A Novel Multivariate Scoring System for Diagnosing Post-Myocardial Infarction Pericarditis Following Percutaneous Coronary Intervention
Abstract Body (Do not enter title and authors here): Introduction: Post-myocardial infarction (MI) pericarditis, particularly after percutaneous coronary intervention (PCI), presents with distinct clinical, laboratory, and electrocardiographic features. Despite its unique presentation, no dedicated diagnostic tools exist for this condition in the post-PCI setting, highlighting the need for a tailored approach. This study aims to develop and validate the first comprehensive clinical scoring system specifically designed to accurately diagnose post-MI pericarditis following PCI, utilizing data available at admission. Methods: In this diagnostic case-control study, we compared 60 patients with confirmed post-PCI pericarditis (verified by echocardiography) from our PCI Registry with 120 control patients with various diagnoses from our hospital database. We evaluated 26 potential predictors, including clinical characteristics, chest pain descriptors, and additional diagnostic tests. Independent predictors for the scoring model were identified using stepwise logistic regression. Results: Among the 17 initial variables associated with pericarditis, five independent predictors were identified: age, chest pain exacerbation with thoracic movement, rising troponin levels, diffuse ST-segment elevation, and C-reactive protein levels. These predictors were incorporated into a scoring system based on their regression coefficients. The model demonstrated excellent discrimination, with a C-statistic of 0.97 (95% CI: 0.93-1.0). A score above 6 points yielded a sensitivity of 95% (95% CI: 85-100) and specificity of 86% (95% CI: 78-93), with positive and negative likelihood ratios of 7.2 (95% CI: 4.2-12) and 0.05 (95% CI: 0.01-0.2), respectively, Figure 1. Conclusion: We have developed the first multivariate scoring system specifically designed to identify post-MI pericarditis in patients undergoing PCI. Its promising accuracy has the potential to enhance early recognition, streamline diagnostic processes, and ultimately improve patient outcomes.
Bolaji, Olayiwola
( Rutgers University New Jersey Medic
, Newark
, New Jersey
, United States
)
Omoru, Okiemute
( Indiana University Purdue University School of Informatics and Computing
, Indianapolis
, Indiana
, United States
)
Upreti, Prakash
( Sands-Constellation Heart Institute, Rochester Regional Health
, Rochester
, New York
, United States
)
Echari, Blanche
( The Brooklyn Hospital Center
, Brooklyn
, New York
, United States
)
Shoar, Saeed
( University of Maryland Capital Region Health
, Largo
, Maryland
, United States
)
Basit, Jawad
( Rawalpindi Medical University
, Rawalpindi
, Pakistan
)
Alraies, M Chadi
( Detroit Medical Center
, Detroit
, Michigan
, United States
)
Author Disclosures:
Olayiwola Bolaji:DO NOT have relevant financial relationships
| Okiemute Omoru:DO NOT have relevant financial relationships
| Prakash Upreti:DO NOT have relevant financial relationships
| Blanche Echari:No Answer
| Saeed Shoar:DO NOT have relevant financial relationships
| Jawad Basit:DO NOT have relevant financial relationships
| M Chadi Alraies:DO NOT have relevant financial relationships
