Cardiovascular disease (CVD) is the main cause of death and disease burden in China and throughout the world, despite the technological advancement and the increasing level of awareness [
In recent years, numerous novel markers of cardiovascular disease have been used in clinical practice. Novel and reliable biomarkers are urgently needed to incorporate within clinical model to help clinicians to both identify patients at high risk for adverse clinical prognosis and provide them with proper prevention program by more accurate prognosis estimation. Apelin, a 77-amino acid peptide secreted by white adipose tissue, functions as the endogenous ligand for the human orphan G protein-coupled receptor (APJ) [
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A MACE is defined as the end point of this study, which referred to the composite of cardiac death, clinically driven target lesion revascularisation, recurrent target vessel myocardial infarction, cardiogenic shock, or demonstrated congestive heart failure. The apelin change rate was defined as the level of apelin-12 at 72 hours after PCI compared with that immediately before PCI. The other clinical outcomes were defined in a previous study [
A total of 32 potential variables were included in this study. The associations of these variables with MACEs were identified using Cox proportional hazards regression models. Backward stepwise selection with the Akaike information criterion (AIC) was used to select variables for the multivariable Cox proportional hazards regression models [
The discrimination and calibration power are two important aspects of the performance of the established nomograms, and they were evaluated by using the concordance index (C-index) and calibration curves, both in evaluation and validation cohorts, respectively. The calibration of the nomogram was assessed by comparing the nomogram-predicted MACEs probability with the observed Kaplan-Meier estimates of MACEs probability. In a perfectly calibrated curve, the predictions should fall on the diagonal 45° line of the calibration plot. Calibration estimates how close the nomogram estimated probability is to the observed probability. A risk score for each patient was generated from the nomogram, which was calculated as a linear combination of the selected variables that were weighted by their respective regression coefficients of the multivariate Cox proportional hazards regression analysis conducted in the evaluation cohort to reflect the probability of MACEs. All patients were divided into two groups (a high-risk group and a low-risk group) according to the median risk score. Patients were further stratified into two subgroups according to the median value of apelin-12 level on admission. The Kaplan-Meier method was used to compare the difference in prognosis between the high- and low-risk groups using the evaluation and validation cohorts.
The clinical usefulness of the nomogram was estimated using the decision curve analysis (DCA) by quantifying the net benefits for a range of threshold probabilities using the combined evaluation and validation cohorts [
The basic characteristics of the patients in the evaluation and validation cohorts are summarized in Table
Participant characteristics in evaluation and validation cohorts.
Variables | Evaluation cohort ( |
Validation cohort ( |
|
---|---|---|---|
Age (years) | 62.60 ± 12.00 | 63.73 ± 11.70 | 0.3537 |
Male, |
250 (77.16%) | 103 (75.74%) | 0.7425 |
Killip’s classification > I, |
92 (28.40%) | 20 (14.71%) | 0.0018 |
Diabetes mellitus, |
99 (30.56%) | 49 (36.03%) | 0.2523 |
Hypertension, |
195 (60.19%) | 69 (50.74%) | 0.0617 |
Myocardial infarction history, |
37 (11.42%) | 18 (13.24%) | 0.5834 |
Anterior wall myocardial infarction, |
160 (49.38%) | 69 (50.74%) | 0.7903 |
Apelin-12 (ng/mL) | 0.82 ± 0.33 | 0.84 ± 0.35 | 0.5605 |
Apelin-12 change rate (%) | 13.74 ± 8.98 | 12.94 ± 8.63 | 0.3783 |
SBP(mmHg) | 131.34 ± 27.20 | 133.13 ± 27.10 | 0.5194 |
Albumin (g/L) | 37.95 ± 3.92 | 37.87 ± 3.64 | 0.8385 |
Haemoglobin (g/L) | 143.46 ± 17.39 | 144.65 ± 16.79 | 0.499 |
Total cholesterol (mmol/L) | 5.67 ± 1.11 | 5.65 ± 1.06 | 0.8583 |
Triglyceride (mmol/L) | 1.09 ± 0.85 | 1.16 ± 0.82 | 0.4159 |
High-density lipoprotein-C (mmol/L) | 1.19 ± 0.27 | 1.24 ± 0.26 | 0.0676 |
Low-density lipoprotein-C (mmol/L) | 3.06 ± 0.71 | 3.00 ± 0.76 | 0.4184 |
Fasting blood glucose (mmol/L) | 7.63 ± 2.52 | 7.76 ± 2.57 | 0.6159 |
White blood cells × 109/L | 9.95 ± 3.72 | 10.40 ± 3.44 | 0.2269 |
Heart rate | 77.54 ± 16.97 | 75.42 ± 17.56 | 0.2268 |
Neutrophil (%) | 75.2 ± 11.71 | 76.89 ± 11.03 | 0.1515 |
Creatinine (mmol/L) | 75.40 ± 25.20 | 72.91 ± 15.58 | 0.2855 |
Uric acid (mmol/L) | 339.19 ± 75.20 | 332.5 ± 70.63 | 0.376 |
Platelet × 109/L | 232.59 ± 56.05 | 231.14 ± 57.02 | 0.8012 |
Blood urea nitrogen (mmol/L) | 6.66 ± 2.05 | 6.89 ± 2.12 | 0.2776 |
Peak creatine kinase MB (U/L) | 126.83 ± 91.26 | 130.85 ± 85.75 | 0.661 |
Peak cTnI (ng/ML) | 16.66 ± 12.87 | 16.47 ± 12.86 | 0.8852 |
Pathological Q wave, |
154 (47.53%) | 67 (49.26%) | 0.735 |
GENSINI score | 72.92 ± 32.17 | 70.18 ± 32.18 | 0.405 |
Left atrial diameter (mm) | 37.53 ± 5.59 | 37.17 ± 5.90 | 0.5356 |
Left ventricular diastolic diameter (mm) | 50.36 ± 6.24 | 50.51 ± 6.36 | 0.8151 |
According to the multivariate Cox proportional hazards regression analysis, 11 candidate clinical variables were found to meet the threshold of
Multivariate Cox proportional hazards regression analysis showing the association of variables with major adverse cardiovascular events.
Nomogram predicting 1- and 2-year major adverse cardiovascular events probability for patients with ST-segment elevation myocardial infarction after primary percutaneous coronary intervention. The nomogram allows the clinician to determine the probability of the 1-year and 2-year risk for an individual patient using a combination of covariates. Using the patient’s age, you can draw a vertical line from that variable to the points scale. After repeating the process for each variable, the scores for each variable can be summed and located on the “Total Points” axis. Finally, a vertical line can be drawn straight down from the plotted total point axis to the probability axis to locate the likelihood of 1-year and 2-year risk.
The nomogram yielded a C-index of 0.758 (95%CI = 0.707 to 0.809) using the evaluation cohort. A widely accepted approach demonstrates that a C-index of more than 0.75 reveals clearly useful discrimination [
The calibration curve for predicting major adverse cardiovascular events probability at (a) 1 year and (b) 2 years in the evaluation cohort and at (c) 1 year and (d) 2 years in the validation cohort. Nomogram-predicted probability of major adverse cardiovascular events is plotted on the
The fitted nomogram used the covariates as input and generated a risk score for each patient in both cohorts. The formula for calculating the risk score is as follows: 0.4215 ∗ (apelin) − 0.3589 ∗ (apelin change rate) + 0.4587 ∗ (age) − 0.7173 ∗ I (pathological Q wave) − 0.6211 ∗ I (myocardial infarction history) − 0.6143 ∗ I (anterior wall myocardial infarction) + 0.5037 ∗ I (Killip’s classification > I) − 0.3868 ∗ (uric acid) + 0.4065 ∗ (total cholesterol) + 1.2534 ∗ (cTnI) + 0.7149 ∗(left atrial diameter). The indicator function (I) equals 1 if the statement in the parentheses is true and is equal to 0 otherwise. The favorable calibration of the nomogram was confirmed in the validation cohort (Figures
After gaining the risk scores from the nomogram, the patients were classified into a low-risk group or a high-risk group using the median risk score as the cutoff value. Kaplan-Meier curves and log-rank test analysis in patients of the low-risk group and high-risk group during 2.5-year follow-up are shown in Figure
Kaplan-Meier survival curves of the evaluation and validation cohorts categorized into low- and high-risk groups. A significant association between the risk score and MACEs was observed in the evaluation cohort (a) and confirmed in the validation cohort (b). Survival curves between the higher apelin-12 group (>0.76 ng/mL) and lower apelin-12 group (≤0.76 ng/mL) in the evaluation cohort (c) and the validation cohort (d).
The decision curve analysis is a novel method that evaluates predictive models from the perspective of clinical consequences. The threshold probability is where the expected benefit of treatment balances the expected benefit of avoiding treatment. When the threshold probability ranged from 0.01 to 0.86 in the combined evaluation and validation cohorts, using the apelin-12 based nomogram to predict MACEs yields a greater net benefit than the treat-all or treat-none strategies. For example, if the possibility of MACEs in a patient is over the threshold probability, then a treatment strategy should be adopted. Therefore, the decision curve analysis indicated that the nomogram is clinically useful. Moreover, the newly developed nomogram in this study also displayed more powerful efficiency of the discrimination for MACEs prediction in the whole cohort compared with the other available risk scores systems (Figure
Decision curve analysis of apelin-12 based nomogram and other already available risk scores in terms of major adverse cardiovascular events risk in the whole cohort. The
In addition, we compared the discrimination of the nomogram with that of other already available risk scores to predict MACEs in the evaluation and validation data sets. The apelin-12 based nomogram discrimination for MACEs prediction was superior to that of the other already available risk scores in the evaluation cohort. The discrimination of the nomogram for MACEs prediction was also enhanced compared with the available risk scores in the validation cohort (Table
Comparisons of C-indexes of the present risk score with other already available risk scores to predict major adverse cardiovascular events in the evaluation and validation data sets.
Risk scores | Evaluation cohort | Validation cohort | ||
---|---|---|---|---|
C-index | 95%CI | C-index | 95%CI | |
Apelin-12 based nomogram | 0.758 | 0.707–0.809 | 0.763 | 0.689–0.837 |
TIMI risk index | 0.625 | 0.568–0.682 | 0.587 | 0.489–0.685 |
PAMI risk score | 0.652 | 0.593–0.711 | 0.657 | 0.559–0.755 |
C-ACS risk score | 0.638 | 0.579–0.697 | 0.614 | 0.514–0.714 |
TIMI = thrombolysis in myocardial infarction; PAMI=primary angioplasty in myocardial infarction; C-ACS =Canada acute coronary syndrome; CI = confidence interval.
Apelin is the endogenous ligand of APJ, which is commonly expressed in several organs and tissues, such as the heart, kidney, lung, and adipose tissue [
It was known that nomograms are commonly used as a prognostic tool in oncology and medicine. They provided individual predictions of future clinical outcomes by combining the effects of various variables associated with these events. As far as we know, this is the first clinical prediction model incorporating apelin-12 for predicting MACEs in patients with STEMI after PCI in a Chinese population. In this study, we have constructed and validated a relatively accurate clinical nomogram, which demonstrated adequate discrimination and calibration power to provide an individualized estimation for the MACEs risk at 1 and 2 years in STEMI patients after PCI. For the construction of the nomogram, 11 significant predictors were screened by the multivariate Cox proportional hazards regression analysis. They were also used to construct the apelin-related nomogram and has presented favorable discrimination and diagnostic value to predict the MACEs risk of patients with STEMI after PCI in the evaluation cohort (C-index: 0.758, 95%CI: 0.707–0.809). The validation cohort further confirmed the clinical significance of the nomogram with the C-index of 0.763 (95CI%: 0.689–0.837), which demonstrated an advantage of individual prediction of the MACEs risk in STEMI patients after PCI. We further compared the discrimination of the nomogram with that of the other already available risk scores to predict MACEs in the two cohorts. The apelin-12 based nomogram discrimination for MACEs prediction was superior to that of the other already available risk scores in both cohorts. These results indicate that these risk scores being derived from Western populations may limit their application in Chinese populations. However, it is still not easy to choose when to use the nomogram. The role of the clinical decision curve analysis is to select the optimal schedule of treatment via analyzing all potential behaviors and outcomes in the clinical decision making process. In the study, according to the results of the DCA related to the apelin-based nomogram, when the threshold probability is >1% and <86%, the use of the nomogram would provide more benefit than either the treat-all-patients approach or the treat-none approach. Furthermore, the newly developed nomogram also displayed more powerful efficiency of discrimination for MACEs prediction in the whole cohort compared with the other available risk scores systems.
When it comes to the clinical application of the nomogram, we can take a 65-year-old male patient who has recently been diagnosed with STEMI with Killip’s classification of III and then performed PCI procedure. The patient presented a history of pathological Q wave, anterior wall myocardial infarction, and left atrial diameter of 35 mm. His apelin-12 level immediately before PCI was 0.79 ng/mL and 72 hours after PCI was 12.36 ng/mL. The lab examination parameters of uric acid, triglyceride, and peak cTnI were 452 mmol/L, 7.66 mmol/L, and 38.5 ng/mL, respectively. He wonders about the probability of MACEs of 1-year and 2-year risk. Using the patient’s age, you can draw a vertical line from that variable to the points scale. After repeating the process for each variable, the scores for each variable can be summed and located on the “Total Points” axis. Finally, a vertical line can be drawn straight down from the plotted total point axis to the survival axis to locate the likelihood of 1-year and 2-year probability. Furthermore, the nomogram successfully stratified STEMI patients into high- and low-risk groups, and the high-risk group revealed a significantly lower probability of MACEs. Therefore, our nomogram may act as a precise and reliable predictive model for MACEs in patients with STEMI after PCI in Chinese populations, which may contribute to patient management.
To improve the primary prevention and management of cardiovascular diseases, several tools have been developed to predict the probability of cardiovascular disease risk in different populations [
In summary, we developed and validated a nomogram incorporating both apelin-12 and clinical risk factors to predict MACES in patients with STEMI after PCI in a Chinese population. Our nomogram showed a satisfactory performance, with a C-index of 0.763. This nomogram can be a precisely individualized predictive tool for prognosis. However, additional studies are needed to determine whether it can be applied to other populations before its implementation into clinical practice.
The data used to support the findings of this study are available from the corresponding author upon request.
Enfa Zhao and Hang Xie contributed equally to this work and should be considered co-first authors.
The authors declare that they have no conflicts of interest.
Enfa Zhao, Hang Xie, and Yushun Zhang were responsible for the conception and design of the study, interpretation of data, drafting, and writing of the article. Enfa Zhao and Hang Xie were responsible for interpretation of data and revision of the intellectual content. All authors participated in final approval of the manuscript and agreed to be accountable for all aspects of the work.
The authors acknowledge the efforts of the Dryad data repository in providing high-quality open resources for researchers.