According to the statistical results of the World Health Organization, coronary artery diseases such as acute coronary syndrome (ACS) have become some of the most frequent causes of death worldwide [1]. ACS includes acute ST-segment elevation myocardial infarction (STEMI), non-ST-segment elevation myocardial infarction (NSTEMI), and unstable angina pectoris (UA) [2]. Percutaneous coronary intervention (PCI) remains the most effective treatment for ACS [3, 4]. However, the incidence of MACE in patients with different risk factors varies, especially in high-risk cases [5, 6]. A predictive model would allow physicians to better identify patients at elevated risk, which would facilitate a more personalized approach to managing these cases.

Several cardiovascular disease risk and prognosis assessment tools have been established in different populations to guide clinical practice [7‐14]. GRACE and Thrombolysis in Myocardial Infarction (TIMI) risk scores are recommended in the guidelines for predicting cardiovascular outcomes (short- and medium-term) for patients with ACS [7, 15‐17]. The CADILLAC risk score is used to predict 30-day and 1-year mortality after PCI for acute myocardial infarction (AMI) [8]. Unfortunately, clinical risk stratification for long-term MACEs for patients with ACS after PCI is not well defined.

In recent years, an increasing number of studies have focused on laboratory testing indicators to predict the prognosis of diseases. Several recent studies have shown that lactate level is an independent prognostic factor that is useful for identifying patients at high risk [18‐21]. Our purpose in designing a nomogram incorporating lactate was to provide a tool for the clinical evaluation of patients with ACS undergoing PCI.

Our results confirmed that several demographic and clinical characteristics reported from previous models developed from other databases can be used to construct a simple model for prognostic evaluation. The 6 most important factors—lactate level, age, LAD stenosis, RCA stenosis, BNP level, and LVEF—contained most of the prognostic information and were incorporated into the nomogram. To our knowledge, this nomogram is the first clinical prediction model incorporating lactate for predicting the long-term risk of MACEs among patients with ACS after PCI. Nomograms are evidence-based and fully personalized tools to guide clinical decision-making and provide patient-friendly, accurate and repeatable predictions without the need for computer software to interpret [30]. Risk stratification is important in determining which medications and revascularization should be used. In addition, the prediction contributes to the development of health care and clinical guidelines for ACS.

Lactate, as an easily and quickly assessed metabolite, has been studied over time in acute cardiac patients to evaluate its prognostic ability [19]. 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 [31]. Harjola et al. found that lactate level (> 2 mmol/L) was independently associated with increased short-term mortality for patients with cardiogenic shock [32]. For patients with STEMI, higher lactate levels were independently associated with 30-day mortality and overall adverse reactions to PCI (in particular, lactate ≥ 1.8 mmol/L) [33]. Marashly et al. found that for patients with cardiogenic shock secondary to acute coronary syndrome, lactate ≥ 2.5 mmol/L could independently predict 30-day all-cause mortality and then established an ACS-MCS score [34]. Ruling out cardiogenic shock, in 766 patients with STEMI and NSTEMI undergoing coronary artery bypass surgery, lactate was a predictor of 30-day and late mortality [35]. In addition, in a study of 1865 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 [18]. Lactate is an important fuel for the stressed heart and is produced by the dehydrogenation of pyruvate, which is synthesized from glycolysis [36, 37]. In a normal heart at rest, β-oxidation of fatty acids provides approximately 60–90% of energy, while pyruvate produces 10–40% [38]. During exercise, the uptake and use of lactate in the myocardium increases, as does the stimulation of β-adrenergic stimulation and shock [39]. Hyperlactatemia can be seen as part of the stress response, including increased metabolic rate, sympathetic nervous system activation, accelerated glycolysis, and improved bioenergy supply [19]. Hyperlactate after ACS may be caused by hypoxia following haemodynamic disorders or by catecholamine-induced aerobic glycolysis in response to stress [39, 40]. 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 risk prediction tool for MACEs integrating lactate to date. Therefore, a well-performed risk prediction model incorporating lactate is urgently needed. It must be acknowledged that certain drugs (e.g., metformin, sodium bicarbonate), preoperatively sustained fasting periods with volume depletion, and even hypertension could have contributed to changes in lactate levels, which could interfere with this evaluation [35]. Unfortunately, we did not calculate lactate clearance, which has been reported to be more reliable on clinical grounds than lactate for risk stratification in different critical illness conditions [41‐43].

For the other five variables, TIMI risk score indicated prior coronary stenosis of 50% or more as an independent predictor for the primary end point (all-cause mortality, MI, or urgent revascularization) occurred by 14 days [16]. In a study of 6755 patients after PCI, Iqbal et al. found that for patients with multivessel disease, untreated proximal LAD and RCA (stenosis > 70%) w ere associated with increased mortality [44]. BNP level was a strong independent predictor of short-term postoperative mortality [45]. Grabowski et al. improved their model’s predictive power by adding BNP to the Killip class and TIMI flow grades [46]. A possible explanation is that an elevated BNP level reflects a larger infarct size and progressive left ventricular remodelling, thus more obviously reflecting the degree of cardiac insufficiency [47]. Similar 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 [48, 49] 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 [50, 51]. The predictive ability of simple age cut-off points of 65 and 75 are similar to that of a more complex model with age as a continuous variable [16]. To overcome or avoid the limitations of a single predictor and achieve high prediction accuracy, we combined six detected predictors into this model. Because of dynamic variations, 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 [7, 8, 14, 16]; Killip class may result in information bias by the judgement error of the clinician’s supervisor. Nomograms are easy to recall and clinically useful.

The model had adequate discrimination and calibration power in the training set (AUC = 0.712–0.762) to predict MACEs and appeared statistically robust in that it was validated in a separate third of the participants (AUC = 0.724–0.818). Discrimination for MACE prediction of the nomogram was superior to GRACE risk score and CADILLAC risk score in both sets, confirming that nomogram was more valuable in predicting MACEs, especially in the long term. The TIMI risk score, published in 2000, predicted the primary end point (all-cause mortality, MI, or severe recurrent ischaemia requiring urgent revascularization) through 14 days after randomization for UA/NSTEMI [52]. The GRACE risk score has been established to predict the risk of death during hospitalization and at 6 months for patients with ACS [7]. To predict 30-day and 1-year mortality risk after PCI for AMI, the PAMI risk score and CADILLAC risk score were established successively [8, 14]. Several studies have proven that in predicting 30-day and 1-year mortality, the CADILLAC risk score showed slight superiority over GRACE, TIMI, and PAMI risk scores [53‐55]. The probable reason is that the CADILLAC risk score emphasizes the importance of LVEF and three-vessel disease [54]. Our nomogram also incorporated these variables. Their predictors, such as heart rate, systolic blood pressure, myocardial enzymes and creatinine, are dynamic. Killip class, postprocedural TIMI flow grade, and ST-segment deviation require the judgement of professional physicians. In addition, previous risk models were derived from Western populations, which limited their application to other populations. In addition, few of the participants were followed up for more than one year.

In regard to the clinical application of the nomogram, we have taken an example of a case of 75 years old (75 points), LAD stenosis 50% (78 points), RCA stenosis 10% (0 point), lactate 1 mmol/L (0 point), BNP 100 pg/ml (0 point) and LVEF 38% (57 points). The total score was 210, and the expected MACE rates after 6 months, 1 year and 4 years were 2%, 3% and 30%, respectively. Patients with ACS undergoing PCI can be thus classified into high- and low-risk groups for 4-year MACEs.

The most attractive aspect features of these models are their accuracy, generalizability, and ease of use. The nomogram is an excellent model to span the entire spectrum of ACS. It is based on a relatively unselected group of patients, representing patients seen in general clinical practice. It includes a new variable, lactate level, that is stable and easily accessible. In addition, the nomogram has an excellent ability to discriminate risk. In the past, risk scores were mostly 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. The nomogram uses the latest clinical data from the past 7 years to reflect the current cardiovascular medical level. Although ACS can be treated in many ways, our study evaluated patient outcomes solely treated with PCI, with fewer uncontrolled variables and more stable clinical events. Unlike traditional risk scores, a follow-up period of up to 4 years is conducive to the evaluation of long-term prognosis.

Limitations also existed in this study. 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 improving the prognosis. Besides, the role of lactate may not be consistent in a general cohort of ACS patients including STEMI, NSTEMI and UA. Since it clearly indicates those patients with hemodinamic compromise and more probably STEMI patients. Subgroup analyses of STEMI, NSTEMI and UA were not performed for lack of adequate detailed information of all patients, resulting in the prediction performance of the model in this three cohorts not being estimated separately. A multi-center validation study, particularly involving other ethnic groups, is required to confirm the performance of the nomogram before clinical application.

In conclusion, a novel prognostic nomogram incorporating lactate level and five other easily available and objective variables can serve as an accurate and favourable prognostic prediction of 6-month, 1-year, and 4-year incidence of MACEs among patients with the entire spectrum of ACS after PCI. This information can help clinicians stratify risk for optimal triage and management.