OHDSI2020 Symposium Study-a-Thon

We are pleased to offer a virtual Study-a-Thon during the 2020 OHDSI Symposium. The Study-a-Thon will take place from Tuesday, October 20 to Wednesday, October 21 and the details are below.

Event Date/time
Kick-off Tues 10:00 AM EDT
Teams begin work Tues 10:45 AM EDT
Report on progress by teams on day 1 Tues 8:00 PM EDT
Teams summarize and initiate plans for day 2 Wed 10:00 AM EDT
Report on progress by teams Wed 8:00 PM EDT
Study windup and conclusions Wed 11:00 PM EDT

PLEASE NOTE: In order to access the virtual environment for the study-a-thon, you must be registered for the Main Symposium on Monday, Oct. 19. If you have not yet registered for the Main Symposium, you may do so below. If you have already registered, please do not register again:

MAIN SYMPOSIUM REGISTRATION

You may register for the Study-a-Thon here:

  STUDY-A-THON REGISTRATION

OHDSI Cardiovascular Clinical Prediction Model Study-a-Thon, Predicting and Recalibrating Outcomes Toward External Understanding Study (PROTEUS)

A two-day study-a-thon will begin the day after the OHDSI symposium, running from Tuesday, October 20 thru Wednesday, October 21. It will focus on two cardiovascular clinical prediction models (CPM) routinely used in clinical practice: the Revised Cardiac Risk Index (RCRI) and the Pooled Cohort Equations. The RCRI is used to predict 30-day risk of cardiovascular complications amongst patients undergoing non-emergent surgical procedures. The Pooled Cohort Equations predict 10-year risk of atherosclerotic cardiovascular disease among adults without pre-existing cardiovascular disease. The community will collaborate to implement these two existing prediction models against the OMOP Common Data Model using the OHDSI PatientLevelPrediction package. We will then apply the package across participating data partners across the OHDSI community to characterize the target populations and estimate their baseline risk, as well as externally validate the performance of the models in real-world settings and determine the potential impact of recalibration on model use in clinical practice.

Participants can contribute by: A) Evaluating data quality and fitness for use of OHDSI participating data partners ; B) Developing and evaluating phenotypes; C) Implementing the open-source R study package to apply the models; C) Running the analysis against their data, D) Synthesize the current literature, and E) interpreting the observational results and summarize how they should be used in clinical practice. We welcome all members of the community to join us on this journey and contribute to any of these activities. Please indicate your preferred role when you register.

ASCVD Risk

1 in 3 deaths are due to cardiovascular disease(1) and primary prevention of atherosclerotic cardiovascular disease (ASCVD) is one of the main therapeutic goals of modern medicine. A recently developed set of clinical prediction models (CPMs) has been integrated into practice guidelines to help risk-stratify patients and target lifestyle and pharmacologic interventions toward individuals who are most likely to benefit (i.e. have highest risk).(2) Major questions have emerged about the accuracy of these CPMs(3) and also about what recalibration methods are required(4) to improve predictions to make them more relevant and guard against harm.(5) In this portion of the OHDSI cardiovascular CPM study-a-thon participants will use OHDSI tools to assess the performance of these models across a set of observational cohorts. This will represent the largest, most geographically diverse and up-to-date validation cohort ever assembled for these important models.

RCRI Surgical Risk

Cardiac complications are common and feared causes of morbidity and mortality following noncardiac surgical procedures and providers are frequently called upon to assess an individual’s preoperative risk.(6) Clinical predictive models (CPMs) have assumed a central role in perioperative patient evaluation where predictions can be leveraged to minimize unnecessary preoperative testing for low-risk patients and improve communication between patient and caregivers (including anesthesiologists and surgeons) about expected risks and possible outcomes. The Revised Cardiac Risk Index (RCRI)(7) is a simple and widely accepted CPM that is recommended for use in this setting. The model, however, was created over 20 years ago and contemporary validations are lacking. Here we have designed a study-a-thon to assess RCRI performance across a set of observational cohorts that together represent the largest, most geographically diverse and up-to-date validation cohort ever assembled.

Outline of Study-a-Thon activities

DAY 1

• apply OHDSI data quality tools to participating partner datasets to determine fitness for use

• develop phenotypes and cohort definitions for the target populations, outcomes, and covariates required for the model

• design and implement a OHDSI study package that uses the PatientLevelPrediction skeleton to externally validate existing models

• execute CohortDiagnostics against participating partner datasets to evaluate phenotypes

• summarize the current literature and write Background/Methods sections for manuscripts

DAY 2

• review CohortDiagnostics results from participating data partners

• execute OHDSI PLP study package across participating data partners

• compile and publish study results as public RShiny application

• review and summarize results in Results/Discussion sections of manuscripts

References

1. Benjamin EJ, Virani SS, Callaway CW, et al. Heart disease and stroke statistics – 2018 update: A report from the American Heart Association. 2018.

2. Goff DC, Lloyd-Jones DM, Bennett G, et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation 2014;129:S49-73.

3. Cook NR, Ridker PM. Calibration of the Pooled Cohort Equations for Atherosclerotic Cardiovascular Disease: An Update. Ann. Intern. Med. 2016;165:786–794.

4. Pennells L, Kaptoge S, Wood A, et al. Equalization of four cardiovascular risk algorithms after systematic recalibration: Individual-participant meta-analysis of 86 prospective studies. Eur. Heart J. 2019;40:621–631.

5. Van Calster B, McLernon DJ, van Smeden M, Wynants L, Steyerberg EW. Calibration: the Achilles heel of predictive analytics. BMC Med. 2019;17:230.

6. Fleisher LA, Fleischmann KE, Auerbach AD, et al. 2014 ACC/AHA guideline on perioperative cardiovascular evaluation and management of patients undergoing noncardiac surgery: A report of the American college of cardiology/American heart association task force on practice guidelines. J. Am. Coll. Cardiol. 2014;64:e77–e137.

7. Lee TH, Marcantonio ER, Mangione CM, et al. Derivation and prospective validation of a simple index for prediction of cardiac risk of major noncardiac surgery. Circulation 1999;100:1043–1049.