Characteristics and outcomes of 627 044 COVID-19 patients living with and without obesity in the United States, Spain, and the United Kingdom

Transforming the Information System for Research in Primary Care (SIDIAP) in Catalonia to the OMOP Common Data Model and Its Use for COVID-19 Research

Purpose

The primary aim of this work was to convert the Information System for Research in Primary Care (SIDIAP) from Catalonia, Spain, to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). Our second aim was to provide a descriptive analysis of COVID-19-related outcomes among the general population.

Patients and Methods

We mapped patient-level data from SIDIAP to the OMOP CDM and we performed more than 3,400 data quality checks to assess its readiness for research. We established a general population cohort as of the 1st March 2020 and identified outpatient COVID-19 diagnoses or tested positive for, hospitalised with, admitted to intensive care units (ICU) with, died with, or vaccinated against COVID-19 up to 30th June 2022.

Results

After verifying the high quality of the transformed dataset, we included 5,870,274 individuals in the general population cohort. Of those, 604,472 had either an outpatient COVID-19 diagnosis or positive test result, 58,991 had a hospitalisation, 5,642 had an ICU admission, and 11,233 died with COVID-19. A total of 4,584,515 received a COVID-19 vaccine. People who were hospitalised or died were more commonly older, male, and with more comorbidities. Those admitted to ICU with COVID-19 were generally younger and more often male than those hospitalised and those who died.

Conclusion

We successfully transformed SIDIAP to the OMOP CDM. From this dataset, a general population cohort of 5.9 million individuals was identified and their COVID-19-related outcomes over time were described. The transformed SIDIAP database is a valuable resource that can enable distributed network research in COVID-19 and beyond.

Mapping the Oncological Basis Dataset to the Standardized Vocabularies of a Common Data Model: A Feasibility Study

In their joint effort against cancer, all involved parties within the German healthcare system are obligated to report diagnostics, treatments, progression, and follow-up information for tumor patients to the respective cancer registries. Given the federal structure of Germany, the oncological basis dataset (oBDS) operates as the legally required national standard for oncological reporting. Unfortunately, the usage of various documentation software solutions leads to semantic and technical heterogeneity of the data, complicating the establishment of research networks and collective data analysis. Within this feasibility study, we evaluated the transferability of all oBDS characteristics to the standardized vocabularies, a metadata repository of the observational medical outcomes partnership (OMOP) common data model (CDM). A total of 17,844 oBDS expressions were mapped automatically or manually to standardized concepts of the OMOP CDM. In a second step, we converted real patient data retrieved from the Hamburg Cancer Registry to the new terminologies. Given our pipeline, we transformed 1773.373 cancer-related data elements to the OMOP CDM. The mapping of the oBDS to the standardized vocabularies of the OMOP CDM promotes the semantic interoperability of oncological data in Germany. Moreover, it allows the participation in network studies of the observational health data sciences and informatics under the usage of federated analysis beyond the level of individual countries.

Influence of obesity on incidence of thrombosis and disease severity in patients with COVID-19: From the CLOT-COVID study

BACKGROUND

The influence of obesity on the development of thrombosis and severity of coronavirus disease 2019 (COVID-19) remains unclear.

METHOD

The CLOT-COVID study was a retrospective multicenter cohort study enrolling 2894 consecutive hospitalized patients with COVID-19 between April 2021 and September 2021 among 16 centers in Japan. The present study consisted of 2690 patients aged over 18 years with available body mass index (BMI), who were divided into an obesity group (BMI ≥30) (N = 457) and a non-obesity group (BMI <30) (N = 2233).

RESULTS

The obesity group showed more severe status of COVID-19 at admission compared with the non-obesity group. The incidence of thrombosis was not significantly different between the groups (obesity group: 2.6 % versus non-obesity group: 1.9 %, p = 0.39), while the incidence of a composite outcome of all-cause death, or requirement of mechanical ventilation or extracorporeal membrane oxygenation during hospitalization was significantly higher in the obesity group (20.1 % versus 15.0 %, p < 0.01). After adjusting confounders in the multivariable logistic regression model, the risk of obesity relative to non-obesity for thrombosis was not significant (adjusted OR, 1.39; 95 % CI, 0.68-2.84, p = 0.37), while the adjusted risk of obesity relative to non-obesity for the composite outcome was significant (adjusted OR, 1.85; 95 % CI, 1.39-2.47, p < 0.001).

CONCLUSIONS

In the present large-scale observational study, obesity was not significantly associated with the development of thrombosis during hospitalization; however, it was associated with severity of COVID-19.

Characteristics and outcomes of COVID-19 patients with and without asthma from the United States, South Korea, and Europe

Objective: Large international comparisons describing the clinical characteristics of patients with COVID-19 are limited. The aim of the study was to perform a large-scale descriptive characterization of COVID-19 patients with asthma.Methods: We included nine databases contributing data from January to June 2020 from the US, South Korea (KR), Spain, UK and the Netherlands. We defined two cohorts of COVID-19 patients ('diagnosed' and 'hospitalized') based on COVID-19 disease codes. We followed patients from COVID-19 index date to 30 days or death. We performed descriptive analysis and reported the frequency of characteristics and outcomes in people with asthma defined by codes and prescriptions.Results: The diagnosed and hospitalized cohorts contained 666,933 and 159,552 COVID-19 patients respectively. Exacerbation in people with asthma was recorded in 1.6-8.6% of patients at presentation. Asthma prevalence ranged from 6.2% (95% CI 5.7-6.8) to 18.5% (95% CI 18.2-18.8) in the diagnosed cohort and 5.2% (95% CI 4.0-6.8) to 20.5% (95% CI 18.6-22.6) in the hospitalized cohort. Asthma patients with COVID-19 had high prevalence of comorbidity including hypertension, heart disease, diabetes and obesity. Mortality ranged from 2.1% (95% CI 1.8-2.4) to 16.9% (95% CI 13.8-20.5) and similar or lower compared to COVID-19 patients without asthma. Acute respiratory distress syndrome occurred in 15-30% of hospitalized COVID-19 asthma patients.Conclusion: The prevalence of asthma among COVID-19 patients varies internationally. Asthma patients with COVID-19 have high comorbidity. The prevalence of asthma exacerbation at presentation was low. Whilst mortality was similar among COVID-19 patients with and without asthma, this could be confounded by differences in clinical characteristics. Further research could help identify high-risk asthma patients.[Box: see text]Supplemental data for this article is available online at https://doi.org/10.1080/02770903.2021.2025392 .

Effect of Obesity on Clinical Outcomes in COVID-19 Patients

Background Obesity is a well-known risk factor for developing severe coronavirus disease 2019 (COVID-19). In this study, we sought to determine the relationship between obesity and poor outcomes in patients with COVID-19 patients at King Abdulaziz University Hospital (KAUH), Jeddah, Saudi Arabia. Methods We conducted a single-centered descriptive study of adult COVID-19 patients hospitalized between March 1 and December 31, 2020, at KAUH. Patients were classified according to body mass index (BMI) as overweight (BMI 25-29.9 kg/m²) or obese (BMI ≥30 kg/m²). The main outcomes were admission to the intensive care unit (ICU), intubation, and death. Results Data were analyzed from 300 COVID-19 patients. Most study participants were overweight (61.8%), and 38.2% were obese. The most significant comorbidities were diabetes (46.8%) and hypertension (41.9%). Both hospital mortality (10.4% for obese; 3.8% for overweight, p = 0.021) and intubation rates (34.6% for obese; 22.7% for overweight, p = 0.004) were significantly higher among obese patients than overweight patients. There was no significant difference in terms of ICU admission rate between both groups. However, intubation rates (34.6% for obese; 22.7% for overweight, p = 0.004) and hospital mortality (10.4% for obese; 3.8% for overweight, p = 0.021) were significantly higher among obese patients than overweight patients. Conclusions This study aimed to describe the effect of high BMI on the clinical outcome of COVID-19 patients in Saudi Arabia. Obesity is significantly correlated with poor clinical outcomes in COVID-19. It is also associated with higher mortality and the need for mechanical ventilation necessitating intensive care unit admission. Patients with higher BMI should be prioritized in the hospital setting, as they have a higher potential of developing severe COVID-19 complications and sequelae.

The US Food and Drug Administration Sentinel System: a national resource for a learning health system

The US Food and Drug Administration (FDA) created the Sentinel System in response to a requirement in the FDA Amendments Act of 2007 that the agency establish a system for monitoring risks associated with drug and biologic products using data from disparate sources. The Sentinel System has completed hundreds of analyses, including many that have directly informed regulatory decisions. The Sentinel System also was designed to support a national infrastructure for a learning health system. Sentinel governance and guiding principles were designed to facilitate Sentinel's role as a national resource. The Sentinel System infrastructure now supports multiple non-FDA projects for stakeholders ranging from regulated industry to other federal agencies, international regulators, and academics. The Sentinel System is a working example of a learning health system that is expanding with the potential to create a global learning health system that can support medical product safety assessments and other research.

A Calculator for COVID-19 Severity Prediction Based on Patient Risk Factors and Number of Vaccines Received

Vaccines have allowed for a significant decrease in COVID-19 risk, and new antiviral medications can prevent disease progression if given early in the course of the disease. The rapid and accurate estimation of the risk of severe disease in new patients is needed to prioritize the treatment of high-risk patients and maximize lives saved. We used electronic health records from 101,039 individuals infected with SARS-CoV-2, since the beginning of the pandemic and until 30 November 2021, in a national healthcare organization in Israel to build logistic models estimating the probability of subsequent hospitalization and death of newly infected patients based on a few major risk factors (age, sex, body mass index, hemoglobin A1C, kidney function, and the presence of hypertension, pulmonary disease, and malignancy) and the number of BNT162b2 mRNA vaccine doses received. The model’s performance was assessed by 10-fold cross-validation: the area under the curve was 0.889 for predicting hospitalization and 0.967 for predicting mortality. A total of 50%, 80%, and 90% of death events could be predicted with respective specificities of 98.6%, 95.2%, and 91.2%. These models enable the rapid identification of individuals at high risk for hospitalization and death when infected, and they can be used to prioritize patients to receive scarce medications or booster vaccination. The calculator is available online.

Association of Obesity With COVID-19 Severity and Mortality: An Updated Systemic Review, Meta-Analysis, and Meta-Regression

Background

Obesity affects the course of critical illnesses. We aimed to estimate the association of obesity with the severity and mortality in coronavirus disease 2019 (COVID-19) patients.

Data Sources

A systematic search was conducted from the inception of the COVID-19 pandemic through to 13 October 2021, on databases including Medline (PubMed), Embase, Science Web, and Cochrane Central Controlled Trials Registry. Preprint servers such as BioRxiv, MedRxiv, ChemRxiv, and SSRN were also scanned.

Study Selection and Data Extraction

Full-length articles focusing on the association of obesity and outcome in COVID-19 patients were included. Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines were used for study selection and data extraction. Our Population of interest were COVID-19 positive patients, obesity is our Intervention/Exposure point, Comparators are Non-obese vs obese patients The chief outcome of the study was the severity of the confirmed COVID-19 positive hospitalized patients in terms of admission to the intensive care unit (ICU) or the requirement of invasive mechanical ventilation/intubation with obesity. All-cause mortality in COVID-19 positive hospitalized patients with obesity was the secondary outcome of the study.

Results

In total, 3,140,413 patients from 167 studies were included in the study. Obesity was associated with an increased risk of severe disease (RR=1.52, 95% CI 1.41-1.63, p<0.001, I2 = 97%). Similarly, high mortality was observed in obese patients (RR=1.09, 95% CI 1.02-1.16, p=0.006, I2 = 97%). In multivariate meta-regression on severity, the covariate of the female gender, pulmonary disease, diabetes, older age, cardiovascular diseases, and hypertension was found to be significant and explained R2 = 40% of the between-study heterogeneity for severity. The aforementioned covariates were found to be significant for mortality as well, and these covariates collectively explained R2 = 50% of the between-study variability for mortality.

Conclusions

Our findings suggest that obesity is significantly associated with increased severity and higher mortality among COVID-19 patients. Therefore, the inclusion of obesity or its surrogate body mass index in prognostic scores and improvement of guidelines for patient care management is recommended.

Extracting Patient-level Social Determinants of Health into the OMOP Common Data Model

Deficiencies in data sharing capabilities limit Social Determinants of Health (SDoH) analysis as part of COVID-19 research. The National COVID Cohort Collaborative (N3C) is an example of an Electronic Health Record (EHR) database of patients tested for COVID-19 that could benefit from a SDoH elements framework that captures various screening instruments in EHR data warehouse systems. This paper uses the University of Washington Enterprise Data Warehouse (a data contributor to N3C) to demonstrate how SDoH can be represented and managed to be made available within an OMOP common data model. We found that these data varied by type of social determinants data and where it was collected, in the time period that it was collected, and in how it was represented.

Mechanisms contributing to adverse outcomes of COVID-19 in obesity

A growing amount of epidemiological data from multiple countries indicate an increased prevalence of obesity, more importantly central obesity, among hospitalized subjects with COVID-19. This suggests that obesity is a major factor contributing to adverse outcome of the disease. As it is a metabolic disorder with dysregulated immune and endocrine function, it is logical that dysfunctional metabolism contributes to the mechanisms behind obesity being a risk factor for adverse outcome in COVID-19. Emerging data suggest that in obese subjects, (a) the molecular mechanisms of viral entry and spread mediated through ACE2 receptor, a multifunctional host cell protein which links to cellular homeostasis mechanisms, are affected. This includes perturbation of the physiological renin-angiotensin system pathway causing pro-inflammatory and pro-thrombotic challenges (b) existent metabolic overload and ER stress-induced UPR pathway make obese subjects vulnerable to severe COVID-19, (c) host cell response is altered involving reprogramming of metabolism and epigenetic mechanisms involving microRNAs in line with changes in obesity, and (d) adiposopathy with altered endocrine, adipokine, and cytokine profile contributes to altered immune cell metabolism, systemic inflammation, and vascular endothelial dysfunction, exacerbating COVID-19 pathology. In this review, we have examined the available literature on the underlying mechanisms contributing to obesity being a risk for adverse outcome in COVID-19.

Predicting COVID-19 severity using major risk factors and received vaccines

BACKGROUND

Vaccines are highly effective in preventing severe disease and death from COVID-19, and new medications that can reduce severity of disease have been approved. However, many countries are facing limited supply of vaccine doses and medications. A model estimating the probabilities for hospitalization and mortality according to individual risk factors and vaccine doses received could help prioritize vaccination and yet scarce medications to maximize lives saved and reduce the burden on hospitalization facilities.

METHODS

Electronic health records from 101,039 individuals infected with SARS-CoV-2, since the beginning of the pandemic and until November 30, 2021 were extracted from a national healthcare organization in Israel. Logistic regression models were built to estimate the risk for subsequent hospitalization and death based on the number of BNT162b2 mRNA vaccine doses received and few major risk factors (age, sex, body mass index, hemoglobin A1C, kidney function, and presence of hypertension, pulmonary disease and malignancy).

RESULTS

The models built predict the outcome of newly infected individuals with remarkable accuracy: area under the curve was 0.889 for predicting hospitalization, and 0.967 for predicting mortality. Even when a breakthrough infection occurs, having received three vaccination doses significantly reduces the risk of hospitalization by 66% (OR=0.339) and of death by 78% (OR=0.223).

CONCLUSIONS

The models enable rapid identification of individuals at high risk for hospitalization and death when infected. These patients can be prioritized to receive booster vaccination and the yet scarce medications. A calculator based on these models is made publicly available on http://covidest.web.app.

Association Between Obesity and COVID-19 Disease Severity in Saudi Population

BACKGROUND

The persistent coronavirus disease 2019 (COVID-19) outbreak has placed a significant burden on the scientific and medical professions. The study examined the association between body mass index (BMI), stratified by category, and severe form of COVID-19, and to explore the influence of demographic characteristics and other known risk factors.

METHODS

This was a retrospective analysis based on COVID-19 data from the Saudi Arabian Ministry of Health. Data were collected for all patients admitted to three main hospitals in Riyadh region between March 1st and July 30, 2020. The effects of BMI, demographic characteristics, clinical presentation, and comorbidities on infection severity were investigated.

RESULTS

A total of 950 patients were included in the study (70% male, 85% aged younger than 60 years old). A total of 55 (5.8%) patients were underweight, 263 (27.7%) were normal weight, 351 (37%) were overweight, 161 (17%) were obese class I, 76 (8%) were obese class II, and 44 (4.6%) were obese class III. Cough, fever, and shortness of breath were the most common symptoms among overweight patients. According to the findings of a bivariate logistic regression study, class III obesity was significantly associated with a more severe form of COVID-19 (odds ratio, 2.874; 95% confidence interval, 1.344-6.149).

CONCLUSION

This study revealed that patients with a BMI ≥40 kg/m² had a higher risk of severe COVID-19 than those with normal weight. This suggests that obesity is a risk factor for severe COVID-19 and influences disease presentation.

Association Between Obesity and Lower Short- and Long-Term Mortality in Coronary Care Unit Patients: A Cohort Study of the MIMIC-III Database

INTRODUCTION

Obesity has long been considered an independent risk factor for cardiovascular diseases (CVD), even in the COVID-19 pandemic. However, recent studies have found that a certain degree of obesity may be beneficial for patients who have already suffered from CVD, which is called the "obesity paradox". Our objective was to investigate whether the obesity paradox existed in coronary care unit (CCU) patients and the relationship between body mass index (BMI) and short- and long-term mortality.

METHODS

We performed a cohort analysis of 3,502 adult CCU patients from the Medical Information Mart for Intensive Care III (MIMIC-III) database. The patients were divided into four groups according to the WHO BMI categories. Both multivariable logistic regression and Cox regression were used to reveal the relation between BMI and mortality. Subgroup analyses were performed based on Simplified Acute Physiology Score (SAPS) and age.

RESULTS

After adjusting for confounders, obese patients had 33% and 30% lower mortality risk at 30-day and 1-year (OR 0.67, 95% CI 0.51 to 0.89; HR 0.70, 95% CI 0.59 to 0.83; respectively) compared with normal-weight patients, while the underweight group were opposite, with 141% and 81% higher in short- and long-term (OR 2.41, 95% CI 1.37 to 4.12; HR 1.81, 95% CI 1.34 to 2.46; respectively). Overweight patients did not have a significant survival advantage at 30-day (OR 0.91, 95% CI 0.70 to 1.17), but did have a 22% lower mortality risk at 1-year (HR 0.78; 95% CI 0.67 to 0.91). The results were consistent after being stratified by SAPS and age.

CONCLUSION

Our study supports that obesity improved survival at both 30-day and 1-year after CCU admission, and the obesity paradox existed in CCU patients.

Causal associations between body fat accumulation and COVID-19 severity: A Mendelian randomization study

Previous studies reported associations between obesity measured by body mass index (BMI) and coronavirus disease 2019 (COVID-19). However, BMI is calculated only with height and weight and cannot distinguish between body fat mass and fat-free mass. Thus, it is not clear if one or both of these measures are mediating the relationship between obesity and COVID-19. Here, we used Mendelian randomization (MR) to compare the independent causal relationships of body fat mass and fat-free mass with COVID-19 severity. We identified single nucleotide polymorphisms associated with body fat mass and fat-free mass in 454,137 and 454,850 individuals of European ancestry from the UK Biobank, respectively. We then performed two-sample MR to ascertain their effects on severe COVID-19 (cases: 4,792; controls: 1,054,664) from the COVID-19 Host Genetics Initiative. We found that an increase in body fat mass by one standard deviation was associated with severe COVID-19 (odds ratio (OR)_{body fat mass} = 1.61, 95% confidence interval [CI]: 1.28-2.04, P = 5.51 × 10^-5; OR_{body fat-free mass} = 1.31, 95% CI: 0.99-1.74, P = 5.77 × 10^-2). Considering that body fat mass and fat-free mass were genetically correlated with each other (r = 0.64), we further evaluated independent causal effects of body fat mass and fat-free mass using multivariable MR and revealed that only body fat mass was independently associated with severe COVID-19 (OR_{body fat mass} = 2.91, 95% CI: 1.71-4.96, P = 8.85 × 10^-5 and OR_{body fat-free mass} = 1.02, 95%CI: 0.61-1.67, P = 0.945). In summary, this study demonstrates the causal effects of body fat accumulation on COVID-19 severity and indicates that the biological pathways influencing the relationship between COVID-19 and obesity are likely mediated through body fat mass.

A standardized analytics pipeline for reliable and rapid development and validation of prediction models using observational health data

BACKGROUND AND OBJECTIVE

As a response to the ongoing COVID-19 pandemic, several prediction models in the existing literature were rapidly developed, with the aim of providing evidence-based guidance. However, none of these COVID-19 prediction models have been found to be reliable. Models are commonly assessed to have a risk of bias, often due to insufficient reporting, use of non-representative data, and lack of large-scale external validation. In this paper, we present the Observational Health Data Sciences and Informatics (OHDSI) analytics pipeline for patient-level prediction modeling as a standardized approach for rapid yet reliable development and validation of prediction models. We demonstrate how our analytics pipeline and open-source software tools can be used to answer important prediction questions while limiting potential causes of bias (e.g., by validating phenotypes, specifying the target population, performing large-scale external validation, and publicly providing all analytical source code).

METHODS

We show step-by-step how to implement the analytics pipeline for the question: 'In patients hospitalized with COVID-19, what is the risk of death 0 to 30 days after hospitalization?'. We develop models using six different machine learning methods in a USA claims database containing over 20,000 COVID-19 hospitalizations and externally validate the models using data containing over 45,000 COVID-19 hospitalizations from South Korea, Spain, and the USA.

RESULTS

Our open-source software tools enabled us to efficiently go end-to-end from problem design to reliable Model Development and evaluation. When predicting death in patients hospitalized with COVID-19, AdaBoost, random forest, gradient boosting machine, and decision tree yielded similar or lower internal and external validation discrimination performance compared to L1-regularized logistic regression, whereas the MLP neural network consistently resulted in lower discrimination. L1-regularized logistic regression models were well calibrated.

CONCLUSION

Our results show that following the OHDSI analytics pipeline for patient-level prediction modelling can enable the rapid development towards reliable prediction models. The OHDSI software tools and pipeline are open source and available to researchers from all around the world.