Detecting Periprocedural Myocardial Infarction in Contemporary Percutaneous Coronary Intervention Trials

Ernest Spitzer, Ton de Vries, Rafael Cavalcante, Marieke Tuinman, Tessa Rademaker-Havinga, Maaike Alkema, Marie-Angele Morel, Osama I. Soliman, Yoshinobu Onuma, Gerrit-Anne van Es, Jan G. P. Tijssen, Eugene McFadden, Patrick W. Serruys

Research output: Contribution to journalArticleAcademicpeer-review

8 Citations (Scopus)

Abstract

This study sought to investigate the differences in detecting (e.g., triggering) periprocedural myocardial infarction (PMI) among 3 current definitions. PMI is a frequent component of primary endpoints in coronary device trials. Identification of all potential suspected events is critical for accurate event ascertainment. Automatic triggers based on study databases prevent underreporting of events. We generated automated algorithms to trigger PMI based on each definition and compared results using data from the RESOLUTE all comers trial. The operationalization of current PMI definitions was achieved by defining programmable algorithms used to interrogate the study database. From a total of 636 PMI triggers, we identified 234 for the World Health Organization extended definition, 382 for the Third Universal definition, and 216 for the Society for Cardiovascular Angiography and Interventions definition. Differences among the biomarkers used, different cutoff values, and in the hierarchy among biomarkers within definitions, yielded a different number of triggers, and identified unique triggers for each definition. Only 38 triggers were consistently identified by all definitions. Availability of ECG data, eCRF data on clinical presentation, and the reporting of >2 post-procedural values of the same biomarker influenced considerably the number of PMI triggers identified. PMI definitions are not interchangeable. The number of triggers identified and consequently the potential number of events varies significantly, highlighting the importance of rigorous methodology when PMI is a component of a powered endpoint. Emphasis on collection of biomarkers, ECG data, and clinical status at baseline may improve the correct identification of PMI triggers
Original languageEnglish
Pages (from-to)658-666
JournalJACC. Cardiovascular interventions
Volume10
Issue number7
DOIs
Publication statusPublished - 2017

Cite this