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

Abstract

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.

Keywords: follow-up; high-risk; primary percutaneous coronary intervention; risk prediction score.

Figure 1

Study sample selection flow diagram.

Figure 2

The establishment procedure of the model. (A1 and A2) Least absolute shrinkage and selection operator (LASSO) regression. The filtering and cross-validation processes of independent variables are shown in (A1 and A2) respectively. Lambda.1se is the lambda value of the optimal efficiency model in the standard error range which gives a model with excellent performance. (B) Forest plot by using the multivariable COX regression; HR, hazard ratio; CABG, coronary artery bypass grafting. (C) Decision tree flow diagram. The binary decision diagram of the variables was shown in (C and D) the risk score nomogram. The score, ranging from 0 to 160, assigned points as follows: for patients younger than 40 years, 100 points; for age 40 to younger than 50 years, 80 points; for age 50 to younger than 60 years, 60 points; for age 60 to younger than 70 years, 40 points; for age 70 to younger than 80 years, 20 points; for patients 80 years or older,0; for Killip II, 7.68; for Killip III, 15.36; for Killip IV, 23.03; for EF at admissio≦50%, 4.62; for previous history of CABG, 20; for in-stent restenosis, 4.81; for stent thrombosis, 9.62; for without complete revascularization, 3.45; for multi-vessel lesion, 18. Age group, 1 stand for age less than 40 years/ 2 stand for age range from 40 to 50 years/ 3 stand for age range from 50 to 60 years/4 stand for age range from 60 to 70 years/ 5 stand for age range from 70 to 80 years/ 6 age stand for age more than 80 years. Killip classification, 1= Killip I, 2= Killip II, 3= Killip III, 4=Killip IV. EF, 0 stands for >50%, 1 stands for less than 50%. History of CABG, 1=with, 0=without; type of lesion, 1= De novo lesion, 2=restenosis, 3= stent thrombosis; complete revascularization, 0=without, 1=with; multivessel disease, 1=with, 0=without. Histogram refers to the score distribution in the derivation cohort. For the variables selected in the nomogram model, the values of different variables can correspond to different scores on the integral line at the top of the nomogram (the score range is 0–160 points) through the projection of the vertical line, and the total score can be obtained by adding up the scores corresponding to the values of each variable. The cumulative occurrence probability of MACCE in 30 days, 3 year, 5 year and 7 years can be obtained from the total score on the prediction line at the bottom of the nomogram. (E) Elements of clinical prediction score and distribution of score among high-risk MI patients who underwent PPCI. (E) The left side has shown the clinical prediction score variable and corresponding points. The right side has shown the graph of distribution of the clinical prediction score.

Figure 3

The internal validation of the model. (A–D) Risk score calibration in the derivation cohort and the internal validation cohort; the stroke events risk score of 3-year (A) 5-year (B) in the derivation cohort and 3-year (C) 5-year (D) in the validation cohort. Calibration is shown as the estimated risk against survival from Kaplan- Meier analysis. Gray line=perfect calibration. (E) showed the survival ROC curve of derivation cohort (AUC=0.767, p<0.001). (F) showed the survival ROC curve of validation cohort (AUC=0.696, p<0.001). AUC, area under the curve; ROC, survival receiver operating characteristic; TP, true positive; FP, false positive.

Figure 4

K-M survival analysis; in the group of predicting MACCE events, the two groups displayed significant difference in both derivation cohort (p<0.001) and validation cohort (p<0.001) shown in (A–D).