mSOFA score (HR 1.29 [95% CI 1.10-1.51], p = 0.002).

CONCLUSION

In Covid-19 ARF, prolonged observation was associated with a mortality benefit at lower mSOFA scores, and increased mortality at higher mSOFA scores compared to early IMV.

Patient care in rapid-expansion intensive care units during the COVID-19 pandemic crisis

Background

The coronavirus-2019 (COVID-19) pandemic highlighted the unfortunate reality that many hospitals have insufficient intensive care unit (ICU) capacity to meet massive, unanticipated increases in demand. To drastically increase ICU capacity, NewYork-Presbyterian/Weill Cornell Medical Center modified its existing operating rooms and post-anaesthesia care units during the initial expansion phase to accommodate the surge of critically ill patients.

Methods

This retrospective chart review examined patient care in non-standard Expansion ICUs as compared to standard ICUs. We compared clinical data between the two settings to determine whether the expeditious development and deployment of critical care resources during an evolving medical crisis could provide appropriate care.

Results

Sixty-six patients were admitted to Expansion ICUs from March 1^st to April 30^th, 2020 and 343 were admitted to standard ICUs. Most patients were male (70%), White (30%), 45–64 years old (35%), non-smokers (73%), had hypertension (58%), and were hospitalized for a median of 40 days. For patients that died, there was no difference in treatment management, but the Expansion cohort had a higher median ICU length of stay (q = 0.037) and ventilatory length (q = 0.015). The cohorts had similar rates of discharge to home, but the Expansion ICU cohort had higher rates of discharge to a rehabilitation facility and overall lower mortality.

Conclusions

We found no significantly worse outcomes for the Expansion ICU cohort compared to the standard ICU cohort at our institution during the COVID-19 pandemic, which demonstrates the feasibility of providing safe and effective care for patients in an Expansion ICU.

Sepsis subphenotyping based on organ dysfunction trajectory

BACKGROUND

Sepsis is a heterogeneous syndrome, and the identification of clinical subphenotypes is essential. Although organ dysfunction is a defining element of sepsis, subphenotypes of differential trajectory are not well studied. We sought to identify distinct Sequential Organ Failure Assessment (SOFA) score trajectory-based subphenotypes in sepsis.

METHODS

We created 72-h SOFA score trajectories in patients with sepsis from four diverse intensive care unit (ICU) cohorts. We then used dynamic time warping (DTW) to compute heterogeneous SOFA trajectory similarities and hierarchical agglomerative clustering (HAC) to identify trajectory-based subphenotypes. Patient characteristics were compared between subphenotypes and a random forest model was developed to predict subphenotype membership at 6 and 24 h after being admitted to the ICU. The model was tested on three validation cohorts. Sensitivity analyses were performed with alternative clustering methodologies.

RESULTS

A total of 4678, 3665, 12,282, and 4804 unique sepsis patients were included in development and three validation cohorts, respectively. Four subphenotypes were identified in the development cohort: Rapidly Worsening (n = 612, 13.1%), Delayed Worsening (n = 960, 20.5%), Rapidly Improving (n = 1932, 41.3%), and Delayed Improving (n = 1174, 25.1%). Baseline characteristics, including the pattern of organ dysfunction, varied between subphenotypes. Rapidly Worsening was defined by a higher comorbidity burden, acidosis, and visceral organ dysfunction. Rapidly Improving was defined by vasopressor use without acidosis. Outcomes differed across the subphenotypes, Rapidly Worsening had the highest in-hospital mortality (28.3%, P-value < 0.001), despite a lower SOFA (mean: 4.5) at ICU admission compared to Rapidly Improving (mortality:5.5%, mean SOFA: 5.5). An overall prediction accuracy of 0.78 (95% CI, [0.77, 0.8]) was obtained at 6 h after ICU admission, which increased to 0.87 (95% CI, [0.86, 0.88]) at 24 h. Similar subphenotypes were replicated in three validation cohorts. The majority of patients with sepsis have an improving phenotype with a lower mortality risk; however, they make up over 20% of all deaths due to their larger numbers.

CONCLUSIONS

Four novel, clinically-defined, trajectory-based sepsis subphenotypes were identified and validated. Identifying trajectory-based subphenotypes has immediate implications for the powering and predictive enrichment of clinical trials. Understanding the pathophysiology of these differential trajectories may reveal unanticipated therapeutic targets and identify more precise populations and endpoints for clinical trials.

Angiopoietin 2 Is Associated with Vascular Necroptosis Induction in Coronavirus Disease 2019 Acute Respiratory Distress Syndrome

Vascular injury is a well-established, disease-modifying factor in acute respiratory distress syndrome (ARDS) pathogenesis. Recently, coronavirus disease 2019 (COVID-19)-induced injury to the vascular compartment has been linked to complement activation, microvascular thrombosis, and dysregulated immune responses. This study sought to assess whether aberrant vascular activation in this prothrombotic context was associated with the induction of necroptotic vascular cell death. To achieve this, proteomic analysis was performed on blood samples from COVID-19 subjects at distinct time points during ARDS pathogenesis (hospitalized at risk, N = 59; ARDS, N = 31; and recovery, N = 12). Assessment of circulating vascular markers in the at-risk cohort revealed a signature of low vascular protein abundance that tracked with low platelet levels and increased mortality. This signature was replicated in the ARDS cohort and correlated with increased plasma angiopoietin 2 levels. COVID-19 ARDS lung autopsy immunostaining confirmed a link between vascular injury (angiopoietin 2) and platelet-rich microthrombi (CD61) and induction of necrotic cell death [phosphorylated mixed lineage kinase domain-like (pMLKL)]. Among recovery subjects, the vascular signature identified patients with poor functional outcomes. Taken together, this vascular injury signature was associated with low platelet levels and increased mortality and can be used to identify ARDS patients most likely to benefit from vascular targeted therapies.

Machine Learning for Identifying Data-Driven Subphenotypes of Incident Post-Acute SARS-CoV-2 Infection Conditions with Large Scale Electronic Health Records: Findings from the RECOVER Initiative

The post-acute sequelae of SARS-CoV-2 infection (PASC) refers to a broad spectrum of symptoms and signs that are persistent, exacerbated, or newly incident in the post-acute SARS-CoV-2 infection period of COVID-19 patients. Most studies have examined these conditions individually without providing concluding evidence on co-occurring conditions. To answer this question, this study leveraged electronic health records (EHRs) from two large clinical research networks from the national Patient-Centered Clinical Research Network (PCORnet) and investigated patients' newly incident diagnoses that appeared within 30 to 180 days after a documented SARS-CoV-2 infection. Through machine learning, we identified four reproducible subphenotypes of PASC dominated by blood and circulatory system, respiratory, musculoskeletal and nervous system, and digestive system problems, respectively. We also demonstrated that these subphenotypes were associated with distinct patterns of patient demographics, underlying conditions present prior to SARS-CoV-2 infection, acute infection phase severity, and use of new medications in the post-acute period. Our study provides novel insights into the heterogeneity of PASC and can inform stratified decision-making in the treatment of COVID-19 patients with PASC conditions.

Prolonged Unconsciousness is Common in COVID-19 and Associated with Hypoxemia

OBJECTIVE

The purpose of this study was to estimate the time to recovery of command-following and associations between hypoxemia with time to recovery of command-following.

METHODS

In this multicenter, retrospective, cohort study during the initial surge of the United States' pandemic (March-July 2020) we estimate the time from intubation to recovery of command-following, using Kaplan Meier cumulative-incidence curves and Cox proportional hazard models. Patients were included if they were admitted to 1 of 3 hospitals because of severe coronavirus disease 2019 (COVID-19), required endotracheal intubation for at least 7 days, and experienced impairment of consciousness (Glasgow Coma Scale motor score <6).

RESULTS

Five hundred seventy-one patients of the 795 patients recovered command-following. The median time to recovery of command-following was 30 days (95% confidence interval [CI] = 27-32 days). Median time to recovery of command-following increased by 16 days for patients with at least one episode of an arterial partial pressure of oxygen (PaO2 ) value ≤55 mmHg (p < 0.001), and 25% recovered ≥10 days after cessation of mechanical ventilation. The time to recovery of command-following  was associated with hypoxemia (PaO2  ≤55 mmHg hazard ratio [HR] = 0.56, 95% CI = 0.46-0.68; PaO2  ≤70 HR = 0.88, 95% CI = 0.85-0.91), and each additional day of hypoxemia decreased the likelihood of recovery, accounting for confounders including sedation. These findings were confirmed among patients without any imagining evidence of structural brain injury (n = 199), and in a non-overlapping second surge cohort (N = 427, October 2020 to April 2021).

INTERPRETATION

Survivors of severe COVID-19 commonly recover consciousness weeks after cessation of mechanical ventilation. Long recovery periods are associated with more severe hypoxemia. This relationship is not explained by sedation or brain injury identified on clinical imaging and should inform decisions about life-sustaining therapies. ANN NEUROL 2022;91:740-755.

Rapidly improving acute respiratory distress syndrome in COVID-19: a multi-centre observational study

Background

Before the pandemic of coronavirus disease (COVID-19), rapidly improving acute respiratory distress syndrome (ARDS), mostly defined by early extubation, had been recognized as an increasingly prevalent subphenotype (making up 15–24% of all ARDS cases), associated with good prognosis (10% mortality in ARDSNet trials). We attempted to determine the prevalence and prognosis of rapidly improving ARDS and of persistent severe ARDS related to COVID-19.

Methods

We included consecutive patients with COVID-19 receiving invasive mechanical ventilation in three intensive care units (ICU) during the second pandemic wave in Greece. We defined rapidly improving ARDS as extubation or a partial pressure of arterial oxygen to fraction of inspired oxygen ratio (PaO₂:FiO₂) greater than 300 on the first day following intubation. We defined persistent severe ARDS as PaO₂:FiO₂ of equal to or less than 100 on the second day following intubation.

Results

A total of 280 intubated patients met criteria of ARDS with a median PaO₂:FiO₂ of 125.0 (interquartile range 93.0–161.0) on day of intubation, and overall ICU-mortality of 52.5% (ranging from 24.3 to 66.9% across the three participating sites). Prevalence of rapidly improving ARDS was 3.9% (11 of 280 patients); no extubation occurred on the first day following intubation. ICU-mortality of patients with rapidly improving ARDS was 54.5%. This low prevalence and high mortality rate of rapidly improving ARDS were consistent across participating sites. Prevalence of persistent severe ARDS was 12.1% and corresponding mortality was 82.4%.

Conclusions

Rapidly improving ARDS was not prevalent and was not associated with good prognosis among patients with COVID-19. This is starkly different from what has been previously reported for patients with ARDS not related to COVID-19. Our results on both rapidly improving ARDS and persistent severe ARDS may contribute to our understanding of trajectory of ARDS and its association with prognosis in patients with COVID-19.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12931-022-02015-8.

Transesophageal echocardiography and risk of respiratory failure in patients who had ischemic stroke or transient ischemic attack: an IDEAL phase 4 study

Objective

Transesophageal echocardiography (TEE) is sometimes used to search for cardioembolic sources after ischemic stroke or transient ischemic attack (TIA). TEE visualizes some sources better than transthoracic echocardiography, but TEE is invasive and may cause aspiration. Few data exist on the risk of respiratory complications after TEE in patients who had stroke or TIA. Our objective was to determine whether TEE was associated with increased risk of respiratory failure in patients who had ischemic stroke or TIA.

Design

This is a retrospective cohort study using administrative data from inpatient and outpatient insurance claims collected by the US federal government’s Centers for Medicare and Medicaid Services.

Setting

Hospitals and outpatient clinics throughout the USA.

Participants

99 081 patients ≥65 years old hospitalized for out-of-hospital ischemic stroke or TIA, defined by validated International Classification of Disease-9/10 diagnosis codes and present-on-admission codes, using claims data from 2008 to 2018 in a random 5% sample of Medicare beneficiaries.

Main outcome measures

Acute respiratory failure, defined as endotracheal intubation and/or mechanical ventilation, starting on the first day after admission through 28 days afterward.

Results

Of 99 081 patients included in this analysis, 73 733 (74.4%) had an ischemic stroke and 25 348 (25.6%) a TIA. TEE was performed in 4677 (4.7%) patients and intubation and/or mechanical ventilation in 1403 (1.4%) patients. The 28-day cumulative risk of respiratory failure after TEE (1.4%; 95% CI 0.8% to 2.7%) was similar to that seen in those without TEE (1.4%; 95% CI 1.4% to 1.5%) (p=0.84). After adjustment for age, sex, race, Charlson comorbidities, diagnosis of stroke versus TIA, intravenous thrombolysis, and mechanical thrombectomy, TEE was not associated with an increased risk of respiratory failure (HR, 0.9; 95% CI 0.6 to 1.2).

Conclusions

In a cohort of older patients who had ischemic stroke or TIA, TEE was not associated with an increased risk of subsequent respiratory failure.

Percutaneous and Open Tracheostomy in Patients With COVID-19: The Weill Cornell Experience in New York City

OBJECTIVE

Report long-term tracheostomy outcomes in patients with COVID-19.

STUDY DESIGN

Review of prospectively collected data.

METHODS

Prospectively collected data were extracted for adults with COVID-19 undergoing percutaneous or open tracheostomy between April 4, 2020 and June 2, 2020 at a major medical center in New York City. The primary endpoint was weaning from mechanical ventilation. Secondary outcomes included sedation weaning, decannulation, and discharge.

RESULTS

One hundred one patients underwent tracheostomy, including 48 percutaneous (48%) and 53 open (52%), after a median intubation time of 24 days (IQR 20, 31). The most common complication was minor bleeding (n = 18, 18%). The all-cause mortality rate was 15% and no deaths were attributable to the tracheostomy. Eighty-three patients (82%) were weaned off mechanical ventilation, 88 patients (87%) were weaned off sedation, and 72 patients (71%) were decannulated. Censored median times from tracheostomy to sedation and ventilator weaning were 8 (95% CI 6-11) and 18 (95% CI 14-22) days, respectively (uncensored: 7 and 15 days). Median time from tracheostomy to decannulation was 36 (95% CI 32-47) days (uncensored: 32 days). Of those decannulated, 82% were decannulated during their index admission. There were no differences in outcomes or complication rates between percutaneous and open tracheostomy. Likelihood of discharge from the ICU was inversely related to intubation time, though the clinical relevance of this was small (HR 0.97, 95% CI 0.943-0.998; P = .037).

CONCLUSION

Tracheostomy by either percutaneous or open technique facilitated sedation and ventilator weaning in patients with COVID-19 after prolonged intubation. Additional study on the optimal timing of tracheostomy in patients with COVID-19 is warranted.

LEVEL OF EVIDENCE

3 Laryngoscope, 131:E2849-E2856, 2021.

Hyperglycemia in acute COVID-19 is characterized by insulin resistance and adipose tissue infectivity by SARS-CoV-2

Individuals infected with SARS-CoV-2 who also display hyperglycemia suffer from longer hospital stays, higher risk of developing acute respiratory distress syndrome (ARDS), and increased mortality. Nevertheless, the pathophysiological mechanism of hyperglycemia in COVID-19 remains poorly characterized. Here, we show that hyperglycemia is similarly prevalent among patients with ARDS independent of COVID-19 status. Yet among patients with ARDS and COVID-19, insulin resistance is the prevalent cause of hyperglycemia, independent of glucocorticoid treatment, which is unlike patients with ARDS but without COVID-19, where pancreatic beta cell failure predominates. A screen of glucoregulatory hormones revealed lower levels of adiponectin in patients with COVID-19. Hamsters infected with SARS-CoV-2 demonstrated a strong antiviral gene expression program in the adipose tissue and diminished expression of adiponectin. Moreover, we show that SARS-CoV-2 can infect adipocytes. Together these data suggest that SARS-CoV-2 may trigger adipose tissue dysfunction to drive insulin resistance and adverse outcomes in acute COVID-19.

A Comparative Analysis of the Respiratory Subscore of the Sequential Organ Failure Assessment Scoring System

Rationale: The Sequential Organ Failure Assessment (SOFA) tool is a commonly used measure of illness severity. Calculation of the respiratory subscore of SOFA is frequently limited by missing arterial oxygen pressure (PaO2) data. Although missing PaO2 data are commonly replaced with normal values, the performance of different methods of substituting PaO2 for SOFA calculation is unclear. Objectives: The study objective was to compare the performance of different substitution strategies for missing PaO2 data for SOFA score calculation. Methods: This retrospective cohort study was performed using the Weill Cornell Critical Care Database for Advanced Research from a tertiary care hospital in the United States. All adult patients admitted to an intensive care unit (ICU) from 2011 to 2019 with an available respiratory SOFA score were included. We analyzed the availability of the PaO2/fraction of inspired oxygen (FiO2) ratio on the first day of ICU admission. In those without a PaO2/FiO2 ratio available, the ratio of oxygen saturation as measured by pulse oximetry to FiO2 was used to calculate a respiratory SOFA subscore according to four methods (linear substitution [Rice], nonlinear substitution [Severinghaus], modified respiratory SOFA, and multiple imputation by chained equations [MICE]) as well as the missing-as-normal technique. We then compared how well the different total SOFA scores discriminated in-hospital mortality. We performed several subgroup and sensitivity analyses. Results: We identified 35,260 unique visits, of which 9,172 included predominant respiratory failure. PaO2 data were available for 14,939 (47%). The area under the receiver operating characteristic curve for each substitution technique for discriminating in-hospital mortality was higher than that for the missing-as-normal technique (0.78 [0.77-0.79]) in all analyses (modified, 0.80 [0.79-0.81]; Rice, 0.80 [0.79-0.81]; Severinghaus, 0.80 [0.79-0.81]; and MICE, 0.80 [0.79-0.81]) (P < 0.01). Each substitution method had a higher accuracy for discriminating in-hospital mortality (MICE, 0.67; Rice, 0.67; modified, 0.66; and Severinghaus, 0.66) than the missing-as-normal technique. Model calibration for in-hospital mortality was less precise for the missing-as-normal technique than for the other substitution techniques at the lower range of SOFA and among the subgroups. Conclusions: Using physiologic and statistical substitution methods improved the total SOFA score's ability to discriminate mortality compared with the missing-as-normal technique. Treating missing data as normal may result in underreporting the severity of illness compared with using substitution. The simplicity of a direct oxygen saturation as measured by pulse oximetry/FiO2 ratio-modified SOFA technique makes it an attractive choice for electronic health record-based research. This knowledge can inform comparisons of severity of illness across studies that used different techniques.

Identifying organ dysfunction trajectory-based subphenotypes in critically ill patients with COVID-19

COVID-19-associated respiratory failure offers the unprecedented opportunity to evaluate the differential host response to a uniform pathogenic insult. Understanding whether there are distinct subphenotypes of severe COVID-19 may offer insight into its pathophysiology. Sequential Organ Failure Assessment (SOFA) score is an objective and comprehensive measurement that measures dysfunction severity of six organ systems, i.e., cardiovascular, central nervous system, coagulation, liver, renal, and respiration. Our aim was to identify and characterize distinct subphenotypes of COVID-19 critical illness defined by the post-intubation trajectory of SOFA score. Intubated COVID-19 patients at two hospitals in New York city were leveraged as development and validation cohorts. Patients were grouped into mild, intermediate, and severe strata by their baseline post-intubation SOFA. Hierarchical agglomerative clustering was performed within each stratum to detect subphenotypes based on similarities amongst SOFA score trajectories evaluated by Dynamic Time Warping. Distinct worsening and recovering subphenotypes were identified within each stratum, which had distinct 7-day post-intubation SOFA progression trends. Patients in the worsening suphenotypes had a higher mortality than those in the recovering subphenotypes within each stratum (mild stratum, 29.7% vs. 10.3%, p = 0.033; intermediate stratum, 29.3% vs. 8.0%, p = 0.002; severe stratum, 53.7% vs. 22.2%, p < 0.001). Pathophysiologic biomarkers associated with progression were distinct at each stratum, including findings suggestive of inflammation in low baseline severity of illness versus hemophagocytic lymphohistiocytosis in higher baseline severity of illness. The findings suggest that there are clear worsening and recovering subphenotypes of COVID-19 respiratory failure after intubation, which are more predictive of outcomes than baseline severity of illness. Distinct progression biomarkers at differential baseline severity of illness suggests a heterogeneous pathobiology in the progression of COVID-19 respiratory failure.

Association of body mass index with morbidity in patients hospitalised with COVID-19

PURPOSE

To evaluate the association between body mass index (BMI) and clinical outcomes other than death in patients hospitalised and intubated with COVID-19.

METHODS

This is a single-centre cohort study of adults with COVID-19 admitted to New York Presbyterian Hospital-Weill Cornell Medicine from 3 March 2020 through 15 May 2020. Baseline and outcome variables, as well as lab and ventilatory parameters, were generated for the admitted and intubated cohorts after stratifying by BMI category. Linear regression models were used for continuous, and logistic regression models were used for categorical outcomes.

RESULTS

The study included 1337 admitted patients with a subset of 407 intubated patients. Among admitted patients, hospital length of stay (LOS) and home discharge was not significantly different across BMI categories independent of demographic characteristics and comorbidities. In the intubated cohort, there was no difference in in-hospital events and treatments, including renal replacement therapy, neuromuscular blockade and prone positioning. Ventilatory ratio was higher with increasing BMI on days 1, 3 and 7. There was no significant difference in ventilator free days (VFD) at 28 or 60 days, need for tracheostomy, hospital LOS, and discharge disposition based on BMI in the intubated cohort after adjustment.

CONCLUSIONS

In our COVID-19 population, there was no association between obesity and morbidity outcomes, such as hospital LOS, home discharge or VFD. Further research is needed to clarify the mechanisms underlying the reported effects of BMI on outcomes, which may be population dependent.

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.

Cytokine signatures of end organ injury in COVID-19

Increasing evidence has shown that Coronavirus disease 19 (COVID-19) severity is driven by a dysregulated immunologic response. We aimed to assess the differences in inflammatory cytokines in COVID-19 patients compared to contemporaneously hospitalized controls and then analyze the relationship between these cytokines and the development of Acute Respiratory Distress Syndrome (ARDS), Acute Kidney Injury (AKI) and mortality. In this cohort study of hospitalized patients, done between March third, 2020 and April first, 2020 at a quaternary referral center in New York City we included adult hospitalized patients with COVID-19 and negative controls. Serum specimens were obtained on the first, second, and third hospital day and cytokines were measured by Luminex. Autopsies of nine cohort patients were examined. We identified 90 COVID-19 patients and 51 controls. Analysis of 48 inflammatory cytokines revealed upregulation of macrophage induced chemokines, T-cell related interleukines and stromal cell producing cytokines in COVID-19 patients compared to the controls. Moreover, distinctive cytokine signatures predicted the development of ARDS, AKI and mortality in COVID-19 patients. Specifically, macrophage-associated cytokines predicted ARDS, T cell immunity related cytokines predicted AKI and mortality was associated with cytokines of activated immune pathways, of which IL-13 was universally correlated with ARDS, AKI and mortality. Histopathological examination of the autopsies showed diffuse alveolar damage with significant mononuclear inflammatory cell infiltration. Additionally, the kidneys demonstrated glomerular sclerosis, tubulointerstitial lymphocyte infiltration and cortical and medullary atrophy. These patterns of cytokine expression offer insight into the pathogenesis of COVID-19 disease, its severity, and subsequent lung and kidney injury suggesting more targeted treatment strategies.

Attributable mortality of acute respiratory distress syndrome: a systematic review, meta-analysis and survival analysis using targeted minimum loss-based estimation

BACKGROUND

Although acute respiratory distress syndrome (ARDS) is associated with high mortality, its direct causal link with death is unclear. Clarifying this link is important to justify costly research on prevention of ARDS.

OBJECTIVE

To estimate the attributable mortality, if any, of ARDS.

DESIGN

First, we performed a systematic review and meta-analysis of observational studies reporting mortality of critically ill patients with and without ARDS matched for underlying risk factor. Next, we conducted a survival analysis of prospectively collected patient-level data from subjects enrolled in three intensive care unit (ICU) cohorts to estimate the attributable mortality of critically ill septic patients with and without ARDS using a novel causal inference method.

RESULTS

In the meta-analysis, 44 studies (47 cohorts) involving 56 081 critically ill patients were included. Mortality was higher in patients with versus without ARDS (risk ratio 2.48, 95% CI 1.86 to 3.30; p<0.001) with a numerically stronger association between ARDS and mortality in trauma than sepsis. In the survival analysis of three ICU cohorts enrolling 1203 critically ill patients, 658 septic patients were included. After controlling for confounders, ARDS was found to increase the mortality rate by 15% (95% CI 3% to 26%; p=0.015). Significant increases in mortality were seen for severe (23%, 95% CI 3% to 44%; p=0.028) and moderate (16%, 95% CI 2% to 31%; p=0.031), but not for mild ARDS.

CONCLUSIONS

ARDS has a direct causal link with mortality. Our findings provide information about the extent to which continued funding of ARDS prevention trials has potential to impart survival benefit.

PROSPERO REGISTRATION NUMBER

CRD42017078313.

Critical carE Database for Advanced Research (CEDAR): An automated method to support intensive care units with electronic health record data

Patients treated in an intensive care unit (ICU) are critically ill and require life-sustaining organ failure support. Existing critical care data resources are limited to a select number of institutions, contain only ICU data, and do not enable the study of local changes in care patterns. To address these limitations, we developed the Critical carE Database for Advanced Research (CEDAR), a method for automating extraction and transformation of data from an electronic health record (EHR) system. Compared to an existing gold standard of manually collected data at our institution, CEDAR was statistically similar in most measures, including patient demographics and sepsis-related organ failure assessment (SOFA) scores. Additionally, CEDAR automated data extraction obviated the need for manual collection of 550 variables. Critically, during the spring 2020 COVID-19 surge in New York City, a modified version of CEDAR supported pandemic response efforts, including clinical operations and research. Other academic medical centers may find value in using the CEDAR method to automate data extraction from EHR systems to support ICU activities.

Hyperglycemia in Acute COVID-19 is Characterized by Adipose Tissue Dysfunction and Insulin Resistance

COVID-19 has proven to be a metabolic disease resulting in adverse outcomes in individuals with diabetes or obesity. Patients infected with SARS-CoV-2 and hyperglycemia suffer from longer hospital stays, higher risk of developing acute respiratory distress syndrome (ARDS), and increased mortality compared to those who do not develop hyperglycemia. Nevertheless, the pathophysiological mechanism(s) of hyperglycemia in COVID-19 remains poorly characterized. Here we show that insulin resistance rather than pancreatic beta cell failure is the prevalent cause of hyperglycemia in COVID-19 patients with ARDS, independent of glucocorticoid treatment. A screen of protein hormones that regulate glucose homeostasis reveals that the insulin sensitizing adipokine adiponectin is reduced in hyperglycemic COVID-19 patients. Hamsters infected with SARS-CoV-2 also have diminished expression of adiponectin. Together these data suggest that adipose tissue dysfunction may be a driver of insulin resistance and adverse outcomes in acute COVID-19.

Shotgun transcriptome, spatial omics, and isothermal profiling of SARS-CoV-2 infection reveals unique host responses, viral diversification, and drug interactions

In less than nine months, the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) killed over a million people, including >25,000 in New York City (NYC) alone. The COVID-19 pandemic caused by SARS-CoV-2 highlights clinical needs to detect infection, track strain evolution, and identify biomarkers of disease course. To address these challenges, we designed a fast (30-minute) colorimetric test (LAMP) for SARS-CoV-2 infection from naso/oropharyngeal swabs and a large-scale shotgun metatranscriptomics platform (total-RNA-seq) for host, viral, and microbial profiling. We applied these methods to clinical specimens gathered from 669 patients in New York City during the first two months of the outbreak, yielding a broad molecular portrait of the emerging COVID-19 disease. We find significant enrichment of a NYC-distinctive clade of the virus (20C), as well as host responses in interferon, ACE, hematological, and olfaction pathways. In addition, we use 50,821 patient records to find that renin-angiotensin-aldosterone system inhibitors have a protective effect for severe COVID-19 outcomes, unlike similar drugs. Finally, spatial transcriptomic data from COVID-19 patient autopsy tissues reveal distinct ACE2 expression loci, with macrophage and neutrophil infiltration in the lungs. These findings can inform public health and may help develop and drive SARS-CoV-2 diagnostic, prevention, and treatment strategies.

Association Between Early Treatment With Tocilizumab and Mortality Among Critically Ill Patients With COVID-19

IMPORTANCE

Therapies that improve survival in critically ill patients with coronavirus disease 2019 (COVID-19) are needed. Tocilizumab, a monoclonal antibody against the interleukin 6 receptor, may counteract the inflammatory cytokine release syndrome in patients with severe COVID-19 illness.

OBJECTIVE

To test whether tocilizumab decreases mortality in this population.

DESIGN, SETTING, AND PARTICIPANTS

The data for this study were derived from a multicenter cohort study of 4485 adults with COVID-19 admitted to participating intensive care units (ICUs) at 68 hospitals across the US from March 4 to May 10, 2020. Critically ill adults with COVID-19 were categorized according to whether they received or did not receive tocilizumab in the first 2 days of admission to the ICU. Data were collected retrospectively until June 12, 2020. A Cox regression model with inverse probability weighting was used to adjust for confounding.

EXPOSURES

Treatment with tocilizumab in the first 2 days of ICU admission.

MAIN OUTCOMES AND MEASURES

Time to death, compared via hazard ratios (HRs), and 30-day mortality, compared via risk differences.

RESULTS

Among the 3924 patients included in the analysis (2464 male [62.8%]; median age, 62 [interquartile range {IQR}, 52-71] years), 433 (11.0%) received tocilizumab in the first 2 days of ICU admission. Patients treated with tocilizumab were younger (median age, 58 [IQR, 48-65] vs 63 [IQR, 52-72] years) and had a higher prevalence of hypoxemia on ICU admission (205 of 433 [47.3%] vs 1322 of 3491 [37.9%] with mechanical ventilation and a ratio of partial pressure of arterial oxygen to fraction of inspired oxygen of <200 mm Hg) than patients not treated with tocilizumab. After applying inverse probability weighting, baseline and severity-of-illness characteristics were well balanced between groups. A total of 1544 patients (39.3%) died, including 125 (28.9%) treated with tocilizumab and 1419 (40.6%) not treated with tocilizumab. In the primary analysis, during a median follow-up of 27 (IQR, 14-37) days, patients treated with tocilizumab had a lower risk of death compared with those not treated with tocilizumab (HR, 0.71; 95% CI, 0.56-0.92). The estimated 30-day mortality was 27.5% (95% CI, 21.2%-33.8%) in the tocilizumab-treated patients and 37.1% (95% CI, 35.5%-38.7%) in the non-tocilizumab-treated patients (risk difference, 9.6%; 95% CI, 3.1%-16.0%).

CONCLUSIONS AND RELEVANCE

Among critically ill patients with COVID-19 in this cohort study, the risk of in-hospital mortality in this study was lower in patients treated with tocilizumab in the first 2 days of ICU admission compared with patients whose treatment did not include early use of tocilizumab. However, the findings may be susceptible to unmeasured confounding, and further research from randomized clinical trials is needed.

AKI Treated with Renal Replacement Therapy in Critically Ill Patients with COVID-19

BACKGROUND

AKI is a common sequela of coronavirus disease 2019 (COVID-19). However, few studies have focused on AKI treated with RRT (AKI-RRT).

METHODS

We conducted a multicenter cohort study of 3099 critically ill adults with COVID-19 admitted to intensive care units (ICUs) at 67 hospitals across the United States. We used multivariable logistic regression to identify patient-and hospital-level risk factors for AKI-RRT and to examine risk factors for 28-day mortality among such patients.

RESULTS

A total of 637 of 3099 patients (20.6%) developed AKI-RRT within 14 days of ICU admission, 350 of whom (54.9%) died within 28 days of ICU admission. Patient-level risk factors for AKI-RRT included CKD, men, non-White race, hypertension, diabetes mellitus, higher body mass index, higher d-dimer, and greater severity of hypoxemia on ICU admission. Predictors of 28-day mortality in patients with AKI-RRT were older age, severe oliguria, and admission to a hospital with fewer ICU beds or one with greater regional density of COVID-19. At the end of a median follow-up of 17 days (range, 1-123 days), 403 of the 637 patients (63.3%) with AKI-RRT had died, 216 (33.9%) were discharged, and 18 (2.8%) remained hospitalized. Of the 216 patients discharged, 73 (33.8%) remained RRT dependent at discharge, and 39 (18.1%) remained RRT dependent 60 days after ICU admission.

CONCLUSIONS

AKI-RRT is common among critically ill patients with COVID-19 and is associated with a hospital mortality rate of >60%. Among those who survive to discharge, one in three still depends on RRT at discharge, and one in six remains RRT dependent 60 days after ICU admission.

Heart rate variability measures for prediction of severity of illness and poor outcome in ED patients with sepsis

INTRODUCTION

This study evaluates the utility of heart rate variability (HRV) for assessment of severity of illness and poor outcome in Emergency Department (ED) patients with sepsis. HRV measures evaluated included low frequency (LF) signal, high frequency (HF) signal, and deviations in LF and HF signal from age-adjusted reference values.

METHODS

This was a prospective, observational study. Seventy-two adult ED patients were assessed within 6 h of arrival.

RESULTS

Severity of illness as defined by sepsis subtype correlated with decreased LF signal (sepsis: 70.68 ± 22.95, severe sepsis: 54.00 ± 28.41, septic shock: 45.54 ± 23.31, p = 0.02), increased HF signal (sepsis: 27.87 ± 19.42, severe sepsis: 44.63 ± 27.29, septic shock: 47.66 ± 20.98, p = 0.01), increasingly negative deviations in LF signal (sepsis: 0.41 ± 24.53, severe sepsis: -21.43 ± 30.09, septic shock -30.39 ± 26.09, p = 0.005) and increasingly positive deviations in HF signal (sepsis: -1.86 ± 21.09, severe sepsis: 20.07 ± 29.03, septic shock: 23.6 ± 24.17, p = 0.004). Composite poor outcome correlated with decreased LF signal (p = 0.008), increased HF signal (p = 0.03), large negative deviations in LF signal (p = 0.004) and large positive deviations in HF signal (p = 0.02). Deviations in LF and HF signal from age-adjusted reference values correlated with individual measures of poor outcome with greater consistency than LF or HF signal.

DISCUSSION

Accounting for the influence of age on baseline HRV signal improves the predictive value of HRV measures in ED patients with sepsis.

Factors Associated With Death in Critically Ill Patients With Coronavirus Disease 2019 in the US

Importance

The US is currently an epicenter of the coronavirus disease 2019 (COVID-19) pandemic, yet few national data are available on patient characteristics, treatment, and outcomes of critical illness from COVID-19.

Objectives

To assess factors associated with death and to examine interhospital variation in treatment and outcomes for patients with COVID-19.

Design, Setting, and Participants

This multicenter cohort study assessed 2215 adults with laboratory-confirmed COVID-19 who were admitted to intensive care units (ICUs) at 65 hospitals across the US from March 4 to April 4, 2020.

Exposures

Patient-level data, including demographics, comorbidities, and organ dysfunction, and hospital characteristics, including number of ICU beds.

Main Outcomes and Measures

The primary outcome was 28-day in-hospital mortality. Multilevel logistic regression was used to evaluate factors associated with death and to examine interhospital variation in treatment and outcomes.

Results

A total of 2215 patients (mean [SD] age, 60.5 [14.5] years; 1436 [64.8%] male; 1738 [78.5%] with at least 1 chronic comorbidity) were included in the study. At 28 days after ICU admission, 784 patients (35.4%) had died, 824 (37.2%) were discharged, and 607 (27.4%) remained hospitalized. At the end of study follow-up (median, 16 days; interquartile range, 8-28 days), 875 patients (39.5%) had died, 1203 (54.3%) were discharged, and 137 (6.2%) remained hospitalized. Factors independently associated with death included older age (≥80 vs <40 years of age: odds ratio [OR], 11.15; 95% CI, 6.19-20.06), male sex (OR, 1.50; 95% CI, 1.19-1.90), higher body mass index (≥40 vs <25: OR, 1.51; 95% CI, 1.01-2.25), coronary artery disease (OR, 1.47; 95% CI, 1.07-2.02), active cancer (OR, 2.15; 95% CI, 1.35-3.43), and the presence of hypoxemia (Pao2:Fio2<100 vs ≥300 mm Hg: OR, 2.94; 95% CI, 2.11-4.08), liver dysfunction (liver Sequential Organ Failure Assessment score of 2-4 vs 0: OR, 2.61; 95% CI, 1.30-5.25), and kidney dysfunction (renal Sequential Organ Failure Assessment score of 4 vs 0: OR, 2.43; 95% CI, 1.46-4.05) at ICU admission. Patients admitted to hospitals with fewer ICU beds had a higher risk of death (<50 vs ≥100 ICU beds: OR, 3.28; 95% CI, 2.16-4.99). Hospitals varied considerably in the risk-adjusted proportion of patients who died (range, 6.6%-80.8%) and in the percentage of patients who received hydroxychloroquine, tocilizumab, and other treatments and supportive therapies.

Conclusions and Relevance

This study identified demographic, clinical, and hospital-level risk factors that may be associated with death in critically ill patients with COVID-19 and can facilitate the identification of medications and supportive therapies to improve outcomes.

Anti-complement C5 therapy with eculizumab in three cases of critical COVID-19

Respiratory failure and acute kidney injury (AKI) are associated with high mortality in SARS-CoV-2-associated Coronavirus disease 2019 (COVID-19). These manifestations are linked to a hypercoaguable, pro-inflammatory state with persistent, systemic complement activation. Three critical COVID-19 patients recalcitrant to multiple interventions had skin biopsies documenting deposition of the terminal complement component C5b-9, the lectin complement pathway enzyme MASP2, and C4d in microvascular endothelium. Administration of anti-C5 monoclonal antibody eculizumab led to a marked decline in D-dimers and neutrophil counts in all three cases, and normalization of liver functions and creatinine in two. One patient with severe heart failure and AKI had a complete remission. The other two individuals had partial remissions, one with resolution of his AKI but ultimately succumbing to respiratory failure, and another with a significant decline in FiO2 requirements, but persistent renal failure. In conclusion, anti-complement therapy may be beneficial in at least some patients with critical COVID-19.