Interview Highlights: An International COVID-19 Study-a-Thon With George Hripcsak

Interview by: Hannah Lin (CC '23)

Cover illustration by: Zoe Chan (CC '22)

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George Hripcsak, MD, MS, is the Vivian Beaumont Allen Professor and Chair of Biomedical Informatics at Columbia University and Director of Medical Informatics Services at New York-Presbyterian Hospital/Columbia.

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​The following is a heavily condensed version of the full interview. If you're interested, read more here.


Can you describe your COVID research at the moment?
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I’ll talk about the international one first: OHDSI [pronounced odyssey]—Observational Health Data Sciences and Informatics—is an international collaboration with 300 researchers, 30 countries, 600 million unique patients in our federated database. Columbia University is the coordinating center for OHDSI, and I’m the director of that coordinating center. 

We were actually supposed to run our annual European symposium at Oxford in the UK just as everything was shutting down, so we turned it into a virtual symposium. It turned into a study-a-thon (like a hackathon to do a study). We spent four days straight, 24 hours a day, working on COVID research, and that is what got us started.

There are three things we do in OHDSI for COVID research: ​

​1. Characterization

Characterization is measuring how often different things happen in a disease. You may have seen news stories that older people, people with hypertension, and people with other various risk factors get the disease or serious complications more often. We find that compared to influenza, COVID is a young person’s disease. COVID-19 still affects older people or those with chronic disease more severely, but healthy people are also getting ill at a rate much higher than ever before. 

2. Population-level estimation

What role does obesity play? Are older people getting sick because they have chronic diseases or are people with chronic disease getting sick because they’re older, or is it both things independently? Once you start asking questions about what is causing an effect, then you move away from characterization to population-level estimation. This includes risk factors and treatment effects.

3. Patient-level prediction

Given my personal risk factors, what’s my risk of being infected? What’s my risk of severe complications? This can be used to decide if it is safe for someone at the hospital to go home. ​

​The first COVID study OHDSI did was actually not on COVID-19 patients, but on patients who were taking hydroxychloroquine and azithromycin to see what their safety risk was. We found that for a 30-day period, hydroxychloroquine is relatively safe, but taken in combination with azithromycin (which many people are doing), there is a notably increased risk of sudden cardiac death due to arrhythmias, due to the known side effects of those two drugs. 

​We’re also doing Columbia-specific studies. We published a characterization study on Columbia patients, where we found a couple things: 

1. As others have shown, older people with hypertension, diabetes, obesity tend to be at greater risk.
2. A high preponderance of kidney disease, which has also been noted in other New York hospitals, but was higher than seen in China or Korea. 
3. Bimodal distribution of intubation. From when symptoms started, 3-4 days is when they had one peak of intubation, and the second, highest peak was at about 9 days. Once you got to 15 days, the risk dropped pretty low, so much so that if you see a patient who started their symptoms 2 or more weeks ago, you had to worry less about intubation and respiratory failure. It might therefore be safer to just send them home for observation rather than having to admit them. 

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Have you faced any setbacks in your research?

For randomized trials, you can easily assign causality. Because you randomize patients to two groups and see which did better, you’re pretty sure that the two groups were equivalent at the beginning. In observational research like we’re doing where we look back in the patient’s database, you don’t know if the group who got the drug and the group who didn’t get the drug were equivalent. 

For example, for patients who were more ill, the doctor might think of giving them hydroxychloroquine more often. Therefore, it might look like patients who got hydroxychloroquine are dying more often. The 2 groups aren’t balanced, and it’s very hard to account for that. 

Would you say that COVID-19 has shifted any of your goals or thoughts about the health record system dramatically?

Electronic health records have been in the news in recent years because of their increased use and the burden they place on providers. In the setting of COVID-19, they were in even more of a hurry than usual, and that probably limited the ability to document things in some ways, so improving electronic health records to reduce the documentation burden would be very useful. ​

​The fact that we were able to do the studies this quickly was actually a benefit of having electronic health records. Now, we’re social distancing. Imagine: in the old days you would have to go to the floor that the patient’s being treated on to get the chart, read it, and abstract it. Now, you can go into the computer and get those data that are entered. I think we need to continue to improve them, but overall, they were a benefit in this case. 

​Are there any common misconceptions about COVID-19 that you’d like to respond to?

The idea that if you’re young, you’re safe is not quite true. With influenza, if you’re young, you’re not completely safe even then. With COVID-19, if you’re young, you’re even less safe. Most people who are young do fine, but not everyone, unfortunately. 

​What is your perspective on the future?

For our work, we’ve learned a lot about how to analyze patients with COVID-19 as opposed to outpatients who, say, have hypertension and started a drug and see how it goes over the course of a year. Now we’re learning how to do the same kind of research, but on patients who come in, and in a day, this happens, and in two days, that happens, because things are happening much more acutely. We need more work on how to do well in this acute setting. And the importance of having these electronic data is just being further verified.