[2022 US Symposium] #66 - Episode and Episode Event Tables Documentation

[mgurley (Michael Gurley)]

This is a great debate. A continuation of an issue debated during ‘Pehnotype Phebruary’:

Phenotype Phebruary Day 4- Multiple Myeloma

I very much like what @jswerdel showed us here. The evaluation clearly illustrates that PPV alone is not enough to evaluate the phenotype. And as @agolozar points out, there is a need to better understand the performance of the phenotypes across all participating data partners. Now, we can’t really expect all of them to do an extensive chart review nor it is feasible to do it on claims data sources. We can use Phevaluator to estimate the performance but it would be really nice to also get an ide…

Phenotype Phebruary Day 19 - Triple negative breast cancer

First I want to thank everyone for the informative phenotype discussions so far. I have learned a lot from reading them. Along with lots of help from my colleagues Darya Kosareva and @agolozar I have created a few cohort definitions for triple negative breast cancer to share. In the process I have learned some lessons about the importance of understanding the vocabulary and how clinical concepts are recorded in actual data. I’d like to summarize the cohorts first then put the clinical definitio…

Phenotype Phebruary Day 26 - Non-Small Cell Lung Cancer

Happy Saturday everyone! It is non-small cell lung cancer (NSCLC) day. I am presenting on behalf of a phantastic group: @dkosareva, @mgurley, @Ajit_Londhe, @CarolynB, Xerxes Pundole, and @jshaw03. And special thanks to @Patrick_Ryan. I am going to make a little bit of a change today and start talking about what we want to define today. Our goal today is to come up with a phenotype for NSCLC. We would like to identify patients with a diagnosis of NSCLC, irrespective of the disease stage. We a…

I would articulate the issue as:

Are there limits to what kind of clinical facts can be represented by standard OMOP, phenotypes and standard analytics?

It is no coincidence that in Phebruary this issue was most hotly debated in phenotypes involving cancer. Cancer research demands the representation of clinical facts like ‘histology’, ‘staging’, ‘disease progression’, and ‘treatment lines’. The OHDSI Oncology Working Group was formed out of difficulties representing these important cancer clinical facts in OMOP.

Thus was introduced:
Structures: EPISODE, EPISODE_EVENT, MEASUREMENT.measurement_event_id, MEASUREMENT.measurement_event_id, MEASUREMENT.meas_event_field_concept_id.
Vocabularies: ICDO3, NAACCR, Cancer Modifier, Hemonc.org, CAP electronic Cancer Checklists (College of American Pathologists)
Standardized ETLs: NAACCR ETL
Treatment Regimen Detection Algorithms: OncoRegimenFinder, Tracer (AJOU University)

It looks like the OHDSI community has formed various ‘factions’ around how to answer the above-articulated issue.

• Faction 1 (the ‘Methodists’):

  • All clinical facts can be represented by current standard OMOP, phenotypes, and standard analytics.
  • We don’t need the EPISODE table.
  • ETL’ers should not interpret source data and derive new data.
  • Rote ETLs and statistics are enough.
  • Members: Patrick Ryan, Gowtham Rao

• Faction 2 (the ‘Derivers’):

  • Not all clinical facts can be represented by standard OMOP, phenotypes, and standard analytics.
  • We need the EPISODE table to represent cancer disease phases and treatment lines.
  • Episodes belong in the standardized derived elements.
  • We need to come up with modifiers (e.g. 734306 = ‘Initial diagnosis’) and conventions on how to populate cancer events in the traditional OMOP standardized clinical event tables.
  • These modifiers and conventions will enable the development of a post-ETL algorithm to derive cancer disease and treatment episodes from the standardized clinical event tables.
  • Episode population should not be opaque and depend on data source context.
  • ETL’ers using modifiers, adhering to conventions and a promise to develop a post-ETL algorithm are enough.
  • Members: Rimma Belenkaya, Robert Miller.

• Faction 3 (the ‘Contextualists’):

  • Not all clinical facts can be represented by standard OMOP, phenotypes, and standard analytics.
  • We need the EPISODE table to represent cancer disease phases and treatment lines.
  • Episodes belong in the standardized clinical data tables.
  • We need to come up with simple semantic targets based on oncology research standards to support the population of disease phases and treatment lines in the EPISODE table.
  • Episode population can only be done in the context of source data by an advanced informatics infrastructure that supports an ETL’er.
  • Such advanced informatics infrastructure will include generating abstractions from unstructured imaging and pathology lab reports and reconciling EHR/claims data with multiple sources: e.g., tumor registry, oncology analytic platforms, and oncology EMRs.
  • Clear semantic targets and institutions with advanced informatics infrastructure are enough.
  • Members: Christain Reich, Asieh Golozar, Michael Gurley

I know these ‘factions’ are overly simplistic. Please take my personal assignments to the factions as an attempt to sharpen the contours of the debate. Nothing more.

One last thing that I will say is that the oncology world is furiously already engaged in the process of interpreting source data and deriving new data. There is a cottage industry of commercial and open-source solutions helping institutions to generate cancer disease phases and treatment lines. Every academic medical cancer center in the country is engaged in such efforts. So fighting against that tide is really a decision for the OHDSI community to be open or closed to incorporating these emerging oncology data assets. One thing the cottage industry has not provided is an open-source clear structure and semantics to represent cancer disease phases and treatment lines. mCode has made significant contributions in this area but is more focused on data collection and transport. Not open data analysis. I think OHDSI has a great opportunity to be that open-source structure and semantics for oncology. But we need to make the target simple enough that folks can achieve populating disease and treatment episodes within our lifetimes.