Key result
Ontology-based identification of cardiac device therapy changes achieved perfect accuracy (F-measure 1.00), outperforming straightforward analysis of standard registry codes (F-measure 0.82).
Why the study?
Does an ontology-based annotation method improve the detection of therapy changes in a CIED registry compared to straightforward registry code analysis?
Does an ontology-based annotation method improve the detection of therapy changes in a CIED registry compared to straightforward registry code analysis?
Absolute Event Rate: 1% vs 0.82%
An ontology-based functional annotation of cardiac devices accurately detects therapy changes in a national registry, outperforming standard registry code analysis.
The patient population benefitting from cardiac implantable electronic devices (CIEDs) is increasing. This study introduces a device annotation method that supports the consistent description of the functional attributes of cardiac devices and evaluates how this method can detect device changes from a CIED registry. We designed the Cardiac Device Ontology, an ontology of CIEDs and device functions. We annotated 146 cardiac devices with this ontology and used it to detect therapy changes with respect to atrioventricular pacing, cardiac resynchronization therapy, and defibrillation capability in a French national registry of patients with implants (STIDEFIX). We then analyzed a set of 6905 device replacements from the STIDEFIX registry. Ontology-based identification of therapy changes (upgraded, downgraded, or similar) was accurate (6905 cases) and performed better than straightforward analysis of the registry codes (F-measure 1.00 versus 0.75 to 0.97). This study demonstrates the feasibility and effectiveness of ontology-based functional annotation of devices in the cardiac domain. Such annotation allowed a better description and in-depth analysis of STIDEFIX. This method was useful for the automatic detection of therapy changes and may be reused for analyzing data from other device registries.
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Rosier et al. (2014) studied Cardiac implantable electronic device (CIED) replacement (n=6,905). Ontology-based functional annotation (Cardiac Device Ontology) vs. NASPE/BPEG registry codes was evaluated on Detection of at least one therapy function change (F-measure). Ontology-based identification of cardiac device therapy changes achieved perfect accuracy (F-measure 1.00), outperforming straightforward analysis of standard registry codes (F-measure 0.82).
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