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. 2020 Jun:138:104138.
doi: 10.1016/j.ijmedinf.2020.104138. Epub 2020 Apr 7.

Guideline-concordant-phenotyping: Identifying patient indications for implantable cardioverter defibrillators from electronic health records

Christopher Manrodt  1 , Anne B Curtis  2 , Dana Soderlund  3 , Gregg C Fonarow  4
Affiliations

Guideline-concordant-phenotyping: Identifying patient indications for implantable cardioverter defibrillators from electronic health records

Christopher Manrodt et al. Int J Med Inform. 2020 Jun.

Abstract

Background: Implantable cardioverter-defibrillators (ICDs) have been shown to reduce sudden cardiac death in appropriately selected patients, but they remain underutilized among indicated patients.

Objective: To develop a new approach to identifying guideline indications among patients implanted with ICDs by creating algorithms that extract data from electronic health records (EHR).

Methods: Published guidelines providing recommendations for ICD use were distilled into categories of diagnoses, measures, procedures, and terminologies. Criteria for each indication category were translated into clinical algorithms using administrative codes, search terms, and other required data. Cardiologists with guideline-development expertise reviewed these algorithms. After developing applications using a subset of data, phenotypes were evaluated against a curated Optum® de-identified EHR dataset, including 94,441 patients with ≥1 procedure codes for ICD implantation or follow-ups from 47 US provider networks.

Results: Guideline-concordant indications were identified in 83.7 % of 49,560 patients with new ICD implants. The percentage of ICD patients with guideline-concordant indications ranged from 69.4%-88.1% for patients whose initial EHR records were 0-6 days to >365 days prior to implant, respectively. Many guideline criteria used data which could only be derived from unstructured provider notes and required significant algorithm development.

Conclusions: Defibrillator implant indications were detected in >80 % of patients receiving ICDs using rule-based algorithms in a curated EHR dataset. Computable phenotypes may enable researchers to analyze EHRs in more reproducible ways, by identifying guideline indications in patients with specific therapies such as ICDs, and, by extension, identifying patients who meet indications yet do not yet have indicated therapies.

Keywords: Electronic health records; Heart failure; Implantable cardioverter defibrillator; Phenotyping.

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Conflict of interest statement

Declaration of Competing Interest CM and DS are employees and shareholders of Medtronic; ABC: Janssen Pharmaceuticals: advisory board; Medtronic, Inc.: honoraria for speaking; data monitoring committee; Abbott: honoraria for speaking; advisory board; Novartis: advisory board; honoraria for speaking; Sanofi Aventis: advisory board; Biotronik: honoraria for speaking; Milestone Pharmaceuticals: advisory board; GCF: reports consulting for Abbott, Amgen, Bayer, CHF Solutions, Janssen, Medtronic, and Novartis.

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