This study presents a risk scoring system which was constructed by nomogram of COX regression including 8 variables selected by XGBoost that predicts 1-year mortality risk of STEMI patients from a nationwide cohort in China, including 244 hospitals. The risk scoring system was comprised of 8 variables: level of Killip class, early reperfusion strategy, use of non-PCI intraoperative anticoagulants, heart rate, gender, age, diagnosis of AWMI and IWMI. It was derived from patients hospitalized from 2015 to 2016 and was validated in patients hospitalized in 2017. The performance of this risk score was satisfactory with discriminatory accuracy based on c statistics in the training set, test set and validation cohort of 0.87, 0.88, and 0.89, respectively.
Previous studies have established several risk scoring systems for estimating the prognosis of STEMI, such as the GRACE score and thrombolysis in myocardial infarction (TIMI) risk score. Those risk scores are derived from studies mainly enrolling patients from Western countries, and both were developed initially only to assess short-term prognoses. Few risk score was developed by Chinese population with STEMI. Although several studies validated the present risk scores in Chinese STEMI patients, those studies were limited by single-center study. Recently, the EPICOR and EPICOR Asia studies used a national wide population to establish a risk-scoring model for 2-year post discharge survival in ACS patients[9, 10]. But the risk score presented a limited clinical value with a c statistics of 0.712 (95% CI, 0.650–0.772) among STEMI patients in China . A risk assessment tool for STEMI patients with a comparative performance of long-term outcomes in Chinese populations still remained to study in detail. Therefore, in our study, we constructed a scoring system based on a national wide population in China to predict 1-year mortality risk for STEMI.
We compared our risk scoring system with the GRACE score among follow-up outcomes at discharged and 6-month, 1-year long follow-up duration (Table 4.). The GRACE score is the most notable risk score for STEMI with highest vitality [13, 14]. It consists of 8 indicators: age, heart rate, SBP, creatinine, level of Killip class and symptoms of cardiac arrest on admission, ECG ST-segment changes and elevated myocardial necrosis markers. Limited study has validated the GRACE score among Chinses patients. Only few single-center study estimated that the accuracy of the GRACE score in predicting all-cause mortality which was ranged 0.766 to 0.789 for c-statistics. In our study, the results showed that the GRACE score have a similar discriminatory accuracy and lower sensitivity than the STEMI risk score. More high-risk STEMI patients would be recognized by the STEMI risk score. It is helpful for health management among Chinese STEMI patients by accessing preventive intervention or more frequently follow-up to decrease death risk at the early stage.
The features selected by XGBoost are mostly coherent with previous reports . Older age, male sex and worse Killip class have been constantly emphasized as major risk factors for adverse outcomes in patients with STEMI. A prior study showed that every additional year of age of the patient with AMI will lead to an increasing risk of 9.3% higher on death. Another study using large population-based cohort observed that female has an higher in-hospital mortality risk than male among patients with STEMI (odds ratio, 1.42; 95% CI, 1.24–1.64). As reported in previous studies, admission HR are predictors of in-hospital and long-term mortality. Every increase of 5 bpm. in heart rate was associated with a 29% increased risk of cardiovascular mortality at 1-year follow-up . AWMI and IWMI were pointed to have different prognosis. With AWMI, significant reduction in left ventricular function is responsible for the hemodynamic compromise. The myocardial mass supplied by the right coronary artery or left circumflex is usually smaller than that of the left atrial dimension, but with proximal right coronary artery associated IWMI, there may be additional hemodynamic compromise due to right ventricular infarction[19–21]. Since the myocardium at risk is greater in patients presenting with AWMI than IWMI, the reported incidence of cardiogenic shock is higher in patients with AWMI compared with those with IWMI.And in-hospital mortality was modestly lower in patients with cardiac shock complicating IWMI vs. AWMI (30.3% vs. 31.9%; OR, 0.80; 95% CI, 0.75–0.86).
In addition to previous reports, we take an added concern of treatment indicators which have more value in the risk stratification for long-term prognosis prediction, especially in China[22, 23]. Poverty, limited healthcare infrastructure for PCI, and poor accessibility to acute emergency medical services are most important system-level limitations for STEMI care in middle-income countries [24, 25]. In 2013, only 36% of AMI patients could receive reperfusion therapy and visited at hospitals equipped with catheterization labs within 12 hours after onset in China. The proportion would be lower among primary hospitals for lacking medical resources . It has been proved that PCI can decrease the 1-year mortality in patients with STEMI . When PCI could not access within 90 minutes, STEMI patients are suggested to have anticoagulants . Under this condition, the factors of early reperfusion strategy and non-PCI intraoperative anticoagulants have import predictive value in China.
Our risk scoring system based on simple patient demographics and subjective symptoms using machine learning and nomogram can be useful for the health management of STEMI patients after discharged. First, our risk scoring system is easy-to-use. It uses information that can be easily provided, such as age, sex, and pre-hospitalize diagnosis. Previous scoring systems [4, 11, 15], including a recently reported deep learning model , require laboratory or radiographic findings as the main variables. Although such models can be helpful in fully equipped medical facilities, they initially consume a certain amount of medical resources and time. Besides, we used nomogram to present the risk scoring system which can calculate the risk directly using graphs, so that the results are more readable and have higher practical value than conventional models. Second, because the variables were selected by a data-driven method, it can recognize predictive factors of the risk scoring system with sufficient accuracy and discrimination. XGBoost is a newly integrated machine learning algorithm based on decision tree. It is more effective than previous methods by using clever penalization of trees and implementation on single, distributed systems and out-of-core computation. With regularization to prevent overfitting and extra parameter defining loss function, XGBoost is expected to be useful in the field of real-world data based study. Previous study demonstrated that XGBoost algorithm showed an improvement in the prediction ability compared with other machine classifiers and conventional risk scores. As our study was drawn from a real-world observation cohort, XGBoost is optimal for our study to identify the independent risk factors of 1-year mortality. The variables included in the risk scoring system were selected by traversal procedure of XGBoost model, according to ACC. This selection method was a data-driven method and can select variables from a large scale of potential factors and can powerfully reduce variance and reduce bias caused by subjective experiences. Then the nomogram was developed based on the selected variables for practical value in clinical application. However, small number of outcomes were observed in the analysis which may cause over matched during the PSM procedure. Extending follow-up time of the cohort or expanding the population scale may improve the results by increasing the observed number of outcomes. We may further validate the risk scoring system in larger scale of population in the future.