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. 2022 Sep:6:e2200056.
doi: 10.1200/CCI.22.00056.

Design and Evaluation of a Computational Phenotype to Identify Patients With Metastatic Breast Cancer Within the Electronic Health Record

Benjamin Neely  1 , Mohammad Shahsahebi  1   2 , Caitlin E Marks  3 , Steve Power  1 , Andrew Kanter  4 , Claire Howell  1 , Terry Hyslop  5 , Jennifer K Plichta  1   3
Affiliations

Design and Evaluation of a Computational Phenotype to Identify Patients With Metastatic Breast Cancer Within the Electronic Health Record

Benjamin Neely et al. JCO Clin Cancer Inform. 2022 Sep.

Abstract

Purpose: Outcomes for patients with metastatic breast cancer (MBC) are continually improving as more effective treatments become available. Granular data sets of this unique population are lacking, and the standard method for data collection relies largely on chart review. Therefore, using electronic health records (EHR) collected at a tertiary hospital system, we developed and evaluated a computational phenotype designed to identify all patients with MBC, and we compared the effectiveness of this algorithm against the gold standard, clinical chart review.

Methods: A cohort of patients with breast cancer were identified according to International Classification of Diseases codes, the institutional tumor registry, and SNOMED codes. Chart review was performed to determine whether distant metastases had occurred. We developed a computational phenotype, on the basis of SNOMED concept IDs, which was applied to the EHR to identify patients with MBC. Contingency tables were used to aggregate and compare results.

Results: A total of 1,741 patients with breast cancer were identified using data from International Classification of Diseases codes, the tumor registry, and/or SNOMED concept identifiers. Chart review of all patients classified each patient as having MBC (n = 416; 23.9%) versus not (n = 1,325; 75.9%). The final computational phenotype successfully classified 1,646 patients (95% accuracy; 82% sensitivity; 99% specificity).

Conclusion: Hospital systems with robust EHRs and reliable mapping to SNOMED have the ability to use standard codes to derive computational phenotypes. These algorithms perform reasonably well and have the added ability to be run at disparate health care facilities. Better tooling to navigate the polyhierarchical structure of SNOMED ontology could yield better-performing computational phenotypes.

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Conflict of interest statement

Steve PowerStock and Other Ownership Interests: Merck Andrew KanterEmployment: Intelligent Medical Objects Inc (I)Leadership: Intelligent Medical Objects IncStock and Other Ownership Interests: Intelligent Medical Objects IncTravel, Accommodations, Expenses: Intelligent Medical Objects Inc Terry HyslopConsulting or Advisory Role: AbbVieTravel, Accommodations, Expenses: AbbVieNo other potential conflicts of interest were reported.

Figures

FIG 1.
FIG 1.
Epic and IMO enabled SNOMED computational phenotype workflow. A list of SNOMED browsers can be found on the National Institutes of Health website. ICD-10-CM, International Classification of Diseases, 10th Revision, Clinical Modification; IMO, Intelligent Medical Objects.
FIG 2.
FIG 2.
Stratified sample scheme. ICD-CM, International Classification of Diseases, Clinical Modification.
See this image and copyright information in PMC

References

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    1. Plichta JK, Thomas SM, Sergesketter AR, et al. : A novel staging system for de novo metastatic breast cancer refines prognostic estimates. Ann Surg 275:784-792, 2022 - PMC - PubMed

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