At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods

By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Early recognition can help provide life-saving targeted anti-coagulation therapy right at admission. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Age, overall presence of symptoms, shortness of breath, and hypertension were found to be key predictors for PE using our extreme gradient boosted model. This analysis based on the, until now, largest global dataset for this set of problems can inform hospital prioritisation policy and guide long term clinical research and decision-making for COVID-19 patients globally. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients

Long-term neurological symptoms after acute COVID-19 illness requiring hospitalization in adult patients: insights from the ISARIC-COVID-19 follow-up study

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Association of Country Income Level With the Characteristics and Outcomes of Critically Ill Patients Hospitalized With Acute Kidney Injury and COVID-19

Introduction: Acute kidney injury (AKI) has been identified as one of the most common and significant problems in hospitalized patients with COVID-19. However, studies examining the relationship between COVID-19 and AKI in low- and low-middle income countries (LLMIC) are lacking. Given that AKI is known to carry a higher mortality rate in these countries, it is important to understand differences in this population.Methods: This prospective, observational study examines the AKI incidence and characteristics of 32,210 patients with COVID-19 from 49 countries across all income levels who were admitted to an intensive care unit during their hospital stay.Results: Among patients with COVID-19 admitted to the intensive care unit, AKI incidence was highest in patients in LLMIC, followed by patients in upper-middle income countries (UMIC) and high-income countries (HIC) (53%, 38%, and 30%, respectively), whereas dialysis rates were lowest among patients with AKI from LLMIC and highest among those from HIC (27% vs. 45%). Patients with AKI in LLMIC had the largest proportion of community-acquired AKI (CA-AKI) and highest rate of in-hospital death (79% vs. 54% in HIC and 66% in UMIC). The association between AKI, being from LLMIC and in-hospital death persisted even after adjusting for disease severity.Conclusions: AKI is a particularly devastating complication of COVID-19 among patients from poorer nations where the gaps in accessibility and quality of healthcare delivery have a major impact on patient outcomes

At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods

By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Early recognition can help provide life-saving targeted anti-coagulation therapy right at admission. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Age, overall presence of symptoms, shortness of breath, and hypertension were found to be key predictors for PE using our extreme gradient boosted model. This analysis based on the, until now, largest global dataset for this set of problems can inform hospital prioritisation policy and guide long term clinical research and decision-making for COVID-19 patients globally. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients

Implementation of recommendations on the use of corticosteroids in severe COVID-19

Importance Research diversity and representativeness are paramount in building trust, generating valid biomedical knowledge, and possibly in implementing clinical guidelines. Objectives To compare variations over time and across World Health Organization (WHO) geographic regions of corticosteroid use for treatment of severe COVID-19; secondary objectives were to evaluate the association between the timing of publication of the RECOVERY (Randomised Evaluation of COVID-19 Therapy) trial (June 2020) and the WHO guidelines for corticosteroids (September 2020) and the temporal trends observed in corticosteroid use by region and to describe the geographic distribution of the recruitment in clinical trials that informed the WHO recommendation. Design, Setting, and Participants This prospective cohort study of 434 851 patients was conducted between January 31, 2020, and September 2, 2022, in 63 countries worldwide. The data were collected under the auspices of the International Severe Acute Respiratory and Emerging Infections Consortium (ISARIC)–WHO Clinical Characterisation Protocol for Severe Emerging Infections. Analyses were restricted to patients hospitalized for severe COVID-19 (a subset of the ISARIC data set). Exposure Corticosteroid use as reported to the ISARIC-WHO Clinical Characterisation Protocol for Severe Emerging Infections. Main Outcomes and Measures Number and percentage of patients hospitalized with severe COVID-19 who received corticosteroids by time period and by WHO geographic region. Results Among 434 851 patients with confirmed severe or critical COVID-19 for whom receipt of corticosteroids could be ascertained (median [IQR] age, 61.0 [48.0-74.0] years; 53.0% male), 174 307 (40.1%) received corticosteroids during the study period. Of the participants in clinical trials that informed the guideline, 91.6% were recruited from the United Kingdom. In all regions, corticosteroid use for severe COVID-19 increased, but this increase corresponded to the timing of the RECOVERY trial (time-interruption coefficient 1.0 [95% CI, 0.9-1.2]) and WHO guideline (time-interruption coefficient 1.9 [95% CI, 1.7-2.0]) publications only in Europe. At the end of the study period, corticosteroid use for treatment of severe COVID-19 was highest in the Americas (5421 of 6095 [88.9%]; 95% CI, 87.7-90.2) and lowest in Africa (31 588 of 185 191 [17.1%]; 95% CI, 16.8-17.3). Conclusions and Relevance The results of this cohort study showed that implementation of the guidelines for use of corticosteroids in the treatment of severe COVID-19 varied geographically. Uptake of corticosteroid treatment was lower in regions with limited clinical trial involvement. Improving research diversity and representativeness may facilitate timely knowledge uptake and guideline implementation

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

Sex differences in post-acute neurological sequelae of SARS-CoV-2 and symptom resolution in adults after COVID-19 hospitalization: An international multicenter prospective observational study

Although it is known that COVID-19 can present with a range of neurological manifestations and in-hospital complications, sparse data exist if these initial neurological symptoms of COVID-19 are closely associated with post-acute neurological sequelae of SARS-CoV-2 (PANSC) and if female versus male sex impacts the symptom resolution. In this international, multicentre, prospective observational study across 407 sites from 15 countries (January/30th/2020-April/30th/2022), we report the prevalence and risk factors of PANSC among hospitalized adults and investigate the differences between males and females on neurological symptom resolution over time. PANSC included altered consciousness/confusion, fatigue/malaise, anosmia, dysgeusia, and muscle aches/joint pain, which were collected at the index hospitalization and during the follow-up assessments. The analysis considered time to resolution of individual and all neurological symptoms. Resulting times were modeled by Weibull regression, assuming mixed-case interval censoring, with sex and age included as covariates. Model results were summarized as cumulative probability functions and age- and sex-adjusted median times to resolution. We included 6,862 hospitalized adults with COVID-19, who had follow-up assessments. The median age of participants was 57 years (39.2% females). Males and females had similar baseline characteristics except that more males (vs. females) were admitted to Intensive Care Unit (30.5% vs. 20.3%) and received mechanical ventilation (17.2% vs. 11.8%). Approximately 70% of patients had multiple neurological symptoms at the first follow-up (median=102 days). Fatigue (49.9%) and myalgia/arthralgia (45.2%) were the most prevalent symptoms of PANSC at the initial follow-up. Reported prevalence in females was generally higher (vs. males) for all symptoms. At 12 months, anosmia and dysgeusia were resolved in most patients, though fatigue, altered consciousness, and myalgia remained unresolved in &gt;10% of the cohort. Females had a longer time to resolution (5.2 vs. 3.4 months) of neurological symptoms at follow-up for those with more than one neurological symptom. In multivariable analysis, males were associated with a shorter time to resolution of symptoms (Hazard Ratio=1.53; 95% Confidence Interval =1.39–1.69). Intensive Care Unit admission was associated with a longer time to the resolution of symptoms (Hazard Ratio =0.68; 95% Confidence Interval=0.60–0.77). Post-discharge stroke was uncommon (0.3% in females; 0.5% in males). Despite the methodological challenges of survey data, this international multicentre prospective cohort study demonstrates that PANSC following index hospitalization is high. Symptom prevalence was higher and took longer to resolve in females than in males. This supports that whilst males were sicker during acute illness, females were disproportionately affected by PANSC

ISARIC-COVID-19 dataset: A Prospective, Standardized, Global Dataset of Patients Hospitalized with COVID-19.

The International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) COVID-19 dataset is one of the largest international databases of prospectively collected clinical data on people hospitalized with COVID-19. This dataset was compiled during the COVID-19 pandemic by a network of hospitals that collect data using the ISARIC-World Health Organization Clinical Characterization Protocol and data tools. The database includes data from more than 705,000 patients, collected in more than 60 countries and 1,500 centres worldwide. Patient data are available from acute hospital admissions with COVID-19 and outpatient follow-ups. The data include signs and symptoms, pre-existing comorbidities, vital signs, chronic and acute treatments, complications, dates of hospitalization and discharge, mortality, viral strains, vaccination status, and other data. Here, we present the dataset characteristics, explain its architecture and how to gain access, and provide tools to facilitate its use