Contemporary Clinical and Coronary Anatomic Risk Model for 30-Day Mortality After Percutaneous Coronary Intervention

Abstract

Background: Percutaneous coronary intervention (PCI) procedures are increasing in clinical and anatomic complexity, likely increasing the calculated risk of mortality. There is need for a real-time risk prediction tool that includes clinical and coronary anatomic information that is integrated into the electronic medical record system. Methods: We assessed 70 503 PCIs performed in 73 Veterans Affairs hospitals from 2008 to 2019. We used regression and machine-learning strategies to develop a prediction model for 30-day mortality following PCI. We assessed model performance with and without inclusion of the Veterans Affairs SYNTAX score (Synergy Between Percutaneous Coronary Intervention With Taxus and Cardiac Surgery), an assessment of anatomic complexity. Finally, the discriminatory ability of the Veterans Affairs model was compared with the CathPCI mortality model. Results: The overall 30-day morality rate was 1.7%. The final model included 14 variables. Presentation status (salvage, emergent, urgent), ST-segment–elevation myocardial infarction, cardiogenic shock, age, congestive heart failure, prior valve disease, chronic kidney disease, chronic lung disease, atrial fibrillation, elevated international normalized ratio, and the Veterans Affairs SYNTAX score were all associated with increased risk of death, while increasing body mass index, hemoglobin level, and prior coronary artery bypass graft surgery were associated with lower risk of death. C-index for the development cohort was 0.93 (95% CI, 0.92–0.94) and for the 2019 validation cohort and the site validation cohort was 0.87 (95% CI, 0.83–0.92) and 0.86 (95% CI, 0.83–0.89), respectively. The positive likelihood ratio of predicting a mortality event in the top decile was 2.87% more accurate than the CathPCI mortality model. Inclusion of anatomic information in the model resulted in significant improvement in model performance (likelihood ratio test P <0.01). Conclusions: This contemporary risk model accurately predicts 30-day post-PCI mortality using a combination of clinical and anatomic variables. This can be immediately implemented into clinical practice to promote personalized informed consent discussions and appropriate preparation for high-risk PCI cases.

The everolimus eluting Synergy MegatronTMdrug‐eluting stent platform: Early outcomes from the European Synergy MegatronTMImplanters' Registry

“…To our knowledge, this is the largest study reporting on the clinical outcomes of this platform. The factors independently associated with TLF in this study are well established predictors of poor short‐term outcomes 14–16 …”

“…The factors independently associated with TLF in this study are well established predictors of poor short-term outcomes. [14][15][16] Aorto-ostial lesions present several obstacles that must be overcome during PCI. Eccentricity and the "funnel" shape of the LM, combined with the fundamental limitations of two-dimensional angiography contribute to high rates of geographical miss in these lesions.…”

Prediction of microvascular obstruction by coronary artery angiography score after acute ST-segment elevation myocardial infarction: a single-center retrospective observational study

“…Coronary artery angiography score is widely used to assess extent and severity of coronary atherosclerotic disease (CAD) burden [ 17 ]. Previous studies proved that the SYNTAX score and Gensini score could predict the no-reflow phenomenon defined by TIMI flow or myocardial color grade post-PCI [ 13 , 14 ].…”

Efficient sensitivity analysis for biomechanical models with correlated inputs

“…Biomechanical models have been maturing over the last decade and are evolving from research tools to models that can be used to support intervention planning by virtually predicting the outcome of possible interventions for patients. [1][2][3][4][5] Now that biomechanical models are being used to support clinical decision-making for individual patients, determining the accuracy of model outcomes and the possible impact of wrong decisions is indispensable. [6][7][8] This resulted in, among others, guidelines documents for assessing the adequacy of concluded verification and validation activities, which demonstrates the credibility of computational models of medical devices, by the American Society of Mechanical Engineers.…”

“…Biomechanical models have been maturing over the last decade and are evolving from research tools to models that can be used to support intervention planning by virtually predicting the outcome of possible interventions for patients 1–5 . Now that biomechanical models are being used to support clinical decision‐making for individual patients, determining the accuracy of model outcomes and the possible impact of wrong decisions is indispensable 6–8 .…”