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Interoperable medical data: The missing link for understanding COVID-19.

ABSTRACT: Being able to link clinical outcomes to SARS-CoV-2 virus strains is a critical component of understanding COVID-19. Here, we discuss how current processes hamper sustainable data collection to enable meaningful analysis and insights. Following the 'Fast Healthcare Interoperable Resource' (FHIR) implementation guide, we introduce an ontology-based standard questionnaire to overcome these shortcomings and describe patient 'journeys' in coordination with the World Health Organization's recommendations. We identify steps in the clinical health data acquisition cycle and workflows that likely have the biggest impact in the data-driven understanding of this virus. Specifically, we recommend detailed symptoms and medical history using the FHIR standards. We have taken the first steps towards this by making patient status mandatory in GISAID ('Global Initiative on Sharing All Influenza Data'), immediately resulting in a measurable increase in the fraction of cases with useful patient information. The main remaining limitation is the lack of controlled vocabulary or a medical ontology.

SUBMITTER: Bauer DC

PROVIDER: S-EPMC8359419 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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Interoperable medical data: The missing link for understanding COVID-19.

Bauer Denis C DC Bauer Denis C DC Metke-Jimenez Alejandro A Maurer-Stroh Sebastian S Tiruvayipati Suma S Wilson Laurence O W LOW Jain Yatish Y Perrin Amandine A Ebrill Kate K Hansen David P DP Vasan Seshadri S SS

Transboundary and emerging diseases 20210129 4

Being able to link clinical outcomes to SARS-CoV-2 virus strains is a critical component of understanding COVID-19. Here, we discuss how current processes hamper sustainable data collection to enable meaningful analysis and insights. Following the 'Fast Healthcare Interoperable Resource' (FHIR) implementation guide, we introduce an ontology-based standard questionnaire to overcome these shortcomings and describe patient 'journeys' in coordination with the World Health Organization's recommendati ...[more]

PMID: 33095970

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Project description:BackgroundThe COVID-19 pandemic has revealed significant challenges in disease forecasting and in developing a public health response, emphasizing the need to manage missing data from various sources in making accurate forecasts.ObjectiveWe aimed to show how handling missing data can affect estimates of the COVID-19 incidence rate (CIR) in different pandemic situations.MethodsThis study used data from the COVID-19/SARS-CoV-2 surveillance system at the National Institute of Hygiene and Epidemiology, Vietnam. We separated the available data set into 3 distinct periods: zero COVID-19, transition, and new normal. We randomly removed 5% to 30% of data that were missing completely at random, with a break of 5% at each time point in the variable daily caseload of COVID-19. We selected 7 analytical methods to assess the effects of handling missing data and calculated statistical and epidemiological indices to measure the effectiveness of each method.ResultsOur study examined missing data imputation performance across 3 study time periods: zero COVID-19 (n=3149), transition (n=1290), and new normal (n=9288). Imputation analyses showed that K-nearest neighbor (KNN) had the lowest mean absolute percentage change (APC) in CIR across the range (5% to 30%) of missing data. For instance, with 15% missing data, KNN resulted in 10.6%, 10.6%, and 9.7% average bias across the zero COVID-19, transition, and new normal periods, compared to 39.9%, 51.9%, and 289.7% with the maximum likelihood method. The autoregressive integrated moving average model showed the greatest mean APC in the mean number of confirmed cases of COVID-19 during each COVID-19 containment cycle (CCC) when we imputed the missing data in the zero COVID-19 period, rising from 226.3% at the 5% missing level to 6955.7% at the 30% missing level. Imputing missing data with median imputation methods had the lowest bias in the average number of confirmed cases in each CCC at all levels of missing data. In detail, in the 20% missing scenario, while median imputation had an average bias of 16.3% for confirmed cases in each CCC, which was lower than the KNN figure, maximum likelihood imputation showed a bias on average of 92.4% for confirmed cases in each CCC, which was the highest figure. During the new normal period in the 25% and 30% missing data scenarios, KNN imputation had average biases for CIR and confirmed cases in each CCC ranging from 21% to 32% for both, while maximum likelihood and moving average imputation showed biases on average above 250% for both CIR and confirmed cases in each CCC.ConclusionsOur study emphasizes the importance of understanding that the specific imputation method used by investigators should be tailored to the specific epidemiological context and data collection environment to ensure reliable estimates of the CIR.

Project description:Patients with COVID-19 present a wide spectrum of disease severity, from asymptomatic cases in the majority to serious disease leading to critical care and even death. Clinically, four different scenarios occur within the typical disease timeline: first, an incubation and asymptomatic period; second, a stage with mild symptoms due mainly to the virus itself; third, in up to 20% of the patients, a stage with severe symptoms where a hyperinflammatory response with a cytokine storm driven by host immunity induces acute respiratory distress syndrome; and finally, a post-acute sequelae (PASC) phase, which present symptoms that can range from mild or annoying to actually quite incapacitating. Although the most common manifestation is acute respiratory failure of the lungs, other organs are also frequently involved. The clinical manifestations of the COVID-19 infection support a key role for endothelial dysfunction in the pathobiology of this condition. The virus enters into the organism via its interaction with angiotensin-converting enzyme 2-receptor that is present prominently in the alveoli, but also in endothelial cells, which can be directly infected by the virus. Cytokine release syndrome can also drive endothelial damage independently. Consequently, a distinctive feature of SARS-CoV-2 infection is vascular harm, with severe endothelial injury, widespread thrombosis, microangiopathy, and neo-angiogenesis in response to endothelial damage. Therefore, endothelial dysfunction seems to be the pathophysiological substrate for severe COVID-19 complications. Biomarkers of endothelial injury could constitute strong indicators of disease progression and severity. In addition, the endothelium could represent a very attractive target to both prevent and treat these complications. To establish an adequate therapy, the underlying pathophysiology and corresponding clinical stage should be clearly identified. In this review, the clinical features of COVID-19, the central role of the endothelium in COVID-19 and in other pathologies, and the potential of specific therapies aimed at protecting the endothelium in COVID-19 patients are addressed.

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Interoperable medical data: The missing link for understanding COVID-19.

Publications

Interoperable medical data: The missing link for understanding COVID-19.

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