Incidence and risk factors for stroke following percutaneous coronary intervention

Adverse Impact of Peri-Procedural Stroke in Patients Who Underwent Percutaneous Coronary Intervention

Peri-procedural stroke (PPS) is an important complication in patients who underwent percutaneous coronary intervention (PCI). The extent to which PPS impacts mortality and outcomes remains to be defined. Consecutive patients who underwent PCI enrolled in the Victorian Cardiac Outcomes Registry (2014 to 2018) were categorized into PPS and no PPS groups. The primary outcome was 30-day major adverse cardiovascular events (MACEs) (composite of mortality, myocardial infarction, stent thrombosis, and unplanned revascularization). Of 50,300 patients, PPS occurred in 0.26% patients (n = 133) (71% ischemic, and 29% hemorrhagic etiology). Patients who developed PPS were older (69 vs 66 years) compared with patients with no PPS, and more likely to have pre-existing heart failure (59% vs 29%), chronic kidney disease (33% vs 20%), and previous cerebrovascular disease (13% vs 3.6%), p <0.01. Among those with PPS, there was a higher frequency of presentation with ST-elevation myocardial infarction (49% vs 18%) and out-of-hospital cardiac arrest (14% vs 2.2%), PCI by way of femoral access (59% vs 46%), and adjunctive thrombus aspiration (12% vs 3.6%), all p = <0.001. PPS was associated with incident 30-day MACE (odds ratio [OR] 2.97, 95% confidence intervals [CIs] 1.86 to 4.74, p <0.001) after multivariable adjustment. Utilizing inverse probability of treatment weighting analysis, PPS remained predictive of 30-day MACE (OR 1.91, 95% CI 1.31 to 2.80, p = 0.001) driven by higher 30-day mortality (OR 2.0, 95% CI 1.35 to 2.96, p = 0.001). In conclusion, in this large, multi-center registry, the incidence of PPS was low; however, its clinical sequelae were significant, with a twofold increased risk of 30-day MACE and all-cause death.

Development and Validation of a Prediction Rule for Major Adverse Cardiac and Cerebrovascular Events in High-Risk Myocardial Infarction Patients After Primary Percutaneous Coronary Intervention

Background and Aims

We aimed to develop a clinical prediction tool to improve the prognosis of major adverse cardiac and cerebrovascular events (MACCE) among high-risk myocardial infarction (MI) patients undergoing primary percutaneous coronary intervention (PCI).

Methods

The present study was a prospective and observational study. A total of 4151 consecutive MI patients who underwent primary PCI at Fuwai Hospital in Beijing, China (January 2010 and June 2017) were enrolled. Forty-eight patients without follow-up data were excluded from the study. The pre-specified criteria (Supplementary Information 1) were chosen to enroll MI patients at high risk for MACCE complications after PCI.

Results

The full model included seven variables, with a risk score of 160 points. Derivation and validation cohort models predicting MACCE had C-statistics of 0.695 and 0.673. The area under the curve (AUC) of the survival receiver operating characteristic curve (ROC) for predicting MACCE was 0.991 and 0.883 in the derivation and validation cohorts, respectively.

Conclusion

The predicted model was internally validated and calibrated in large cohorts of patients with high-risk MI receiving primary PCI to predict MACCE and showed modest accuracy in the derivation and validation cohorts.

Estimation of Major Adverse Cardiovascular Events in Patients With Myocardial Infarction Undergoing Primary Percutaneous Coronary Intervention: A Risk Prediction Score Model From a Derivation and Validation Study

Background: The population with myocardial infarction (MI) undergoing primary percutaneous coronary intervention (PPCI) is growing, but validated models to guide their clinical management are lacking. This study aimed to develop and validate prognostic models to predict major adverse cardiovascular events (MACEs) in patients with MI undergoing PPCI.

Methods and Results: Models were developed in 4,151 patients with MI who underwent PPCI in Fuwai Hospital between January 2010 and June 2017, with a median follow-up of 698 days during which 544 MACEs occurred. The predictors included in the models were age, a history of diabetes mellitus, atrial fibrillation, chronic kidney disease, coronary artery bypass grafting, the Killip classification, ejection fraction at admission, the high-sensitivity C-reactive protein (hs-CRP) level, the estimated glomerular filtration rate, the d-dimer level, multivessel lesions, and the culprit vessel. The models had good calibration and discrimination in the derivation and internal validation with C-indexes of 0.74 and 0.60, respectively, for predicting MACEs. The new prediction model and Thrombolysis in Myocardial Infarction (TIMI) risk score model were compared using the receiver operating characteristic curve. The areas under the curve of the new prediction model and TIMI risk score model were 0.806 and 0.782, respectively (difference between areas = 0.024 < 0.05; z statistic, 1.718).

Conclusion: The new prediction model could be used in clinical practice to support risk stratification as recommended in clinical guidelines.