The long-term clinical outcomes of ACS patients after PCI vary, so an accurate predictive model is required for identification. Risk prediction can help clinicians identify high-risk groups, guide follow-up and individualized treatment. In addition, the prediction contributes to the development of health care and clinical guidelines for ACS. Nomogram is evidence-based and fully personalized tool to regulate clinical decision-making and provides patient friendly, accurate and repeatable predictions without the need for computer software to interpret.  Therefore, we have developed and validated a nomogram with satisfying stability and accuracy. The most important 6 factors—lactate, age, LAD stenosis, RCA stenosis, BNP and LVEF—contained most of the prognostic information has been included.
Harjola et al. found lactate level (༞2 mmol/L) independently associated with an increased short-term mortality in patients with cardiogenic shock。 A meta-analysis showed a greater reduction in lactate concentrations in survivors than in non-survivors, whether following cardiac surgery, cardiogenic shock, or cardiac arrest.  In STEMI patients, higher lactate levels were independently associated with 30-day mortality and overall adverse reactions to PCI (in particular, lactate ≥ 1.8 mmol/L).  Besides, in a study of 1,865 patients with ACS, elevated lactate levels (≥ 1.8 mmol/L) at admission were an independent predictor of 30-day and 180-day all-cause mortality.  In terms of energy supply for heart,in a normal heart, at rest, β-oxidation of fatty acids provides about 60%-90% of energy while pyruvate produces 10%-40%. Lactate produced by dehydrogenation of pyruvate which synthesized from glycolysis, is also an important fuel for the stressed heart.   During exercise, the uptake and use of lactate in the myocardium increases, as does the stimulation of β-adrenergic stimulation and shock.  Hyperlactatemia can be seen as part of the stress response, including increased metabolic rate, sympathetic nervous system activation, accelerated glycolysis, and improved bioenergy supply.  Hyper-lactate after ACS may be caused by hypoxia following hemodynamic disorders or by catecholamine-induced aerobic glycolysis in response to stress. [27, 39] These studies suggest that lactate may play an important role in the course of ACS. To the best of our knowledge, however, there has been no such risk prediction tool for MACE containing lactate so far. Therefore, it is significant to set up this risk model.
For the other five variables, TIMI flow grades reported significant coronary stenosis as an independent predictor.  In a study of 6,755 patients after PCI, Iqbal et al. found that in patients with multivessel disease, untreated proximal LAD and RCA were associated with increased mortality.  BNP level was a strong independent predictor of short-term postoperative mortality. Grabowski et al. have improved their predictive power by adding BNP to the Killip class and TIMI flow grades.  The possible explanation is that the elevated BNP level reflects a larger infarct size and progressive left ventricular remodeling, thus more obviously reflecting the degree of cardiac insufficiency. The same to BNP, LVEF also serves as a reference index for cardiac function, to supply important prognostic information and should be included in approaches for stratifying risk after myocardial infarction.  Many studies have reported that age is a significant risk factor for clinical events (cardiac death, target vessel myocardial infarction, and clinically driven target vessel revascularization) after PCI.   The predictive ability of simple age cutoff points of 65 and 75 are similar to that of a more complex model age as a continuous variable. 
The COX regression analysis results of our study consistent with the above results, that is, lactate, LAD stenosis, RCA stenosis, BNP, LVEF and age were important predictors. In order to overcome or avoid the limitations of a single predictor and achieve high prediction accuracy, we combined six detected predictors to construct a nomogram model. Because of dynamic, the nomogram did not include clinical symptoms and signs, such as Killip class, heart rate, and systolic blood pressure, which are significantly associated with ACS mortality.   And Killip class may result in information bias by the judgment error of the clinician's supervisor. Nomogram is easy to recall and clinically useful.
Favorable discrimination power of the nomogram has been ascertained by drawing the time-dependent ROC of nomogram in the training set (AUC = 0.712–0.762) and validation set (AUC = 0.724–0.818). The calibration curve showed consistency between the predicted MACE after 6 months, 1 year and 4 years and the actual results.
TIMI Risk Score, published in 2000, predicted primary end point (all-cause mortality, MI, or severe recurrent ischemia requiring urgent revascularization) through 14 days after randomization for UA/ NSTEMI. GRACE risk score has been established to predict the risk of death during hospitalization and at 6 months in patients with ACS.  To predict 30-day and 1-year mortality risk after PCI in AMI,PAMI risk score and CADILLAC risk score established successively.   Several studies has proved that in predicting the 30-day and 1-year mortality, CADILLAC risk score showed slight superiority than Grace, TIMI, and PAMI risk scores. 
The probable reason is CADILLAC risk score considers LVEF and three-vessel disease.  Our nomogram also takes LVEF and specific coronary angiography results into account. Furthermore, data from both our training set and validation set confirmed that nomogram was superior in predicting the MACE in ACS patients after PCI than the above several risk scores.
In order to achieve better results in the actual prediction, we used the nomogram to calculate the total score of MACE risk. ACS Patients after PCI can be classified into the "high risk" group (score ≥ 285.1) and the "low risk" group (score < 285.1) based on the cutoff values determined by X-tile analysis. Kaplan-Meier analysis showed that the incidence of MACE was statistically different between the two groups. It helps more accurately monitoring of high-risk patients to personalized health management and increase cost-effectiveness.
Several strengths could be found in the study. In the past, risk scores were almost based on western populations, while the population of patients with ACS after PCI in the East, especially in China, was much larger, requiring a specialized prediction model. Our nomogram uses the latest clinical data from the past 7 years to reflect current cardiovascular medical standards. Between PCI, drug therapy, and coronary bypass surgery, our study evaluated patient outcomes solely treated with PCI, with fewer uncontrolled variables and more stability and accuracy. Our nomogram has combined an independent and new risk factor, lactate, that is an easily accessible indicator. Unlike traditional forecasting models, a follow-up period of up to 4 years, which is conducive to the evaluation of both short-term and long-term prognosis. The most attractive aspect of the nomogram is its good discrimination and calibration power.
Limitations also existed in this study. Our data came from the same medical center. Independent external validation is required to confirm the performance of the nomogram before the clinical application. Although lactate has certain predictive ability, the detection time and collection method of lactate are not unified and clear. Some clinical drugs may cause changes in lactate without the improvement of the prognosis.