Deep phenotyping of 34,128 patients hospitalised with COVID-19 and a comparison with 81,596 influenza patients in America, Europe and Asia: an international network study

Protocol for developing a personalised prediction model for viral suppression among under-represented populations in the context of the COVID-19 pandemic

INTRODUCTION

Sustained viral suppression, an indicator of long-term treatment success and mortality reduction, is one of four strategic areas of the 'Ending the HIV Epidemic' federal campaign launched in 2019. Under-represented populations, like racial or ethnic minority populations, sexual and gender minority groups, and socioeconomically disadvantaged populations, are disproportionately affected by HIV and experience a more striking virological failure. The COVID-19 pandemic might magnify the risk of incomplete viral suppression among under-represented people living with HIV (PLWH) due to interruptions in healthcare access and other worsened socioeconomic and environmental conditions. However, biomedical research rarely includes under-represented populations, resulting in biased algorithms. This proposal targets a broadly defined under-represented HIV population. It aims to develop a personalised viral suppression prediction model using machine learning (ML) techniques by incorporating multilevel factors using All of Us (AoU) data.

METHODS AND ANALYSIS

This cohort study will use data from the AoU research programme, which aims to recruit a broad, diverse group of US populations historically under-represented in biomedical research. The programme harmonises data from multiple sources on an ongoing basis. It has recruited ~4800 PLWH with a series of self-reported survey data (eg, Lifestyle, Healthcare Access, COVID-19 Participant Experience) and relevant longitudinal electronic health records data. We will examine the change in viral suppression and develop personalised viral suppression prediction due to the impact of the COVID-19 pandemic using ML techniques, such as tree-based classifiers (classification and regression trees, random forest, decision tree and eXtreme Gradient Boosting), support vector machine, naïve Bayes and long short-term memory.

ETHICS AND DISSEMINATION

The institutional review board approved the study at the University of South Carolina (Pro00124806) as a Non-Human Subject study. Findings will be published in peer-reviewed journals and disseminated at national and international conferences and through social media.

Obesity, Hypovitaminosis D, and COVID-19: the Bermuda Triangle in Public Health

PURPOSE OF REVIEW

The COVID-19 pandemic has challenged public health to a significant extent by markedly increasing morbidity and mortality. Evidence suggests that obesity and hypovitaminosis D constitute important risk factors for SARS-CoV-2 infection, severity of disease, and poor outcomes. Due to their high prevalence globally, obesity and hypovitaminosis D are considered pandemics. This review presents current epidemiologic and genetic data linking obesity, hypovitaminosis D, and COVID-19, highlighting the importance of the convergence of three pandemics and their impact on public health. We also briefly summarize potential mechanisms that could explain these links.

RECENT FINDINGS

Epidemiologic data have shown that obesity is an independent risk factor for COVID-19, severe disease and death, and genetic evidence has suggested a causal association between obesity-related traits and COVID-19 susceptibility and severity. Additionally, obesity is independently associated with hypovitaminosis D, which is highly prevalent in subjects with obesity. Hypovitaminosis D is independently associated with a higher risk for COVID-19, severity, hospitalization, infectious complications, acute respiratory distress syndrome, and poor outcomes. However, genome-wide association studies have not revealed any causal association between vitamin D levels and the risk for COVID-19, while there is no robust evidence for a beneficial role of vitamin D supplementation in the prevention and treatment of COVID-19. In the context of the ongoing COVID-19 pandemic, the epidemiologic impact of obesity and hypovitaminosis D is emphasized. Efforts to increase public awareness and reinforce preventive and therapeutic measures against obesity and hypovitaminosis D are strongly required.

Comparison of the clinical parameters of patients with COVID-19 and influenza using blood test data: a retrospective cross-sectional survey

Objective

The characteristic features, including blood test data, of the novel coronavirus disease 2019 (COVID-19) versus influenza have not been defined. We therefore compared the clinical parameters, including blood test data, of COVID-19 and influenza.

Methods

This retrospective cross-sectional survey was conducted at Juntendo University Nerima Hospital. We recruited patients diagnosed with COVID-19 between 1 January 2020 and 31 December 2020 who underwent blood tests. For comparison, we recruited an equivalent number of patients who were diagnosed with influenza and who underwent blood tests.

Results

During the study period, 228 patients (male:female, 123 [54.0%]:105 [46.0%]; age, 54.68 ± 18.98 years) were diagnosed with COVID-19. We also recruited 228 patients with influenza (male:female, 129 [56.6%]:99 [43.4%]; age, 69.6 ± 21.25 years). An age of 15 to 70 years (vs. 71 years), breathing difficulty, and malaise were significantly more common in patients with COVID-19 than in those with influenza. However, nausea, body temperature >38.1°C, and white blood cell count >9000/μL were more common in patients with influenza.

Conclusions

Our results are useful for differentiating COVID-19 from influenza, and these findings will be extremely helpful for future practice as we learn to coexist with COVID-19.

Applying Machine Learning in Distributed Data Networks for Pharmacoepidemiologic and Pharmacovigilance Studies: Opportunities, Challenges, and Considerations

Increasing availability of electronic health databases capturing real-world experiences with medical products has garnered much interest in their use for pharmacoepidemiologic and pharmacovigilance studies. The traditional practice of having numerous groups use single databases to accomplish similar tasks and address common questions about medical products can be made more efficient through well-coordinated multi-database studies, greatly facilitated through distributed data network (DDN) architectures. Access to larger amounts of electronic health data within DDNs has created a growing interest in using data-adaptive machine learning (ML) techniques that can automatically model complex associations in high-dimensional data with minimal human guidance. However, the siloed storage and diverse nature of the databases in DDNs create unique challenges for using ML. In this paper, we discuss opportunities, challenges, and considerations for applying ML in DDNs for pharmacoepidemiologic and pharmacovigilance studies. We first discuss major types of activities performed by DDNs and how ML may be used. Next, we discuss practical data-related factors influencing how DDNs work in practice. We then combine these discussions and jointly consider how opportunities for ML are affected by practical data-related factors for DDNs, leading to several challenges. We present different approaches for addressing these challenges and highlight efforts that real-world DDNs have taken or are currently taking to help mitigate them. Despite these challenges, the time is ripe for the emerging interest to use ML in DDNs, and the utility of these data-adaptive modeling techniques in pharmacoepidemiologic and pharmacovigilance studies will likely continue to increase in the coming years.

COVID-19 Pandemic Between Severity Facts and Prophylaxis

Before COVID-19, many viruses have infected humans, so what caused COVID-19 to be considered as a pandemic? COVID-19 belongs to the coronavirus family that includes severe acute respiratory syndrome (SARS) and Middle East Respiratory Syndrome (MERS). This family has caused a large number of deaths all over the world. How risky is the novel coronavirus? People and their careers were disrupted, and many businesses all over the world are now closed. From here, it seems to us that this virus is something that can make people feel afraid. In this article, we will try to understand the severity of this virus, and then disclose the available ways to confront it and ways that might improve the ability to face it, either now or in the future. Upon comparing COVID-19 with seasonal flu, we have found that COVID-19 is about 10 times more deadly, although it is not the most infectious virus. In this review, we will discuss how healthy nutrition and lifestyle may help to prevent and treat diseases, and especially COVID-19. We will focus on how to follow healthy nutrition habits and lifestyles to stop the dangers of COVID-19.

Data Consult Service: Can we use observational data to address immediate clinical needs?

OBJECTIVE

A number of clinical decision support tools aim to use observational data to address immediate clinical needs, but few of them address challenges and biases inherent in such data. The goal of this article is to describe the experience of running a data consult service that generates clinical evidence in real time and characterize the challenges related to its use of observational data.

MATERIALS AND METHODS

In 2019, we launched the Data Consult Service pilot with clinicians affiliated with Columbia University Irving Medical Center. We created and implemented a pipeline (question gathering, data exploration, iterative patient phenotyping, study execution, and assessing validity of results) for generating new evidence in real time. We collected user feedback and assessed issues related to producing reliable evidence.

RESULTS

We collected 29 questions from 22 clinicians through clinical rounds, emails, and in-person communication. We used validated practices to ensure reliability of evidence and answered 24 of them. Questions differed depending on the collection method, with clinical rounds supporting proactive team involvement and gathering more patient characterization questions and questions related to a current patient. The main challenges we encountered included missing and incomplete data, underreported conditions, and nonspecific coding and accurate identification of drug regimens.

CONCLUSIONS

While the Data Consult Service has the potential to generate evidence and facilitate decision making, only a portion of questions can be answered in real time. Recognizing challenges in patient phenotyping and designing studies along with using validated practices for observational research are mandatory to produce reliable evidence.

Understanding the Co-Epidemic of Obesity and COVID-19: Current Evidence, Comparison with Previous Epidemics, Mechanisms, and Preventive and Therapeutic Perspectives

PURPOSE OF REVIEW

A growing body of evidence suggests that obesity and increased visceral adiposity are strongly and independently linked to adverse outcomes and death due to COVID-19. This review summarizes current epidemiologic data, highlights pathogenetic mechanisms on the association between excess body weight and COVID-19, compares data from previous pandemics, discusses why COVID-19 challenges the "obesity paradox," and presents implications in prevention and treatment as well as future perspectives.

RECENT FINDINGS

Data from meta-analyses based on recent observational studies have indicated that obesity increases the risks of infection from SARS-CoV-2, severe infection and hospitalization, admission to the ICU and need of invasive mechanical ventilation (IMV), and the risk of mortality, particularly in severe obesity. The risks of IMV and mortality associated with obesity are accentuated in younger individuals (age ≤ 50 years old). The meta-inflammation in obesity intersects with and exacerbates underlying pathogenetic mechanisms in COVID-19 through the following mechanisms and factors: (i) impaired innate and adaptive immune responses; (ii) chronic inflammation and oxidative stress; (iii) endothelial dysfunction, hypercoagulability, and aberrant activation of the complement; (iv) overactivation of the renin-angiotensin-aldosterone system; (v) overexpression of the angiotensin-converting enzyme 2 receptor in the adipose tissue; (vi) associated cardiometabolic comorbidities; (vii) vitamin D deficiency; (viii) gut dysbiosis; and (ix) mechanical and psychological issues. Mechanistic and large epidemiologic studies using big data sources with omics data exploring genetic determinants of risk and disease severity as well as large randomized controlled trials (RCTs) are necessary to shed light on the pathways connecting chronic subclinical inflammation/meta-inflammation with adverse COVID-19 outcomes and establish the ideal preventive and therapeutic approaches for patients with obesity.

Impact of COVID-19 on the Health of the General and More Vulnerable Population and Its Determinants: Health Care and Social Survey-ESSOC, Study Protocol

This manuscript describes the rationale and protocol of a real-world data (RWD) study entitled Health Care and Social Survey (ESSOC, Encuesta Sanitaria y Social). The study's objective is to determine the magnitude, characteristics, and evolution of the COVID-19 impact on overall health as well as the socioeconomic, psychosocial, behavioural, occupational, environmental, and clinical determinants of both the general and more vulnerable population. The study integrates observational data collected through a survey using a probabilistic, overlapping panel design, and data from clinical, epidemiological, demographic, and environmental registries. The data will be analysed using advanced statistical, sampling, and machine learning techniques. The study is based on several measurements obtained from three random samples of the Andalusian (Spain) population: general population aged 16 years and over, residents in disadvantaged areas, and people over the age of 55. Given the current characteristics of this pandemic and its future repercussions, this project will generate relevant information on a regular basis, commencing from the beginning of the State of Alarm. It will also establish institutional alliances of great social value, explore and apply powerful and novel methodologies, and produce large, integrated, high-quality and open-access databases. The information described here will be vital for health systems in order to design tailor-made interventions aimed at improving the health care, health, and quality of life of the populations most affected by the COVID-19 pandemic.

Chronic respiratory diseases are predictors of severe outcome in COVID-19 hospitalised patients: a nationwide study

Background

Influenza epidemics were initially considered to be a suitable model for the COVID-19 epidemic, but there is a lack of data concerning patients with chronic respiratory diseases (CRDs), who were supposed to be at risk of severe forms of COVID-19.

Methods

This nationwide retrospective cohort study describes patients with prior lung disease hospitalised for COVID-19 (March–April 2020) or influenza (2018–2019 influenza outbreak). We compared the resulting pulmonary complications, need for intensive care and in-hospital mortality depending on respiratory history and virus.

Results

In the 89 530 COVID-19 cases, 16.03% had at least one CRD, which was significantly less frequently than in the 45 819 seasonal influenza patients. Patients suffering from chronic respiratory failure, chronic obstructive pulmonary disease, asthma, cystic fibrosis and pulmonary hypertension were under-represented, contrary to those with lung cancer, sleep apnoea, emphysema and interstitial lung diseases. COVID-19 patients with CRDs developed significantly more ventilator-associated pneumonia and pulmonary embolism than influenza patients. They needed intensive care significantly more often and had a higher mortality rate (except for asthma) when compared with patients with COVID-19 but without CRDs or patients with influenza.

Conclusions

Patients with prior respiratory diseases were globally less likely to be hospitalised for COVID-19 than for influenza, but were at higher risk of developing severe COVID-19 and had a higher mortality rate compared with influenza patients and patients without a history of respiratory illness.

There was a higher risk of developing severe COVID-19 and a higher mortality rate among patients with chronic respiratory diseases. This study suggests that these patients should have priority access to SARS-CoV-2 vaccination.https://bit.ly/3bcp2HC

Characterizing the Anticancer Treatment Trajectory and Pattern in Patients Receiving Chemotherapy for Cancer Using Harmonized Observational Databases: Retrospective Study

BACKGROUND

Accurate and rapid clinical decisions based on real-world evidence are essential for patients with cancer. However, the complexity of chemotherapy regimens for cancer impedes retrospective research that uses observational health databases.

OBJECTIVE

The aim of this study is to compare the anticancer treatment trajectories and patterns of clinical events according to regimen type using the chemotherapy episodes determined by an algorithm.

METHODS

We developed an algorithm to extract the regimen-level abstracted chemotherapy episodes from medication records in a conventional Observational Medical Outcomes Partnership (OMOP) common data model (CDM) database. The algorithm was validated on the Ajou University School Of Medicine (AUSOM) database by manual review of clinical notes. Using the algorithm, we extracted episodes of chemotherapy from patients in the EHR database and the claims database. We also developed an application software for visualizing the chemotherapy treatment patterns based on the treatment episodes in the OMOP-CDM database. Using this software, we generated the trends in the types of regimen used in the institutions, the patterns of the iterative chemotherapy use, and the trajectories of cancer treatment in two EHR-based OMOP-CDM databases. As a pilot study, the time of onset of chemotherapy-induced neutropenia according to regimen was measured using the AUSOM database. The anticancer treatment trajectories for patients with COVID-19 were also visualized based on the nationwide claims database.

RESULTS

We generated 178,360 treatment episodes for patients with colorectal, breast, and lung cancer for 85 different regimens. The algorithm precisely identified the type of chemotherapy regimen in 400 patients (average positive predictive value >98%). The trends in the use of routine clinical chemotherapy regimens from 2008-2018 were identified for 8236 patients. For a total of 12 regimens (those administered to the largest proportion of patients), the number of repeated treatments was concordant with the protocols for standard chemotherapy regimens for certain cases. In addition, the anticancer treatment trajectories for 8315 patients were shown, including 62 patients with COVID-19. A comparative analysis of neutropenia showed that its onset in colorectal cancer regimens tended to cluster between days 9-15, whereas it tended to cluster between days 2-8 for certain regimens for breast cancer or lung cancer.

CONCLUSIONS

We propose a method for generating chemotherapy episodes for introduction into the oncology extension module of the OMOP-CDM databases. These proof-of-concept studies demonstrated the usability, scalability, and interoperability of the proposed framework through a distributed research network.

Comparison of clinical characteristics and disease outcome of COVID-19 and seasonal influenza

While several studies have described the clinical course of patients with coronavirus disease 2019 (COVID-19), direct comparisons with patients with seasonal influenza are scarce. We compared 166 patients with COVID-19 diagnosed between February 27 and June 14, 2020, and 255 patients with seasonal influenza diagnosed during the 2017-18 season at the same hospital to describe common features and differences in clinical characteristics and course of disease. Patients with COVID-19 were younger (median age [IQR], 59 [45-71] vs 66 [52-77]; P < 0001) and had fewer comorbidities at baseline with a lower mean overall age-adjusted Charlson Comorbidity Index (mean [SD], 3.0 [2.6] vs 4.0 [2.7]; P < 0.001) than patients with seasonal influenza. COVID-19 patients had a longer duration of hospitalization (mean [SD], 25.9 days [26.6 days] vs 17.2 days [21.0 days]; P = 0.002), a more frequent need for oxygen therapy (101 [60.8%] vs 103 [40.4%]; P < 0.001) and invasive ventilation (52 [31.3%] vs 32 [12.5%]; P < 0.001) and were more frequently admitted to the intensive care unit (70 [42.2%] vs 51 [20.0%]; P < 0.001) than seasonal influenza patients. Among immunocompromised patients, those in the COVID-19 group had a higher hospital mortality compared to those in the seasonal influenza group (13 [33.3%] vs 8 [11.6%], P = 0.01). In conclusion, we show that COVID-19 patients were younger and had fewer baseline comorbidities than seasonal influenza patients but were at increased risk for severe illness. The high mortality observed in immunocompromised COVID-19 patients emphasizes the importance of protecting these patient groups from SARS-CoV-2 infection.

Severity Prediction for COVID-19 Patients via Recurrent Neural Networks

The novel coronavirus disease-2019 (COVID-19) pandemic has threatened the health of tens of millions of people worldwide and imposed heavy burden on global healthcare systems. In this paper, we propose a model to predict whether a patient infected with COVID-19 will develop severe outcomes based only on the patient's historical electronic health records (EHR) prior to hospital admission using recurrent neural networks. The model predicts risk score that represents the probability for a patient to progress into severe status (mechanical ventilation, tracheostomy, or death) after being infected with COVID-19. The model achieved 0.846 area under the receiver operating characteristic curve in predicting patients' outcomes averaged over 5-fold cross validation. While many of the existing models use features obtained after diagnosis of COVID-19, our proposed model only utilizes a patient's historical EHR to enable proactive risk management at the time of hospital admission.

Incorporation of Korean Electronic Data Interchange Vocabulary into Observational Medical Outcomes Partnership Vocabulary

OBJECTIVES

We incorporated the Korean Electronic Data Interchange (EDI) vocabulary into Observational Medical Outcomes Partnership (OMOP) vocabulary using a semi-automated process. The goal of this study was to improve the Korean EDI as a standard medical ontology in Korea.

METHODS

We incorporated the EDI vocabulary into OMOP vocabulary through four main steps. First, we improved the current classification of EDI domains and separated medical services into procedures and measurements. Second, each EDI concept was assigned a unique identifier and validity dates. Third, we built a vertical hierarchy between EDI concepts, fully describing child concepts through relationships and attributes and linking them to parent terms. Finally, we added an English definition for each EDI concept. We translated the Korean definitions of EDI concepts using Google.Cloud.Translation.V3, using a client library and manual translation. We evaluated the EDI using 11 auditing criteria for controlled vocabularies.

RESULTS

We incorporated 313,431 concepts from the EDI to the OMOP Standardized Vocabularies. For 10 of the 11 auditing criteria, EDI showed a better quality index within the OMOP vocabulary than in the original EDI vocabulary.

CONCLUSIONS

The incorporation of the EDI vocabulary into the OMOP Standardized Vocabularies allows better standardization to facilitate network research. Our research provides a promising model for mapping Korean medical information into a global standard terminology system, although a comprehensive mapping of official vocabulary remains to be done in the future.

The Acquisition of Multidrug-Resistant Bacteria in Patients Admitted to COVID-19 Intensive Care Units: A Monocentric Retrospective Case Control Study

Whether the risk of multidrug-resistant bacteria (MDRB) acquisition in the intensive care unit (ICU) is modified by the COVID-19 crisis is unknown. In this single center case control study, we measured the rate of MDRB acquisition in patients admitted in COVID-19 ICU and compared it with patients admitted in the same ICU for subarachnoid hemorrhage (controls) matched 1:1 on length of ICU stay and mechanical ventilation. All patients were systematically and repeatedly screened for MDRB carriage. We compared the rate of MDRB acquisition in COVID-19 patients and in control using a competing risk analysis. Of note, although we tried to match COVID-19 patients with septic shock patients, we were unable due to the longer stay of COVID-19 patients. Among 72 patients admitted to the COVID-19 ICUs, 33% acquired 31 MDRB during ICU stay. The incidence density of MDRB acquisition was 30/1000 patient days. Antimicrobial therapy and exposure time were associated with higher rate of MDRB acquisition. Among the 72 SAH patients, 21% acquired MDRB, with an incidence density was 18/1000 patient days. The septic patients had more comorbidities and a greater number of previous hospitalizations than the COVID-19 patients. The incidence density of MDRB acquisition was 30/1000 patient days. The association between COVID-19 and MDRB acquisition (compared to control) risk did not reach statistical significance in the multivariable competing risk analysis (sHR 1.71 (CI 95% 0.93–3.21)). Thus, we conclude that, despite strong physical isolation, acquisition rate of MDRB in ICU patients was at least similar during the COVID-19 first wave compared to previous period.

Deep phenotyping of 34,128 adult patients hospitalised with COVID-19 in an international network study

Comorbid conditions appear to be common among individuals hospitalised with coronavirus disease 2019 (COVID-19) but estimates of prevalence vary and little is known about the prior medication use of patients. Here, we describe the characteristics of adults hospitalised with COVID-19 and compare them with influenza patients. We include 34,128 (US: 8362, South Korea: 7341, Spain: 18,425) COVID-19 patients, summarising between 4811 and 11,643 unique aggregate characteristics. COVID-19 patients have been majority male in the US and Spain, but predominantly female in South Korea. Age profiles vary across data sources. Compared to 84,585 individuals hospitalised with influenza in 2014-19, COVID-19 patients have more typically been male, younger, and with fewer comorbidities and lower medication use. While protecting groups vulnerable to influenza is likely a useful starting point in the response to COVID-19, strategies will likely need to be broadened to reflect the particular characteristics of individuals being hospitalised with COVID-19.

What Factors Increase the Risk of Complications in SARS-CoV-2-Infected Patients? A Cohort Study in a Nationwide Israeli Health Organization

BACKGROUND

Reliably identifying patients at increased risk for coronavirus disease (COVID-19) complications could guide clinical decisions, public health policies, and preparedness efforts. Multiple studies have attempted to characterize at-risk patients, using various data sources and methodologies. Most of these studies, however, explored condition-specific patient cohorts (eg, hospitalized patients) or had limited access to patients' medical history, thus, investigating related questions and, potentially, obtaining biased results.

OBJECTIVE

This study aimed to identify factors associated with COVID-19 complications from the complete medical records of a nationally representative cohort of patients, with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.

METHODS

We studied a cohort of all SARS-CoV-2-positive individuals, confirmed by polymerase chain reaction testing of either nasopharyngeal or saliva samples, in a nationwide health organization (covering 2.3 million individuals) and identified those who suffered from serious complications (ie, experienced moderate or severe symptoms of COVID-19, admitted to the intensive care unit, or died). We then compared the prevalence of pre-existing conditions, extracted from electronic health records, between complicated and noncomplicated COVID-19 patient cohorts to identify the conditions that significantly increase the risk of disease complications, in various age and sex strata.

RESULTS

Of the 4353 SARS-CoV-2-positive individuals, 173 (4%) patients suffered from COVID-19 complications (all age ≥18 years). Our analysis suggests that cardiovascular and kidney diseases, obesity, and hypertension are significant risk factors for COVID-19 complications. It also indicates that depression (eg, males ≥65 years: odds ratio [OR] 2.94, 95% CI 1.55-5.58; P=.01) as well as cognitive and neurological disorders (eg, individuals ≥65 years old: OR 2.65, 95% CI 1.69-4.17; P<.001) are significant risk factors. Smoking and presence of respiratory diseases do not significantly increase the risk of complications.

CONCLUSIONS

Our analysis agrees with previous studies on multiple risk factors, including hypertension and obesity. It also finds depression as well as cognitive and neurological disorders, but not smoking and respiratory diseases, to be significantly associated with COVID-19 complications. Adjusting existing risk definitions following these observations may improve their accuracy and impact the global pandemic containment and recovery efforts.