Identification of the high-risk patient in primary percutaneous coronary intervention: development and validation of a novel predictive index. (25th November 2020) - Blake, S; Proscia, C; Eleuteri, A; Groves, D; Stables, R.H
Abstract: Background: Primary percutaneous coronary intervention (PPCI) is the best treatment for patients with ST elevation myocardial infarction (STEMI). Several risk scores have been created to help risk-stratify these patients but few of these can be calculated in-lab, during the acute event. Development of a score that could be applied during PPCI could aid operators' decisions regarding adjunctive therapies and post-procedural surveillance which could improve patient outcomes. This study aimed to develop a simple, practical risk model that could be applied during PPCI to identify high-risk patients. Methods: Demographic, clinical and outcome data were collected for all patients, as part of the HEAT-PPCI trial, who underwent PPCI for suspected STEMI between February 2012 and November 2013 at our hospital. Independent predictors of the composite outcome of 28-day mortality or severe impairment of LV function (ejection fraction ≤35%) were identified using multiple logistic regression. A risk model was fitted and internal validation was performed by bootstrapping. External validation was performed on a separate cohort of patients with STEMI. Results: The derivation cohort included 1271 patients, with 131/1271 = 10.3% experiencing the composite outcome of 28-day mortality or poor LV function. Three variables were required to predict the outcome: age (OR:2.07, 95% CI 1.55 to 2.78), location of the culprit artery (OR:6.16, 95% CI 4.00 to 9.47), myocardial blush grade post-PPCI (OR:2.32, 95% CI 1.39 to 3.88). External validation was performed on 324 patients undergoing PPCI from a different centre. The model showed good discrimination on ROC-curve analysis (c statistic 0.79, 95% CI 0.75 to 0.83) and performed well on external validation (c statistic 0.87, 95% CI 0.72 to 0.95). Accuracy of the risk model on the validation data was improved by simple recalibration. The model was used to create a risk prediction chart that can be used in-lab during PPCI (Figure 1). Conclusions: We have developed a risk model that accurately predicts 28-day mortality or poor LV function following STEMI using age, culprit location and myocardial blush grade. The model can assist operators in identifying high-risk patients during PPCI. Funding Acknowledgement: Type of funding source: Public hospital(s). Main funding source(s): National Health Service, UK