Cardiovascular disease and severe hypoxemia associated with higher rates of non-invasive respiratory support failure in COVID-19

RationaleAcute hypoxemic respiratory failure (AHRF) is the major complication of coronavirus disease 2019 (COVID-19), yet optimal respiratory support strategies are uncertain. ObjectivesTo describe outcomes with high-flow oxygen delivered through nasal cannula (HFNC) and non-invasive positive pressure ventilation (NIPPV) in COVID-19 AHRF and identify individual factors associated with failure. MethodsWe performed a retrospective cohort study of hospitalized adults with COVID-19 treated with HFNC and/or NIPPV to describe rates of success (live discharge without endotracheal intubation (ETI)), and identify characteristics associated with failure (ETI and/or in-hospital mortality) using Fine-Gray sub-distribution hazard models. ResultsA total of 331 and 747 patients received HFNC and NIPPV as the highest level of non-invasive respiratory support, respectively; 154 (46.5%) in the HFNC cohort and 167 (22.4%) in the NIPPV cohort were successfully discharged without requiring ETI. In adjusted models, significantly increased risk of HFNC and NIPPV failure was seen among patients with cardiovascular disease (subdistribution hazard ratio (sHR) 1.82; 95% confidence interval (CI), 1.17-2.83 and sHR 1.40; 95% CI 1.06-1.84), respectively, and among those with lower oxygen saturation to fraction of inspired oxygen (SpO2/FiO2) ratio at HFNC and NIPPV initiation (sHR, 0.32; 95% CI 0.19-0.54, and sHR 0.34; 95% CI 0.21-0.55, respectively). ConclusionsA significant proportion of patients receiving non-invasive respiratory modalities for COVID-19 AHRF achieved successful discharge without requiring ETI, with lower success rates among those with cardiovascular disease or more severe hypoxemia. The role of non-invasive respiratory modalities in COVID-19 related AHRF requires further consideration.

Development and calibration of a simple mortality risk score for hospitalized COVID-19 adults

ObjectivesMortality risk scores, such as SOFA, qSOFA, and CURB-65, are quick, effective tools for communicating a patients prognosis and guiding therapeutic decisions. Most use simple calculations that can be performed by hand. While several COVID-19 specific risk scores exist, they lack the ease of use of these simpler scores. The objectives of this study were (1) to design, validate, and calibrate a simple, easy-to-use mortality risk score for COVID-19 patients and (2) to recalibrate SOFA, qSOFA, and CURB-65 in a hospitalized COVID-19 population. DesignRetrospective cohort study incorporating demographic, clinical, laboratory, and admissions data from electronic health records. SettingMulti-hospital health system in New York City. Five hospitals were included: one quaternary care facility, one tertiary care facility, and three community hospitals. ParticipantsPatients (n=4840) with laboratory-confirmed SARS-CoV2 infection who were admitted between March 1 and April 28, 2020. Main outcome measuresGrays K-sample test for the cumulative incidence of a competing risk was used to assess and rank 48 different variables associations with mortality. Candidate variables were added to the composite score using DeLongs test to evaluate their effect on predictive performance (AUC) of in-hospital mortality. Final AUCs for the new score, SOFA, qSOFA, and CURB-65 were assessed on an independent test set. ResultsOf 48 variables investigated, 36 (75%) displayed significant (p<0.05 by Grays test) associations with mortality. The variables selected for the final score were (1) oxygen support level, (2) troponin, (3) blood urea nitrogen, (4) lymphocyte percentage, (5) Glasgow Coma Score, and (6) age. The new score, COBALT, outperforms SOFA, qSOFA, and CURB-65 at predicting mortality in this COVID-19 population: AUCs for initial, maximum, and mean COBALT scores were 0.81, 0.91, and 0.92, compared to 0.77, 0.87, and 0.87 for SOFA. We provide COVID-19 specific mortality estimates at all score levels for COBALT, SOFA, qSOFA, and CURB-65. ConclusionsThe COBALT score provides a simple way to estimate mortality risk in hospitalized COVID-19 patients with superior performance to SOFA and other scores currently in widespread use. Evaluation of SOFA, qSOFA, and CURB-65 in this population highlights the importance of recalibrating mortality risk scores when they are used under novel conditions, such as the COVID-19 pandemic. This studys approach to score design could also be applied in other contexts to create simple, practical and high-performing mortality risk scores. Trial registrationNA Funding sourceThe authors declare that there was no external funding provided. Summary boxO_ST_ABSWhat is already known on this topicC_ST_ABSO_LIMortality risk scores are widely used in clinical settings to facilitate communication with patients and families, guide goals of care discussions, and optimize resource allocation. C_LIO_LIAlthough popular mortality risk scores like SOFA, qSOFA, and CURB-65 are routinely used in COVID-19 populations, they were originally calibrated in different contexts and their true performance among hospitalized COVID-19 patients is unknown. C_LIO_LISeveral dedicated COVID-19 mortality risk scores have been created during the 2019-2020 pandemic, but all use complicated formulae or machine learning algorithms and are difficult or impossible to calculate by hand, limiting their applicability at the bedside. C_LI What this study addsO_LIWe describe a data-driven, simple, and hand-calculable COVID-specific mortality risk score (COBALT) that has superior performance to SOFA, qSOFA, and CURB-65 in a hospitalized COVID-19 patient population. C_LIO_LIWe provide COVID-specific mortality estimates for SOFA, qSOFA, and CURB-65 using data from 4840 patients in a large and diverse New York City multihospital health system. C_LI

COVID-19 Hospitalization is More Frequent and Severe in Down Syndrome

BackgroundIndividuals with rare disorders, like Down syndrome (DS) are historically understudied. Currently, it is not known how COVID-19 pandemic affects individuals with DS. Herein, we report an analysis of individuals with DS who were hospitalized with COVID-19 in the Mount Sinai Health System in New York City, USA. MethodsIn this retrospective, single-center study of 4,615 patients hospitalized with COVID-19, we analyzed all patients with DS admitted in the Mount Sinai Health System. Hospitalization rates, clinical and outcomes were assessed. FindingsContrary to an expected number of one, we identified six patients with DS. We found that patients with DS are at an 8.9-fold higher risk of hospitalization with COVID-19 when compared to non-DS patients. Hospitalized DS individuals are on average 10 years younger than non-DS patients with COVID-19. Moreover, type 2 diabetes mellitus appears to be an important driver of this susceptibility to COVID-19. Finally, patients with DS have more severe outcomes than controls, and are more likely to progress to sepsis in particular. InterpretationWe demonstrate that individuals with DS represent a higher risk population for COVID-19 compared to the general population and conclude that particular care should be taken for both the prevention and treatment of COVID-19 in these patients. FundingNational Institute of Allergy and Infectious Diseases. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed and Google Scholar on May 26, 2020, for articles describing the features of patients in Down syndrome infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), using the search terms "SARS-CoV-2" or "COVID-19" and "Down syndrome" or "Trisomy 21." We found only one case report describing a cluster of 4 cases of COVID-19 in a healthcare facility for patients with mental retardation. Added value of this studyWe compared the hospitalization rates of DS patients to over 4,500 individuals without DS, and we assessed comorbidities and outcomes of individuals with DS compared to age, race, and sex-matched controls hospitalized with COVID-19. To the best of our knowledge, we provide the first evidence that patients with DS with are at higher risk of hospitalization with COVID-19 and more severe disease progression than non-DS patients. Implications of all the available evidenceWe demonstrate that individuals with DS are a high-risk population for COVID-19 and suggest appropriate measures should be taken for both the prevention and treatment of COVID-19 in these patients.

Machine Learning to Predict Mortality and Critical Events in COVID-19 Positive New York City Patients

Coronavirus 2019 (COVID-19), caused by the SARS-CoV-2 virus, has become the deadliest pandemic in modern history, reaching nearly every country worldwide and overwhelming healthcare institutions. As of April 20, there have been more than 2.4 million confirmed cases with over 160,000 deaths. Extreme case surges coupled with challenges in forecasting the clinical course of affected patients have necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods for achieving this are lacking. In this paper, we use electronic health records from over 3,055 New York City confirmed COVID-19 positive patients across five hospitals in the Mount Sinai Health System and present a decision tree-based machine learning model for predicting in-hospital mortality and critical events. This model is first trained on patients from a single hospital and then externally validated on patients from four other hospitals. We achieve strong performance, notably predicting mortality at 1 week with an AUC-ROC of 0.84. Finally, we establish model interpretability by calculating SHAP scores to identify decisive features, including age, inflammatory markers (procalcitonin and LDH), and coagulation parameters (PT, PTT, D-Dimer). To our knowledge, this is one of the first models with external validation to both predict outcomes in COVID-19 patients with strong validation performance and identify key contributors in outcome prediction that may assist clinicians in making effective patient management decisions. One-Sentence SummaryWe identify clinical features that robustly predict mortality and critical events in a large cohort of COVID-19 positive patients in New York City.