Evidence of gender bias in the diagnosis and management of COVID-19 patients: A Big Data analysis of Electronic Health Records

Ancochea, Julio; Izquierdo, José Luis; Soriano, Joan B.

doi:10.1101/2020.07.20.20157735

Cited by 11 publications

(7 citation statements)

References 34 publications

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“…The reported sex-dependent differences in the symptomatology of COVID-19 at onset have been further confirmed by our group using similar methods [32] and should be interpreted in light of data suggesting that female teenagers and young adult women are significantly more affected by the disease than their male counterparts [32]. In this regard, our results warrant further investigations aimed at closing the gender gap in the ongoing pandemic [33].…”

Section: Principal Findingssupporting

confidence: 83%

Clinical Characteristics and Prognostic Factors for Intensive Care Unit Admission of Patients With COVID-19: Retrospective Study Using Machine Learning and Natural Language Processing (Preprint)

Alonso¹,

Ancochea²,

Soriano³

2020

Preprint

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BACKGROUND Many factors involved in the onset and clinical course of the ongoing COVID-19 pandemic are still unknown. Although big data analytics and artificial intelligence are widely used in the realms of health and medicine, researchers are only beginning to use these tools to explore the clinical characteristics and predictive factors of patients with COVID-19. OBJECTIVE Our primary objectives are to describe the clinical characteristics and determine the factors that predict intensive care unit (ICU) admission of patients with COVID-19. Determining these factors using a well-defined population can increase our understanding of the real-world epidemiology of the disease. METHODS We used a combination of classic epidemiological methods, natural language processing (NLP), and machine learning (for predictive modeling) to analyze the electronic health records (EHRs) of patients with COVID-19. We explored the unstructured free text in the EHRs within the Servicio de Salud de Castilla-La Mancha (SESCAM) Health Care Network (Castilla-La Mancha, Spain) from the entire population with available EHRs (1,364,924 patients) from January 1 to March 29, 2020. We extracted related clinical information regarding diagnosis, progression, and outcome for all COVID-19 cases. RESULTS A total of 10,504 patients with a clinical or polymerase chain reaction–confirmed diagnosis of COVID-19 were identified; 5519 (52.5%) were male, with a mean age of 58.2 years (SD 19.7). Upon admission, the most common symptoms were cough, fever, and dyspnea; however, all three symptoms occurred in fewer than half of the cases. Overall, 6.1% (83/1353) of hospitalized patients required ICU admission. Using a machine-learning, data-driven algorithm, we identified that a combination of age, fever, and tachypnea was the most parsimonious predictor of ICU admission; patients younger than 56 years, without tachypnea, and temperature <39 degrees Celsius (or >39 ºC without respiratory crackles) were not admitted to the ICU. In contrast, patients with COVID-19 aged 40 to 79 years were likely to be admitted to the ICU if they had tachypnea and delayed their visit to the emergency department after being seen in primary care. CONCLUSIONS Our results show that a combination of easily obtainable clinical variables (age, fever, and tachypnea with or without respiratory crackles) predicts whether patients with COVID-19 will require ICU admission.

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Section: Principal Findingssupporting

confidence: 83%

Clinical Characteristics and Prognostic Factors for Intensive Care Unit Admission of Patients With COVID-19: Retrospective Study Using Machine Learning and Natural Language Processing (Preprint)

Alonso¹,

Ancochea²,

Soriano³

2020

Preprint

Self Cite

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“…If only those with more severe symptoms are tested this will affect the denominator of the CFR and will depend on the testing strategy of each country. For example, if tests are restricted to the more severe cases and if females tend to have a less severe disease, this would selectively underestimate the number in the CFR denominator and inflate the female CFR [ 26 ]. In such a case the male excess in CFR would be underestimated.…”

Section: Discussionmentioning

confidence: 99%

Sex differences in the case-fatality rates for COVID-19—A comparison of the age-related differences and consistency over seven countries

et al. 2021

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Background Early in the COVID-19 pandemic, it was noted that males seemed to have higher case-fatality rates than females. We examined the magnitude and consistency of the sex differences in age-specific case-fatality rates (CFRs) in seven countries. Methods Data on the cases and deaths from COVID-19, by sex and age group, were extracted from the national official agencies from Denmark, England, Israel, Italy, Spain, Canada and Mexico. Age-specific CFRs were computed for males and females separately. The ratio of the male to female CFRs were computed and meta-analytic methods were used to obtained pooled estimates of the male to female ratio of the CFRs over the seven countries, for all age-groups. Meta-regression and sensitivity analysis were conducted to evaluate the age and country contribution to differences. Results The CFRs were consistently higher in males at all ages. The pooled M:F CFR ratios were 1.71, 1.88, 2.11, 2.11, 1.84, 1.78 and 1.49, for ages 20–29, 30–39, 40–49, 50–59, 60–69, 70–79, 80+ respectively. In meta-regression, age group and country were associated with the heterogeneity in the CFR ratios. Conclusions The sex differences in the age-specific CFRs are intriguing. Sex differences in the incidence and mortality have been found in many infectious diseases. For COVID-19, factors such as sex differences in the prevalence of underlying diseases may play a part in the CFR differences. However, the consistently greater case-fatality rates in males at all ages suggests that sex-related factors impact on the natural history of the disease. This could provide important clues as to the mechanisms underlying the severity of COVID-19 in some patients.

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“…In an AGILE-like methodology, applying continuous improvement ( 55 ), demos were regularly performed, and feedbacks shortly implemented. The SARS-CoV-2 being a novel virus, the scientific literature was closely scrutinized to incorporate new relevant elements, such as gender-related indicators, which were included after ( 56 ) published their research on gender bias in COVID-19 diagnosis.…”

Section: Resultsmentioning

confidence: 99%

An Automated Dashboard to Improve Laboratory COVID-19 Diagnostics Management

Maury

Boldi

Greub

et al. 2021

Front. Digit. Health

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Background: In response to the COVID-19 pandemic, our microbial diagnostic laboratory located in a university hospital has implemented several distinct SARS-CoV-2 RT-PCR systems in a very short time. More than 148,000 tests have been performed over 12 months, which represents about 405 tests per day, with peaks to more than 1,500 tests per days during the second wave. This was only possible thanks to automation and digitalization, to allow high throughput, acceptable time to results and to maintain test reliability. An automated dashboard was developed to give access to Key Performance Indicators (KPIs) to improve laboratory operational management.Methods: RT-PCR data extraction of four respiratory viruses—SARS-CoV-2, influenza A and B and RSV—from our laboratory information system (LIS), was automated. This included age, gender, test result, RT-PCR instrument, sample type, reception time, requester, and hospitalization status etc. Important KPIs were identified and the visualization was achieved using an in-house dashboard based on the open-source language R (Shiny).Results: The dashboard is organized into three main parts. The “Filter” page presents all the KPIs, divided into five sections: (i) general and gender-related indicators, (ii) number of tests and positivity rate, (iii) cycle threshold and viral load, (iv) test durations, and (v) not valid results. Filtering allows to select a given period, a dedicated instrument, a given specimen, an age range or a requester. The “Comparison” page allows a custom charting of all the available variables, which represents more than 182 combination. The “Data” page, gives the user an access to the raw data in tables format, with possibility of filtering, allowing for a deeper analysis and data download. Informations are updated every 4 h.Conclusions: By giving a rapid access to a huge number of up-to-date information, represented using the most relevant visualization types, without the burden of timely data extraction and analysis, the dashboard represents a reliable and user-friendly tool for operational laboratory management. The dashboard represents a reliable and user-friendly tool improving the decision-making process, resource planning and quality management.

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Evidence of gender bias in the diagnosis and management of COVID-19 patients: A Big Data analysis of Electronic Health Records

Cited by 11 publications

References 34 publications

Clinical Characteristics and Prognostic Factors for Intensive Care Unit Admission of Patients With COVID-19: Retrospective Study Using Machine Learning and Natural Language Processing (Preprint)

Clinical Characteristics and Prognostic Factors for Intensive Care Unit Admission of Patients With COVID-19: Retrospective Study Using Machine Learning and Natural Language Processing (Preprint)

Sex differences in the case-fatality rates for COVID-19—A comparison of the age-related differences and consistency over seven countries

An Automated Dashboard to Improve Laboratory COVID-19 Diagnostics Management

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