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OHDSI Face-to-Face at Columbia May2-3: Community study-a-thon

I am proposing on behalf of Elad Sharon from the NIH:

To compare the risk of opportunistic infections (will provide list of those) between Patients with ALCL/Hodgkin treated with CD30 inhibitors (brentuximab) and Patients with ALCL/Hodgkin with other drug therapies (any antineoplastics or immunmodulators), we will estimate the population-level effect of exposure on the rate ratio of the outcome during the period from 1 day after exposure start to 90 days after exposure end.

Reason this is interesting: CD-30 inhibitors are fairly new and CD-30 might be involved in fighting opportunistic (as opposed the ordinary day-to-day infections you pick up from your sneazing and coughing neighbor on a long distant flight) infections. This could be a good experiment to support or reject this hypothesis.

See below the proposal from Guy Brussele from the Department of Respiratory Medicine, Ghent University Hospital Belgium and Katia Verhamme from the Department of Medical Informatics of the Erasmus MC:

Does use of inhaled long-acting muscarinic antagonists (LAMA) without concomitant use of inhaled corticosteroids in patients with asthma increase mortality risk?

Asthma is a chronic disease of the airways, associated with chronic airway inflammation, variable airflow limitation and variable symptoms of cough, shortness of breath and chest tightness. Viral respiratory tract infections, and exposure to allergens and pollutants can induce asthma attacks (i.e. asthma exacerbations), which can be life-threatening. In 2015, world-wide 400.000 patients with asthma died due to asthma (Global Burden of Disease Study, Lancet RM 2017). The mainstay of treatment of asthma are inhaled corticosteroids (ICS). In patients with moderate to severe asthma (Global Initiative for Asthma [GINA] steps 3-5), long-acting bronchodilators are added to the maintenance treatment with ICS: either long-acting beta2-agonists (LABA) and/or long-acting muscarinic antagonists (LAMA). In asthma, most clinical evidence of add-on therapy with LAMA to ICS (with or without LABA) is available for tiotropium (Spiriva [Handihaler or Respimat]), but other LAMAs encompass glycopyrronium, umeclidinium and aclidinium.

For LABA, it is well established that LABA monotherapy (without concomitant therapy with ICS) in patients with asthma is associated with an increased risk of asthma attacks and mortality. Therefore, a LABA should always be added to an ICS when treating patients with moderate-to-severe persistent asthma; ideally in one single fixed dose combination inhaler (ICS + LABA). For LAMA, it is not yet known whether LAMA monotherapy (without concomitant therapy with ICS) in patients with asthma is associated with an increased risk of asthma attacks and/or mortality. However, since both LABA and LAMA do not reduce the chronic airway inflammation in asthma, we put forward the hypothesis that in patients with asthma LAMA monotherapy – similar to LABA monotherapy – increases the risk of mortality. Moreover, since there are no fixed dose combination inhalers of LAMA and ICS, the risk of asthma patients using a LAMA without an ICS is substantial.

Clinical research question
Is use of inhaled long-acting muscarinic antagonists (LAMA) without concomitant use of inhaled corticosteroids (ICS) in patients with asthma associated with an increased mortality risk?

In patients with asthma,
A) is use of monotherapy of inhaled LAMA without concomitant use of ICS associated with an increased risk of mortality compared with use of LAMA and ICS?
B) Is use of inhaled LAMA (with or without LABA; with or without Leukotriene Receptor Antagonist [LTRA]) without concomitant use of ICS associated with an increased risk of mortality compared with use of LAMA and ICS (with or without LABA; with or without Leukotriene Receptor Antagonist [LTRA])?

5- core elements of the comparative cohort design:
1) The target exposure cohort:
Patients with asthma (age: 12 – 50 years) exposed to LAMA without ICS;
Either (A) monotherapy with LAMA, or (B) therapy with LAMA and (LABA and/or LTRA).
2) The comparator cohort:
Patients with asthma (age: 12 – 50 years) exposed to LAMA and ICS;
Either (A) therapy with LAMA and ICS only, or (B) therapy with LAMA and ICS and (LABA and/or LTRA).
3) The outcome (cohort):
Mortality (all-cause mortality);
eventually in a second stage: respiratory mortality (asthma-related mortality).
4) A time-at-risk period:
From the first day of treatment with LAMA or LAMA and ICS till end of cohort study, end of follow-up or death.
5) A model specification:
Matching of subjects of the comparator cohort with subjects in the target exposure cohort on age, gender and propensity score (e.g. smoking, asthma severity, asthma control, previous asthma exacerbations, previous asthma hospitalizations …).

The choice of the age range (12 - 50 years) is based upon the following rationale:

  1. The LAMA tiotropium is indicated for the (add-on) treatment of
    asthma from the age of 12 years onwards;
  2. LAMA monotherapy
    (without ICS) and LAMA + LABA combination therapy (loose or fixed
    combination inhalers; with or without ICS) are indicated for the
    maintenance treatment of COPD, which mainly affects subjects older
    than 50 years.
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Looove this topic! Indeed, GINA protocol says that LAMA shouldn’t be used otherwise than as add-on therapy, which is reasonable from the medical perspective. I haven’t found any reliable research on LAMA monotherapy, which I believe is due to an inappropriate risk for patients who are prescribed LAMA only. So, eventually, I’m quite excited to investigate it!
Something that I want to mention: why don’t we add asthma exacerbations as another criterion?And why can’t we exclude patients with COPD to extend the upper age limit?

Getting back on track: I want to propose a relative research question:

To compare the risk of asthma exacerbations (and now I believe I should add mortality here) between patients with Chronic Obstructive Pulmonary Disease (COPD) C/D and inhaled corticosteroids withdrawal and patients with COPD C/D who have never used ICS, we will estimate the population-level effect of exposure on the rate ratio of the outcome during the period from 1 day after last exposure start to 180 days after exposure end.

There have been several trials focused on ICS withdrawal although they have discrepancies in the definition of outcome, treatment used, eligibility criteria etc. Additionally, patients with no use of ICS weren’t included in any of these studies.

Adding a reference:


Sudden cardiac death, acute myocardial infarction, and stroke are rare but serious cardiovascular events among users of serotonin 1b/1d agonists (triptans). Since triptans are known to have such adverse effects, physicians evaluate patients for cardiovascular risk factors prior prescribing these medications. As the result, lower reports of cardiovascular conditions are being reported among users of triptans, but it is not known how much triptans may contribute to serious cardiovascular events.


We intend to evaluate both short-term and long-term effect of triptans to cause serious cardiovascular events compared with new users.

Research question:

To compare the risk of serious cardiovascular events (sudden cardiac death, acute myocardial infarction, and stroke) between new users of triptans and patients who continuously used triptans for 1 year (short-term) and more than 1 year (long-term), we will estimate the population level effect of exposure on the hazards of the outcome during the period from 1 day after exposure start to 0 days after exposure end.

Research Question:
Is the risk of osteoporosis or fracture increased in postmenopausal hypothyroid patients using high strength levothyroxine compared to patients using low strength levothyroxine?
Long-term use of levothyroxine has been associated with decreased bone mineral density, particularly in postmenopausal females on greater than replacement doses or in women receiving suppressive doses. Levothyroxine is used in the treatment of hypothyroidism and patients should be given the minimum dose necessary for desired clinical and biochemical response to limit risks for osteoporosis.

From: BMJ 2011; 342 doi: https://doi.org/10.1136/bmj.d2238

“Hypothyroidism is common in older people, particularly women,1 and over 20% of older people receive levothyroxine replacement long term.2 With normal ageing, thyroid hormone production, secretion, and degradation decreases,3 4 5 and therefore older people with hypothyroidism have lower requirements for levothyroxine replacement than younger people.3 5 Most people with hypothyroidism are diagnosed in early or middle adulthood,6 thus most will have been treated for many years by the time they reach older age. Although regular monitoring of levothyroxine doses is indicated,7 8 evidence suggests that the dose often remains unchanged as people age,9 10 and over 20% of older adults are overtreated,11 12 13 14 leading to iatrogenic hyperthyroidism.
Chronic hyperthyroidism may increase the risk of fractures, particularly in older people and postmenopausal women who already have a higher risk of osteoporosis and fractures.13 15 16 17 Studies have found that higher compared with lower doses of levothyroxine replacement18 19 20 and subclinical hyperthyroidism21 are associated with a lower bone density and bone quality, as measured by ultrasonography.22 An excess of thyroid hormone can also affect neuromuscular function and muscle strength23 and increase the risk of arrhythmias24 25 and falls,15which can raise the risk of fractures independent of bone density. Previous studies of the association between levothyroxine and fractures have had mixed results,15 26 27 28 29 30 largely because of small sample sizes and the inclusion of younger, lower risk populations. This problem has not been dealt with adequately in older women, and older people in general, who are at higher risk of fractures,15 26 31 more likely to be treated with levothyroxine,11.”

1) Target Cohort (T) : On treatment post menopausal (age >=50 years) patients with hypothyroidism exposed to high strength (From above publication defined as: >=0.044 mg/day) levothyroxine.
2) Comparator Cohort ©: On treatment post menopausal (age >=50 years) patients with hypothyroidism exposed to low strength (From above publication defined as: <0.044 mg/day) levothyroxine.
3) Outcome Cohort (O): Osteoporosis or Fracture
4) Model Type: Cox
5) Time at Risk Start and End: From the first day of treatment with high or low strength levothyroxine until the end of cohort study, end of follow-up or death.
6) Methods to adjust for bias: Use propensity score matching or stratification with or without trimming, use negative controls
tagging @Frank as he may share an interest in this topic

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There is a reported association of developing CKD with exposure to proton pump inhibitors in several small cohorts in the literature. This is particularly concerning given the widespread use of these drugs and the rising prevalence of CKD (defined as an eGFR < 60 mL/min) that currently affects 1 in 7 adults in the United States - the reasons for which are not clear at present. Proton pump inhibitors are commonly prescribed drugs but are frequently given to individuals who also have significant comorbidities such as diabetes and obesity (also known risk factors for the development of CKD). A large observational cohort study that is either able to detect a change in the GFR over time (calculated from serum creatinine, age, gender and race) or the development of new onset CKD, defined as an estimated GFR < 60mL/min, following the initiation of PPI use (after adjustment for major comorbidities) would result in significant change in clinical practice worldwide.
Currently small cohort studies have been plagued with inadequate adjustment, inability to create propensity matched control cohorts. As a result, there has yet to be a significant change in clinical practice. The results of a large adequately powered, propensity score matched study or a study that excludes individuals with comorbidities commonly associated with CKD would have an immediate impact on patient care.
Target Cohort – patients aged age 18 years and older with chronic exposure to proton pump inhibitors who do not have evidence of chronic kidney disease (defined as an estimated GFR of < 60mL/min) at the time of initiation of therapy
Comparator group: Patients aged 18 years and older with no exposure to proton pump inhibitors
Outcome: Incidence of chronic kidney disease
Model: Cox model for time to event analysis (development of CKD) with stratification by drug exposure duration. Regression for assessing rate of decline in GFR associated with exposure to PPI
Adjustment: propensity score using age, diabetes, hypertension, African American race and eGFR at the time of entry into the study.
Potential exclusions: patients exposed to known nephrotoxic agents - specifically chronic NSAID use.

Hi everyone! Hope it’s not too late, but wanted to toss out another 2 ideas about cardiovascular health based on recent guidelines

  1. The 2016 ESC/EAS (Europe) and 2017 AACE (Endocrinology, American) guidelines both note that patients with elevated triglycerides may have substantially increased ASCVD (Artherosclerotic Cardiovascular Disease) risk, even those who are already on statins for LDL lowering. They recommend considering fibrates for these patients to improve CV outcomes but there’s no randomized control trial that has proven this and just a few studies that support this. Therefore, the study would be:
    To compare the risk of ASCVD between patients with diagnosis of hyperlipidemia (HLD) on statins and elevated triglycerides who are treated with fibrates and patients with HLD on statins and elevated triglycerides not on fibrates. Estimate population-level effect of exposure on the hazards of outcome (ASCVD) from 1 day after exposure start to 0 days after exposure end (on treatment, or ITT)

  2. I’ve heard rumors from multiple sources that new ACC/AHA guidelines will be coming out at the upcoming ACC conference, and these new guidelines will go back to cholesterol targets instead of just grouping people into risk categories and doing high or low-intensity statin (as in the 2013 ACC/AHA guidelines). Essentially that numbers matter, not just the risk category. So another idea for study related to this would be:
    To compare the risk of ASCVD between patients with diagnosis of HLD on statins with ideal cholesterol levels and patients with HLD on statins with elevated cholesterol. Could also stratify further across different groups of cholesterol levels to see how risk changes (i.e. for every 50mg/dL) but that’s like running multiple studies. Estimate population-level effect of exposure on the hazards of outcome (ASCVD) from 30 day after exposure to statin (about how long it takes the level to drop on a statin) to 0 days after exposure end (on treatment)

Quick question - what does C/D stands for? Sorry didn’t get that one. Thanks

Hi all,

Here is my research question:

Research Question: To determine the comparative risk developing malignancy in patients with moderate to severe rheumatoid arthritis treated with tofacitinib and tumor necrosis factor inhibitors (TNFi).
Rationale: There is a significant concern for developing malignancy in patients treated with TNFi, other non-TNFi biologics, and targeted small molecules. To date, only limited evidence to guide clinical practice.
Patient population: new users of 1) tofacitinib or 2) TNFi for moderate to severe rheumatoid arthritis
Outcome: Time to develop any type of malignancy.
Model: Cox regression model

Thanks @rchen for posting this question. Canagliflozin is a Janssen product and I and others on my team have done a good bit of work on to examine this product’s real-world safety and effectiveness. In fact, we’ve recently completed a study that addresses the amputation risk question that you identified. The study was registered on clinicaltrials.gov (https://clinicaltrials.gov/ct2/show/NCT03492580). We have also publicly posted the full protocol and all associated source code in the OHDSI github repository (https://github.com/OHDSI/StudyProtocols/tree/master/AhasHfBkleAmputation). The results are under review, and we plan to share our experience with this study as soon as the results can be made available. So, while I encourage anyone interested in this topic to reach out to me and would be happy to see this study executed across other sites across the OHDSI network, it would be ‘cheating’ for us to pick this study idea for the F2F meeting, since we want to take an idea from scratch all the way through design, protocol development, code implement, and network study execution in the two days while we are together, and too much of the work has already been done.

Thanks @Evan_Minty for posting this question. Yes, I agree aspirin exposure may be difficult across many data partners. I also wonder how we would work through thinking about prevalent exposure status in this case, since it sounds like the T/C cohorts would be indexed off the bladder cancer diagnosis. Nonetheless, it sounds clinically important, so it seems worth considering the question as we vote for our study choice for the F2F.

Thanks @Vojtech_Huser for posting this question. I hadn’t seen this trial, and it seems potentially quite important from a public health perspective that an antibiotic that is so commonly used could potentially increase mortality, cardiovascular death, and cerebrovascular disease. It’s also a nice example of a randomized trial that was performed which potentially could be valuable to replicate in real-world data, where the exposed population in clinical practice may be much broader than the subjects enrolled in the trial. The trial is only 4400 patients, whereas I expect we have millions of exposures to clarithromycin and amoxicillin across our network. I think it is noteworthy that the effect sizes are so small (HR=1.1 could easily be within the range of systematic error that we may estimate through empirical calibration). We also have the concern about the adequacy of capture of mortality across our network, and we likely have variable follow-up with a much smaller proportion being observed for 10 years. All that said, seems a worthy topic to consider in our community vote when picking a topic. Thanks!

Thanks @christian_reich and Elad. I’ve heard from several folks in the community about interest in studying infections with immunosuppressive agents, including chemotherapies and biologics, so it seems like a good area to have some focus. I don’t know anything about brentuximab and I don’t have much exposure to the drug in the datasets I have access to (n<1000 patients), but maybe others in the community have more sample to work with. I would think we’d need to be careful about defining an appropriate comparator, since in some databases, particularly claims data, many antineoplastics are non-specifically coded as procedural administrations and we may not know the specific active ingredients. I know @rchen is doing some good detective work for OHDSI’s collaboration with NCI to try to characterize the boundaries for how cancer treatment exposure is captured, but I am not sure how much findings there will generalize across the rest of the OHDSI community.

Thanks @rijnbeek, Guy, and Katia. This is a very well-written project specification, and while I don’t know much in the asthma space, you make a compelling argument for why understanding the effect of inhaled corticosteroids among patients on LAMA on mortality could have a large public health impact.

As with some of the other project ideas, one concern I have is the adequacy of capture of death information across many of our data partners. Particularly for this problem where any effect difference we observe is likely be small, and may be well within the systematic error due to outcome misclassification. Forgive my ignorance for a potentially basic clinical question, but for LAMA and ICS, are we comfortable that dispensing records from pharmacy systems or prescriptions written in EHRs are sufficient proxies for exposure (and inferring concomitant use) or would there be consider about patient’s non-adherence or switching/augmentation of asthma treatments could also introduce systematic error due to exposure misclassification?

In any case, seems like a good question to add to our ballot for voting…

Thanks @aostropolets, so if I understand the question, you are basically asking: among patients with COPD, is starting and stopping ICS better or worse than never starting ICS at all for the risk of asthma exacerbations and mortality?

I am struggling to think about how we would define the index date for the target and comparator cohort in this case. In the target cohort, for many databases, we can infer when ICS exposure started based on dispensing or prescription written, but I suspect we will have much less precision around a withdrawal date (though we can see when dispensings discontinued or no more refills ordered). For the comparator cohort, if the COPD patients aren’t exposed to ICS, is there some other treatment we would expect to see that we could index off of (LAMA or LABA)? Is the COPD Grading C/D something that we can infer based on medication history?

Anyway, thanks for the submission, I’ll add it to our ballot to vote on.

Thanks @abedtash_hamed. It’s an interesting use case to consider a ‘known side effect’ that may have a lower real-world risk than what was previously observed in trials, because clinicians are now imposing their own risk minimization strategies.

I think you raise an important type of problem that I haven’t really wrestled with before, namely how do you examine the short-term effects vs. long-term effects of a product. If there was some active comparator to use in the study, I could imagine specifying a fixed short-term window like ‘to compare risk of CV events amongst patients with migraine who are new users of triptans vs. patients with migraine who are new users of indomethacin, during the period from 1d from exposure start to 180d from exposure start’. And then, to examine a longer-term effect, a question could be specified like: ‘to compare risk of CV events amongst patients with migraine who are new users of triptans who remain continuously exposed for over 1 year vs. patients with migraine who are new users of indomethacin who remain continuously exposed for over 1 year, during the period from 1 year from exposure start until end of observation’. (note, in this second question, since we require the 1 year of continous exposure, we can’t use that time as part of our time-at-risk).

What I can’t quite work my head around is how to compare short-term exposure with long-term exposure. It’s not clear how to reconcile the time-at-risk, since length of exposure is determining the comparator. Perhaps that’s what we could sort out at the F2F, if this problem is selected in the voting.

Thanks @jill_hardin, given the extremely high prevalence of use of levothyroxine, it seems like any effect of dosing could be quite important from a public health perspective.

One of the challenges that I don’t think we’ve overcome as a community is how to think about the time-varying nature of dosing. In the claims data I’m most familiar with, we can try to infer average daily dose from each pharmacy dispensing record using the strength of the product * quantity / days supply. But we need to think about how to handle successive dispensings and how to handle dosing during gaps and periods of overlap. We’d also need to think about how to handle patients who may switch from low-to-high dose or vice versa. Perhaps there’s enough people who start on and remain on low dose who can be compared with people who start and remain on high dose, but I don’t know one way or the other if ‘stable dose’ users are generalizable to the population at large.

Anyway, you’ve done a good job of laying out the problem and rationale, so it’ll be up to the community to vote .

Thanks @sm2206, there’s been a lot of work examining the effects of proton pump inhibitors in various observational databases, and I agree that many of the studies I’ve seen in the literature have had some serious methodological shortcomings, so it’d be nice to see what evidence we could generate if we applied the OHDSI community best practices to the question. It would be interesting to see how a comparison of PPI vs. H2RAs using a large-scale propensity score matched new user design across the OHDSI network would stack up with studies using different covariate adjustment strategies, such as the recent paper by Lazurus et al.

One challenge I anticipate is exposure misclassification, since both PPI and H2RA are available over-the-counter and may not be fully captured in many of our data partners. So, there could be some tension between using the most recent and timely data vs. using the time period where prescription coverage offered most exposures. I suspect this could be worked out if this question is selected in voting.

Thanks @rchen. I appreciate the vantage point of looking the clinical guidelines as a source of inspiration for where we can make important contributions. It seems there’s areas of opportunities for determining which recommendations may exist which are not fully supported by high-quality evidence (or could be further augmented with real-world observational studies), and also I’ve seen several areas in guidelines where recommendations cannot be made because of the lack of availability of any evidence to support alternatives. It seems the effect of fibrates to supplement statin use in reducing cardiovascular risk should be better understood if its going to be part of recommendations for how to handle specific subpopulations with uncontrolled hyperlipidemia. I’ll add this to our ballot so we can see how the rest of the community feels about it.

Thanks @BridgetWang. I’m very excited by this question. There is an ongoing RCT that is scheduled to report out next year to address this question (https://clinicaltrials.gov/ct2/show/NCT02092467), so OHDSI has an opportunity to ‘predict’ the RCT results using our observational data network, which has important methodological value for demonstrating the potential role of real-world data to augment, and in some circumstances, replace the need for RCTs, as has been proposed for consideration by the FDA and as part of the 21st Century Cures Act. The trial involves an active comparator, so we can define target and comparator cohorts for these drugs quite easily. Also, both trial outcomes (cancer, MACE) should be observable in most claims and EHR systems, as are most of the study inclusion criteria. There seems to be a lot of uncertainty about the safety of biologics in RA even after a lot of small clinical trials, so examining these outcomes in the real-world clinical practice across the OHDSI network could be an important contribution to the field.

Let’s see if the community is as excited as I am to pursue this question, I’ll add it to the ballot.

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