Yulanka S Castro-Dominguez1, Yongfei Wang1, Karl E Minges1, Robert L McNamara2, John A Spertus3, Gregory J Dehmer4, John C Messenger5, Kimberly Lavin6, Cornelia Anderson6, Kristina Blankinship6, Nestor Mercado7, Julie M Clary8, Anwar D Osborne9, Jeptha P Curtis1, Matthew A Cavender10. 1. Department of Medicine (Cardiology), Yale School of Medicine, New Haven, Connecticut, USA; Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut, USA. 2. Department of Medicine (Cardiology), Yale School of Medicine, New Haven, Connecticut, USA. 3. Cardiovascular Research, Saint Luke's Mid America Heart Institute/University of Missouri-Kansas City, Kansas City, Missouri, USA. 4. Department of Medicine (Cardiology), Virginia Tech Carilion School of Medicine, Roanoke, Virginia, USA. 5. Department of Medicine (Cardiology), University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA. 6. American College of Cardiology, Washington, DC, USA. 7. Division of Cardiology, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA. 8. Division of Cardiology, Indiana University School of Medicine, Indianapolis, Indiana, USA. 9. Department of Emergency Medicine, Emory University, Atlanta, Georgia, USA. 10. Department of Medicine (Cardiology), University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA. Electronic address: matt.cavender@unc.edu.
Abstract
BACKGROUND: Standardization of risk is critical in benchmarking and quality improvement efforts for percutaneous coronary interventions (PCIs). In 2018, the CathPCI Registry was updated to include additional variables to better classify higher-risk patients. OBJECTIVES: This study sought to develop a model for predicting in-hospital mortality risk following PCI incorporating these additional variables. METHODS: Data from 706,263 PCIs performed between July 2018 and June 2019 at 1,608 sites were used to develop and validate a new full and pre-catheterization model to predict in-hospital mortality, and a simplified bedside risk score. The sample was randomly split into a development cohort (70%, n = 495,005) and a validation cohort (30%, n = 211,258). The authors created 1,000 bootstrapped samples of the development cohort and used stepwise selection logistic regression on each sample. The final model included variables that were selected in at least 70% of the bootstrapped samples and those identified a priori due to clinical relevance. RESULTS: In-hospital mortality following PCI varied based on clinical presentation. Procedural urgency, cardiovascular instability, and level of consciousness after cardiac arrest were most predictive of in-hospital mortality. The full model performed well, with excellent discrimination (C-index: 0.943) in the validation cohort and good calibration across different clinical and procedural risk cohorts. The median hospital risk-standardized mortality rate was 1.9% and ranged from 1.1% to 3.3% (interquartile range: 1.7% to 2.1%). CONCLUSIONS: The risk of mortality following PCI can be predicted in contemporary practice by incorporating variables that reflect clinical acuity. This model, which includes data previously not captured, is a valid instrument for risk stratification and for quality improvement efforts.
BACKGROUND: Standardization of risk is critical in benchmarking and quality improvement efforts for percutaneous coronary interventions (PCIs). In 2018, the CathPCI Registry was updated to include additional variables to better classify higher-risk patients. OBJECTIVES: This study sought to develop a model for predicting in-hospital mortality risk following PCI incorporating these additional variables. METHODS: Data from 706,263 PCIs performed between July 2018 and June 2019 at 1,608 sites were used to develop and validate a new full and pre-catheterization model to predict in-hospital mortality, and a simplified bedside risk score. The sample was randomly split into a development cohort (70%, n = 495,005) and a validation cohort (30%, n = 211,258). The authors created 1,000 bootstrapped samples of the development cohort and used stepwise selection logistic regression on each sample. The final model included variables that were selected in at least 70% of the bootstrapped samples and those identified a priori due to clinical relevance. RESULTS: In-hospital mortality following PCI varied based on clinical presentation. Procedural urgency, cardiovascular instability, and level of consciousness after cardiac arrest were most predictive of in-hospital mortality. The full model performed well, with excellent discrimination (C-index: 0.943) in the validation cohort and good calibration across different clinical and procedural risk cohorts. The median hospital risk-standardized mortality rate was 1.9% and ranged from 1.1% to 3.3% (interquartile range: 1.7% to 2.1%). CONCLUSIONS: The risk of mortality following PCI can be predicted in contemporary practice by incorporating variables that reflect clinical acuity. This model, which includes data previously not captured, is a valid instrument for risk stratification and for quality improvement efforts.
Authors: Christopher P Kovach; Colin I O'Donnell; Stanley Swat; Jacob A Doll; Mary E Plomondon; Richard Schofield; Javier A Valle; Stephen W Waldo Journal: Cardiovasc Revasc Med Date: 2021-11-06
Authors: Jingjing Song; Yupeng Liu; Wenyao Wang; Jing Chen; Jie Yang; Jun Wen; Jun Gao; Chunli Shao; Yi-Da Tang Journal: Front Cardiovasc Med Date: 2022-08-17
Authors: Lorenzo Azzalini; Milan Seth; Devraj Sukul; Javier A Valle; Edouard Daher; Brett Wanamaker; Michael T Tucciarone; Anwar Zaitoun; Ryan D Madder; Hitinder S Gurm Journal: PLoS One Date: 2022-09-26 Impact factor: 3.752