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OHDSI virtual study-a-thon to support COVID-19 response, to take place 26-29Mar2020...Collaborators wanted!

(Rimma Belenkaya) #16

“At Mount Sinai School of Medicine, NYC, physicians have identified specific patterns in the lungs as markers of the disease as it develops over the course of a week and a half.” https://www.mountsinai.org/about/newsroom/2020/mount-sinai-physicians-the-first-in-us-analyzing-lung-disease-in-coronavirus-patients-from-china-press-release

They studied about 500 CT-Scans of Chinese patients along with some additional clinical data.

Mount Sinai has an OMOP instance (which is also a part of the NYC-CDRN), this could be another opportunity supporting this effort.

(Seng Chan You) #17

I’ve added novel vocabulary of Korea (KCD-7) for COVID-19 to here. Needs standard vocabulary for them.
Sorry most of them are not in English. I’ll translate them.

(Daniel Rubin) #18

@Yeon_Hee_Kim how does one sign up for the data-thon?

(Seng Chan You) #19

@dlrubin Please see below:

(Dmytry Dymshyts) #20

We have just released 2020 March SNOMED version that includes novel coronavirus concepts.

To accommodate for immediate need for concepts to represent ideas related to novel COVID-19 diagnostics, treatment and research, IHTSDO has released interim version of SNOMED International on 9th of march.

In OMOP CDM, SNOMED Vocabulary is built from three different sources: SNOMED International release, SNOMED US edition and SNOMED UK edition. Latter two are based on International release.

We were intending to delay January until April to release to synchronize with local editions to reduce inconsistencies and standard concept duplication. However, we had to force the update today to include changes related to novel coronavirus pandemic. Plans to synchronize releases have been delayed until July release, which will hopefully be finalized this October.

Updates to LOINC and ICD10 are in progress.

Here’s the corresponding github issue.

(Patrick Ryan) #21


Wow, what a difference a week makes! Last week, we announced we’d hold a OHDSI virtual study-a-thon on COVID-19 in lieu of the OHDSI EU Symposium, not knowing exactly how we’d do it or what we’d do and mildly anxious that we only had three weeks to prepare for the event. Ah, the good old days, when the biggest anxiety in life was meeting logistics…

In the last 7 days, I have been tremendously impressed by how the OHDSI community has rallied together to figure how how we can collaborate to collectively generate evidence to promote better health decisions and better care. We are still very much looking forward to the OHDSI virtual study-a-thon on Mar26-29, but it very clear that the work needed to support public health can’t and isn’t waiting to start for two weeks, and whatever we do during the study-a-thon isn’t going to be the end of our efforts to inform the COVID-19 pandemic response. Unfortunately, it looks likely that we are going to be on this journey together for quite awhile.

The good news is that it’s also become abundantly clear that we can (and will!) make meaningful contributions to the current crisis through responsible analysis across our international data network. There are a lot of open questions that real-world evidence is best equipped to answer, either because observational data is the best or only current source of information, or because retrospective analyses of these data is the most efficient way to generate insights to address the immediate urgency until prospective data collection and research can be completed.

I do want to reinforce the differentiated value that I see we can make as a community. Lots of groups around the world are working hard to do COVID-19 case detection, and there is quite a bit the world will learn from those cases. But much of what we will learn can’t be determined today, rather it will unfold over the next several months as the disease progresses and prospective data collection of the longitudinal experience of these patients is captured. A key strength of our OHDSI community is our ability to design and execute retrospective analysis of existing healthcare data across our international data network. Together, we have access to the historical experience from hundreds of millions of patients, and it is our responsibility to learn whatever we can from those past experiences to inform the current situation…now not later. So, for those of you on the analytics side of the house, I encourage all of you to not idly speculate: ‘what would I want to do if I eventually get access to data with COVID-19 cases?’ but rather take action on the question: ‘how can I learn from the data I currently have access to?’.

And for those of you with a data focus, the efforts you are putting in today to standardize your data, be it conversion of legacy warehouses or real-time data streams, will pay dividends. I am certain that it’ll remain the case that no one data source will have sufficient information to meet our needs, so the only viable path forward is to work together as a network and learn from our collective resources. The prospective data collection underway today will become the retrospective analysis opportunity tomorrow, and we need to be prepared on both the data and analytics side to realize the potential of what we can deliver for the public health good.

A quick update on community activities that I am aware of:

@SCYou is doing heroic work, leading efforts in Korea to use recent national claims data from HIRA, standardizing the HIRA to OMOP CDM and preparing to apply OHDSI analysis packages. Chan, you are a true inspiration to all of us. Whatever we can do to support you, you’ve got it.

@Daniel_Prieto has been in close contact with UK NHS to determine how OHDSI can support their needs. We are hopeful that we will be able to use more recent CPRD and HES data to inform our current activities.

@mvanzandt is leading efforts to get data refreshed across various Iqvia datasets so that the most recent data can be made available for distributed analysis. I have heard that others in the community are also hard at work trying to get whatever is the most recent data possible accessible in their OMOP CDM instance. Thank you for these efforts, they will make a difference.

@Christian_Reich, @Alexdavv , @Dymshyts and entire vocabulary team are hard at work critically reviewing the source codes and standard concepts, with particular focus on diagnosis of viral disease, symptoms and complications, respiratory procedures, and associated measurements. In addition to adding new standard concepts for COVID-19 diagnosis and tests, they will be revising concept_relationships to enable more accurate analysis. Christian will announce to community when a new vocab is released, which we will encourage everyone in the OHDSI community to download and refresh your ETL so that we’re all on the same page.

@jennareps, @Rijnbeek and I have begun designing patient-level prediction studies. Across the world, towns, states, and countries are all aiming to ‘flatten the curve’, by employing a series of public health measures aimed at delaying the communal transmission of COVID-19 such that the number of infected patients are sufficiently spread out over time that the capacity of our healthcare system can accommodate the needs of the ill. Certainly all people should be heeding the advice of their government and public health officials, useful information from US CDC is here: https://www.cdc.gov/coronavirus/2019-ncov/index.html. In situations where demand for health services exceed supply, prioritization tools based on disease severity become valuable and education for the public about why they shouldn’t seek unnecessary care can make a difference. We are proposing 3 patient-level prediction studies that can inform this discussion: 1) Amongst T: persons with an outpatient visit (GP, urgent care, ER) who have flu or flu-like symptoms, who are O: persons who are admitted to hospital with flu or pneumonia, in TAR: 0d-30d from outpatient visit? The goal of this prediction is to identify, based on the medical history prior to the first encounter, which patients are likely going to need hospitalization. 2) Amongst T: persons with an outpatient visit (GP or urgent care) who have flu or flu-like symptoms but do not have pneumonia and who are not admitted to hospital on the same day or next day, who are O: persons who are admitted to hospital with pneumonia, in TAR: 2d-30d from outpatient visit? The goal with this analysis is to help with assurance of the public that if they are sent home, the likelihood of something bad happening is low so they should follow doctor’s advice and CDC recommendations for self-care. 3) Amongst T: persons with inpatient admission with pneumonia due to viral origin, who are O1: persons requiring intensive care services or O2: persons who die, TAR: during the hospital stay? The goal here is to use medical history information prior to admission to help with triaging those who arrive at hospital to determine which cases are likely to be more severe.

@schuemie and @Daniel_Prieto have begun designing population-level estimation studies. Based on feedback we’ve gathered from clinicians across the world, it seems several areas are attempting use of various agents as treatment for COVID-19-positive patients, such as hydroxychloroquine, antivirals like protease inhibitors and remdesivir, and immunosuppressants like tocilizumab, despite little immediate COVID-19 efficacy evidence and uncertainty about the long-term safety of these medicines in these off-target populations. Prophylactic use of these medicines for asymptomatic patients who were potentially exposed to COVID-19 is being discussed. These developments further increase the need to more comprehensively understand the real-world effects of these products overall, within subpopulations with existing viral disease, and among subgroups at higher risk for COVID-19 complications. Comparative cohort and self-controlled analyses will be developed to examine causal effects of exposure on incidence of viral disease and associated complications, as well as safety outcomes.

We recognize the need to design characterization studies. FDA has encouraged us to prioritize work to delineate the natural history of the disease where possible, but also believe that developing and evaluating phenotypes for the constellation of diagnostic patterns observed during prior viral outbreaks, including the past several flu seasons, would be useful as foundational research which can be utilized not only for the current outbreak purposes but also for other similar viral diseases research and surveillance in general. @Christian_Reich and I are coordinating the development and evaluation of phenotype algorithms to identify persons with viral diseases and associated symptoms and complications, as well as anticipated treatments, which can be used as the cohort inputs into our characterization, estimation, and prediction studies. The quality of our analyses are largely depending on the quality of our phenotypes, so it is important that we use clinical expertise, data domain knowledge, and empirical tools to produce definitions that we can consistently apply across the community, and not end up with an array of different half-baked definitions that will add noise to our evidence generating process. We will be using the new CohortDiagnostics package to support our iterative development, and PheValuator to estimate measurement error where appropriate. We will use atlas.ohdsi.org as a landing place for all finalized conceptsets and cohort definitions (thanks @lee_evans for your help in administrating that!).

How can you help?

  1. If you are interested in participating in the study-a-thon, but haven’t yet signed up, please do so here.

  2. If you are one of the 103 people who have already signed up for the study-a-thon, thank you! If you marked that your expertise is in literature review and evidence synthesis, then you can expect to receive a private email from me enlisting your support on some preparatory research. So the whole community knows what I’ll be asking, there are already multiple prediction models that have created in and around the space of community-acquired pneumonia, but they vary substantially by their target population, outcome definition, decision context, what information is needed as predictors, and the extent to which they were developed or evaluated on observational data like we have within our community. We need to summarize this prior knowledge to inform our future prediction designs. Also, past work has been published in defining and validating phenotypes for viral disease, symptoms, and complications, but it has not been compiled together to examine the heterogeneity of approaches and determine a best practice moving forward. We need to summarize what is known about the safety and effectiveness of treatments under consideration for COVID-19 to find the specific gaps that we can fill. If we can do an OHDSI-community-crowd-sourced literature review on these topics, it will lay a solid foundation for our future research together. We need leaders to coordinate the community effort, so that’ll be my ask.

  3. If you have access to a patient-level data in OMOP CDM and haven’t yet done so, please run the OHDSI concept prevalence study, led by @aostropolets . We are using this aggregate summary information to directly inform the phenotype development process to ensure that as we define conceptsets, we are representing all concepts that are actually captured across the community as completely as possible. It has already proven invaluable to figure out the different ways that flu-like symptoms are showing up around the world, and will only increase in value as more and more of you participate.

  4. If you have access to a patient-level data formatted in the OMOP CDM and can execute analyses to support the cause, please sign up here. All analyses will be prepared as R packages, which you’ll be able to build and execute in RStudio, and which will produce aggregate summary statistics (no patient-level data) which can be shared to the study coordinate for synthesis across the participating data partners. If you haven’t set up your local environment yet to run an OHDSI network study, useful resources are available in the Book of OHDSI, and Anna’s OHDSI concept prevalence study study would be a great first study to cut your teeth on.

Urgent: Vocabulary Release for the upcoming Covid-19 Study-a-thon
[OHDSI COVID-19 response] Community Update 17 March 2020
(Michael Shamberger) #22

One question to try to answer in the study-a-thon: Does evidence support that taking anti inflammatories can cause adverse outcome?

(Theresa Burkard) #23

@Michael_Shamberger, I was also wondering this.
Apparently this information is based on a hypothesis from a Lancet Correspondence (see attachment). The correspondence talks about evidence from prior studies about ACE inhibitors being a risk factor for Covid-19 infection. Furthermore, they speculate about ibuprofen (and thiazolidinediones) also upregulating ACE2 and therefore being also risk factors.

However it seems, @Patrick_Ryan, that the study questions are set?

Another thought of mine was on clustering patients with severe covid-19 progression. There seems to be certain groups that are rather affected or have worse outcomes of Covid-19. But what if we are missing a certain (smaller) group that we would not think of? This is highly speculative and not sure whether it is possible with OHDSI tools, but I thought that would be also an interesting aspect.

(Nigel Hughes) #24

There has been vexed discussion over decades over the merits of anti-pyretics and anti-inflammatories with regards to fever and infection.

For instance this study from 1990: https://www.ncbi.nlm.nih.gov/pubmed/2172402

Some would argue that inhibiting normal immunological response with OTC drugs not warranted, and indeed immunological efficiency at higher body temperature is well understood. Impacting on abnormal immunological responses is another matter of course.

In France they have allegedly seen younger cases who took ibuprofen, vs. the usual older population.

Again, looking at all concomitant medications may prove very useful in terms of any incidental findings, but OTC and illicit drugs, such as cannabis, will likely not be available in clinical record data.

(Seng Chan You) #25

I’ve added this question to the github.

In my previous experience related with H2-blockers, we could identify substantial number of patients using OTC drugs in OHDSI network, though it might not cover the most (https://data.ohdsi.org/RanitidineCancerRisk/).

(Christian Reich) #26

OTC drugs can be prescribed in the US. They are not always paid for by the insurance, but the prices are low anyway and comparable to the co-payments.

(John Frias Morales, DrBA, MS) #27

Check this out http://predictivehealthcare.pennmedicine.org/2020/03/14/accouncing-chime.html

(Ross Williams) #28

I saw this statement from the ISH which is of relevance: https://ish-world.com/news/a/A-statement-from-the-International-Society-of-Hypertension-on-COVID-19/

although could well still be an interesting question as my reading is there is a lack of evidence rather than evidence to disprove the theory

(Alexander Sivura) #29

(Nigel Hughes) #30

Via https://infoviewer.infodesk.com/infodisplay/article/80cb16dd-72d6-4ab6-a55c-7b33dd163a38.html

Trials Begin of Existing Drugs as Potential Coronavirus Treatments

Last Modified: 05:06 PM, Mon Mar 16, 2020

16 March 2020

Word Count: 409

( Reuters ) Drugs used for treating arthritis are being trialed as treatments for people who have caught the coronavirus, as private companies and scientific researchers rush to find ways of helping patients and slowing infection. Sanofi and Regeneron Pharmaceuticals said on Monday they had begun a clinical trial of their rheumatoid arthritis drug Kevzara as a coronavirus treatment, while in Spain a separate trial is studying if a combination of two drugs can slow down the spread of coronavirus among humans.

(Updates with details on clinical trials in Spain)

March 16 (Reuters) - Drugs used for treating arthritis are being trialed as treatments for people who have caught the coronavirus, as private companies and scientific researchers rush to find ways of helping patients and slowing infection.

Sanofi and Regeneron Pharmaceuticals said on Monday they had begun a clinical trial of their rheumatoid arthritis drug Kevzara as a coronavirus treatment, while in Spain a separate trial is studying if a combination of two drugs can slow down the spread of coronavirus among humans.

Enrolments for the mid-to-late stage trial of Kevzara, an infection-fighting protein known as a monoclonal antibody, will begin immediately and test up to 400 patients, Sanofi and Regeneron said in a joint statement.

Regeneron in February announced a partnership with the U.S. Department of Health and Human Services to develop a coronavirus treatment, and had said it would focus on monoclonal antibodies.

Meanwhile, Barcelona-based researchers said on Monday they would administer a drug used to treat HIV - containing darunavir and cobicistat - to a coronavirus-infected person. The patient’s close contacts would be administered hydroxychloroquine, a drug for malaria and rheumatoid conditions, such as arthritis.

"The goal of our study is to separate the transmission chains," Oriol Mitja, researcher at Germans Trias i Pujol Research Institute, told a news briefing.

Patients with coronavirus can infect between 5% and 15% of the people they come into contact with during the 14 days after starting to show symptoms, he said.

The trial’s goal is to reduce that number below 14 days and also to reduce the percentage of contacts infected and researchers plan to analyze the results in 21 days.

Around 200 patients with coronavirus and 3,000 of their close contacts will take part in the trial, which has private and public funding.

Mitja - who plans to talk with World Health Organization officials this week - said there were two other similar projects in Australia and the United States, but that his is in the lead.

Also on Monday, Madrid’s La Paz-Carlos III hospital announced another trial to administer Remdesivir, a drug similar to a nucleotide and intended to fight viral replication, to voluntary patients with serious and milder cases of coronavirus, Madrid’s regional government said in a statement.

About 1,000 patients will take part in the European-wide trial, it said. (Reporting by Manas Mishra in Bengaluru and Joan Faus in Barcelona; Editing by Sriraj Kalluvila and Alexander Smith)

(Emma Vos) #31

First of all, great initiative! The knowledge and reach of the OHDSI community are enormous, which is very valuable in this critical COVID-19 period.
For everyone to benefit from the outcome of this study-a-thon, it is important to publish the data and the results in a FAIR (Findable, Accessible, Interoperable, Reusable, https://www.go-fair.org/fair-principles/) compliant manner. The OMOP CDM itself already ensures interoperability, and by focussing on the other FAIR principles as well we could maximize the value of the study-a-thon output and help decision makers and other stakeholders navigate the data.

We (The Hyve) would like to offer our services, if beneficial for this study-a-thon, to help to publish the output of this study-a-thon in a FAIR way. In light of the current EHDEN project, this fits the EHDEN FAIR roadmap The Hyve is currently involved in.

(Shyam Visweswaran) #32

In the Accrual of patients to Clinical Trials (ACT) network, we are starting to collect new and old codes for Covid-19 to build an ontology. See https://github.com/shyamvis/covid-phenotyping and https://github.com/shyamvis/covid-phenotyping/blob/master/codes.md

(Gregory Klebanov) #33

Dear Friends,

Odysseus is standing up the COVID-19 OHDSI/OMOP response team that can help institutions and organizations with converting their data into OMOP CDM format to assist with the upcoming COVID-19 studies. We do not have a lot of time left until March 26 - it is given - but let’s think about beyond that, it is long term. We will need to have data available not just for this study-a-thon but many months after that as well. If you need help with converting your data to support COVID-19, please reach - we will help. We will figure out the funding later lets first get the data and evidence in place. Also, please reach out for any collaborations well - the more the merrier

(André Tadeu de Carvalho) #34

Dataset from Johns Hopkins University Center for Systems Science and Engineering - https://github.com/CSSEGISandData/COVID-19

(Sajan Khosla) #35

Two things:

  1. Is there any way we could leverage our China OHDSI network to understand their willingness to use the China hospital data?
  2. Could we be looking into things like Length of Stay or 30 Day mortality in patients hospitalised with viral pneumonia to understand patterns of antiviral use and potential differences in endpoints?

Looking forward to contributing towards this effort