Laboratory Tests
- Tiffany J. Callahan (Primary Annotator)
- Adrianne Stefanski Domain Experts:
Clinicians 🏥
- Tellen D. Bennett
- James A. Feinstein
- Blake Martin
Biocurators 🖥️ 🔬
- Tiffany J. Callahan
- Adrianne Stefanski
- Nicole Vasilevsky
- Xingmin Aaron Zhang
- Leigh Carmody
- Peter N. Robinson
The goal of this project was to map condition codes drawn from the Observational Medical Outcomes Partnership (OMOP) common data model to the Open Biomedical Ontologies (OBO). Specifically, we annotated all unique LOINC codes assigned to at least 1 patient (n=29,128 codes) to two OBO ontologies.
“The Open Biological and Biomedical Ontology (OBO) Foundry is a collective of ontology developers that are committed to collaboration and adherence to shared principles. The mission of the OBO Foundry is to develop a family of interoperable ontologies that are both logically well-formed and scientifically accurate.” -OBO Foundry
Currently, there are very few annotations (i.e. mappings or connecting of similar concepts from different sources) that exist between clinical terminologies and the OBO ontologies. Creating these mappings enables transition into a reproducible research framework where clinical observations can be viewed within the context of their underlying molecular mechanism(s).
This task will use the Human Phenotype Ontology (HPO) and the Human Disease Ontology (DOID):
Human Phenotype Ontology (HPO)
The Human Phenotype Ontology (HPO) provides a standardized vocabulary of phenotypic abnormalities encountered in human disease. Each term in the HPO describes a phenotypic abnormality, such as Atrial septal defect. - HPO
Our goal, was to map all unique LOINC laboratory test results (i.e. low, normal, or high), assigned to at least one pediatric patient, to HPO. For laboratory tests, each result is considered independently in order to find the best possible mapping to an ontology concept. The table below provides an example of what a mapping looks like: Our goal, is to map all unique concepts assigned to at least one pediatric patient, across four clinical domains (i.e. conditions or diagnoses, medications, laboratory measurements, and procedures). This form will be used to document our efforts to verify a subset of the 2,735 mappings between a LOINC laboratory result and an ontology term from the HPO. See example mappings below:
| LOINC | Result | HPO |
|---|---|---|
| LOINC_28606-2 : 1-Methylhistidine/Creatinine [Ratio] in Urine | Low | Decreased urinary 1-methylhistidine |
| LOINC_28606-2 : 1-Methylhistidine/Creatinine [Ratio] in Urine | Normal | NOT(Abnormal urinary methylhistidine concentration) |
| LOINC_28606-2 : 1-Methylhistidine/Creatinine [Ratio] in Urine | High | Increased urinary 1-methylhistine |
The following tasks were performed to map our LOINC laboratory test results to the HPO:
Export LOINC Laboratory test Results
- Export each SNOMED CT id and it’s most senior ancestors from a pediatric instance of the OMOP common data model. The SQL code that was used to retrieve these codes is stored as a GitHub Gist and can be found here. For convenience, the queries are also shown below:
-- MEASUREMENTS: compute a new variable that provides an indicator for each patient's lab result versus their
-- reference range
SELECT measurement_concept_id AS OMOP_ID,
concept_code AS LOINC_ID,
concept_name AS Label,
lab_result AS Result
FROM
(SELECT m.person_id, c.concept_code, m.measurement_concept_id, c.concept_name, m.value_as_number, m.range_low, m.range_high, m.measurement_source_value,
CASE
WHEN CAST(m.value_as_number as numeric) > CAST(m.range_high AS numeric) THEN "High"
WHEN CAST(m.value_as_number as numeric) < CAST(m.range_low AS numeric) THEN "Low"
ELSE "Normal"
END AS lab_result
FROM DB.measurement m
JOIN DB.concept c
ON m.measurement_concept_id = c.concept_id
WHERE (m.range_low IS NOT NULL AND m.range_high IS NOT NULL AND m.value_as_number IS NOT NULL)
AND concept_name != "No matching concept"
AND c.vocabulary_id = "LOINC"
AND c.domain_id = "Measurement"
GROUP BY m.person_id, c.concept_code, m.measurement_concept_id, c.concept_name, m.measurement_source_value, m.value_as_number, m.range_low, m.range_high, lab_result)
GROUP BY measurement_concept_id, concept_code, concept_name, lab_result
ORDER BY measurement_concept_id, result DESC;
A subset (n=270) of pediatric-specific laboratory test result mappings were independently validated by five domain experts (i.e. three pediatric clinicians, a PhD-level molecular biologist, and a master’s-level epidemiologist).
To perform this validation, a Qualtrics survey (see QR code) was designed so that each question featured a laboratory test description and set of reasonable HPO concepts.
The survey was completed by all experts between October and December (2018). After completion, any laboratory test mapping that did not meet agreement by at least one clinician and both the biologist/epidemiologist were re-evaluated with one clinician until consensus was reached (n=58 lab results). These terms were additionally vetted on the loinc2hpoAnnotation GitHub tracker by the entire team of HPO biocurators.
The subset of randomly selected LOINC codes that will be verified (n=731 lab tests), can be found here. For the purposes of this document, a screenshot of the table is also shown below (click on image to enlarge).
To verify or search the ontologies for alternative terms, please use the following:
- To verify each of the mappings, please work row-by-row considering each LOINC lab result code within the context of the HPO mappings that have been provided.
- The goal is to find the best mapping between a single ontology term and a LOINC laboratory test result.
- Additional information on this mapping process, including the new terms we requested in order to complete this mapping, can be found in the Human Phenotype Ontology GitHub tracker.